Search

In this experiment, the linear CCD mixing concentration online detection device was studied under five concentrations of carmine solution: 0.1 g/L, 0.3 g/L, 0.5 g/L, 0.7 g/L, and 0.9 g/L, for the factors that can affect the detection accuracy in the real spraying process (spray flow rate, spray pressure, liquid temperature, and light intensity). The results show the following results: different spray flow rates have less influence on the concentration detection results; the greater the concentration of the solution, the less the influence of the spray pressure on the detection; the smaller the concentration of the solution, the greater the influence of the spray pressure on the detection; the greater the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid less.

To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas.In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance.In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p0.001) than the clinical nomogram.DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value.• DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Metho...

The precise temperature distribution measurement is crucial in many industrial fields, where ultrasonic tomography (UT) has broad application prospects and significance. In order to improve the resolution of reconstructed temperature distribution images and maintain high accuracy, a novel two-step reconstruction method is proposed in this article. First, the problem of solving the temperature distribution is converted to an optimization problem and then solved by an improved version of the equilibrium optimizer (IEO), in which a new nonlinear time strategy and novel population update rules are deployed. Then, based on the low-resolution and high-precision images reconstructed by IEO, Gaussian process regression (GPR) is adopted to enhance image resolution and keep the reconstruction errors low. After that, the number of divided grids and the parameters of IEO are also further studied to improve the reconstruction quality. The results of numerical simulations and experiments indicate that high-resolution images with low reconstruction errors can be reconstructed effectively by the proposed IEO-GPR method, and it also shows excellent robust performance. For a complex three-peak temperature distribution, a competitive accuracy with 3.10% and 2.37% error at root-mean-square error and average relative error is achieved, respectively. In practical experiment, the root-mean-square error of IEO-GPR is 0.72%, which is at least 0.89% lower than that of conventional algorithms.

Decompressive craniectomy is widely used to treat medically refractory intracranial hypertension. There were still few studies focusing on the complications between titanium cranioplasty with non-titanium materials cranioplasty. Our systematic review and meta-analysis aimed to assess the complications following titanium cranioplasty and to make a comparison with nontitanium materials. A systematic review was used to review titanium cranioplasty characters in recent articles. A systematic literature review and meta-analysis were performed by using PubMed/MEDLINE, Scopus, the Cochrane databases and Embase for studies reporting on cranioplasty procedures that compared complication outcomes between titanium with non-titanium materials. The final 15 studies met inclusion criteria and represented 2258 cranioplasty procedures (896 titanium, 1362 nontitanium materials). Overall complications included surgical site infection, hematoma, implant exposure, seizure, cerebrospinal fluid leak, imprecise fitting. Titanium cranioplasty was associated with a significant decrease in overall complications rate (OR, 0.72; P = 0.007), hematoma rate (OR, 0.31; P = 0.0003) and imprecise fitting rate (OR, 0.35; P = 0.04). However, it also suggested that titanium cranioplasty can be greatly increased implant exposure rate (OR, 4.11; P 0.00001). Our results confirmed the advantages of titanium cranioplasty in reducing complications including hematoma, imprecise fitting, and also suggested that clinicians should pay more attention to postoperative implant exposure. With new synthetic materials emerging, it would also be interesting to study the cost-effect and functional outcomes associated with cranioplasty materials.

The disadvantages of poor selectivity, thermal instability and nonlinear output for metal oxide semiconductor (MOS) gas sensors restrict their detection accuracy for flammable and toxic gases. In this paper, a low frequency dynamic temperature modulation detection method using rectangular wave was designed and first proposed herein. The temperature modulation detection mechanism was also provided in detail. The flammable gases CH4 and CO were detected successfully using this method by self-made indirectly Pt/SnO2 sensors. The results show that the response time of the Pt/SnO2 sensors is 9 s and 8 s, and the recovery time is 25 s and 23 s for 100 ppm CH4 and CO, using the rectangular wave temperature modulation with a working temperature of 335-382 °C and an optimized frequency of 100 mHz. The temperature modulation has more advantages than steady-state constant temperature method in power consumption and working temperature. The temperature-modulated response for CH4 and CO, are highly linear with the gas concentration without using the log coordinates. The selectivity of the Pt/SnO2 sensors for 500 ppm CH4 and CO is enhanced from 2.03 using the traditional steady-state constant temperature detection to 2.56 using the temperature modulation method. Compared with the steady-state constant temperature method, both the selectivity and linearity of the response output for the Pt/SnO2 sensors are improved by this method, and an effective detection can be realized for a wide gas concentration range of 0–500 ppm CH4 and CO. It provides a new potential detection pathway for MOS-based gas sensors.

