Progress on COVID-19 Chemotherapeutics Discovery and Novel Technology

COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel highly contagious and pathogenic coronavirus that emerged in late 2019. SARS-CoV-2 spreads primarily through virus-containing droplets and small particles of air pollution, which greatly increases the risk of inhaling these virus particles when people are in close proximity. COVID-19 is spreading across the world, and the COVID-19 pandemic poses a threat to human health and public safety. To date, there are no specific vaccines or effective drugs against SARS-CoV-2. In this review, we focus on the enzyme targets of the virus and host that may be critical for the discovery of chemical compounds and natural products as antiviral drugs, and describe the development of potential antiviral drugs in the preclinical and clinical stages. At the same time, we summarize novel emerging technologies applied to the research on new drug development and the pathological mechanisms of COVID-19.     

Information Bottleneck Signal Processing and Learning to Maximize Relevant Information for Communication Receivers

Digital communication receivers extract information about the transmitted data from the received signal in subsequent processing steps, such as synchronization, demodulation and channel decoding. Technically, the receiver-side signal processing for conducting these tasks is complex and hence causes bottleneck situations in terms of power, delay and chip area. Typically, many bits per sample are required to represent and process the received signal in the digital receiver hardware accurately. In addition, demanding arithmetical operations are required in the signal processing algorithms. A popular recent trend is designing entire receiver chains or some of their crucial building blocks from an information theoretical perspective. Signal processing blocks with very simple mathematical operations can be designed to directly maximize the relevant information that flows through them. At the same time, a strong quantization reduces the number of bits processed in the receiver to further lower the complexity. The described system design approach follows the principle of the information bottleneck method. Different authors proposed various ideas to design and implement mutual information-maximizing signal processing units. The first important aim of this article is to explain the fundamental similarities between the information bottleneck method and the functionalities of communication receivers. Based on that, we present and investigate new results on an entire receiver chain that is designed following the information bottleneck design principle. Afterwards, we give an overview of different techniques following the information bottleneck design paradigm from the literature, mainly dealing with channel decoding applications. We analyze the similarities of the different approaches for information bottleneck signal processing. This comparison leads to a general view on information bottleneck signal processing which goes back to the learning of parameters of trainable functions that maximize the relevant mutual information under compression.     

Multiscale Dense U-Net: A Fast Correction Method for Thermal Drift Artifacts in Laboratory NanoCT Scans of Semi-Conductor Chips

The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset.     

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.     

Detecting Errors with Zero-Shot Learning

Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, some researchers focus their attention on learning the semantic distribution of data for error detection; however, the low error rate in real datasets makes it hard to collect negative samples for training supervised deep learning models. Most of the existing deep-learning-based error detection algorithms solve the class imbalance problem by data augmentation. Due to the inadequate sampling of negative samples, the features learned by those methods may be biased. In this paper, we propose an AEGAN (Auto-Encoder Generative Adversarial Network)-based deep learning model named SAT-GAN (Self-Attention Generative Adversarial Network) to detect errors in relational datasets. Combining the self-attention mechanism with the pre-trained language model, our model can capture semantic features of the dataset, specifically the functional dependency between attributes, so that no rules or constraints are needed for SAT-GAN to identify inconsistent data. For the lack of negative samples, we propose to train our model via zero-shot learning. As a clean-data tailored model, SAT-GAN tries to recognize error data as outliers by learning the latent features of clean data. In our evaluation, SAT-GAN achieves an average F1-score of 0.95 on five datasets, which yields at least 46.2% F1-score improvement over rule-based methods and outperforms state-of-the-art deep learning approaches in the absence of rules and negative samples.     

PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.     

Human Remains Identification Using Micro-CT,Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact

(1) Teeth, in humans, represent the most resilient tissues. However, exposure to concentrated acids might lead to their dissolving, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper aims to introduce a novel method for the 3D reconstruction of dental patterns as a crucial step for the digital identification of dental records. (2) With a combination of modern methods, including micro-computed tomography, cone-beam computer tomography, and attenuated total reflection, in conjunction with Fourier transform infrared spectroscopy and artificial intelligence convolutional neural network algorithms, this paper presents a method for 3D-dental-pattern reconstruction, and human remains identification. Our research studies the morphology of teeth, bone, and dental materials (amalgam, composite, glass-ionomer cement) under different periods of exposure to 75% sulfuric acid. (3) Our results reveal a significant volume loss in bone, enamel, dentine, as well as glass-ionomer cement. The results also reveal a significant resistance by the composite and amalgam dental materials to the impact of sulfuric acid, thus serving as strong parts in the dental-pattern mosaic. This paper also probably introduces the first successful artificial intelligence application in automated-forensic-CBCT segmentation. (4) Interdisciplinary cooperation, utilizing the mentioned technologies, can solve the problem of human remains identification with a 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.     

当代性与“做以成人”

福柯哲学从多个维度体现了当代性与现代性的对照或张力,而“成人”始终是这一没有中心的哲学的一个中心话题。一个时代的哲学气质决定了人之为人。当代性指称的是以精神的失落为表征的一个特定时代的别一种精神,当代人也因此是抛弃信仰、告别理想、丧失情感的人。人在现代性进程中是“学”而成的,即主体或人是依据一些普遍法则被塑造而成的;而在当代性进程中,人却是“做”而成的,即主体或人是通过自己的行为被塑造而成的。福柯认为其写作的目的是为了改变自己,为了让自己的形象多样化。其人其说正好体现了从“学以成人”到“做以成人”的重要转换,尽管前者以某种方式被包含在了后者之中。     

石墨炔原子催化剂的崭新道路:基于自验证机器学习方法的筛选策略

近年来, 原子催化剂(ACs)引起了广泛的研究关注. 目前该领域的长足发展受限于贵金属的使用和单原子催化剂(SACs)的性能有限. 本文总结了利用密度泛函理论(DFT)和机器学习(ML)方法筛选高效的基于石墨炔(GDY)的原子催化剂的工作. 研究表明, Pd, Co, Pt和Hg可以形成稳定的零价过渡金属-石墨炔组合(TM-GDY), 而镧系-过渡金属的双原子催化剂(Ln-TM DAC)组合通过f-d轨道耦合作用可以获得有效的催化性能提升. 进一步分析表明, 主族元素与过渡金属和镧系金属的结合可以通过p轨道耦合保持高电活性, 从而构成高度稳定的GDY-DAC系统, 机器学习算法也揭示了s,p轨道的作用. 此外, 理论算法技术在筛选催化水分解析氢反应(HER)的高效组合上也表现出了优越性, 创新性地预测了石墨炔-原子催化剂在实际催化反应中的潜能. 本综合评述可为未来设计新型原子催化剂提供新的思路与策略.     

Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology

Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is needed to improve detection and reduce complications from invasive cystoscopy. Here, we present a rapid spectroscopic method to detect the recurrence of NMIBC in urine. Urine samples from previously-diagnosed NMIBC patients (n = 62) were collected during their follow-up visits before cystoscopy examination. Cystoscopy results were recorded (41 cancer-free and 21 recurrence) and attenuated total refraction Fourier transform infrared (ATR-FTIR) spectra were acquired from urine samples using direct application. Spectral processing and normalization were optimized using parameter grid searching. We assessed their technical variability through multivariate analysis and principal component analysis (PCA). We assessed 35 machine learning models on a training set (70%), and the performance was evaluated on a held-out test set (30%). A Regularized Random Forests (RRF) model achieved a 0.92 area under the receiver operating characteristic (AUROC) with 86% sensitivity and 77% specificity. In conclusion, our spectroscopic liquid biopsy approach provides a promising technique for the early identification of NMIBC with a less invasive examination.