The Model 2.0 and Friends: An Interim Report

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation! We look forward to the group’s suggestions for these projects

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge, this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly, MAPSeg is the first framework that can be applied to $\textbf{centralized}$, $\textbf{federated}$, and $\textbf{test-time}$ UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset, and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.Comment: CVPR 2024 camera-ready (8 pages, 3 figures) with the supplemental materials (5 pages, 4 figures). Xuzhe Zhang and Yuhao Wu are co-first authors. Andrew F. Laine and Yun Wang are co-senior supervising author

A prognostic estimation model based on mRNA-sequence data for patients with oligodendroglioma

BackgroundThe diagnosis of oligodendroglioma based on the latest World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 5) criteria requires the codeletion of chromosome arms 1p and 19q and isocitrate dehydrogenase gene (IDH) mutation (mut). Previously identified prognostic indicators may not be completely suitable for patients with oligodendroglioma based on the new diagnostic criteria. To find potential prognostic indicators for oligodendroglioma, we analyzed the expression of mRNAs of oligodendrogliomas in Chinese Glioma Genome Atlas (CGGA).MethodsWe collected 165 CGGA oligodendroglioma mRNA-sequence datasets and divided them into two cohorts. Patients in the two cohorts were further classified into long-survival and short-survival subgroups. The most predictive mRNAs were filtered out of differentially expressed mRNAs (DE mRNAs) between long-survival and short-survival patients in the training cohort by least absolute shrinkage and selection operator (LASSO), and risk scores of patients were calculated. Univariate and multivariate analyses were performed to screen factors associated with survival and establish the prognostic model. qRT-PCR was used to validate the expression differences of mRNAs.ResultsA total of 88 DE mRNAs were identified between the long-survival and the short-survival groups in the training cohort. Seven RNAs were selected to calculate risk scores. Univariate analysis showed that risk level, age, and primary-or-recurrent status (PRS) type were statistically correlated with survival and were used as factors to establish a prognostic model for patients with oligodendroglioma. The model showed an optimal predictive accuracy with a C-index of 0.912 (95% CI, 0.679–0.981) and harbored a good agreement between the predictions and observations in both training and validation cohorts.ConclusionWe established a prognostic model based on mRNA-sequence data for patients with oligodendroglioma. The predictive ability of this model was validated in a validation cohort, which demonstrated optimal accuracy. The 7 mRNAs included in the model would help predict the prognosis of patients and guide personalized treatment

Investigation of Mechanical Properties and Plastic Deformation Behavior of (Ti45Cu40Zr10Ni5)100−xAlx Metallic Glasses by Nanoindentation

The effect of Al addition on mechanical properties and plastic deformation behavior of (Ti45Cu40Zr10Ni5)100−xAlx (x = 0, 2, 4, 6 and 8) amorphous alloy ribbons have been investigated by nanoindentation. The hardness and elastic modulus do not simply increase with the increase of Al content. The alloy with 8 at.% Al exhibits the highest hardness and elastic modulus. The serrations or pop-in events are strongly dependent on the loading rate and alloy composition

Calculation of inlet capacitance for long-duration induction voltage test of single-phase three-winding converter transformers

The converter transformer is one of the core equipment in the high-voltage DC (HVDC) transmission project. The capacity of the converter transformer is much larger than that of an ordinary AC transformer, and its main function is to convert the AC system voltage to the phase change voltage required by the converter. The long-duration induction voltage test is an important technical means to assess the insulation strength of electrical equipment, and the calculation of the inlet capacitance of the converter transformer in the test design is extremely critical. This paper conducts circuit and mathematical modeling based on the structure of a single-phase three-winding converter transformer, calculates the equivalent capacitance between each winding of the converter transformer and each winding to ground by the model, and uses each equivalent capacitance to calculate the voltage added inlet capacitance, then obtains the appropriate compensation inductance. Moreover, the calculated inlet capacitance is verified by using the field test data, and the verification results show the reasonableness of the model. Finally, the calculation results are analyzed for errors and possible sources of errors are pointed out. This inlet capacitance calculation method has some universality and is expected to be promoted and applied in this field

Clinical Value of Combined Determination of Serum B7-H4 with Carcinoembryonic Antigen, Osteopontin, or Tissue Polypeptide-Specific Antigen for the Diagnosis of Colorectal Cancer

Aim. B7-H4 is member of the B7 family that negatively regulates the immune response, which are associated with tumor development and prognosis. The present study is aimed at examining serum B7-H4 expression and exploring its contribution to diagnosis in patients with colorectal cancer. Methods. We determined serum expressions of B7-H4, carcinoembryonic antigen (CEA), osteopontin (OPN), and tissue polypeptide-specific antigen (TPS) in 59 patients with colorectal cancer and 29 healthy volunteers and analyzed the diagnostic value of B7-H4 combined with CEA, OPN, or TPS detection for colorectal cancer. B7-H4, OPN, and TPS serum expressions were measured by enzyme-linked immunosorbent assay, and CEA was measured by electrochemical luminescence detection. Results. Serum B7-H4 levels were significantly higher in colorectal cancer patients compared with paired normal controls (P=0.001). B7-H4 serum level was positively correlated with infiltration depth, tumor masses, and lymph node metastasis (P=0.004, P=0.016, and P=0.0052, respectively). We also detected serum expression of B7-H4 before and after radical resection and showed that B7-H4 levels decreased significantly during the first week postoperation (P=0.0064). We used receiver operating characteristic (ROC) curve analysis to indicate the potential diagnostic values of these markers. The areas under the ROC curves (AUC) for B7-H4, OPN, TPS, and CEA were 0.867, 0.805, 0.812, and 0.833, respectively. The optimal sensitivity and specificity of B7-H4 for discriminating between colon cancer patients and healthy controls were 88.2% and 86.7%, respectively, using a cut-off of value of 78.89 ng/mL. However, combined ROC analysis using B7-H4 and CEA revealed an AUC of 0.929, with a sensitivity of 98.9% and a specificity of 80.4% for discriminating colon cancer patients from healthy controls. Conclusions. B7-H4 was highly expressed in the serum in colorectal cancer patients. Detection of B7-H4 plus CEA showed significantly increased sensitivity and specificity for discriminating between colorectal cancer patients and healthy controls compared to individual detection of these markers. Combined detection of serum B7-H4 and CEA may thus have the potential to become a new laboratory method for the early clinical diagnosis and prognostic evaluation of colorectal cancer

A Modal Frequency Estimation Method of Non-Stationary Signal under Mass Time-Varying Condition Based on EMD Algorithm

A method to estimate modal frequency based on empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is proposed. This method can decrease the difficulties in identifying modal frequency of combine harvesters. First, we used 16 acceleration sensors installed at different test points to collect vibration signals of a corn combine harvester under operating conditions (mass time-varying conditions). Second, we calculated mean value, variance and root mean square (RMS) value of the vibration signals, and analyzed its stationarity of vibration signals. Third, the main frequencies of the 16 points were extracted using the EMD and EEMD methods. Finally, we considered modal frequencies identified by the SSI algorithm as standard, and calculated the fitting degrees of the EMD and EEMD methods. The results show that in different time periods (0~60 s and 60~120 s), the maximum differences of the mean value, variance and RMS value of signals were 0.8633, 171.1629 and 11.3767, and the vibration signal under the operating condition of field harvesting can be regarded as a typical non-stationary random vibration signal. The EMD method had more modal aliasing than EEMD, and when we obtained the fitting equations of EMD, EEMD and SSI methods, the value of the Euler distance between the EMD fitting equation and the SSI fitting equation was 446.7883, while that for EEMD and SSI was 417.2845. The vibration frequencies calculated by the EEMD method is closer to the modal frequencies identified by SSI algorithm. The proposed method provides a reference for modal frequency identification and vibration control in a complex working environment

Simulation analysis on magnetic core loss characteristic of valve reactor in UHVDC system

Valve reactors (VR) with multiple magnetic cores are important electrical components in ultra-high voltage direct current systems. The impact current and high-order harmonics during their operation can lead to additional losses and temperature rise in the valve reactors, posing a threat to insulation aging, and even causing serious incidents such as combustion and explosions. Therefore, it is necessary to accurately quantify and analyze the loss characteristics of valve reactors under different magnetic materials. This article first theoretically analyzes the electromagnetic and temperature characteristics during the actual operation of VR; then, it analyzes and calculates the magnetic field distribution and electromagnetic losses of a single magnetic core. Finally, it establishes a three-dimensional equivalent model of VR and conducts transient electromagnetic-thermal coupled finite element simulation to reveal the magnitude and spatial distribution of VR temperature changes

Tipping Point Detection Using Reservoir Computing

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system’s changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems