Stroke-Related Visceral Alterations: A Voxel-Based Neuroanatomic Localization Study.

OBJECTIVE: Functional and morphologic changes in extracranial organs can occur after acute brain injury. The neuroanatomic correlates of such changes are not fully known. Herein, we tested the hypothesis that brain infarcts are associated with cardiac and systemic abnormalities (CSAs) in a regionally specific manner. METHODS: We generated voxelwise p value maps of brain infarcts for poststroke plasma cardiac troponin T (cTnT) elevation, QTc prolongation, in-hospital infection, and acute stress hyperglycemia (ASH) in 1,208 acute ischemic stroke patients prospectively recruited into the Heart-Brain Interactions Study. We examined the relationship between infarct location and CSAs using a permutation-based approach and identified clusters of contiguous voxels associated with p < 0.05. RESULTS: cTnT elevation not attributable to a known cardiac reason was detected in 5.5%, QTc prolongation in the absence of a known provoker in 21.2%, ASH in 33.9%, and poststroke infection in 13.6%. We identified significant, spatially segregated voxel clusters for each CSA. The clusters for troponin elevation and QTc prolongation mapped to the right hemisphere. There were 3 clusters for ASH, the largest of which was in the left hemisphere. We found 2 clusters for poststroke infection, one associated with pneumonia in the left and one with urinary tract infection in the right hemisphere. The relationship between infarct location and CSAs persisted after adjusting for infarct volume. INTERPRETATION: Our results show that there are discrete regions of brain infarcts associated with CSAs. This information could be used to bootstrap toward new markers for better differentiation between neurogenic and non-neurogenic mechanisms of poststroke CSAs. ANN NEUROL 2023;94:1155-1163.

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.

Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published methodology is often incomplete, and the results are rarely validated in external testing data sets. Purpose To characterize the type, prevalence, and statistical impact of methodologic errors present in radiomic ML studies. Materials and Methods Radiomic ML publications were reviewed for the presence of performance-inflating methodologic flaws. Common flaws were subsequently reproduced with randomly generated features interpolated from publicly available radiomic data sets to demonstrate the precarious nature of reported findings. Results In an assessment of radiomic ML publications, the authors uncovered two general categories of data analysis errors: inconsistent partitioning and unproductive feature associations. In simulations, the authors demonstrated that inconsistent partitioning augments radiomic ML accuracy by 1.4 times from unbiased performance and that correcting for flawed methodologic results in areas under the receiver operating characteristic curve approaching a value of 0.5 (random chance). With use of randomly generated features, the authors illustrated that unproductive associations between radiomic features and gene sets can imply false causality for biologic phenomenon. Conclusion Radiomic machine learning studies may contain methodologic flaws that undermine their validity. This study provides a review template to avoid such flaws. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Jacobs in this issue.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. PURPOSE: This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. STUDY TYPE: Retrospective and prospective. POPULATION: For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). FIELD STRENGTH/SEQUENCE: 5T and 3T, T2-weighted turbo spin echo imaging. ASSESSMENT: Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. STATISTICAL TESTS: Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. RESULTS: In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. DATA CONCLUSION: In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. EVIDENCE LEVEL: 2 Technical Efficacy: Stage 2.

Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data.

Deep learning (DL) algorithms have shown remarkable potential in automating various tasks in medical imaging and radiologic reporting. However, models trained on low quantities of data or only using data from a single institution often are not generalizable to other institutions, which may have different patient demographics or data acquisition characteristics. Therefore, training DL algorithms using data from multiple institutions is crucial to improving the robustness and generalizability of clinically useful DL models. In the context of medical data, simply pooling data from each institution to a central location to train a model poses several issues such as increased risk to patient privacy, increased costs for data storage and transfer, and regulatory challenges. These challenges of centrally hosting data have motivated the development of distributed machine learning techniques and frameworks for collaborative learning that facilitate the training of DL models without the need to explicitly share private medical data. The authors describe several popular methods for collaborative training and review the main considerations for deploying these models. They also highlight publicly available software frameworks for federated learning and showcase several real-world examples of collaborative learning. The authors conclude by discussing some key challenges and future research directions for distributed DL. They aim to introduce clinicians to the benefits, limitations, and risks of using distributed DL for the development of medical artificial intelligence algorithms. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications.

Hundreds of imaging-based artificial intelligence (AI) models have been developed in response to the COVID-19 pandemic. AI systems that incorporate imaging have shown promise in primary detection, severity grading, and prognostication of outcomes in COVID-19, and have enabled integration of imaging with a broad range of additional clinical and epidemiologic data. However, systematic reviews of AI models applied to COVID-19 medical imaging have highlighted problems in the field, including methodologic issues and problems in real-world deployment. Clinical use of such models should be informed by both the promise and potential pitfalls of implementation. How does a practicing radiologist make sense of this complex topic, and what factors should be considered in the implementation of AI tools for imaging of COVID-19? This critical review aims to help the radiologist understand the nuances that impact the clinical deployment of AI for imaging of COVID-19. We review imaging use cases for AI models in COVID-19 (e.g., diagnosis, severity assessment, and prognostication) and explore considerations for AI model development and testing, deployment infrastructure, clinical user interfaces, quality control, and institutional review board and regulatory approvals, with a practical focus on what a radiologist should consider when implementing an AI tool for COVID-19.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.

