How Multi-Illuminant Scenes Affect Automatic Colour Balancing

Many illumination-estimation methods are based on the assumption that the imaged scene is lit by a single course of illumination; however, this assumption is often violated in practice. We investigate the effect this has on a suite of illumination-estimation methods by manually sorting the Gehler et al. ColorChecker set of 568 images into the 310 of them that are approximately single-illuminant and the 258 that are clearly multiple-illuminant and comparing the performance of the various methods on the two sets. The Grayworld, Spatio-Spectral-Statistics and Thin-Plate-Spline methods are relatively unaffected, but the other methods are all affected to varying degrees

Comparison of protein interaction networks reveals species conservation and divergence

BACKGROUND: Recent progresses in high-throughput proteomics have provided us with a first chance to characterize protein interaction networks (PINs), but also raised new challenges in interpreting the accumulating data. RESULTS: Motivated by the need of analyzing and interpreting the fast-growing data in the field of proteomics, we propose a comparative strategy to carry out global analysis of PINs. We compare two PINs by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs). Using this approach we perform twenty-one pairwise comparisons among the seven recently available PINs of E.coli, H.pylori, S.cerevisiae, C.elegans, D.melanogaster, M.musculus and H.sapiens. In spite of the incompleteness of data, PIN comparison discloses species conservation at the network level and the identified CoNSs are also functionally conserved and involve in basic cellular functions. We investigate the yeast CoNSs and find that many of them correspond to known complexes. We also find that different species harbor many conserved interaction regions that are topologically identical and these regions can constitute larger interaction regions that are topologically different but similar in framework. Based on the species-to-species difference in CoNSs, we infer potential species divergence. It seems that different species organize orthologs in similar but not necessarily the same topology to achieve similar or the same function. This attributes much to duplication and divergence of genes and their associated interactions. Finally, as the application of CoNSs, we predict 101 protein-protein interactions (PPIs), annotate 339 new protein functions and deduce 170 pairs of orthologs. CONCLUSION: Our result demonstrates that the cross-species comparison strategy we adopt is powerful for the exploration of biological problems from the perspective of networks

MGTUNet: An new UNet for colon nuclei instance segmentation and quantification

Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer. Cellular nuclei instance segmentation and classification, and nuclear component regression tasks can aid in the analysis of the tumor microenvironment in colon tissue. Traditional methods are still unable to handle both types of tasks end-to-end at the same time, and have poor prediction accuracy and high application costs. This paper proposes a new UNet model for handling nuclei based on the UNet framework, called MGTUNet, which uses Mish, Group normalization and transposed convolution layer to improve the segmentation model, and a ranger optimizer to adjust the SmoothL1Loss values. Secondly, it uses different channels to segment and classify different types of nucleus, ultimately completing the nuclei instance segmentation and classification task, and the nuclei component regression task simultaneously. Finally, we did extensive comparison experiments using eight segmentation models. By comparing the three evaluation metrics and the parameter sizes of the models, MGTUNet obtained 0.6254 on PQ, 0.6359 on mPQ, and 0.8695 on R2. Thus, the experiments demonstrated that MGTUNet is now a state-of-the-art method for quantifying histopathological images of colon cancer.Comment: Published in BIBM2022(regular paper),https://doi.org/10.1109/BIBM55620.2022.999566

LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively. Finally, we use the results of multi-label pathological image classification to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202

CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.Comment: Submitted to BIBM202

Curcumin suppresses leukemia cell proliferation by downregulation of P13K/AKT/mTOR signalling pathway

Purpose: To investigate the effect of curcumin ester on the proliferation of leukemia cell lines in vitro. Methods: Changes in WEHI-3 and THP 1 cell viabilities were measured using Cell Counting Kit 8 (CCK 8). Analysis of cell cycle and determination of apoptosis were carried out using propidium iodide and Annexin V fluorescein isothiocyanate staining. Transmission electron microscopy was used for observing the presence of apoptotic features in cells. Results: Treatment with curcumin ester for 72 h caused significant reduction in the proliferation of WEHI-3 and THP 1 cells. Curcumin ester, at a dose of 50 µM, decreased the proliferations of WEHI-3 and THP 1 cells to 28 and 32 %, respectively. On exposure to curcumin ester for 72 h, cell cycle in WEHI-3 cells was arrested in G1/G0 phase. Curcumin ester at doses of 25, 30 and 50 µM enhanced apoptosis in WEHI-3 cells to 46, 58 and 64 %, respectively. Curcumin ester suppressed the levels of phosphoinositide 3 kinase (PI3K), protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) protein and mRNA in WEHI-3 cells. In curcumin ester-treated WEHI-3 cells, the presence of apop¬totic bodies increased significantly and concentration-dependently. Conclusion: These results demonstrate that curcumin ester inhibits leukemia cell proliferation by inducing apoptosis and arresting cell cycle in G1/G0 phase, probably via suppression of PI3K, AKT and mTOR, and promotion of PTEN. Thus, curcumin ester has potentials for use in the development of an effective treatment strategy for leukemia

Analysis of a Single Hemodialysis on Phosphate Removal of the Internal Fistula Patients by Mathematical and Statistical Methods

Chronic kidney disease related mineral and bone disease (CKD-MBD) is a worldwide challenge in hemodialysis patients. In china, the number of dialysis patients is growing but few data are available about their bone disorders. In the current study, we aimed to evaluate the effect of clinical factors on the serum phosphorus clearance in the 80 maintenance hemodialysis (MHD) patients. Six clinical factors were identified for their association with the serum phosphorus clearance using the analysis of Spearman’s single linear correlation, including predialysis serum phosphate level, CRR, membrane surface area of the dialyzer, effective blood flow rate, the blood chamber volume, and hematocrit. In an overall multivariate analysis, pre-P, CRR, membrane SA, and Qb were identified as independent risk factors associated with the serum phosphorus clearance. In conclusion, HD could effectively clear serum phosphorus. The analysis of CRR might help to estimate serum phosphorus reduction ratio

Enhance Reasoning for Large Language Models in the Game Werewolf

This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. Unlike augmenting LLMs with prompt engineering, Thinker directly harnesses knowledge from databases and employs various optimization techniques. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks such as natural language processing, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge. Our framework is presented using a 9-player Werewolf game that demands dual-system reasoning. We introduce a communication protocol between LLMs and the Thinker, and train the Thinker using data from 18800 human sessions and reinforcement learning. Experiments demonstrate the framework's effectiveness in deductive reasoning, speech generation, and online game evaluation. Additionally, we fine-tune a 6B LLM to surpass GPT4 when integrated with the Thinker. This paper also contributes the largest dataset for social deduction games to date

pN1 but not pN0/N2 predicts survival benefits of prophylactic cranial irradiation in small-cell lung cancer patients after surgery.

Background Prophylactic cranial irradiation has been shown to reduce brain metastases and provide survival benefits in small-cell lung cancer (SCLC). However, its role in limited-stage SCLC patients after surgery remains unclear. Further, it is unknown whether the effect of prophylactic cranial irradiation is generalizable in these patients with different pathological nodal (N0-N2) stages, a state indicating the presence of tumor metastases. Methods We combined data from a single medical center and Surveillance, Epidemiology, and End Results database. Propensity score matching analyses were performed (1:2) to evaluate the role of prophylactic cranial irradiation in SCLC patients after surgery. Cox proportional hazards regression model was used to identify predictors of survival. Results 124 (18.7%) out of 664 surgically-treated SCLC patients received prophylactic cranial irradiation treatment. Within the entire cohort, multivariate Cox regression analysis identified dataset source, age, pathological T and N stages, adjuvant chemotherapy, resection type, and histology as independent prognostic factors for overall survival. Prophylactic cranial irradiation appeared to be associated with a better overall survival, but the difference is marginally significant (P=0.063). Further, we stratified patients based on the pathological N0-N2 stages using propensity score matching analyses, which showed that prophylactic cranial irradiation treatment was superior to non-prophylactic cranial irradiation treatment for surgically-treated SCLC patients with N1 stage only (univariate analysis: P=0.026; multivariate Cox: P=0.004), but not N0/N2 stage (univariate analysis: P=0.65 and P=0.28, respectively; multivariate Cox: P=0.99 and P=0.35, respectively). Conclusions Prophylactic cranial irradiation provides survival benefits for SCLC patients with pN1 after surgery but not with pathological N0/N2 stage. Our findings may provide helpful stratifications for clinical decision-making of prophylactic cranial irradiation intervention in SCLC patients