Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage

Plastid Phylogenomic Insights into the Inter-Tribal Relationships of Plantaginaceae

Plantaginaceae, consisting of 12 tribes, is a diverse, cosmopolitan family. To date, the inter-tribal relationships of this family have been unresolved, and the plastome structure and composition within Plantaginaceae have seldom been comprehensively investigated. In this study, we compared the plastomes from 41 Plantaginaceae species (including 6 newly sequenced samples and 35 publicly representative species) representing 11 tribes. To clarify the inter-tribal relationships of Plantaginaceae, we inferred phylogenic relationships based on the concatenated and coalescent analyses of 68 plastid protein-coding genes. PhyParts analysis was performed to assess the level of concordance and conflict among gene trees across the species tree. The results indicate that most plastomes of Plantaginaceae are largely conserved in terms of genome structure and gene content. In contrast to most previous studies, a robust phylogeny was recovered using plastome data, providing new insights for better understanding the inter-tribal relationships of Plantaginaceae. Both concatenated and coalescent phylogenies favored the sister relationship between Plantagineae and Digitalideae, as well as between Veroniceae and Hemiphragmeae. Sibthorpieae diverged into a separate branch which was sister to a clade comprising the four tribes mentioned above. Furthermore, the sister relationship between Russelieae and Cheloneae is strongly supported. The results of PhyParts showed gene tree congruence and conflict to varying degrees, but most plastid genes were uninformative for phylogenetic nodes, revealing the defects of previous studies using single or multiple plastid DNA sequences to infer the phylogeny of Plantaginaceae

Quasi-Steady-State CEST Prediction Based on TCN-LSTM

An important topic in chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is that certain CEST effects (such as amide proton transfer effects) require sufficiently long saturation time to reach steady state, which makes CEST imaging less practical in clinical application. To address this issue, we develop a deep learning-based model to predict quasi-steady-state (QUASS) CEST from experimentally acquired CEST images with short saturation time. The study described in this paper are outlined as follows: 1) Bloch-McConnell equation is designed to obtain simulated CEST Z-spectra data, in which all possible parameters of the equation were optimized to automatically acquire large amount of training data for reflecting metabolite combinations; 2) tumor-bearing rat model was established on a 7T horizontal diameter small animal MRI scanner, allowing ground-truth generation; 3) by combining the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM) in temporal modelling, a TCN-LSTM model is developed to predict QUASS CEST data. (4) To evaluate the performance of TCN-LSTM, the multilayer perceptron (MLP), recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), BiLSTM and TCN are included in comparison experiment. In terms of absolute error modulus, mutual information (MI), structural similarity (SSIM) and feature similarity (FSIM), the results show that TCN-LSTM provides better prediction results than its counterparts

CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy

Objective: Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). Methods: In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. Results: A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. Conclusion: CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed

Effect of Y2O3 on the Electrical Contact Behavior of Al2O3-Cu/MoTa Composites

With the massive penetration of electronics into human life, higher demands are placed on electrical contacts. Among them, the lifetime of electrical contacts and safety are the most concerning. In this research, Al2O3-Cu/25Mo5Ta and 0.5Y2O3/Al2O3-Cu/25Mo5Ta composites were prepared by using ball milling and powder metallurgy methods. The two composites were subjected to 10,000 contact opening and closing electrical contact experiments and the arc duration and arc energy were analyzed. The results show that the addition of Y2O3 has a slight effect on the mechanical properties of the Al2O3-Cu/25Mo5Ta composites but has a significant effect on the electrical contact performance. Y2O3 can reduce the mass loss of the electrical contacts during the electrical contact process, which prolongs their service life. The addition of Y2O3 decreased the average arc duration and arc energy of the electrical contact material by 21.53% and 18.02%, respectively, under the experimental conditions of DC 30 V, 10 A. TEM results showed that nanoscale YTaO4 with excellent thermal stability was generated during the sintering process, which has a positive effect on the electrical contact performance of the composites

Effect of Y₂O₃ on the Electrical Contact Behavior of Al₂O₃-Cu/MoTa Composites

With the massive penetration of electronics into human life, higher demands are placed on electrical contacts. Among them, the lifetime of electrical contacts and safety are the most concerning. In this research, Al2O3-Cu/25Mo5Ta and 0.5Y2O3/Al2O3-Cu/25Mo5Ta composites were prepared by using ball milling and powder metallurgy methods. The two composites were subjected to 10,000 contact opening and closing electrical contact experiments and the arc duration and arc energy were analyzed. The results show that the addition of Y2O3 has a slight effect on the mechanical properties of the Al2O3-Cu/25Mo5Ta composites but has a significant effect on the electrical contact performance. Y2O3 can reduce the mass loss of the electrical contacts during the electrical contact process, which prolongs their service life. The addition of Y2O3 decreased the average arc duration and arc energy of the electrical contact material by 21.53% and 18.02%, respectively, under the experimental conditions of DC 30 V, 10 A. TEM results showed that nanoscale YTaO4 with excellent thermal stability was generated during the sintering process, which has a positive effect on the electrical contact performance of the composites

MTHFR Ala222Val polymorphism and clinical characteristics confer susceptibility to suicide attempt in chronic patients with schizophrenia

Patients with schizophrenia (SCZ) exhibit higher suicide rates than the general population. However, the molecular mechanism responsible for the high rate of suicidal behavior in SCZ remains poorly understood. MTHFR Ala222Val (C677T; rs 1801133) polymorphism has repeatedly demonstrated to play a pathological role in numerous mental disorders, but none of these studies focused on the susceptibility of suicidal behavior in SCZ. In the present cross-sectional study, we recruited 957 chronic inpatients with SCZ and 576 healthy controls to assess the psychopathological symptoms of SCZ and compare the frequency of the MTHFR Ala222Val genotype in both suicide attempters and non-attempters. Our results demonstrated no significant differences in MTHFR Ala222Val genotype and allele distributions between the SCZ patients and controls (p > 0.05), but showed a statistical significance in the distribution of Ala/Val genotype between suicide attempters and non-attempters (p < 0.05). Further logistic regression analysis showed that MTHFR Ala222Val genotype, psychopathological symptoms, number of cigarettes smoked per day and drinking status were related to suicide attempts in SCZ (p < 0.05). Our study demonstrated that MTHFR Ala222Val polymorphism and some clinical characteristics might confer susceptibility to suicide in patients with SCZ

Microstructure and electrical contact behavior of Al2O3–Cu/30W3SiC(0.5Y2O3) composites

The aim of this study is to observe the organization and properties of Al2O3–Cu/WSiC–Y2O3 composites and to investigate the effect of Y2O3 incorporation on the electrical contact properties of the composites. Preparation of Al2O3–Cu/30W3SiC(0.5Y2O3) composites by rapid hot-pressing sintering technique. The conductivity of the composites is 59.3 %IACS and 58.3 %IACS, respectively. Hardness is 179 HV and 183 HV, respectively. Thermal conductivity is 108 W/(m·k) and 275 W/(m·k), respectively. The dense structure of the composites and the homogeneous distribution of reinforcing phases give the composites excellent overall performance. The addition of Y2O3 increases the resistance of the materials to arc erosion. The amount of material transfer and loss is significantly reduced, and the arc ablation phenomenon is reduced. The arc duration was significantly reduced from 4.48 ms to 0.44 ms, 5.72 ms–4.68 ms, 6.23 ms–5.52 ms, and 12.5 ms–8.59 ms, respectively; and the melt force was significantly reduced to 58.9%, 81.4%, 87.5%, and 89.4% of the original

Factors of Negative Affect in Elderly Patients With Substance Use Disorders During COVID-19 Pandemic

Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has become the greatest public health emergency and has attracted global attention. During the COVID-19 pandemic, the negative affect (NA) of elderly patients with substance use disorders (SUDs) has also become a more serious public concern. The current study aims to clarify the NA and its influencing factors in elderly patients with SUDs during the pandemic. Methods: Two psychiatrists conducted semi-structured interviews with 77 SUD patients aged above 50 years to collect their demographical information and certain drug use characteristics. Barratt Impulse Scale and the Positive and Negative Affect Scale were used to obtain information about patients' self-reported impulsivity and NA. Results: Univariate linear regression analysis showed that NA was positively correlated with the frequency of drug use, type of SUDs, cravings during COVID-19, and impulsivity. Multiple linear regression analysis showed that being female, higher frequency of drug use, stronger cravings, and greater impulsiveness jointly accounted for the variation of NA in elderly patients with SUDs. Conclusions: This study confirmed that, during the COVID-19 pandemic, gender, frequency of drug use, cravings, and impulsivity were associated with NA in elderly patients with SUDs. This study provided a theoretical basis for clinicians to reduce the patients' NA