Are 4f-Orbitals Engaged in Covalent Bonding Between Lanthanides and Triphenylphosphine Oxide? An Oxygen K-Edge X-ray Absorption Spectroscopy and Density Functional Theory Study.

The bonding covalency between trivalent lanthanides (Ln = La, Pr, Nd, Eu, Gd) and triphenylphosphine oxide (TPPO) is studied by X-ray absorption spectra (XAS) and density functional theory (DFT) calculations on the LnCl3(TPPO)3 complexes. The O, P, and Cl K-edge XAS for the single crystals of LnCl3(TPPO)3 were collected, and the spectra were interpreted based on DFT calculations. The O and P K-edge XAS spectra showed no significant change across the Ln series in the LnCl3(TPPO)3 complexes, unlike the Cl K-edge XAS spectra. The experimental O K-edge XAS spectra suggest no mixing between the Ln 4f- and the O 2p-orbitals in the LnCl3(TPPO)3 complexes. DFT calculations indicate that the amount of the O 2p character per Ln-O bond is less than 0.1% in the Ln 4f-based orbitals in all of the LnCl3(TPPO)3 complexes. The experimental spectra and theoretical calculations demonstrate that Ln 4f-orbitals are not engaged in the covalent bonding of lanthanides with TPPO, which contrasts the involvement of U 5f-orbitals in covalent bonding in the UO2Cl2(TPPO)2 complex. Results in this work reinforce our previous speculation that bonding covalency is potentially responsible for the extractability of monodentate organophosphorus ligands toward metal ions.

Synthesis, Structure, and Theoretical Calculations on NpO2Br42.

The actinide-halogen complexes (AnO2X42-, X = Cl, Br, and I) are the simplest and most representative compounds for studying the bonding nature of actinides with ligands. In this work, we attempted to synthesize the crystals of NpO2X42- (X = Cl, Br, and I). The crystals of NpO2Cl42- and NpO2Br42- were successfully synthesized, in which the structure of NpO2Br42- was obtained for the first time. The crystal of NpO2I42- could not be obtained due to the rapid reduction of Np(VI) to Np(V) by I-. The molecular structures of NpO2Cl42- and NpO2Br42- were characterized by single-crystal X-ray diffraction and infrared, Raman, and UV-Vis-NIR absorption spectroscopy. The complexes of NpO2X42- (X = Cl, Br, and I) were also investigated by density functional theory calculations, and the calculated vibration frequencies and absorption features were comparable to the experimental results. Both the experimental results and theoretical calculations demonstrate the strengthened Np-O bonds and the weakened Np-X bonds across the NpO2X42- series; however, the population analysis on the frontier molecular orbitals (MOs) of NpO2X42- indicates a slight reduction in the Np-O bonding covalency and an enhancement in the Np-X bonding covalency from NpO2Cl42- to NpO2I42-. Results in this work have enriched the crystal database of the AnO2X42- family and provided insights into the bonding nature in the actinide complexes with soft- and hard-donor ligands.

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography.

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.

Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms.

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment. To solve this problem, we propose a deep reinforcement learning based and maximum-entropy-based soft actor-critic (SAC) algorithm. In the proposed algorithm, we add an improved Wolpertinger architecture to the original SAC algorithm in order to reduce the number of interactions and improve the accuracy of the algorithm. The results show that the proposed algorithm shows excellent performance in various scenarios of jamming and achieves accurate, fast, and continuous jamming for both sides of the communication.

Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis.

OBJECTIVE: Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. METHODS: We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). RESULTS: In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). CONCLUSION: High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. KEY POINTS: • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.

Open set recognition algorithm based on Conditional Gaussian Encoder.

For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the original form of the signal as much as possible and deep neural network is used to extract useful information. CG-Encoder adopts residual network structure and a new Kullback-Leibler (KL) divergence is defined. In the training phase, the known classes are approximated to different Gaussian distributions in the latent space and the discrimination between classes is increased to improve the recognition performance of the known classes. In the testing phase, a specific and effective OSR algorithm flow is designed. Simulation experiments are carried out on 9 jamming types. The results show that the CSR and OSR performance of CG-Encoder is better than that of the other three kinds of network structures. When the openness is the maximum, the open set average accuracy of CG-Encoder is more than 70%, which is about 30% higher than the worst algorithm, and about 20% higher than the better one. When the openness is the minimum, the average accuracy of OSR is more than 95%.