Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification

Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-the-art approaches apply a two-stage learning framework to alleviate the class-imbalance issue, where the first stage focuses on training of a general feature extractor and the second stage focuses on fine-tuning the classifier head for class rebalancing. However, existing two-stage approaches do not consider the fine-grained property between different diseases, often causing the first stage less effective for medical image classification than for natural image classification tasks. In this study, we propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Extensive experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches, suggesting that metric learning can be used as an effective plug-in component in the two-stage framework for fine-grained class-imbalanced image classification tasks

Robust Ranking Explanations

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.Comment: Accepted to IMLH (Interpretable ML in Healthcare) workshop at ICML 2023. arXiv admin note: substantial text overlap with arXiv:2212.1410

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.MethodsWe applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.ResultsThe scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS’s prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.ConclusionOur research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients

Affective Decoding for Empathetic Response Generation

Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding

TMT-Based Quantitative Proteomic Analysis Reveals the Key Role of Cell Proliferation and Apoptosis in Intestine Regeneration of <i>Apostichopus japonicus</i>

Sea cucumbers are widely known for their powerful regenerative abilities, which allow them to regenerate a complete digestive tract within a relatively short time following injury or autotomy. Recently, even though the histological changes and cellular events in the processes of intestinal regeneration have been extensively studied, the molecular machinery behind this faculty remains unclear. In this study, tandem mass tag (TMT)-based quantitation was utilized to investigate protein abundance changes during the process of intestine regeneration. Approximately 538, 445, 397, 1012, and 966 differential proteins (DEPs) were detected (p < 0.05) between the normal and 2, 7, 12, 20, and 28 dpe stages, respectively. These DEPs also mainly focus on pathways of cell proliferation and apoptosis, which were further validated by 5-Ethynyl-2′-deoxyuridine (EdU) or Tunel-based flow cytometry assay. These findings provide a reference for a comprehensive understanding of the regulatory mechanisms of various stages of intestinal regeneration and provide a foundation for subsequent research on changes in cell fate in echinoderms

Provable Robust Saliency-based Explanations

Robust explanations of machine learning models are critical to establishing human trust in the models. The top-$k$ intersection is widely used to evaluate the robustness of explanations. However, most existing attacking and defense strategies are based on $\ell_p$ norms, thus creating a mismatch between the evaluation and optimization objectives. To this end, we define explanation thickness for measuring top-$k$ salient features ranking stability, and design the \textit{R2ET} algorithm based on a novel tractable surrogate to maximize the thickness and stabilize the top salient features efficiently. Theoretically, we prove a connection between R2ET and adversarial training; using a novel multi-objective optimization formulation and a generalization error bound, we further prove that the surrogate objective can improve both the numerical and statistical stability of the explanations. Experiments with a wide spectrum of network architectures and data modalities demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining model accuracy

Antibacterial and Antifungal Properties of a Novel Antimicrobial Peptide GK-19 and Its Application in Skin and Soft Tissue Infections Induced by MRSA or Candida albicans

The increasing resistance of human pathogens promotes the development of novel antimicrobial agents. Due to the physical bactericidal mechanism of membrane disruption, antimicrobial peptides are considered as potential therapeutic candidates without inducing microbial resistance. Scorpion venom-derived peptide, Androctonus amoreuxi Antimicrobial Peptide 1 (AamAP1), has been proved to have broad-spectrum antimicrobial properties. However, AamAP1 can induce hemolysis and shows strong toxicity against mammalian cells. Herein, the antimicrobial activity and mechanism of a novel synthetic antimicrobial peptide, GK-19, derived from AamAP1 and its derivatives, was evaluated. Five bacteria and three fungi were used to evaluate the antimicrobial effects of GK-19 in vitro. Scalded mice models combined with skin and soft tissue infections (SSTIs) were used to evaluate its applicability. The results indicated that GK-19 could not only inhibit Gram-positive and Gram-negative bacterial growth, but also kill fungi by disrupting the microbial cell membrane. Meanwhile, GK-19 showed negligible toxicity to mammalian cells, low hemolytic activity and high stability in plasma. Furthermore, in scalded mice models combined with SSTIs induced by either Methicillin-Resistant Staphylococcus aureus (MRSA) or Candida albicans, GK-19 showed significant antimicrobial and healing effects. Overall, it was demonstrated that GK-19 might be a promising drug candidate in the battle against drug-resistant bacterial and fungal infections

Analysis of population‐based colorectal cancer screening in Guangzhou, 2011‐2015

Abstract Objective To analyze the detection rates of colorectal cancer (CRC) and polyps by population‐based screening in Guangzhou. Methods From January 2011 to December 2015, the residents aged 30‐79 were selected for CRC screening. The residents were conducted Questionnaires and/or FOBT to assess high‐risk groups, the free colonoscopy examination was recommended, and the results were evaluated in detail. Results There were 98 927 residents involving screening, 5306 high‐risk residents identified (males 1859 and females 3447), and 4713 subjects underwent colonoscopy (males 1690 and females 3023). CRC was seen in 55 individuals (males 28 and females 27), and the detection rates in male were higher than in female (P = 0.019). And the detection rates increasing with age, for people over 60 years old, were obviously higher than those younger (x2 = 18.64, P = 0.000924). The polyps were seen in 1458 (30.94%) cases， and 1420 subjects received pathological examination (adenomas 971 and non‐adenomatous polyps 449). Advanced adenomas were seen in 462 cases (males 240 and females 222) and 509 cases of non‐advanced adenomas (males 255 and females 254). For advanced adenomas, the detection rates in male were higher than female (14.20% vs 7.34%, P = 2.64 × 10−14). For the detection rates of adenomas or advanced adenomas by age, the people over 40 years were higher than younger (20.91% vs 3.61% P = 7.87 × 10−6; 9.94% vs 2.41%, P = 0.009). Conclusions For Guangzhou residents, the detection rates of CRC and adenoma were 1.17% and 20.60%. The detection rates of CRC increasing with age, for people over 60 years old, were obviously higher than those younger. But for people over 40 years, the detection rate of adenoma and advanced adenoma was higher than younger. So for people over 40 years, the CRC screening is recommended

Affective Decoding for Empathetic Response Generation

Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding