Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation

Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades. Deep neural networks (DNNs) are the current state-of-the-art methods for MSVS. However, the existing DNNs are often designed manually, which is time-consuming and error-prone. In addition, the network architectures are usually pre-defined, and not adapted to the training data. To address these issues, we introduce a Neural Architecture Search (NAS) method to the structure design of DNNs for MSVS. Specifically, we propose a new multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling operators to extract multi-resolution features. Based on the NAS, we then develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by automatically searching the effective MRP-CNN structures using genetic algorithms, optimized in terms of a single-objective considering only separation performance, or multi-objective considering both the separation performance and the model complexity. The multi-objective E-MRP-CNN gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Quantitative and qualitative evaluations on the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the proposed framework over several recent baselines

Guiding AMR Parsing with Reverse Graph Linearization

Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic graph into a sequence of nodes and edges and generate the linearized graph directly, have achieved good performance. However, we observed that these approaches suffer from structure loss accumulation during the decoding process, leading to a much lower F1-score for nodes and edges decoded later compared to those decoded earlier. To address this issue, we propose a novel Reverse Graph Linearization (RGL) enhanced framework. RGL defines both default and reverse linearization orders of an AMR graph, where most structures at the back part of the default order appear at the front part of the reversed order and vice versa. RGL incorporates the reversed linearization to the original AMR parser through a two-pass self-distillation mechanism, which guides the model when generating the default linearizations. Our analysis shows that our proposed method significantly mitigates the problem of structure loss accumulation, outperforming the previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 dataset, respectively. The code are available at https://github.com/pkunlp-icler/AMR_reverse_graph_linearization.Comment: Findings of EMNLP202

IL-9 Secreted by Leukemia Stem Cells Induces Th1-Skewed CD4+ T-Cells, which Promote Their Expansion.

In acute myeloid leukemia (AML), leukemia stem and progenitor cells (LSCs and LPCs) interact with various cell types in the bone marrow (BM) microenvironment, regulating their expansion and differentiation. To study the interaction of CD4+ and CD8+ T-cells in the BM with LSCs and LPCs, we analyzed their transcriptome and predicted cell-cell interactions by unbiased high-throughput correlation network analysis. We found that CD4+ T-cells in the BM of AML patients were activated and skewed towards Th1-polarization whereas IL-9 producing (Th9) CD4+ T-cells were absent. In contrast to normal hematopoietic stem cells (HSCs), LSCs produced IL-9 and the correlation modelling predicted IL9 in LSCs as a main hub-gene that activates CD4+ T-cells in AML. Functional validation revealed that IL-9R signaling in CD4+ T-cells leads to activation of the JAK-STAT pathway that induces the upregulation of KMT2A, KMT2C genes resulting in methylation on histone H3 at lysine 4 (H3K4) to promote genome accessibility and transcriptional activation. This induced Th1-skewing, proliferation and effector cytokine secretion, including interferon (IFN)-ɣ and tumor necrosis factor (TNF)-α. IFN-ɣ and to a lesser extend TNF-α produced by activated CD4+ T-cells, induced the expansion of LSCs. In accordance with our findings, high IL9 expression in LSCs and high IL9R, TNF and IFNG expression in BM-infiltrating CD4+ T-cells correlated with worse overall survival in AML. Thus, IL-9 secreted by AML LSCs shapes a Th1-skewed immune environment that promotes their expansion by secreting IFN-ɣ and TNF-α

Electrotunable liquid sulfur microdroplets.

Manipulating liquids with tunable shape and optical functionalities in real time is important for electroactive flow devices and optoelectronic devices, but remains a great challenge. Here, we demonstrate electrotunable liquid sulfur microdroplets in an electrochemical cell. We observe electrowetting and merging of sulfur droplets under different potentiostatic conditions, and successfully control these processes via selective design of sulfiphilic/sulfiphobic substrates. Moreover, we employ the electrowetting phenomena to create a microlens based on the liquid sulfur microdroplets and tune its characteristics in real time through changing the shape of the liquid microdroplets in a fast, repeatable, and controlled manner. These studies demonstrate a powerful in situ optical battery platform for unraveling the complex reaction mechanism of sulfur chemistries and for exploring the rich material properties of the liquid sulfur, which shed light on the applications of liquid sulfur droplets in devices such as microlenses, and potentially other electrotunable and optoelectronic devices

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster. Unmanned Aerial Vehicles (UAVs) equipped with remote sensing capabilities offer distinct advantages, including high-resolution imagery and exceptional mobility, making them well suited for monitoring flood extent and identifying rescue targets during floods. However, there are some challenges in interpreting rescue information in real time from flood images captured by UAVs, such as the complexity of the scenarios of UAV images, the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform. Thus, we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets (i.e., pedestrians and vehicles trapped by floods). The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model. The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer. Our experimental results demonstrate that the Intersection over Union (IoU) for flood water extraction reaches an impressive 80%, and the IoU for real-time flood water extraction stands at a commendable 76.4%. The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue

Paired Single-B-Cell Transcriptomics and Receptor Sequencing Reveal Activation States and Clonal Signatures That Characterize B Cells in Acute Myeloid Leukemia

BACKGROUND: Acute myeloid leukemia (AML) is associated with a dismal prognosis. Immune checkpoint blockade (ICB) to induce antitumor activity in AML patients has yielded mixed results. Despite the pivotal role of B cells in antitumor immunity, a comprehensive assessment of B lymphocytes within AML\u27s immunological microenvironment along with their interaction with ICB remains rather constrained. METHODS: We performed an extensive analysis that involved paired single-cell RNA and B-cell receptor (BCR) sequencing on 52 bone marrow aspirate samples. These samples included 6 from healthy bone marrow donors (normal), 24 from newly diagnosed AML patients (NewlyDx), and 22 from 8 relapsed or refractory AML patients (RelRef), who underwent assessment both before and after azacitidine/nivolumab treatment. RESULTS: We delineated nine distinct subtypes of B cell lineage in the bone marrow. AML patients exhibited reduced nascent B cell subgroups but increased differentiated B cells compared with healthy controls. The limited diversity of BCR profiles and extensive somatic hypermutation indicated antigen-driven affinity maturation within the tumor microenvironment of RelRef patients. We established a strong connection between the activation or stress status of naïve and memory B cells, as indicated by AP-1 activity, and their differentiation state. Remarkably, atypical memory B cells functioned as specialized antigen-presenting cells closely interacting with AML malignant cells, correlating with AML stemness and worse clinical outcomes. In the AML microenvironment, plasma cells demonstrated advanced differentiation and heightened activity. Notably, the clinical response to ICB was associated with B cell clonal expansion and plasma cell function. CONCLUSIONS: Our findings establish a comprehensive framework for profiling the phenotypic diversity of the B cell lineage in AML patients, while also assessing the implications of immunotherapy. This will serve as a valuable guide for future inquiries into AML treatment strategies

Reverse Phase Proteomic Array Profiling of Asparagine Synthetase Expression in Newly Diagnosed Acute Myeloid Leukemia

Asparaginase-based therapy is a cornerstone in acute lymphoblastic leukemia (ALL) treatment, capitalizing on the methylation status of the asparagine synthetase (ASNS) gene, which renders ALL cells reliant on extracellular asparagine. Contrastingly, ASNS expression in acute myeloid leukemia (AML) has not been thoroughly investigated, despite studies suggesting that AML with chromosome 7/7q deletions might have reduced ASNS levels. Here, we leverage reverse phase protein arrays to measure ASNS expression in 810 AML patients and assess its impact on outcomes. We find that AML with inv(16) has the lowest overall ASNS expression. While AML with deletion 7/7q had ASNS levels slightly lower than those of AML without deletion 7/7q, this observation was not significant. Low ASNS expression correlated with improved overall survival (46 versus 54 weeks, respectively

High-stability dead-end anode proton exchange membrane fuel cells by purge optimization

The crossover of nitrogen and oxygen from cathode to anode aggravates the non-uniformity inside dead-end anode proton exchange membrane fuel cell (DEA-PEMFC), inducing some other effects, such as carbon corrosion, to cause irreversible damage to catalyst. Therefore, developing a purge strategy according to the non-uniformity is necessary to improve its stability. In this study, the effects of operating parameters on the uneven electrical-thermal-water performance are investigated based on a three-dimensional transient model of DEA-PEMFC. Afterwards, a purge optimization is carried out based on the uneven distribution of field variables. The results show that the calculated standard deviation (STDEV) of overvoltage is reduced first and then increased quickly for all the cases. Therefore, the purge should be started when the STDEV approaches the minimum value, to avoid the irreversible damage to DEA-PEMFC, achieving high-stability output performance meanwhile. On this basis, the purge interval is optimized to 100 s, which is suitable for almost all the discussed cases. The purge duration is reduced to 0.2 s. In this situation, the minimum voltage is decreased by about 0.95% compared with the maximum value, indicating a good voltage stability. This study is beneficial to provide guidance for the efficient and long-term operation of DEA-PEMFC

The Short- and Long-Term Readmission of Four Major Categories of Digestive System Cancers: Does Obesity or Metabolic Disorder Matter?

Purpose: Patients with digestive system cancers (DSCs) are at a high risk for hospitalizations; however, the risk factors for readmission remain unknown. Here, we established a retrospective cohort study to assess the association between metabolic obesity phenotypes and readmission risks of DSC. Experimental design: A total of 142,753 and 74,566 patients at index hospitalization were ultimately selected from the Nationwide Readmissions Database (NRD) 2018 to establish the 30-day and 180-day readmission cohorts, respectively. The study population was classified into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Multivariate Cox regression analysis was used to estimate the effect of metabolic obesity phenotypes on DSC readmission. Results: The MUNO phenotype had 1.147-fold (95% CI: 1.066, 1.235; p \u3c 0.001) increased 180-day readmission risks in patients with neoplasm of the upper digestive tract. The MUNO phenotype had 1.073-fold (95% CI: 1.027, 1.121; p = 0.002) increased 30-day readmission risks and 1.067-fold (95% CI: 1.021, 1.115; p = 0.004) increased 180-day readmission risks in patients with neoplasm of the lower digestive tract. The MUNO and MUO phenotypes were independent risk factors of readmission in patients with liver or pancreatic neoplasm. Metabolic obesity status was independently associated with a high risk of severe and unplanned hospitalization within 30 days or 180 days. Conclusion: Both obesity and metabolic abnormalities are associated with a high risk for the poor prognosis of DSC patients. The effect of metabolic categories on the short- or long-term readmission of liver or pancreas cancers may be stronger than that of obesity