Model People Auscultation System Based on Capacitive Sensor

The medical teaching needs auscultation training, so a model people auscultation training system was designed based on capacitive sensing principle. The PIC32 CPU with charging time measuring unit was used as the system core. Capacitance sensors were set in different parts of the model, the sampled signal was digitalized and processed, the cancelling jitter algorithm and dynamic average filtering was used for improving signal, and then the simulation audio was played. At the same time, the acquisition data was sent to the workstation through Zigbee RF module for being processed. The experience results showed that the system could simulate the audio signal from the different model parts, and it’s useful for raising the training effect; the algorithms of dynamic average filtering and cancelling dithering played important role for keeping on the system stable

NegDL: Privacy-Preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy

Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System

Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming for data analysts. To address this issue, we introduce InsightPilot, an LLM (Large Language Model)-based, automated data exploration system designed to simplify the data exploration process. InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining. Then, these analysis intents are concretized by issuing corresponding intentional queries (IQueries) to create a meaningful and coherent exploration sequence. In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts and simplifies the exploration process for users. By employing an LLM to iteratively collaborate with a state-of-the-art insight engine via IQueries, InsightPilot is effective in analyzing real-world datasets, enabling users to gain valuable insights through natural language inquiries. We demonstrate the effectiveness of InsightPilot in a case study, showing how it can help users gain valuable insights from their datasets

XInsight: eXplainable Data Analysis Through The Lens of Causality

In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the underlying causes of the knowledge acquired by EDA is crucial. However, it remains under-researched. This study promotes a transparent and explicable perspective on data analysis, called eXplainable Data Analysis (XDA). For this reason, we present XInsight, a general framework for XDA. XInsight provides data analysis with qualitative and quantitative explanations of causal and non-causal semantics. This way, it will significantly improve human understanding and confidence in the outcomes of data analysis, facilitating accurate data interpretation and decision making in the real world. XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact. XInsight uses a set of design concepts and optimizations to address the inherent difficulties associated with integrating causality into XDA. Experiments on synthetic and real-world datasets as well as a user study demonstrate the highly promising capabilities of XInsight

Mechanism of circular RNA hsa_circ_0012779 expression in nasopharyngeal carcinoma and its influence on cell biological behavior

Background and purpose: Circular RNA (circRNA) plays an important regulatory role in the development of a variety of tumors. However, the abnormal expression and biological function of circRNA in nasopharyngeal carcinoma remain unclear. This study aimed to explore the effect of hsa_circ_0012779 on the biological behavior of nasopharyngeal carcinoma cells and its molecular mechanism. Methods: The expression of hsa_circ_0012779 in human immortalized nasopharyngeal epithelial cell line NP69 and nasopharyngeal carcinoma cell lines CNE2, 5-8F, HNE1 and SUNE1 was detected by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR). Cell counting kit-8 (CCK-8) assay and transwell invasion assay were used to detect the effect of hsa_circ_0012779 on the proliferation and invasion ability of nasopharyngeal carcinoma cells. The protein level of ELAV like protein 1 (ELAVL1) in NPC cells with hsa_circ_0012779 knockdown was detected by Western blot. The binding of hsa_circ_0012779 and ELAVL1 was verified by RNA pull-down assay. Results: hsa_circ_0012779 was highly expressed in nasopharyngeal carcinoma tissues and cells. Knockdown hsa_circ_0012779 could inhibit the proliferation and invasion of nasopharyngeal carcinoma cells. hsa_circ_0012779 bound to RNA-binding protein ELAVL1 to promote its expression and colocalization in cytoplasm. In the meanwhile, the effect of knockdown hsa_circ_0012779 on nasopharyngeal carcinoma cells could be reversed by the overexpression of ELAVL1. Conclusion: hsa_circ_0012779 promote the expression of ELAVL1 and thus promote the proliferation and invasion of nasopharyngeal carcinoma, and influence the occurrence and development of nasopharyngeal carcinoma

UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer

The development of a colorectal adenoma (CA) into carcinoma (CRC) is a long and stealthy process. There remains a lack of reliable biomarkers to distinguish CA from CRC. To effectively explore underlying molecular mechanisms and identify novel lipid biomarkers promising for early diagnosis of CRC, an ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS) method was employed to comprehensively measure lipid species in human serum samples of patients with CA and CRC. Results showed significant differences in serum lipid profiles between CA and CRC groups, and 85 differential lipid species (P < 0.05 and fold change > 1.50 or < 0.67) were discovered. These significantly altered lipid species were mainly involved in fatty acid (FA), phosphatidylcholine (PC), and triacylglycerol (TAG) metabolism with the constituent ratio > 63.50%. After performance evaluation by the receiver operating characteristic (ROC) curve analysis, seven lipid species were ultimately proposed as potential biomarkers with the area under the curve (AUC) > 0.800. Of particular value, a lipid panel containing docosanamide, SM d36:0, PC 36:1e, and triheptanoin was selected as a composite candidate biomarker with excellent performance (AUC = 0.971), and the highest selected frequency to distinguish patients with CA from patients with CRC based on the support vector machine (SVM) classification model. To our knowledge, this study was the first to undertake a lipidomics profile using serum intended to identify screening lipid biomarkers to discriminate between CA and CRC. The lipid panel could potentially serve as a composite biomarker aiding the early diagnosis of CRC. Metabolic dysregulation of FAs, PCs, and TAGs seems likely involved in malignant transformation of CA, which hopefully will provide new clues to understand its underlying mechanism

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance

Small RNA-Directed Epigenetic Natural Variation in Arabidopsis thaliana

Progress in epigenetics has revealed mechanisms that can heritably regulate gene function independent of genetic alterations. Nevertheless, little is known about the role of epigenetics in evolution. This is due in part to scant data on epigenetic variation among natural populations. In plants, small interfering RNA (siRNA) is involved in both the initiation and maintenance of gene silencing by directing DNA methylation and/or histone methylation. Here, we report that, in the model plant Arabidopsis thaliana, a cluster of ∼24 nt siRNAs found at high levels in the ecotype Landsberg erecta (Ler) could direct DNA methylation and heterochromatinization at a hAT element adjacent to the promoter of FLOWERING LOCUS C (FLC), a major repressor of flowering, whereas the same hAT element in ecotype Columbia (Col) with almost identical DNA sequence, generates a set of low abundance siRNAs that do not direct these activities. We have called this hAT element MPF for Methylated region near Promoter of FLC, although de novo methylation triggered by an inverted repeat transgene at this region in Col does not alter its FLC expression. DNA methylation of the Ler allele MPF is dependent on genes in known silencing pathways, and such methylation is transmissible to Col by genetic crosses, although with varying degrees of penetrance. A genome-wide comparison of Ler and Col small RNAs identified at least 68 loci matched by a significant level of ∼24 nt siRNAs present specifically in Ler but not Col, where nearly half of the loci are related to repeat or TE sequences. Methylation analysis revealed that 88% of the examined loci (37 out of 42) were specifically methylated in Ler but not Col, suggesting that small RNA can direct epigenetic differences between two closely related Arabidopsis ecotypes