Construction of a structural enzyme adsorption/kinetics model to elucidate additives associated lignin-cellulase interactions in complex bioconversion system.

Enzymatic hydrolysis is a rate-limiting process in lignocellulose biorefinery. The reaction involves complex enzyme-substrate and enzyme-lignin interactions in both liquid and solid phases, and has not been well characterized numerically. In this study, a kinetic model was developed to incorporate dynamic enzyme adsorption and product inhibition parameters into hydrolysis simulation. The enzyme adsorption coefficients obtained from Langmuir isotherm were fed dynamically into first-order kinetics for simulating the equilibrium enzyme adsorption in hydrolysis. A fractal and product inhibition kinetics was introduced and successfully applied to improve the simulation accuracy on adsorbed enzyme and glucose concentrations at different enzyme loadings, lignin contents, and in the presence of bovine serum albumin (BSA) and lysozyme. The model provided numerical proof quantifying the beneficial effects of both additives, which improved the hydrolysis rate by reducing the nonproductive adsorption of enzyme on lignin. The hydrolysis rate coefficient and fractal exponent both increased with increasing enzyme loadings, and lignin inhibition exhibited with increasing fractal exponent. Compared with BSA, the addition of lysozyme exhibited higher hydrolysis rates, which was reflected in the larger hydrolysis rate coefficients and smaller fractal exponents in the simulation. The model provides new insights to support process development, control, and optimization.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer.

OBJECTIVES: Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer. METHODS: In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance. RESULTS: Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94. CONCLUSIONS: MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer. KEY POINTS: • MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. METHODS: Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. RESULTS: Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. CONCLUSIONS: These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.

Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making. PURPOSE: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma. STUDY TYPE: Retrospective. POPULATION: In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T/T2 -weighted and T1 contrast-enhanced sequences. ASSESSMENT: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. STATISTICAL TESTS: Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. RESULTS: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set. DATA CONCLUSION: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.

PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome. MATERIALS AND METHODS: Clinical data were collected on patients treated with uterine fibroid embolization at the Hospital of the University of Pennsylvania from 2007 to 2018. Fibroids for each patient were manually segmented by an abdominal radiologist on a T1-weighted contrast-enhanced (T1C) sequence and a T2-weighted sequence of MR imaging obtained before and after embolization. A residual convolutional neural network (ResNet) model to predict clinical outcome was trained using MR imaging obtained before the procedure. RESULTS: Inclusion criteria were met by 727 fibroids in 409 patients. At clinical follow-up, 85.6% (n = 350) of 409 patients (590 of 727 fibroids; 81.1%) experienced symptom resolution or improvement, and 14.4% (n = 59) of 409 patients (137 of 727 fibroids; 18.9%) had no improvement or worsening symptoms. The T1C trained model achieved a test accuracy of 0.847 (95% confidence interval [CI], 0.745-0.914), sensitivity of 0.932 (95% CI, 0.833-0.978), and specificity of 0.462 (95% CI, 0.232-0.709). In comparison, the average of 4 radiologists achieved a test accuracy of 0.722 (95% CI, 0.609-0.813), sensitivity of 0.852 (95% CI, 0.737-0.923), and specificity of 0.135 (95% CI, 0.021-0.415). CONCLUSIONS: This study demonstrates that deep learning based on a ResNet model achieves good accuracy in predicting outcome of uterine fibroid embolization. If further validated, the model may help clinicians better identify patients who can most benefit from this therapy and aid clinical decision making.

Detecting phthalate esters in sludge particulates from wastewater treatment plants.

This study proposed a method for analysis of 10 phthalate esters compounds from wastewater treatment plant sludges. The analytical efficiency of GC-MS for of target compounds was verified by a standard mixture of phthalate esters. The response factors related to the respective internal standards from a five-point calibration curve quantified the phthalate esters in individual compounds. Based on the literature compiled by environmental agencies, new generation phthalate compounds have been developed, such as di-iso-nonyl phthalate (DiNP), di-iso-decyl phthalate (DiDP), as alternative to conventional phthalates. The analytical results showed that the total PAEs concentration was in the range from 7.4 to 138.6 mg kg-1 dw in these seven analyzed sludge samples. More, di-iso-nonyl phthalate (DiNP), di-iso-decyl phthalate (DiDP) and bis(2-ethylhexyl) phthalate (DEHP) contributed to over 99% of PAEs in the sludge. The correlation between total PAEs concentration in household and sewage flow treated at seven WWTPs and concentrations of DEHP, DiNP and DiDP was significant.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas.

PURPOSE: Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. METHODS: Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. RESULTS: Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). CONCLUSION: Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

Effects of atmospheric-plasma system on energy efficiency improvement and emissions reduction from a diesel engine.

Efficient energy usage and energy saving is one of the nowadays necessity for all scientists of IC engine. This is because of the current environmental challenges that have tremendously increased concerning air pollution, particularly pollutant emissions from vehicles. Yet, industries and governments alike have disregarded this phenomenon which has been considerably contributing to climate change. It is against this background that, the research works carried out in this present study is predominantly focusing on improving energy efficiency and reducing emission levels from diesel engines. This can be achieved with the help of atmospheric-plasma system which can offer a noble solution to the above-mentioned challenges due to its potential to improve combustion efficiency which leads to energy efficiency, while reducing emission levels from diesel engines. In this study, the performance and emissions of a diesel generator supplemented with an atmospheric-plasma system was examined. The diesel engine was used to examine the effects of fuel composition, or brake specific fuel consumption, thermal efficiency and pollutant emissions at different plasma system voltages. To this end, we equally examined the effects of atmospheric-plasma system on energy efficiency improvement and emissions reduction from diesel engine as the main purpose of this study. We do so by testing the diesel-fueled engine generator under the atmospheric-plasma system. The tests were carried out at a constant state condition with the engine running at 2200 rpm with torque and power outputs of 10.4 Nm (75% of the max load) and 2.1 kW, separately, for the tested fuels and this was used to increase the output voltage of the plasma system during this study. The plasma system ionized the intake air and improved the formation of free radicals upon combustion. During this study, the output voltage of the plasma was set within the range of 0-7 kV. The experimental results have indicated that formaldehyde, acetaldehyde and acrolein account for more than 75% of total carbonyl compounds emissions. Simultaneously, it was also observed from the results that higher plasma system voltage reduces pollutants emissions levels. Hence, such reduction is predominantly evident for nitrogen oxides, particulate matters and carbon monoxide. However, the marginal improvements of engine performance and emissions reduction become insignificant when the plasma system voltage reaches 6 kV. On the other hand, increasing the amount of plasma system voltages in diesel engine continues to significantly reduce pollutant emissions.

AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity.

Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.

Reduced expression of DNA repair genes and chemosensitivity in 1p19q codeleted lower-grade gliomas.

BACKGROUND: Lower-grade gliomas (LGGs, defined as WHO grades II and III) with 1p19q codeletion have increased chemosensitivity when compared to LGGs without 1p19q codeletion, but the mechanism is currently unknown. METHODS: RNAseq data from 515 LGG patients in the Cancer Genome Atlas (TCGA) were analyzed to compare the effect of expression of the 9 DNA repair genes located on chromosome arms 1p and 19q on progression free survival (PFS) and overall survival (OS) between patients who received chemotherapy and those who did not. Chemosensitivity of cells with DNA repair genes knocked down was tested using MTS cell proliferation assay in HS683 cell line and U251 cell line. RESULTS: The expression of 9 DNA repair genes on 1p and 19q was significantly lower in 1p19q-codeleted tumors (n = 175) than in tumors without the codeletion (n = 337) (p < 0.001). In LGG patients who received chemotherapy, lower expression of LIG1, POLD1, PNKP, RAD54L and MUTYH was associated with longer PFS and OS. This difference between chemotherapy and non-chemotherapy groups in the association of gene expression with survival was not observed in non-DNA repair genes located on chromosome arms 1p and 19q. MTS assays showed that knockdown of DNA repair genes LIG1, POLD1, PNKP, RAD54L and MUTYH significantly inhibited recovery in response to temozolomide when compared with control group (p < 0.001). CONCLUSIONS: Our results suggest that reduced expression of DNA repair genes on chromosome arms 1p and 19q may account for the increased chemosensitivity of LGGs with 1p19q codeletion.

Thermogravimetric Analysis of Textile Dyeing Sludge (TDS) in N2/CO2/O2 Atmospheres and its Combustion Model with Coal.

The combustion characteristics of textile dyeing sludge (TDS) in N2/O2, CO2/O2, and N2/CO2 atmospheres, and blends of TDS with coal were analyzed using TGA (thermogravimetric analysis). Results showed that the replacement of N2 by CO2 resulted in negative effects on the combustion and pyrolysis of TDS. Comparing N2/O2 and CO2/O2 atmospheres, combustion of TDS was easier in a N2/O2 atmosphere, but the residual mass after TDS pyrolysis in pure CO2 was less than that in N2 by approximately 4.51%. When the proportion of TDS was 30-50% in the blends of coal with TDS, a synergistic interaction clearly occurred, and it significantly promoted combustion. In considering different combustion parameters, the optimal proportion of TDS may be between 20-30%. The activation energy Ea value decreased from 155.6 kJ/mol to 53.35 kJ/mol with an increasing TDS proportion from 0% to 50%, and it rapidly decreased when the TDS proportion was below 20%.