Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Investigation of the Cofiring Process of Raw or Torrefied Bamboo and Masson Pine by Using a Cone Calorimeter

Cofiring characteristics of raw or torrefied bamboo and masson pine blends with different blend ratios were investigated by cone calorimetry, and its ash performance from cofiring was also determined by a YX-HRD testing instrument, X-ray fluorescence, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Results showed that bamboo and masson pine had the different physicochemical properties. Torrefaction improved fuel performances, resulting in a more stable cofiring process. It also decreased the heat release rate, total heat release, and total suspended particulates of fuels, especially CO2 and CO release. Masson pine ash mainly included CaO, SiO2, Fe2O3, K2O, and Al2O3. Bamboo ash was mainly composed of K2O, SiO2, MgO, and SO3. There were different melting temperatures and trends between different samples. The synergistic reaction of ash components was found during the cofiring process. The surface morphology of blend ash changed with the variation of bamboo or masson pine content

Nitrogen Self-Doped Activated Carbons Derived from Bamboo Shoots as Adsorbent for Methylene Blue Adsorption

Bamboo shoots, a promising renewable biomass, mainly consist of carbohydrates and other nitrogen-related compounds, such as proteins, amino acids and nucleotides. In this work, nitrogen self-doped activated carbons derived from bamboo shoots were prepared via a simultaneous carbonization and activation process. The adsorption properties of the prepared samples were evaluated by removing methylene blue from waste water. The factors that affect the adsorption process were examined, including initial concentration, contact time and pH of methylene blue solution. The resulting that BSNC-800-4 performed better in methylene blue removal from waste water, due to its high specific surface area (2270.9 m2 g−1), proper pore size (2.19 nm) and relatively high nitrogen content (1.06%). Its equilibrium data were well fitted to Langmuir isotherm model with a maximum monolayer adsorption capacity of 458 mg g−1 and a removal efficiency of 91.7% at methylene blue concentration of 500 mg L−1. The pseudo-second-order kinetic model could be used to accurately estimate the carbon material’s (BSNC-800-4) adsorption process. The adsorption mechanism between methylene blue solution and BSNC-800-4 was controlled by film diffusion. This study provides an alternative way to develop nitrogen self-doped activated carbons to better meet the needs of the adsorption applications

The role of smoking and alcohol in mediating the effect of gastroesophageal reflux disease on lung cancer: A Mendelian randomization study

Observational studies have suggested a positive association between gastroesophageal reflux disease and lung cancer, but due to the existence of confounders, it remains undetermined whether gastroesophageal reflux disease (GERD) has a causal association with lung cancer. Therefore, Mendelian randomization (MR) analyses were applied to investigate the relationship between the two conditions. Two-sample Mendelian randomization analysis was utilized with summary genetic data from the European Bioinformatics Institute (602,604 individuals) and International Lung Cancer Consortium, which provides information on lung cancer and its histological subgroups. Furthermore, we used two-step Mendelian randomization and multivariable Mendelian randomization to estimate whether smoking initiation (311,629 cases and 321,173 controls) and alcohol intake frequency (n = 462,346) mediate any effect of gastroesophageal reflux disease on lung cancer risk. The Mendelian randomization analyses indicated that gastroesophageal reflux disease was associated with and significantly increased the risk of lung cancer (ORIVW = 1.35, 95% CI = 1.18–1.54; p = 1.36 × 10–5). Smoking initiation and alcohol intake frequency mediated 35% and 3% of the total effect of gastroesophageal reflux disease on lung cancer, respectively. The combined effect of these two factors accounted for 60% of the total effect. In conclusion, gastroesophageal reflux disease is associated with an increased risk of lung cancer, and interventions to reduce smoking and alcohol intake may reduce the incidence of lung cancer

LncRNA TP73-AS1 Promotes Cell Proliferation and Inhibits Cell Apoptosis in Clear Cell Renal Cell Carcinoma Through Repressing KISS1 Expression and Inactivation of PI3K/Akt/mTOR Signaling Pathway

Background/Aims: Emerging evidence suggests that long non-coding RNAs (lncRNAs) play a vital regulatory role in the pathogenesis and progression of renal cell carcinoma (RCC). We aim to determine lncRNA profiles in clear cell RCC (ccRCC) and investigate key lncRNAs involved in ccRCC tumorigenesis and progression. Methods: RNA sequencing technique and qPCR were used to determine the candidate lncRNAs in ccRCC tissues. The correlations between lncRNA P73 antisense RNA 1T (TP73-AS1) levels and survival outcomes were analyzed to elucidate its clinical significance. The underlying mechanisms of TP73-AS1 in ccRCC were analyzed through in vitro functional assays. Results: We found TP73-AS1 was upregulated in 40 ccRCC tissues compared with adjacent normal renal tissues and increased TP73-AS1 was correlated to aggressive clinicopathologic features and unfavorable prognosis. Knockdown of TP73-AS1 suppressed cell proliferation, invasion and induced cell apoptosis. We also identified KISS-1 metastasis-suppressor (KISS1) was significantly upregulated in TP73-AS1 knockdown cells. Further, we revealed that TP73-AS1 suppressed KISS1 expression through the interaction with Enhancer of zeste homolog 2 (EZH2) and the specific binding to KISS1 gene promoter region. Knockdown of KISS1 partly reversed TP73-AS1 knockdown-induced inhibition of cell proliferation and promotion of apoptosis. We further determined that TP73-AS1 knockdown activated PI3K/Akt/mTOR signaling pathway, while overexpression of TP73-AS1 induced inhibition of PI3K/Akt/mTOR pathway and these effects could be partly abolished by overexpression of KISS1. Conclusion: In conclusion, we identified that TP73-AS1 as an oncogenic lncRNA in the development of ccRCC and a potential target for human renal carcinoma treatment

Mid-late Holocene temperature and precipitation variations in the Guanting Basin, upper reaches of the Yellow River

The reconstruction of prehistoric temperature and precipitation variations in the upper reaches of the Yellow River is essential for understanding the cultural evolution of the region, but related information is sparse due to the limitations of the available proxies. Recent studies have shown that microbial glycerol dialkyl glycerol tetraethers (GDGTs) are promising tools for reconstructing mean annual temperature (MAT) and mean annual precipitation (MAP) in terrestrial deposits. In this study, we reconstructed mid-late Holocene climatic changes using GDGT distributions in a loess-paleosol sequence in the Lajia Ruins of the Neolithic Qijia Culture, Guanting Basin, in the southwestern end of the Chinese Loess Plateau. Our GDGT records show that MAP decreased from ca. 600 mm to 430 mm, while MAT decreased from 11.9 degrees C to 8.0 degrees C, during the past ca. 7000 yr, and a drastic decline in MAP (70 mm), accompanied by a 0.8 degrees C decline in MAT, occurred at 3800-3400 yr BP. Our results provide direct evidence supporting a hypothesis that the flourishing (4200-4000 yr BP) and decline (4000-3600 yr BP) of the Qijia culture (mainly based on millets cultivation) and subsequent rise of the Xindian/Kayue culture (3600-2600 yr BP), based on mixed agriculture of sheep husbandry and millets cultivation were triggered by climate change

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease