Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining

Existing contrastive learning methods for anomalous sound detection refine the audio representation of each audio sample by using the contrast between the samples' augmentations (e.g., with time or frequency masking). However, they might be biased by the augmented data, due to the lack of physical properties of machine sound, thereby limiting the detection performance. This paper uses contrastive learning to refine audio representations for each machine ID, rather than for each audio sample. The proposed two-stage method uses contrastive learning to pretrain the audio representation model by incorporating machine ID and a self-supervised ID classifier to fine-tune the learnt model, while enhancing the relation between audio features from the same ID. Experiments show that our method outperforms the state-of-the-art methods using contrastive learning or self-supervised classification in overall anomaly detection performance and stability on DCASE 2020 Challenge Task2 dataset.Comment: To appear in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Sounds

Different machines can exhibit diverse frequency patterns in their emitted sound. This feature has been recently explored in anomaly sound detection and reached state-of-the-art performance. However, existing methods rely on the manual or empirical determination of the frequency filter by observing the effective frequency range in the training data, which may be impractical for general application. This paper proposes an anomalous sound detection method using self-attention-based frequency pattern analysis and spectral-temporal information fusion. Our experiments demonstrate that the self-attention module automatically and adaptively analyses the effective frequencies of a machine sound and enhances that information in the spectral feature representation. With spectral-temporal information fusion, the obtained audio feature eventually improves the anomaly detection performance on the DCASE 2020 Challenge Task 2 dataset.Comment: Published in INTERSPEECH 202

Effects of Epac1 on Diabetic Retinal Inflammation

An ever-growing body of research suggests that inflammation is one of the primary causes of diabetic retinopathy, as the inflammation can lead to insulin resistance. Beta-adrenergic receptor agonists can reduce the inflammation in human retinal endothelial cells (HRECs), but are not a viable treatment due to systemic effects. Epac1 lies downstream of beta-adrenergic receptor signaling, and it may have the capability to reduce inflammation by acting as an alternative pathway for beta-adrenergic receptor agonists to block inflammatory cytokines such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 beta (IL-1B). We hypothesized that the Epac1 agonist will decrease cytokine levels, leading to improved insulin signal transduction in the retina. HRECs were grown in normal (5mM) or high glucose (25mM). Some cells were not treated with the Epac1 agonist and serve as controls. Western blotting was done using primary antibodies for total and phosphorylated insulin receptor substrate-1 (IRS-1), insulin receptor (IR) and Akt, as well as beta actin as a control for loading. Anti-Rabbit IgG/HRP was used for secondary antibodies. ELISA analyses were done for protein levels of TNF-alpha and IL-1B. We are not done with data analyses, but we expect to find that Epac1 will increase insulin receptor and Akt phosphorylation, while reducing TNF-alpha and IL-1B levels

Transformer-based Autoencoder with ID Constraint for Unsupervised Anomalous Sound Detection

Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.Comment: Accepted by EURASIP Journal on Audio, Speech, and Music Processin

The next widespread bamboo flowering poses a massive risk to the giant panda

The IUCN Red List has downgraded several species from “endangered” to “vulnerable” that still have largely unknown extinction risks. We consider one of those downgraded species, the giant panda, a bamboo specialist. Massive bamboo flowering could be a natural disaster for giant pandas. Using scenario analysis, we explored possible impacts of the next bamboo flowering in the Qinling and Minshan Mountains that are home to most giant pandas. Our results showed that the Qinling Mountains could experience large-scale bamboo flowering leading to a high risk of widespread food shortages for the giant pandas by 2020. The Minshan Mountains could similarly experience a large-scale bamboo flowering with a high risk for giant pandas between 2020 and 2030 without suitable alternative habitat in the surrounding areas. These scenarios highlight thus-far unforeseen dangers of conserving giant pandas in a fragmented habitat. We recommend advance measures to protect giant panda from severe population crashes when flowering happens. This study also suggests the need to anticipate and manage long-term risks to other downgraded species

Epac1 Blocks NLRP3 Inflammasome to Reduce IL-1 β

Inflammation is an important component of diabetic retinal damage. We previously reported that a novel β-adrenergic receptor agonist, Compound 49b, reduced Toll-like receptor 4 (TLR4) signaling in retinal endothelial cells (REC) grown in high glucose. Others reported that TLR4 activates high-mobility group box 1 (HMGB1), which has been associated with the NOD-like receptor 3 (NLRP3) inflammasome. Thus, we hypothesized that Epac1, a downstream mediator of β-adrenergic receptors, would block TLR4/HMGB1-mediated stimulation of the NLRP3 inflammasome, leading to reduced cleavage of caspase-1 and interleukin-1 beta (IL-1β). We generated vascular specific conditional knockout mice for Epac1 and used REC grown in normal and high glucose treated with an Epac1 agonist and/or NLRP3 siRNA. Protein analyses were done for Epac1, TLR4, HMGB1, NLRP3, cleaved caspase-1, and IL-1β. Loss of Epac1 in the mouse retinal vasculature significantly increased all of the inflammatory proteins. Epac1 effectively reduced high glucose-induced increases in TLR4, HMGB1, cleaved caspase-1, and IL-1β in REC. Taken together, the data suggest that Epac1 reduces formation of the NLRP3 inflammasome to reduce inflammatory responses in the retinal vasculature

Distribution, occurrence characteristics and geological origin of typical hazardous elements in low-medium ash coal of Huainan coalfield

With the continuous development, processing and utilization of coal resources in our province, the existence of typical harmful elements in coal is bound to have a negative impact on the efficient and clean utilization of coal resources. Based on the mass data analysis of typical hazardous elements in coal of Huainan coalfield, taking the main coal seams (No.13-1, No.11-2, No.8, No.6, No.4 and No.1) as the research objects, the occurrence characteristics and geological genesis of typical hazardous elements were comprehensively analyzed by the means of ICP-MS, AFS, stepwise chemical extraction and cluster analysis. The results were as follows: ① Typical hazardous elements Cr, Co, Se and Pb in coal of Huainan coalfield are “lightly enriched”, Hg is “highly enriched” and other elements are in “normal range”; ② The high value area of As element content in Huainan coalfield was mainly located in the west of coalfield; the high value region of Hg element was located in the east of coalfield, followed by the west; the remaining eight elements (Cr, Mn, Co, Ni, Se, Cd, Sb and Pb) were all higher in coal of Panji mining area of Huainan coalfield (especially near Pansan coal mine). The late magmatic hydrothermal process in Panji-Zhuji region may be the main reason for the relative enrichment of hazardous elements such as Cr, Mn, Co, Ni, Se, Cd, Sb and Pb. ③ There were many ion-exchange States in the occurrence state of Hg, and the inorganic components brought by magmatic hydrothermal action may have little influence on the enrichment degree of Hg element. The magmatic hydrothermal intrusion in the late diagenesis period had no obvious influence on As, and the low content of As in coal may be related to the low content of As in coal-forming plants

Enhancement of Canonical Wnt/β-Catenin Signaling Activity by HCV Core Protein Promotes Cell Growth of Hepatocellular Carcinoma Cells

BACKGROUND: The Hepatitis C virus (HCV) core protein has been implicated as a potential oncogene or a cofactor in HCV-related hepatocellular carcinoma (HCC), but the underlying mechanisms are unknown. Overactivation of the Wnt/β-catenin signaling is a major factor in oncogenesis of HCC. However, the pathogenesis of HCV core-associated Wnt/β-catenin activation remains to be further characterized. Therefore, we attempted to determine whether HCV core protein plays an important role in regulating Wnt/β-catenin signaling in HCC cells. METHODOLOGY: Wnt/β-catenin signaling activity was investigated in core-expressing hepatoma cells. Protein and gene expression were examined by Western blot, immunofluorescence staining, RT-qPCR, and reporter assay. PRINCIPAL FINDINGS: HCV core protein significantly enhances Tcf-dependent transcriptional activity induced by Wnt3A in HCC cell lines. Additionally, core protein increases and stabilizes β-catenin levels in hepatoma cell line Huh7 through inactivation of GSK-3β, which contributes to the up-regulation of downstream target genes, such as c-Myc, cyclin D1, WISP2 and CTGF. Also, core protein increases cell proliferation rate and promotes Wnt3A-induced tumor growth in the xenograft tumor model of human HCC. CONCLUSIONS/SIGNIFICANCE: HCV core protein enhances Wnt/β-catenin signaling activity, hence playing an important role in HCV-associated carcinogenesis