ImportanceHigh-grade gliomas (HGGs) constitute the most common and aggressive primary brain tumor, with 5-year survival rates of 30.9% for grade 3 gliomas and 6.6% for grade 4 gliomas. The add-on efficacy of interferon alfa is unclear for the treatment of HGG.ObjectivesTo compare the therapeutic efficacy and toxic effects of the combination of temozolomide and interferon alfa and temozolomide alone in patients with newly diagnosed HGG.Design, Setting, and ParticipantsThis multicenter, randomized, phase 3 clinical trial enrolled 199 patients with newly diagnosed HGG from May 1, 2012, to March 30, 2016, at 15 Chinese medical centers. Follow-up was completed July 31, 2021, and data were analyzed from September 13 to November 24, 2021. Eligible patients were aged 18 to 75 years with newly diagnosed and histologically confirmed HGG and had received no prior chemotherapy, radiotherapy, or immunotherapy for their HGG.InterventionsAll patients received standard radiotherapy concurrent with temozolomide. After a 4-week break, patients in the temozolomide with interferon alfa group received standard temozolomide combined with interferon alfa every 28 days. Patients in the temozolomide group received standard temozolomide.Main Outcomes and MeasuresThe primary end point was 2-year overall survival (OS). Secondary end points were 2-year progression-free survival (PFS) and treatment tolerability.ResultsA total of 199 patients with HGG were enrolled, with a median follow-up time of 66.0 (95% CI, 59.1-72.9) months. Seventy-nine patients (39.7%) were women and 120 (60.3%) were men, with ages ranging from 18 to 75 years and a median age of 46.9 (95% CI, 45.3-48.7) years. The median OS of patients in the temozolomide plus interferon alfa group (26.7 [95% CI, 21.6-31.7] months) was significantly longer than that in the standard group (18.8 [95% CI, 16.9-20.7] months; hazard ratio [HR], 0.64 [95% CI, 0.47-0.88]; P = .005). Temozolomide plus interferon alfa also significantly improved median OS in patients with O6-methylguanine-DNA methyltransferase (MGMT) unmethylation (24.7 [95% CI, 20.5-28.8] months) compared with temozolomide (17.4 [95% CI, 14.1-20.7] months; HR, 0.57 [95% CI, 0.37-0.87]; P = .008). Seizure and influenzalike symptoms were more common in the temozolomide plus interferon alfa group, with 2 of 100 (2.0%) and 5 of 100 (5.0%) patients with grades 1 and 2 toxic effects, respectively (P = .02). Finally, results suggested that methylation level at the IFNAR1/2 promoter was a marker of sensitivity to temozolomide plus interferon alfa.Conclusions and RelevanceCompared with the standard regimen, temozolomide plus interferon alfa treatment could prolong the survival time of patients with HGG, especially the MGMT promoter unmethylation variant, and the toxic effects remained tolerable.Trial RegistrationClinicalTrials.gov Identifier: NCT01765088

Background: The progression and metastasis of cancers are associated with systematic immune inflammation and nutritional dysfunction. The systemic immune-inflammation index and prognostic nutritional index (PNI) have shown a prognostic impact in several malignancies. Therefore, our study aimed to evaluate immune inflammation and nutritional index prognostic significance in patients with medulloblastoma (MB).Methods: We retrospectively analyzed 111 patients with MB between 2001 and 2021 at our institution. The optimal cutoff values for systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte counts ration (MLR), and PNI were evaluated with receiver operating characteristic (ROC) curve analysis. Clinical characteristics and SII, NLR, MLR, and PNI were tested with the Pearson's chi-squared test. The Kaplan–Meier survival curves and the Cox proportional hazards model were used to evaluate the effects of immune inflammation and nutritional index on overall survival (OS).Results: Receiver operating characteristic curve analysis determined the optimal SII, NLR, MLR, and PNI cutoff values of 2,278, 14.83, 0.219, and 56.5 that significantly interacts with OS and divided the patients into two groups. Comparative survival analysis exhibited that the high-SII cohort had significantly shorter OS (p = 0.0048) than the low-SII cohort. For the univariate analysis, the results revealed that preoperative hydrocephalus (p = 0.01), SII (p = 0.006), albumin–bilirubin score (ALBI) (p = 0.04), and coSII–PNI were predictors of OS. In the multivariate analysis, preoperative hydrocephalus (p < 0.001), ALBI (p = 0.010), SII (p < 0.001), and coSII–PNI as independent prognostic factors were significantly correlated with OS.Conclusion: The preoperative SII, ALBI, and coSII–PNI serve as robust prognostic biomarkers for patients with MB undergoing surgical resection.

Background: Anlotinib is a multi-target anti-angiogenic agent. This retrospective study aimed to evaluate the efficacy and safety of anlotinib alone or in combination with temozolomide for the treatment of recurrent high-grade glioma.Materials and Methods: The clinical data of patients with recurrent high-grade glioma treated with anlotinib alone or in combination with temozolomide in our cancer center were collected and analyzed. Treatment response was evaluated according to the response assessment for neuro-oncology criteria. Progression-free survival, progression-free survival at 6 months, overall survival, and overall survival at 12 months were evaluated by Kaplan–Meier method and compared by log-rank test.Results: Between August 2019 and December 2020, 31 patients with recurrent high-grade glioma (21 of grade 4 and 10 of grade 3) were enrolled in this study. Seventeen patients received anlotinib alone and 14 received anlotinib plus temozolomide. All patients were heavily treated, the median lines of previous treatments were 2, and the median Karnofsky score was 60. At the data cutoff date, the median progression-free survival was 4.5 months and the progression-free survival at 6 months was 43.5%. The median overall survival was 7.7 months, and the overall survival at 12 months was 26.7%. The progression-free survival at 6 months and the overall survival at 12 months for 21 patients with grade 4 glioma was 40.2 and 27.9%, respectively. The tumor objective response rate was 41.9% in all patients and 33.3% in patients with grade 4 glioma. No grade 3 or worse treatment-related adverse events were recorded during the treatment.Conclusion: Anlotinib alone or in combination with temozolomide showed encouraging efficacy and favorable tolerability in patients with recurrent high-grade glioma who had been heavily treated.

The fault diagnosis of hydrogen sensors is of great significance. However, it is difficult to collect data samples for some modes of hydrogen sensor signals, so the data samples may be unbalanced, which can seriously affect the fault diagnosis results. In this paper, we present a novel convolutional neural network (CNN)-based deep convolutional generative adversarial network (DCG) method (DCG-CNN) for gas sensor fault diagnosis. First, we transform the 1D fault signals of the gas sensor into 2D gray images for end-to-end conversion with no signal data information loss. Second, we use the DCG to enrich the 2D gray images of small fault data samples, which results in balanced sensor fault datasets. Third, we use the CNN method to improve the accuracy of fault diagnosis. In order to understand the internal mechanism of the CNN, we further visualize the learned feature maps of fault data samples in each layer of the CNN and try to analyze the reasons for the method's high performance. The fault diagnosis accuracy of the DCG-CNN is shown to be higher than that of other traditional methods.

ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

In the mining process of coal, oil, and natural gas, there are often a large number of toxic, hazardous and explosive gases, which pose a greater safety hazard. These gases usually exist in the form of mixtures. Accurately identifying the composition of the mixed gas is of great significance to the prevention of safety accidents. In order to improve the accuracy of the component recognition of the mixed gas, this paper proposes a mixed gas component recognition algorithm based on the combination of principal component analysis (PCA) and k-nearest neighbor algorithm (KNN). PCA is used to extract the characteristics of the sensor array signal to obtain the characteristic value of the gas, and then KNN is used to realize the recognition of the gas type. The results show that the recognition rate of the feature quantity after dimensionality reduction as input is higher than that before dimensionality reduction. Finally, PCA and KNN algorithm and PCA and support vector machine (SVM) algorithm are compared for the recognition rate of mixed gas. Experimental results show that the proposed method has a recognition rate of 96.88% for mixed gas components.

Gases in the real environment always exist in the form of mixtures, and effective identification of gas types to reduce the occurrence of safety incidents has become an important direction in the field of gas analysis research. This paper proposes a mixed gas identification method based on PCA and PSO-LSSVM. This method uses principal component analysis (PCA) to reduce the dimensionality of the sensor array output signal, and uses the Relief algorithm to select data. The particle swarm algorithm is used to iteratively optimize the relevant parameters in the least squares support vector machine model, and the PSO-LSSVM model is constructed to qualitatively analyze the composition of the mixed gas. This article uses a public data set of mixed gases of ethylene, methane and carbon monoxide to conduct experiments. The experimental results show that the method used in this paper can effectively identify the type of mixed gas, and the recognition accuracy rate reaches 91.67%. The method proposed in this paper can provide a research foundation for the follow-up analysis of the mixed gas concentration.

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

High-grade glioma is the most common malignant primary brain tumor in the central nervous system. Multiple strategies such as surgery, radiotherapy, and chemotherapy have been used, but the prognosis of patients with high-grade glioma remains poor. No standard treatment exists for recurrent gliomas; however, combination therapies of programmed cell death protein 1 blockades with antiangiogenic agents have demonstrated promising effects in different solid tumors. We have initiated a clinical trial designed to evaluate the safety and efficiency of neoadjuvant therapy using camrelizumab and apatinib in patients with recurrent highgrade gliomas. In this prospective, Phase II, singlearm study, patients with recurrent highgrade gliomas will receive singledose intravenous injection of camrelizumab (200 mg) and daily oral administration of apatinib (250 mg/day for 7 days) 14 days before surgery for recurrent tumor. Sequential therapy will begin 2 weeks after surgery with the biweekly injection of camrelizumab and 4 weeks after surgery with the daily administration of apatinib. Treatment of camrelizumab and apatinib will be continued until disease progression or unacceptable toxicity or death. The trial is planned to enroll 30 patients. Up-to date (March 31, 2022), 12 patients had been enrolled, in which, 9 were GBM. Three patients died, while 4 cases on trial more than 6 months, the longest already 1 year. Although an evaluation is still impossible to be conducted yet, some patients have shown a promising outcome. We will present updated results on the meeting. These preliminary data suggest that this study would be worthwhile. This study was approved by the Ethics Committee of Sun Yatsen University Cancer Center (Guangzhou, China; approval No. SLB202014901). This study was registered with ClinicalTrials.gov under identifier NCT04588987.

Purpose We aim to investigate the impacts of extent of resection and adjuvant radiotherapy on survival of high-grade meningiomas (WHO grade II–III) according to modern diagnosis and management. Methods Patients with high-grade meningiomas were identified in the Surveillance Epidemiology and End Results (SEER) database between 2000 and 2015 and used for survival analysis. Propensity score matching (PSM) was conducted to reduce selection bias. Another 92 patients from Sun Yat-sen University Cancer Center (SYSUCC) were used for validation. Results 530 patients were enrolled from SEER. Patients with gross total resection (GTR) had no significantly different overall survival (OS) compared with those with subtotal resection (STR), even after performing PSM between these two groups. Multivariable analysis found that age ≥ 65 years (HR 2.22, P 6 cm (HR 1.59, P = 0.004) and grade III tumor (HR 4.31, P

Background and Aim: Approximately 60% of glioblastoma multiforme (GBM) patients possess an unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) gene, which confers a limited response to standard-of-care treatment with temozolomide (TMZ), resulting in a lower survival. Dianhydrogalactitol (VAL-083) is a novel bi-functional DNA-targeting agent that induces interstrand cross-links at N7-guanine, leading to DNA double-strand breaks and ultimately cell death. VAL-083 circumvents MGMT-mediated repair of the O6 guanine alkylator TMZ. A Phase 2 study has been initiated for VAL-083 in newly diagnosed MGMT unmethylated GBM. Subjects and Methods: The study has two parts: part 1 is a dose–escalation and induction format to enroll up to ten patients in which they received VAL-083 at 20, 30, or 40 mg/m2 per day for 3 days every 21 days concurrently with standard radiation treatment and VAL-083 for up to eight additional cycles. Part 2 comprises an expansion phase to enroll up to twenty additional patients. This study was performed with approval by the Institutional Review Board of Sun Yat-sen University Cancer Center (B2016-058-01) on January 13, 2017, and registered with the ClinicalTrials.gov (NCT03050736) on February 13, 2017. Results: After completion of dose escalation, VAL-083, 30 mg/m2 per day, in combination with radiation therapy, was generally safe and well tolerated. At the cutoff date, 23 patients had been enrolled, 14 of whom had been treated in the expansion phase. Consistent with prior studies, myelosuppression was the most common adverse event. Pharmacokinetic assessment indicated that the levels of VAL-083 were as high in the cerebrospinal fluid as in plasma, 2 h postinfusion. Of the 22 patients who had reached their four precycle magnetic resonance imaging assessments, 12 were assessed with disease progression, with a median progression-free survival of 9.9 (95% confidence interval 7.3–12.0) months for all the patients studied. Conclusion: These preliminary data support VAL-083 as a potentially valuable treatment option for newly diagnosed GBM.

Background and Aim: Guidelines recommend adjuvant treatment for patients with high-grade gliomas and low-grade gliomas with high risk of progression. In clinical practice, however, treatments may not conform to these suggested guidelines. In this study, we reviewed the treatments and outcomes in patients with gliomas at Sun Yat-Sen University Cancer Center (SYSUCC), China. Materials and Methods: Medical records and radiologic images of 1215 glioma patients who underwent surgery at the center from 2000 to 2017 were retrospectively reviewed, and their clinicopathological characteristics, treatment method, and overall survival (OS) were analyzed. The study was approved by the Ethics Committee of SYSUCC on February 20, 2019 (approval No. GZR2019-219). Results: A total of 1001 patients diagnosed with glioma (initially World Health Organization 2007 criteria, then 2016 criteria) were enrolled, including 90 patients with Grade I, 307 Grade II, 239 Grade III, and 365 Grade IV gliomas. A total of 331 of 604 patients with high-grade glioma (54.8%) and 159 of 397 with low-grade glioma (40.1%) received postsurgical radiotherapy, and 285 patients with high-grade tumors (47.1%) and 80 with low-grade tumors (20.2%) received adjuvant chemotherapy. The median OS was 17.5 months for Grade IV and 43.1 months for Grade III gliomas. The median OS of patients with low-grade glioma was not reached. The 5-year survival rates of patients with Grades I, II, III, and IV gliomas were 94.7%, 73.7%, 45.7%, and 18.6%, respectively. Multivariate analysis identified onset age, preoperative seizure, tumor location, pathological subtype, resection extent, and postsurgical treatment as independent predictors of OS in patients with high-grade gliomas. Patients with high-grade glioma who received postsurgical treatment had better survival than those without adjuvant therapy (Grade III: 52.6 vs. 20.3 months, P = 0.012; Grade IV: 22.6 vs. 12.1 months, P < 0.001). Among patients with diffuse low-grade gliomas, age, performance status, preoperative seizure, Ki-67 index, tumor subtype, and resection extent were associated with clinical outcomes. Conclusion: Glioma patients are not always treated according to guidelines. Although standard care may lead to favorable prognoses, individualized treatments may be more acceptable and result in better outcomes and should thus be considered in routine clinical practice.

