Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module

Although Convolutional Neural Networks (CNNs) have achieved promising results in image classification, they still are vulnerable to affine transformations including rotation, translation, flip and shuffle. The drawback motivates us to design a module which can alleviate the impact from different affine transformations. Thus, in this work, we introduce a more robust substitute by incorporating distribution learning techniques, focusing particularly on learning the spatial distribution information of pixels in images. To rectify the issue of non-differentiability of prior distribution learning methods that rely on traditional histograms, we adopt the Kernel Density Estimation (KDE) to formulate differentiable histograms. On this foundation, we present a novel Differentiable Arithmetic Distribution Module (DADM), which is designed to extract the intrinsic probability distributions from images. The proposed approach is able to enhance the model's robustness to affine transformations without sacrificing its feature extraction capabilities, thus bridging the gap between traditional CNNs and distribution-based learning. We validate the effectiveness of the proposed approach through ablation study and comparative experiments with LeNet

MedChatZH: a Better Medical Adviser Learns from Better Instructions

Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and LLMs.Comment: 7 pages, 3 figure

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.Methods: We screened all 162 “kidney transplant”–related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan–Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis.Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response–related pathways were found in the C4 group, which had poor prognosis.Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO

The novel PIAS-like protein hZimp10 is a transcriptional co-activator of the p53 tumor suppressor

The tumor suppressor, p53, plays critical roles in the cell cycle progression, DNA repair and apoptosis. The PIAS proteins (protein inhibitor of activated STAT) were originally identified as inhibitors of the JAK-STAT pathway. Subsequently, crosstalk between the PIAS proteins and other signaling pathways has been shown to be involved in various cellular processes. Particularly, previous studies have demonstrated that PIAS proteins regulate p53-mediated transcription through sumoylation. hZimp10, also named zmiz1, is a novel PIAS-like protein and functions as a transcriptional co-activator. We recently identified p53 to be an hZimp10 interacting protein in the yeast two-hybrid screen. The interaction between p53 and hZimp10 was confirmed by GST pull-down and co-immunoprecipitation assays. Co-localization of p53 and hZimp10 proteins was also observed within cell nuclei by immunostaining. Moreover, we show that expression of exogenous hZimp10 enhances the transcriptional activity of p53 and knockdown of endogenous hZimp10 reduces the transcriptional activity of p53. Furthermore, using chromatin immunoprecipitation assays, we demonstrate that hZimp10 binds to p53 on the p21 promoter. Finally, p53-mediated transcription is significantly impaired in Zimp10 null embryonic fibroblasts. Taken together, these results provide the first line of evidence to demonstrate a role for Zimp10 in regulating p53 function

Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks

Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the information overload resulting from the large number of network parameters. In this paper, we propose Frequency Regularization to restrict the non-zero elements of the network parameters in the frequency domain. The proposed approach operates at the tensor level, and can be applied to almost all network architectures. Specifically, the tensors of parameters are maintained in the frequency domain, where high-frequency components can be eliminated by zigzag setting tensor elements to zero. Then, the inverse discrete cosine transform (IDCT) is used to reconstruct the spatial tensors for matrix operations during network training. Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization. Comprehensive evaluations on various state-of-the-art network architectures, including LeNet, Alexnet, VGG, Resnet, ViT, UNet, GAN, and VAE, demonstrate the effectiveness of the proposed frequency regularization. For a very small accuracy decrease (less than 2%), a LeNet5 with 0.4M parameters can be represented by only 776 float16 numbers (over $1100\times$ reduction), and a UNet with 34M parameters can be represented by only 759 float16 numbers (over $80000\times$ reduction). In particular, the original size of the UNet model is reduced from 366 Mb to 4.5 Kb

Rituximab treatment for lupus nephritis: A systematic review

Purpose: We used the Cochrane systematic review to analyze the effectiveness and safety of rituximab for lupus nephritis. Methods: Systematic search was performed among Cochrane clinical controlled trials database, MEDLINE, MEDLINE-IN-Process and Other Non-Indexed Citations, EMBASE, EBSCO CINAHL, CNKI, VIP and Wanfang database from the establishment of the database to February 2016. The effectiveness and safety were evaluated in terms of the complete remission rate, total remission rate, urinary protein, Systemic Lupus Erythematosus Disease Activity Index changes and adverse events rate. Data were analyzed by the Review Manager Software version 5.3. Results: Five RCTs that met the inclusion criteria, including a total of 238 patients, were enrolled in our study. The results showed that the complete remission rate in rituximab group was a significantly higher than that of cyclophosphamide group. The difference between the two groups was statistically significant (OR=2.80, 95%CI(1.08,7.26), P=0.03). But there was no significant difference between the two groups in partial and total remission rate. The complete remission rate, partial remission rate and total remission rate in rituximab treatment group was similar compared with mycophenolate mofetil group and rituximab combined with cyclophosphamide group. The adverse reaction rate was also similar among the groups. Conclusion: The study systematically analyzed the effectiveness and safety of rituximab for lupus nephritis, which suggested that the complete remission rate of rituximab in the treatment of lupus nephritis was a significantly higher than that of cyclophosphamide group, while the effectiveness and safety was of no difference compared with cyclophosphamide and mycophenolate mofetil

Mapping the Interactions of HBV cccDNA with Host Factors

Hepatitis B virus (HBV) infection is a major health problem affecting about 300 million people globally. Although successful administration of a prophylactic vaccine has reduced new infections, a cure for chronic hepatitis B (CHB) is still unavailable. Current anti-HBV therapies slow down disease progression but are not curative as they cannot eliminate or permanently silence HBV covalently closed circular DNA (cccDNA). The cccDNA minichromosome persists in the nuclei of infected hepatocytes where it forms the template for all viral transcription. Interactions between host factors and cccDNA are crucial for its formation, stability, and transcriptional activity. Here, we summarize the reported interactions between HBV cccDNA and various host factors and their implications on HBV replication. While the virus hijacks certain cellular processes to complete its life cycle, there are also host factors that restrict HBV infection. Therefore, we review both positive and negative regulation of HBV cccDNA by host factors and the use of small molecule drugs or sequence-specific nucleases to target these interactions or cccDNA directly. We also discuss several reporter-based surrogate systems that mimic cccDNA biology which can be used for drug library screening of cccDNA-targeting compounds as well as identification of cccDNA-related targets

Mapping the Interactions of HBV cccDNA with Host Factors

Int J Mol Sci