Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series

Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variations. Firstly, MBTS is grouped based on inter-modal distances, then each group with minimum intra-modal variations can be effectively modeled by individual encoders. Besides, to enhance the multi-scale feature extraction (encoder), various patch lengths and mask ratios are designed to generate tokens with semantic information at different scales and diverse contextual perspectives respectively. Finally, cross-modal contrastive learning is proposed to maximize consistency among inter-modal groups, maintaining useful information and eliminating noises. Experiments against four bio-medical applications show that MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in human activity recognition, and by 1.14% F1-score in obstructive sleep apnea-hypopnea syndrome.Comment: 4 pages, 3 figures, submitted to ICASSP 202

SP70 is a novel biomarker of hepatocellular carcinoma

BackgroundTumor-specific protein 70 (SP70) was identified as a new biomarker associated with the proliferation and invasion of cancer cells. This study aimed to investigate the expression of SP70 in hepatocellular carcinoma (HCC) and assess its clinical value in the diagnosis and prediction of early HCC recurrence.MethodsA total of 1049 subjects from the First Affiliated Hospital of Nanjing Medical University were recruited in this study. Serum SP70, alpha-fetoprotein (AFP) and prothrombin induced by vitamin K absence II (PIVKA-II) were measured. The diagnostic performance for HCC was obtained using the receiver operating characteristic (ROC) curve, and recurrence-free survival (RFS) was calculated using the Kaplan–Meier method. Univariate and multivariate analyses were performed to identify predictive factors of RFS.ResultsSP70 was highly expressed in HCC cells and HCC tissue. Serum SP70 levels in the HCC group were significantly higher than in the benign liver diseases group and healthy control group (P<0.001). SP70 combined with AFP showed the best diagnostic performance (AUC=0.909, 95%CI [confidence interval]=0.890–0.929). Kaplan–Meier analysis revealed that patients with high SP70 levels had shorter median RFS than those with low SP70 levels (P=0.003). In addition, high SP70 levels were significantly associated with shorter RFS (P=0.037) in the AFP-negative subgroup. Univariate and multivariate analyses confirmed that preoperative serum SP70 level, serum AFP, tumor diameter and microvascular invasion were independent prognostic factors of RFS.ConclusionSP70 is a promising biomarker in diagnosing HCC. High preoperative serum SP70 level is associated with an increased risk of early relapse and could be used as a valuable marker to predict early recurrence of HCC after resection

Single‐cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy

Abstract Diabetic retinopathy (DR) is a leading cause of irreversible blindness in the working‐age populations. Despite decades of research on the pathogenesis of DR for clinical care, a comprehensive understanding of the condition is still lacking due to the intricate cellular diversity and molecular heterogeneity involved. Single‐cell RNA sequencing (scRNA‐seq) has made the high‐throughput molecular profiling of cells across modalities possible which has provided valuable insights into complex biological systems. In this review, we summarise the application of scRNA‐seq in investigating the pathogenesis of DR, focusing on four aspects. These include the identification of differentially expressed genes, characterisation of key cell subpopulations and reconstruction of developmental ‘trajectories’ to unveil their state transition, exploration of complex cell‒cell communication in DR and integration of scRNA‐seq with genome‐wide association studies to identify cell types that are most closely related to DR risk genetic loci. Finally, we discuss the future challenges and expectations associated with studying DR using scRNA‐seq. We anticipate that scRNA‐seq will facilitate the discovery of mechanisms and new treatment targets in the clinical care landscape for patients with DR. Key points Progress in scRNA‐seq for diabetic retinopathy (DR) research includes studies on DR patients, non‐human primates, and the prevalent mouse models. scRNA‐seq facilitates the identification of differentially expressed genes, pivotal cell subpopulations, and complex cell‐cell interactions in DR at single‐cell level. Future scRNA‐seq applications in DR should target specific patient subsets and integrate with single‐cell and spatial multi‐omics approaches

An Improved Nested Named-Entity Recognition Model for Subject Recognition Task under Knowledge Base Question Answering

In the subject recognition (SR) task under Knowledge Base Question Answering (KBQA), a common method is by training and employing a general flat Named-Entity Recognition (NER) model. However, it is not effective and robust enough in the case that the recognized entity could not be strictly matched to any subjects in the Knowledge Base (KB). Compared to flat NER models, nested NER models show more flexibility and robustness in general NER tasks, whereas it is difficult to employ a nested NER model directly in an SR task. In this paper, we take advantage of features of a nested NER model and propose an Improved Nested NER Model (INNM) for the SR task under KBQA. In our model, each question token is labeled as either an entity token, a start token, or an end token by a modified nested NER model based on semantics. Then, entity candidates would be generated based on such labels, and an approximate matching strategy is employed to score all subjects in the KB based on string similarity to find the best-matched subject. Experimental results show that our model is effective and robust to both single-relation questions and complex questions, which outperforms the baseline flat NER model by a margin of 3.3% accuracy on the SimpleQuestions dataset and a margin of 11.0% accuracy on the WebQuestionsSP dataset

Causality between major depressive disorder and functional dyspepsia: a two-sample Mendelian randomization study

BackgroundTo investigate the causal relationship between major depression and functional dyspepsia using two-sample Mendelian randomization.MethodsData for major depression and functional dyspepsia were obtained from genome-wide association studies. We selected Single Nucleotide Polymorphisms (SNPs) strongly associated with severe depression. Mendelian randomization analysis was conducted using methods such as Inverse-Variance Weighted (IVW), MR-Egger, and Weighted Median Estimator (WME). Sensitivity analysis was performed to assess the robustness of the results.ResultsA total of 31 eligible SNPs were identified as instrumental variables for major depression. IVW analysis indicated a positive causal relationship between the two conditions (β = 0.328; SE = 0.137; p = 0.017), suggesting that severe depression increases the risk of functional dyspepsia (OR = 1.389; 95% CI: 1.062–1.816). Sensitivity tests showed no evidence of heterogeneity or horizontal pleiotropy (p > 0.05).ConclusionMR analysis had shown that major depressive disorder is associated with an increased risk of functional dyspepsia

Formation of LiF-rich Cathode-Electrolyte Interphase by Electrolyte Reduction

The capacityof transitionmetal oxide cathodefor Li-ionbatteriescan be furtherenhancedby increas-ing the chargingpotential.However,these high voltagecathodessufferfrom fast capacitydecaybecausethelargevolumechangeof cathodebreaksthe activematerialsand cathode-electrolyteinterphase(CEI),resultingin electrolytepenetrationinto brokenactivematerialsand continuousside reactionsbetweencath-ode and electrolytes.Herein,a robustLiF-richCEI wasformedby potentiostaticreductionof fluorinatedelec-trolyteat a low potentialof 1.7 V. By takingLiCoO2asa modelcathode,we demonstratethat the LiF-richCEImaintainsthe structuralintegrityand suppresseselectro-lyte penetrationat a high cut-offpotentialof 4.6 V. TheLiCoO2with LiF-richCEI exhibiteda capacityof198 mAhghttps://doi.org/10.1002/anie.20220273

Tuning Interface Lithiophobicity for Lithium Metal Solid-State Batteries

Solid-state lithium batteries (SSLBs) using garnet electrolytes potentially have a higher energy density and are safer than liquid organic electrolyte Li-ion batteries. However, SSLBs face challenges of Li dendrite and high interface resistance. In this work, we overcome both challenges by doping strontium (Sr) into lithium anodes. Different from all previous metal/metal oxide coating on garnet or Li alloy anodes that form lithiophilic interlayer, Li-Sr/SrO-doped Li2O are enriched on the interface forming a lithiophilic/lithiophobic bifunctional layer. The interlayer reduces the interfacial resistance and also suppresses lithium dendrite. The stability of the lithiophobic SrO-doped Li2O against Li prevents reducing the garnet and suppresses Li dendrite, which distinguishes it from all reported alloy electron-conducting interlayers. The optimized Li-Sr|garnet|Li-Sr symmetric cell achieves a critical current density of 1.3 mA/cm2 and can be cycled for 1,000 cycles under 0.5 mA/cm2 at room temperature. The bifunctional lithiophilic/lithiophobic interlayer provides a new strategy for high-performance garnet solid-state lithium batteries

Enhancing Li\u3csup\u3e+\u3c/sup\u3e Transport in NMC811||Graphite Lithium-Ion Batteries at Low Temperatures by Using Low-Polarity-Solvent Electrolytes

LiNixCoyMnzO2 (x+y+z=1)||graphite lithium-ion battery (LIB) chemistry promises practical applications. However, its low-temperature (≤ −20 °C) performance is poor because the increased resistance encountered by Li+ transport in and across the bulk electrolytes and the electrolyte/electrode interphases induces capacity loss and battery failures. Though tremendous efforts have been made, there is still no effective way to reduce the charge transfer resistance (Rct) which dominates low-temperature LIBs performance. Herein, we propose a strategy of using low-polarity-solvent electrolytes which have weak interactions between the solvents and the Li+ to reduce Rct, achieving facile Li+ transport at sub-zero temperatures. The exemplary electrolyte enables LiNi0.8Mn0.1Co0.1O2||graphite cells to deliver a capacity of ≈113 mAh g−1 (98 % full-cell capacity) at 25 °C and to remain 82 % of their room-temperature capacity at −20 °C without lithium plating at 1/3C. They also retain 84 % of their capacity at −30 °C and 78 % of their capacity at −40 °C and show stable cycling at 50 °C

Pyropia yezoensis genome reveals diverse mechanisms of carbon acquisition in the intertidal environment

The nori producing seaweed Pyropia yezoensis has heteromorphic generations that occupy distinct habitats. Here, via genome assembly, transcriptome analysis, and 13 C isotope labeling, the authors show the interplay between inorganic carbon availability and life cycle evolution in the intertidal environment