Scalar products of the open XYZ chain with non-diagonal boundary terms

With the help of the F-basis provided by the Drinfeld twist or factorizing F-matrix of the eight-vertex solid-on-solid (SOS) model, we obtain the determinant representations of the scalar products of Bethe states for the open XYZ chain with non-diagonal boundary terms. By taking the on shell limit, we obtain the determinant representations (or Gaudin formula) of the norms of the Bethe states.Comment: Latex file, 28 page

Application of Local Wave Decomposition in Seismic Signal Processing

Local wave decomposition (LWD) method plays an important role in seismic signal processing for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. The LWD method is an effective way to decompose a seismic signal into several individual components. Each component represents a harmonic signal localized in time, with slowly varying amplitudes and frequencies, potentially highlighting different geologic and stratigraphic information. Empirical mode decomposition (EMD), the synchrosqueezing transform (SST), and variational mode decomposition (VMD) are three typical LWD methods. We mainly study the application of the LWD method especially EMD, SST, and VMD in seismic signal processing including seismic signal de‐noising, edge detection of seismic images, and recovery of the target reflection near coal seams

Domain wall partition function of the eight-vertex model with a non-diagonal reflecting end

With the help of the Drinfeld twist or factorizing F-matrix for the eight-vertex SOS model, we obtain the explicit determinant expression of the partition function of the eight-vertex model with a generic non-diagonal reflecting end and domain wall boundary condition. Our result shows that, contrary to the eight-vertex model without a reflection end, the partition function can be expressed as a single determinant.Comment: Latex file, 25 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1107.562

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications

Different physiological roles of insulin receptors in mediating nutrient metabolism in zebrafish

Insulin, the most potent anabolic hormone, is critical for somatic growth and metabolism in vertebrates. Type 2 diabetes, which is the primary cause of hyperglycemia. results from an inability of insulin to signal glycolysis and gluconeogenesis. Our previous study showed that double knockout of insulin receptor a (insra) and b (insrb) caused beta-cell hyperplasia and lethality from 5 to 16 days postfertilization (dpf) (Yang BY, Zhai G, Gong YL, Su JZ, Han D, Yin Z, Xie SQ. Sci Bull (Beijing) 62: 486-492, 2017). In this study, we characterized the physiological roles of Insra and Insrb. in somatic growth and fueling metabolism, respectively. A high-carbohydrate diet was provided for insulin receptor knockout zebrafish from 60 to 120 dpf to investigate phenotype inducement and amplification. We observed hyperglycemia in both insra-/- fish and insrb-/- fish. Impaired growth hormone signaling, increased visceral adiposity, and fatty liver were detected in insrb-/- fish, which are phenotypes similar to the lipodystrophy observed in mammals. More importantly, significantly diminished protein levels of P-PPAR alpha, P-STATS, and IGF-1 were also observed in insrb-/- fish. In insra-/- fish, we observed increased protein content and decreased lipid content of the whole body. Taken together, although Insra and Insrb show overlapping roles in mediating glucose metabolism through the insulin-signaling pathway, Insrb is more prone to promoting lipid catabolism and protein synthesis through activation of the growth hormone-signaling pathway, whereas Insra primarily acts to promote lipid synthesis via glucose utilization.</p

Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer

Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets

Calibration of X-ray telescope prototypes at PANTER

We report a ground X-ray calibration of two X-ray telescope prototypes at the PANTER X-ray Test Facility, of the Max-Planck-Institute for Extraterrestrial Physics, in Neuried, Germany. The X-ray telescope prototypes were developed by the Institute of Precision Optical Engineering (IPOE) of Tongji University, in a conical Wolter-I configuration, using thermal glass slumping technology. Prototype #1 with 3 layers and Prototype #2 with 21 layers were tested to assess the prototypes' on-axis imaging performance. The measurement of Prototype #1 indicates a Half Power Diameter (HPD) of 82" at 1.49 keV. As for Prototype #2, we performed more comprehensive measurements of on-axis angular resolution and effective area at several energies ranging from 0.5-10 keV. The HPD and effective area are 111" and 39 cm^2 at 1.49 keV, respectively, at which energy the on-axis performance of the prototypes is our greatest concern.Comment: 11 pages, 9 figure