Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several neural network (NN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptim

Controlled Radical Polymerization of Vinyl Acetate Mediated by a Bis(imino)pyridine Vanadium Complex

Source type: Prin

Dinitrogen partial reduction by formally zero- and divalent vanadium complexes supported by the bis-iminopyridine system

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Reactivity of chromium complexes of a bis(imino)pyridine ligand : highly active ethylene polymerization catalysts carrying the metal in a formally low oxidation state

A divalent chromium complex of bis(imino)pyridine, {2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}CrCl2 (1), was prepared with the aim of studying its reactivity with alkylating agents. Upon treatment with MeLi, the metal center was both reduced and alkylated, forming {2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}CrMe(-Me)Li(THF)3 (2). Complex 1 is also conveniently reduced with either NaH or metallic sodium to give the new species {2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}CrCl (3). Despite the appearance of the metal center in a rare monovalent oxidation state, the square-planar geometry of the Cr atom suggests that the metal is most likely divalent, with the electron housed in the ligand * orbital. When it is activated with MAO, complex 3 is a very and even more active catalyst for the polymerization of ethylene than either the -CrCl2 or -CrCl3 derivative of this ligand system and yet produces polymers with similar properties. Subsequent reactivity studies of complex 3 have allowed the isolation of several products. Reaction with either LiCH2Si(CH3)3 or MeLi resulted in deprotonation of one of the methyl groups on the ligand backbone, forming {2-[2,6-(i-Pr)2PhN=C(CH3)]-6-[2,6-(i-Pr)2PhNC=CH2](C5H3N)}Cr(THF) (4) and {2-[2,6-(i-Pr)2PhN=C(CH3)]-6-[2,6-(i-Pr)2PhNC=CH2](C5H3N)}Cr(-Me)Li(THF)3 (5), respectively. On the other hand, alkylation with AlMe3 allowed the successful preparation of another organochromium species, {2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}CrCH3 (7), along with small amounts of the byproduct {2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}Cr(-Cl)2Al(CH3)2 (6). Interestingly, complex 7, which also has the deceiving connectivity of a monovalent species, displays an even greater activity for ethylene polymerization than all of the other species reported herein, again producing a polymer with nearly identical characteristics. Activation with IBAO revealed a deactivation pathway similar to that observed with the FeCl2 system. In this case, the stronger reducing power of IBAO resulted in the usual reduction not only of the ligand backbone but also of the metal center. As a result of the metal reduction, partial transmetalation of the ligand system occurred, with formation of [4-{2,6-[2,6-(i-Pr)2PhN=C(CH3)]2(C5H3N)}Al2(i-Bu)3(-Cl)]Cr-(6-C7H8) (8). By being catalytically inactive, the partly transmetalated 8 suggests that ligand demetalation is a possible catalyst deactivation pathway

Reaction of a Redox-Active Ligand Complex of Nickel with Dioxygen Probes Ligand-Radical Character

(Chemical Equation Presented) Bis(imino)pyridine complex [Ni{2,6-(ArN=CMe)2C5H3N}Cl] (where Ar = 2,6-iPr2C6H3) was synthesized by reduction of the corresponding dichloride complex and characterized as a ligand-radical complex of NiII. Reaction of this complex with O 2 caused intraligand C-C bond cleavage to afford the Ni complex of the new iminoethylpyridylcarboxamidato ligand, which also was isolated as the corresponding carboxamide, 6-(ArN=CMe)C5H3N-2-C(O)NHAr. This reaction serves as an example of small-molecule activation effected directly at the redoxactive bis(imino)pyridine ligand without an overall oxidation state change at the Ni center.close171

Multi-module-based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normalwaveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several Artificial Neural Network (ANN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime