Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection.

Introduction: As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods: The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results: Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion: Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

How does the COVID-19 affect earnings management: Empirical evidence from China.

Using China's A-share listed companies from 2018 to 2020, this paper examines the impact of COVID-19 on earnings management. The results reveal that: (1) The COVID-19 shock intensifies earnings management, which is reflected in the increasing accrual-based earnings management and real earnings management. Moreover, when enterprises face a higher degree of financial constraints, this shock effect is more evident. (2) Enterprises in industries and regions where COVID-19 is more severe are more affected by the suspension of work and production caused by the epidemic prevention policies, so these enterprises choose accrual-based earnings management through accounting items rather than carrying out earnings management through real activities. (3) Further analysis finds that, enterprises with more investment opportunities have more evident earnings management caused by the COVID-19 shock. However, high-quality auditing has an inhibitory effect on accrual-based earnings management caused by the COVID-19 shock but has no inhibitory effect on real earnings management.

A mechanism for SARS-CoV-2 RNA capping and its inhibition by nucleotide analog inhibitors.

Decoration of cap on viral RNA plays essential roles in SARS-CoV-2 proliferation. Here, we report a mechanism for SARS-CoV-2 RNA capping and document structural details at atomic resolution. The NiRAN domain in polymerase catalyzes the covalent link of RNA 5' end to the first residue of nsp9 (termed as RNAylation), thus being an intermediate to form cap core (GpppA) with GTP catalyzed again by NiRAN. We also reveal that triphosphorylated nucleotide analog inhibitors can be bonded to nsp9 and fit into a previously unknown "Nuc-pocket" in NiRAN, thus inhibiting nsp9 RNAylation and formation of GpppA. S-loop (residues 50-KTN-52) in NiRAN presents a remarkable conformational shift observed in RTC bound with sofosbuvir monophosphate, reasoning an "induce-and-lock" mechanism to design inhibitors. These findings not only improve the understanding of SARS-CoV-2 RNA capping and the mode of action of NAIs but also provide a strategy to design antiviral drugs.

A mechanism for SARS-CoV-2 RNA capping and its inhibition by nucleotide analogue inhibitors

Decoration of cap on viral RNA plays essential roles in SARS-CoV-2 proliferation. Here we report a mechanism for SARS-CoV-2 RNA capping and document structural details at atomic resolution. The NiRAN domain in polymerase catalyzes the covalent link of RNA 5’ end to the first residue of nsp9 (termed as RNAylation), thus being an intermediate to form cap core (GpppA) with GTP catalyzed again by NiRAN. We also reveal that triphosphorylated nucleotide analogue inhibitors can be bonded to nsp9 and fit into a previously unknown ‘Nuc-pocket’ in NiRAN, thus inhibiting nsp9 RNAylation and formation of GpppA. S-loop (residues 50-KTN-52) in NiRAN presents a remarkable conformational shift observed in RTC bound with sofosbuvir monophosphate, reasoning an ‘induce-and-lock’ mechanism to design inhibitors. These findings not only improve the understanding of SARS-CoV-2 RNA capping and the mode of action of NAIs, but also provide a strategy to design antiviral drugs. Graphical Structural analyses reveal how proteins from SARS-CoV-2 cooperate and use GTP to form the cap on viral mRNA, and how this process is interrupted by nucleotide analogues that serve as antiviral drugs.

How does the COVID-19 affect earnings management: empirical evidence from China

Using China’s A-share listed companies from 2018 to 2020, this paper examines the impact of COVID-19 on earnings management. The results reveal that: (1) The COVID-19 shock intensifies earnings management, which is reflected in the increasing accrual-based earnings management and real earnings management. Moreover, when enterprises face a higher degree of financial constraints, this shock effect is more evident. (2) Enterprises in industries and regions where COVID-19 is more severe are more affected by the suspension of work and production caused by the epidemic prevention policies, so these enterprises choose accrual-based earnings management through accounting items rather than carrying out earnings management through real activities. (3) Further analysis finds that, enterprises with more investment opportunities have more evident earnings management caused by the COVID-19 shock. However, high-quality auditing has an inhibitory effect on accrual-based earnings management caused by the COVID-19 shock but has no inhibitory effect on real earnings management.

Pyrolysis of COVID-19 disposable masks and catalytic cracking of the volatiles.

The disposable masks generated in the battle against COVID-19 has attracted wide attention in the world. Pyrolysis can convert the masks into useful chemicals and fuels. In this work, the masks are pyrolyzed at temperatures of 400-580 °C and the volatiles generated are cracked without or with catalysts at 440-580 °C. The catalysts used include metal oxides (Al2O3, kaolin, Fe2O3, CeO2, TiO2) and molecular sieves (HZSM5, HY, ß(25H), ß(60H)). The yields and composition of gas and liquid products are studied in detail where the tetrahydrofuran (THF) soluble compounds are defined as the liquid product and the n-hexane soluble compounds are defined as the oil. The liquid product and the oil were identified by GC-MS and quantified by GC. Results indicate that 440 °C is sufficient for the masks' pyrolysis and the yields of gas, liquid product and oil are 23.4, 74.7 and 42.1 wt%, respectively. About 30% of the liquid product are C6-C35 hydrocarbons while about 70% are C36-C70 hydrocarbons trapped in the GC column (termed as column residue). The gas products are mainly C5, propylene and butene, accounting for 54.8%, 22.8% and 14.5% of the total gas product, respectively. Cracking of volatiles over various catalysts converts the liquid product mainly to propylene, butene and smaller organic gases. TiO2, HY and ß(60H) are good catalysts, especially ß(60H), which increases the yield of gas product to 86.5 wt% with 73.0% being ethylene, propylene and butene at 580 °C.

The Role of Imaging in the Detection and Management of COVID-19: A Review.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

Cryo-EM Structure of an Extended SARS-CoV-2 Replication and Transcription Complex Reveals an Intermediate State in Cap Synthesis.

Transcription of SARS-CoV-2 mRNA requires sequential reactions facilitated by the replication and transcription complex (RTC). Here, we present a structural snapshot of SARS-CoV-2 RTC as it transitions toward cap structure synthesis. We determine the atomic cryo-EM structure of an extended RTC assembled by nsp7-nsp82-nsp12-nsp132-RNA and a single RNA-binding protein, nsp9. Nsp9 binds tightly to nsp12 (RdRp) NiRAN, allowing nsp9 N terminus inserting into the catalytic center of nsp12 NiRAN, which then inhibits activity. We also show that nsp12 NiRAN possesses guanylyltransferase activity, catalyzing the formation of cap core structure (GpppA). The orientation of nsp13 that anchors the 5' extension of template RNA shows a remarkable conformational shift, resulting in zinc finger 3 of its ZBD inserting into a minor groove of paired template-primer RNA. These results reason an intermediate state of RTC toward mRNA synthesis, pave a way to understand the RTC architecture, and provide a target for antiviral development.

Coupling of N7-methyltransferase and 3'-5' exoribonuclease with SARS-CoV-2 polymerase reveals mechanisms for capping and proofreading.

The capping of mRNA and the proofreading play essential roles in SARS-CoV-2 replication and transcription. Here, we present the cryo-EM structure of the SARS-CoV-2 replication-transcription complex (RTC) in a form identified as Cap(0)-RTC, which couples a co-transcriptional capping complex (CCC) composed of nsp12 NiRAN, nsp9, the bifunctional nsp14 possessing an N-terminal exoribonuclease (ExoN) and a C-terminal N7-methyltransferase (N7-MTase), and nsp10 as a cofactor of nsp14. Nsp9 and nsp12 NiRAN recruit nsp10/nsp14 into the Cap(0)-RTC, forming the N7-CCC to yield cap(0) (7MeGpppA) at 5' end of pre-mRNA. A dimeric form of Cap(0)-RTC observed by cryo-EM suggests an in trans backtracking mechanism for nsp14 ExoN to facilitate proofreading of the RNA in concert with polymerase nsp12. These results not only provide a structural basis for understanding co-transcriptional modification of SARS-CoV-2 mRNA but also shed light on how replication fidelity in SARS-CoV-2 is maintained.

[Consensus of Chinese experts on diagnosis and treatment processes of acute myocardial infarction in the context of prevention and control of COVID-19 (first edition)].

The SARS-CoV-2 epidemic starting in Wuhan in December, 2019 has spread rapidly throughout the nation. The control measures to contain the epidemic also produced influences on the transport and treatment process of patients with acute myocardial infarction (AMI), and adjustments in the management of the patients need to be made at this particular time. AMI is characterized by an acute onset with potentially fatal consequence, a short optimal treatment window, and frequent complications including respiratory infections and respiratory and circulatory failure, for which active on-site treatment is essential. To standardize the management and facilitate the diagnosis and treatment, we formulated the guidelines for the procedures and strategies for the diagnosis and treatment of AMI, which highlight 5 Key Principles, namely Nearby treatment, Safety protection, Priority of thrombolysis, Transport to designated hospitals, and Remote consultation. For AMI patients, different treatment strategies are selected based on the screening results of SARS-CoV-2, the time window of STEMI onset, and the vital signs of the patients. During this special period, the cardiologists, including the interventional physicians, should be fully aware of the indications and contraindications of thrombolysis. In the transport and treatment of AMI patients, the physicians should strictly observe the indications for patient transport with appropriate protective measurements of the medical staff.