Purpose Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation-based radiomics framework to predict if HGG patients would have long or short OS time. Methods First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multifeature collaborative sparse representation classification to combine the information of multimodal images to classify OS time. Results Three experiments were performed on the two datasets provided by different institutions. Specifically, the proposed model was trained and independently tested on dataset 1 (135 subjects), on dataset 2 (86 subjects), and on the combination of dataset 1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities), and 87.93% on the combined dataset (one modality). Conclusions The sparse representation theory provides reasonable solutions to feature extraction, feature selection, and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome.

Purpose Isocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI). Methods In this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non‐enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all‐relevant feature selection and a random forest classification for predicting IDH1. Four single‐region models and a model combining all‐region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients). Results Among the four single‐region radiomics models, the edema model achieved the best accuracy of 96% and the best F1‐score of 0.75 while the non‐enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor‐core model (accuracy 0.96, AUC 0.86 and F1‐score 0.75) and the whole‐tumor model (accuracy 0.96, AUC 0.88 and F1‐score 0.75) was slightly better than the single‐regional models. The 8‐feature all‐region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1‐score 0.78. Among all models, the model combining all‐region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1‐score 0.84. Conclusions The radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all‐region features performed better than the single‐region models, while combining age with all‐region features achieved the best performance.

Purpose Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization that complements diffusion and dynamic contrast-enhanced MRI. However, quantification of in vivo APT effect is challenging because of concomitant semisolid magnetization transfer (MT) and nuclear overhauser enhancement effects. A direct saturation corrected (DISC) chemical exchange saturation transfer (CEST) analysis has been recently proposed that simplifies the determination of in vivo CEST effects. Our present study aimed to extend the DISC analysis to pulsed radiofrequency CEST MRI and evaluate it at 3T. Methods Nine adult male Sprague-Dawley rats implanted C6 gliomas underwent multiparametric MRI of T1 , T2 , CEST, and T1 -weighted gadolinium-enhanced imaging 1 day before and 3 days after chemoradiotherapy. The routine MT asymmetry, 3-point method, and the extended DISC analysis were compared in tumor characterization with histology as a reference. Regional variations were assessed by 1-way analysis of variance. Results T1 , T2 , and MT asymmetry and the DISC CEST effects showed significant alterations in tumor/necrosis with respect to the contralateral reference (P 0.05), whereas the 3-point method detected no regional alteration at both time points (P > 0.05). Conclusion Our study translated DISC CEST MRI to 3T, evaluated it in glioma rat models, and confirmed its advantages in resolving tumor heterogeneity over the routine asymmetry and 3-point analyses.

Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Background: Medulloblastoma (MB) is the most common malignant brain tumor of childhood. Studies have shown that the link between socio-economic status, morbidity and mortality from major cancers has changed significantly over the past 50 years, and therefore the socio-economic variation in cancer incidence is dynamic. Meanwhile, Socio-economic status (SES) characteristics of patients with medulloblastoma and the influence of Socio-economic status on their prognosis have been rarely discussed. Methods: Purpose of our study was aiming to understand ethnic, gender and socioeconomic disparities in medulloblastoma by using the SEER database. Propensity score matching of cases with controls by gender was conducted at the ratio of 1:1. Multivariate cox proportional hazards model was used to assess SES impact and clinically relevant variables of medulloblastoma specific death and overall survival. Independent prognostic factors determined by multivariate analysis were used to construct nomograms. Results: 2660 patients were enrolled after the matching. Study showed unemployed (MBSD, high level vs. low level, HR = 1.334) (OS, high level vs. low level, HR = 1.311) and marital status (OS, married vs unmarried/unknow., HR = 0.706) were important factors affecting the prognosis of medulloblastoma in male. Meanwhile, median household income (MBSD, Quartile 1 vs. Quartile 3, HR = 1.029) (OS, Quartile 1 vs. Quartile 2, HR = 1.809) (OS, Quartile 1 vs. Quartile 3, HR = 1.592), residence (MBSD, urban vs. rural, HR = 0.414) and insurance status (MBSD, insured vs. uninsured/unknow, HR = 0.566) (OS, insured vs. uninsured/unknow, HR = 0.573) were significant factors affecting the prognosis of medulloblastoma in female. Through the calibration plot and C-index test, our nomogram was also of predictive significance. Conclusions: The unique features of medulloblastoma have provided a scenario for the analysis of the impact of racial, ethnic, gender and socioeconomic factors. The current findings have important public health implications for achieving the goal of a healthy population. Given the known morbidity rates and the long-term psychological, financial and medical burdens that these children and their families must bear, it is critical to identify and address these gaps.

Chemical gas sensor array is a common information acquisition device in current machine olfactory systems. However, due to the characteristics of the sensitive materials in the chemical gas sensor, the measurement quality of sensor array decreases in long-term practical application, which gradually affects the analytical results of the machine olfactory system as the time goes on. The self-validating sensor has fault detection and isolation module, fault diagnosis module, fault recovery module and uncertainty analysis module. These functions of self-validating sensor are suitable for monitoring the reliability of the chemical sensor array. In this paper, the self-validating sensor technology is introduced into chemical gas sensor array to improve the measurement quality, so as to improve the overall performance of machine olfactory system. A prototype of self-validating chemical sensor array is designed to illustrate the feasibility of its application in machine olfactory system.

With the development of urbanization, more and more attention has been paid to the problem of urban air pollution. The expansion of city scale results in the dynamic change of pollutant distribution. The monitoring equipment based on optical analysis instrument cannot meet the new demand of spatiotemporal dynamic monitoring of pollution gas distribution in the region. The portable air pollution monitoring equipment can realize the regional monitoring of pollutants and is easy to form a monitoring network. A prototype of a portable air pollution monitoring system based on chemical sensor array is designed in this paper. The monitoring system is mounted in a portable case and can monitor the working state of the system and the process of gas measurement through the touch screen. The instrument can monitor the concentration of environmental pollution gas in real time and display the concentration change curve. It is also equipped with 4G wireless module, which can transmit the pollution gas information remotely. The reliability and effectiveness of the designed monitoring system are proved by testing under laboratory conditions.

Background Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment. Materials and methods Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively. Results A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit. Conclusion Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.

Twist is a transcription factor involved in the process of epithelial to mesenchymal transition (EMT) of carcinoma cells, and the promotion of invasion of gliomas through the mesenchymal adjusting process. However, its clinical significance in human glioma has not yet to be understood. To delineate the clinical-pathological significance and prognostic value of Twist, the expression of Twist was evaluated by Immunohistochemistry for 187 glioma samples. We found that Twist demonstrated frequent nuclear expression in the glioma samples and its expression levels were associated with tumor grade (P0.001). Furthermore, high Twist expression was correlated with a poor outcome in patients with glioma (P=0.001), particularly with high grade glioma (P=0.026). Interestingly, Twist expression showed positive correlation with microvascular density (MVD) (r=0.145, P=0.048) as well as vasculogenic mimicry (VM) (r=0.273, P0.001) in the tumors. These results suggest that Twist could be a predictor for poor prognosis in glioma patients. Additionally, Twist expression was associated with two major microcirculation patterns: endothelial-dependent vessels and VM in glioma, indicating that Twist could be a potential molecular target for anti-glioma therapy.

Background Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the efficacy of quantitative parameters measured by dual-energy spectral computed tomographic (CT) for this differentiation. Methods Twenty-eight patients were examined by dual-energy spectral CT. The effective and normalized atomic number (Zeff and Zeff-N, respectively); spectral Hounsfield unit curve (λHU) slope; and iodine and normalized iodine concentration (IC and ICN, respectively) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2 months were used for final diagnosis. Nonparametric and t-tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), and accuracy were calculated using receiver operating characteristic (ROC) curves. Predictive probabilities were used to generate ROC curves to determine the diagnostic value. Results Examination of pre-contrast λHU, Zeff, Zeff-N, IC, ICN, and venous phase ICN showed no significant differences in quantitative parameters (P > 0.05). Venous phase λHU, Zeff, Zeff-N, and IC in glioma recurrence were higher than in treatment-related changes (P 3, achieving 66.7, 91.7, 83.3, and 91.7% sensitivity; 100.0, 77.8, 88.9, and 77.8% specificity; 100.0, 73.3, 83.3, and 73.3% PPV; 81.8, 93.3, 88.9, and 93.3% NPV; and 86.7, 83.3, 86.7, and 83.3% accuracy, respectively. The respective areas under the curve (AUCs) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes. Conclusions Glioma recurrence could be potentially differentiated from treatment-related changes based on quantitative values measured by dual-energy spectral CT imaging.

Twist is a transcription factor involved in the process of epithelial to mesenchymal transition (EMT) of carcinoma cells, and the promotion of invasion of gliomas through the mesenchymal adjusting process. However, its clinical significance in human glioma has not yet to be understood. To delineate the clinical-pathological significance and prognostic value of Twist, the expression of Twist was evaluated by Immunohistochemistry for 187 glioma samples. We found that Twist demonstrated frequent nuclear expression in the glioma samples and its expression levels were associated with tumor grade (P

Background Vessels with different microcirculation patterns are required for glioblastoma (GBM) growth. However, details of the microcirculation patterns in GBM remain unclear. Here, we examined the microcirculation patterns of GBM and analyzed their roles in patient prognosis together with two well‐known GMB prognosis factors (O6‐methylguanine DNA methyltransferase [MGMT] promoter methylation status and isocitrate dehydrogenase [IDH] mutations). Methods Eighty GBM clinical specimens were collected from patients diagnosed between January 2000 and December 2012. The microcirculation patterns, including endothelium‐dependent vessels (EDVs), extracellular matrix‐dependent vessels (ECMDVs), GBM cell‐derived vessels (GDVs), and mosaic vessels (MVs), were evaluated by immunohistochemistry (IHC) and immunofluorescence (IF) staining in both GBM clinical specimens and xenograft tissues. Vascular density assessments and three‐dimensional reconstruction were performed. MGMT promoter methylation status was determined by methylation‐specific PCR, and IDH1/2 mutations were detected by Sanger sequencing. The relationship between the microcirculation patterns and patient prognosis was analyzed by Kaplan‐Meier method. Results All 4 microcirculation patterns were observed in both GBM clinical specimens and xenograft tissues. EDVs were detected in all tissue samples, while the other three patterns were observed in a small number of tissue samples (ECMDVs in 27.5%, GDVs in 43.8%, and MVs in 52.5% tissue samples). GDV‐positive patients had a median survival of 9.56 months versus 13.60 months for GDV‐negative patients (P = 0.015). In MGMT promoter‐methylated cohort, GDV‐positive patients had a median survival of 6.76 months versus 14.23 months for GDV‐negative patients (P = 0.022). Conclusion GDVs might be a negative predictor for the survival of GBM patients, even in those with MGMT promoter methylation.

BACKGROUND: The present study explored the predictive value of systemic inflammatory indexes in diagnosing grade III gliomas of oligodendroglial origin. METHODS: A retrospective study of 154 patients with grade III gliomas was conducted. Systemic inflammatory indexes, including neutrophil-to-lymphocyte ratio (NLR), albumin-to-gamma-glutamyl transferase ratio (AGR), platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, prognostic nutritional index, and fibrinogen-to-albumin ratio, were reviewed. The resulting predictive model was externally validated using a demographic-matched cohort of 49 grade III glioma patients. RESULTS: In the training set, gliomas of oligodendroglial origin tended to have a lower NLR (P=0.018) and a higher AGR (P=0.036) than those with tumors of astrocytic origin. Moreover, both NLR and AGR had predictive value for oligodendroglial tumors, when compared with astrocytic tumors. The best diagnostic value was obtained using NLR + AGR (AUC =64.9%, 95% CI: 55.5–74.3%, P=0.005). In the validation set, NLR + AGR satisfactorily predicted the presence of oligodendroglial tumors (AUC =66.5%, 95% CI: 50.6–82.4%, P

The intraoperative tumor-free principles refer to the operation techniques that must be carried out to prevent the exfoliation and planting of tumor cells during the operation, so as to prevent local recurrence and distant metastasis. Following the intraoperative tumor-free principles has been valued in resection of extracranial solid tumors. However, the significance of intraoperative tumor-free principles for brain tumors is still unclear. We retrospectively analyzed 106 patients with primary glioblastoma who underwent resection following the intraoperative tumor-free principles from 2010 to 2016. By February 28, 2019, 11 (10.4%) patients were lost to follow-up. The median overall survival (OS) was 20.2 months, and the 1-, 3-, and 5-year survival rates were 71%, 30%, and 26%, respectively. For patients with complete tumor resection, the median OS was 24.8 months, and the 1-, 3-, and 5-year survival rates were 80%, 38%, and 36%, respectively. Patients who received postoperative Stupp regimen had a median OS of 74.1 months, and the 1-, 3-, and 5-year survival rates were 82%, 51%, and 51%, respectively. A total of 36 patients had complete tumor resection followed by Stupp regimen. Their 1-, 3-, and 5-year survival rates were 94%, 68%, and 68%, respectively. Median OS was not reached. The patient with the longest OS had survived for 108 months and was still alive. In summary, the OS of patients in this study was relatively longer than that reported in most previous literatures, which suggests that following the intraoperative tumor-free principles in resection of GBM can benefit patient survival.

e14019 Background: High-grade glioma (HGG) is the most common malignant brain tumor and lacks effective treatment regimen. Anlotinib is a multikinase inhibitor blocking angiogenesis and tumor cell proliferation simultaneously. This study was performed to evaluate the efficacy and safety of anlotinib alone or in combination with temozolomide (TMZ) in the treatment of recurrent HGG. Methods: This is a single-center, retrospective study. Eligible patients (pts) were diagnosed with pathologically confirmed high grades (WHO III/IV) glioma and had recurrent or progressive disease on or after prior treatment. Other key eligibility criteria included Karnofsky Performance Status (KPS) ≥ 40, aged 16 ̃75 years and having at least one measurable lesion (RANO criteria). Pts were administrated with anlotinib once daily for 14 days every 3 weeks till disease progression, intolerable toxicities or death. The initial dose was 12mg for younger pts ( < 40 years old) with KPS ≥ 60 and 10 mg for others. Combination treatment was allowed if previous TMZ was effective and tolerable. TMZ was administered on dose-dense schedule (150mg/m2, QD, d1-d7 and d15-d21 every 28 days) or metronomic schedule (25-50mg/m2 QD). The primary endpoint was progression-free survival at 6 months (PFS6m) accessed according to RANO criteria. The second endpoints included overall survival (OS), objective response rate (ORR) and disease control rate (DCR). Results: Between August 2019 and June 2020, 23 pts with HGG (15 grade IV; 8 grade III; 12 males, 11 females) were enrolled. The median age and median KPS was 42 years and 60. 16 pts have multifocal or disseminated disease. 18 pts received ≥2 lines previous treatment. At the data cutoff date on September 2020, the median duration of treatment was 9 weeks (range: 3-33). The PFS6m was 39.1% and the median PFS was 4.2 months (95% CI: 2.8, 5.6). The median OS was not reached (95% CI: NE, NE) and the OS at 12 months (OS12m) was 54.8%. 8 pts observed tumor response and 9 pts had stable disease. The ORR and DCR were 34.8% and 73.9% respectively. The results of survival analysis for subgroups were summarized in table below. Grade 1 or 2 treatment-related adverse events (TRAEs) occurred in 65.2% pts. No ≥ grade 3 TRAE was found. All hematological TRAEs occurred in patients received combination regimen. No TRAE-induced treatment termination occurred. The lower incidence of TRAE may partly attributed to that most pts (18/23) received lower initial dose (10mg) of anlotinib and the relatively shorter treatment duration. Conclusions: This study showed treatment with anlotinib alone or in combination with TMZ had promising efficacy and favorable tolerability in patients with recurrent HGG.[Table: see text]

To enhance the reliability of multifunctional sensors, a novel data validation strategy is presented by handling independent and related variables separately. The maximal information coefficient (MIC), which can measure the strength of the correlation between two variables, is applied to divide all variables of multifunctional sensors into related and independent. For one thing, the k-nearest neighbor (kNN) rule is introduced to accomplish fault detection and isolation of independent variables, and the grey predictive model GM(1,1), which has the advantages of low computation burden and high accuracy, is adopted to achieve data recovery of faulty independent variables. For another, the kernel principal component analysis (KPCA), which can handle possible non-linearity of data, is employed to realize fault detection of related variables. An iterative reconstruction-based contribution (IRBC) method is developed to isolate all faulty related variables, and data recovery of them are implemented using a fuzzy similarity (FS)-based reconstruction method based on the spatial correlations among related variables. An experimental system for multifunctional sensors is built to evaluate the proposed strategy, and the performance comparisons with its counterparts are also conducted.

Purpose This paper aims to enhance the reliability of self-validating multifunctional sensors. Design/methodology/approach An effective fault detection, isolation and data recovery (FDIR) strategy by using kernel principal component analysis (KPCA) coupled with gray bootstrap and fault reconstruction methods. Findings The proposed FDIR strategy is able to the address fault detection, isolation and data recovery problem of self-validating multifunctional sensors efficiently. Originality/value A KPCA-based model which can overcome the limitation of existing linear-based models is used to achieve the fault detection task. By using gray bootstrap method, the position of all faulty sensitive units can be calculated even under the multiple faults situation. A reconstruction-based contribution method is adopted to evaluate the amplitudes of the fault signals, and the fault-free output of the faulty sensitive units can be used to replace the fault output.

A novel fault detection, isolation, and data recovery (FDIR) approach for self-validating multifunctional sensors is presented in this paper. To improve the fault detection accuracy under multiple steady conditions for multifunctional sensors, a sparse non-negative matrix factorization (SNMF)-based model is employed to accomplish fault detection through a combination of newly proposed $C^{2}$ and squared prediction error ( SPE ) statistics. Furthermore, a self-adaptive multiple-variable reconstruction strategy (SMVR) is proposed to achieve high accuracy on multiple-fault isolation and data recovery for faulty sensitive units. The performance of the proposed approach is fully verified in a real experimental system for self-validating multifunctional sensors, and it is compared with those of other fault detection models, such as principal component analysis (PCA), non-negative matrix factorization (NMF), and fault isolation algorithms, such as PCA-based contribution plots and SNMF-based contribution plots. The experimental results demonstrate that the proposed approach provides an excellent solution to the FDIR of self-validating multifunctional sensors.

Existing deep convolutional neural network-based survival analysis neither consider the modern attention mechanism nor use 3D tomographic medical images such as magnetic resonance images (MRI). This paper for the first time presents a 3D deep convolutional neural network using attention mechanism for survival prediction from multiparametric MRI in glioblastoma (GBM) patients. The attention module is incorporated into the residual network to enhance the representation power of meaningful features while suppress unimportant ones. The proposed model achieves an C-index of 0.71 in the training dataset and 0.68 in an independent test dataset, which outperforms both the traditional Cox model (0.60,0.54) and the non-attentive model (0.63,0.61). It indicates that the proposed 3D attention network has the potential of offering better performance in predicting survival using MRI than traditional survival analysis.

Air pollution is one of the major threats to human health. The monitoring of toxic NO2 gas in urban air emission pollution is becoming increasingly important. Thus, the development of an NO2 sensor with low power consumption, low cost, and high performance is urgent. In this paper, a planar structural micro hotplate gas sensor based on an AlN ceramic substrate with an annular Pt film heater was designed and prepared by micro-electro-mechanical system (MEMS) technology, in which Pt/Nb/In2O3 composite semiconductor oxide was used as the sensitive material with a molar ratio of In:Nb = 9:1. The annular thermal isolation groove was designed around the heater to reduce the power consumption and improve the thermal response rate. Furthermore, the finite element simulation analysis of the thermal isolation structure of the sensor was carried out by using ANSYS software. The results show that a low temperature of 94 &deg, C, low power consumption of 150 mW, and low concentration detection of 1 to 10 ppm NO2 were simultaneously realized for the Nb-doped In2O3-based gas sensor. Our findings provide a promising strategy for the application of In2O3-based sensors in highly effective and low concentration NO2 detection.