Sentiment Lexicon Induction and Interpretable Multiple-instance Learning in Financial Markets

Sentiment analysis has been widely used in the domain of finance. There are two most common textual sentiment analysis methods in finance: \textit{dictionary-based approach} and \textit{machine learning approach}. The dictionary-based method is the most convenient and efficient method to extract sentiments from the text, but the words in the dictionary are limited and cannot capture the full scope of a particular domain. Additionally, it is expensive and unsustainable to manually create and maintain domain-specific dictionary using expert opinions. Deep learning models become mainstream methods in sentiment analysis because of their better performance by utilizing extra information on a larger corpus and more complex model structures. However, deep learning models often suffer from the interpretability problem. This thesis is an attempt to address the issues of both methods. It proposes a machine learning method to do a corpus-based sentiment lexicon induction, which extends the sentiment dictionary that is customized to analyze corporate conference calls. The new extended dictionary is shown to have a better performance than the original dictionary in terms of the three-day returns of the companies in the MSCI universe. It also proposes a highly interpretable attention-based multiple-instance learning model to perform sentiment classification. It also shows that the newly proposed model has comparable accuracy performance to the state-of-the-art sequential models with better interpretability. A keyword ranking is also generated by the model as a by-product. A new sentiment dictionary is also generated by the deep learning method and shows even better performance than both the extended dictionary and the original dictionary

Using Network Processor to Establish Security Agent for AODV Routing Protocol

Network Processor (NP) is optimized to perform special network functionalities. It has highly parallel processing architecture to achieve high performance. Ad hoc network is an exciting research aspect due to the characters of self-organization、 dynamically changing topology and temporary network life. However, all the characters make the security problem more serious. Denial-of-Service (DoS) attack is the main puzzle in the security of Ad hoc network. A novel NP-based security scheme is proposed to combat the attack in AODV routing protocol. Security agent is established by a hardware thread in NP. Agent can update itself at some interval by the trustworthiness of the neighbor nodes. Agent can trace the RREQ and RREP messages stream to aggregate the key information to link list and analyze them by intrusion detection algorithm. NS2 simulator is expanded to validate the security scheme. Simulation results show that NP-based security scheme is highly effective to detect and block DoS attack

Marmoset Viral Hepatic Inflammation Induced by Hepatitis C Virus Core Protein via IL-32.

Common marmosets infected with GB virus-B (GBV-B) chimeras containing hepatitis C virus (HCV) core and envelope proteins (CE1E2p7) developed more severe hepatitis than those infected with HCV envelope proteins (E1E2p7), suggesting that HCV core protein might be involved in the pathogenesis of viral hepatitis. The potential role of HCV core in hepatic inflammation was investigated. Six individual cDNA libraries of liver tissues from HCV CE1E2p7 or E1E2p7 chimera-infected marmosets (three animals per group) were constructed and sequenced. By differential expression gene analysis, 30 of 632 mRNA transcripts were correlated with the immune system process, which might be associated with hepatitis. A protein-protein interaction network was constituted by STRING database based on these 30 differentially expressed genes (DEGs), showing that IL-32 might play a central regulatory role in HCV core-related hepatitis. To investigate the effect of HCV core protein on IL-32 production, HCV core expressing and mock constructs were transfected into Huh7 cells. IL-32 mRNA and secretion protein were detected at significantly higher levels in cells expressing HCV core protein than in those without HCV core expression (P < 0.01 and P < 0.001, respectively). By KEGG enrichment analysis and using the specific signaling pathway inhibitor LY294002 for inhibition of PI3K, IL-32 expression was significantly reduced (P < 0.001). In conclusion, HCV core protein induces an increase of IL-32 expression via the PI3K pathway in hepatic cells, which played a major role in development of HCV-related severe hepatitis

A high infectious simian adenovirus type 23 vector based vaccine efficiently protects common marmosets against Zika virus infection.

Zika virus (ZIKV) has spread in many countries or territories causing severe neurologic complications with potential fatal outcomes. The small primate common marmosets are susceptible to ZIKV, mimicking key features of human infection. Here, a novel simian adenovirus type 23 vector-based vaccine expressing ZIKV pre-membrane-envelope proteins (Sad23L-prM-E) was produced in high infectious titer. Due to determination of immunogenicity in mice, a single-dose of 3×108 PFU Sad23L-prM-E vaccine was intramuscularly inoculated to marmosets. This vaccine raised antibody titers of 104.07 E-specific and 103.13 neutralizing antibody (NAb), as well as robust specific IFN-γ secreting T-cell response (1,219 SFCs/106 cells) to E peptides. The vaccinated marmosets, upon challenge with a high dose of ZIKV (105 PFU) six weeks post prime immunization, reduced viremia by more than 100 folds, and the low level of detectable viral RNA (103.66) and T-cell response (>726 SFCs/106 PBMCs) were acquired 1-2 weeks post exposure to ZIKV, while non-vaccinated control marmosets developed long-term high titer of ZIKV (105.73 copies/ml) (P<0.05). No significant pathological lesions were observed in marmoset tissues. Sad23L-prM-E vaccine was detectable in spleen, liver and PBMCs at least 4 months post challenge. In conclusion, a prime immunization with Sad23L-prM-E vaccine was able to protect marmosets against ZIKV infection when exposed to a high dose of ZIKV. This Sad23L-prM-E vaccine is a promising vaccine candidate for prevention of ZIKV infection in humans

Evaluation of Reactivity of Monoclonal Antibodies Against Omp25 of Brucella spp.

Brucellosis is a serious zoonosis occurring mainly in developing countries, and its diagnosis is largely dependent on serologic detection and bacterial culture. In this study, we developed the murine monoclonal antibodies (mAbs) against a conserved and major outer membrane protein 25 (Omp25) of Brucella species (B. spp.) for use in clinical diagnosis. The mAbs to Omp25 were produced by hybridoma technique, which were utilized for developing various immunoassays for detection of Brucellae, including Western blot (WB), enzyme-linked immunosorbent assay (ELISA), immunochemical staining (ICS), immunofluorescence staining (IFS), and flow cytometry assay (FCM). A number of five mAbs (2B10, 4A12, 4F10, 6C12, and 8F3) specific to Omp25 were selected, including 2 IgG1, 2 IgG2a, and 1 IgG2b. Among them, mAbs 6C12, 8F3, and 4A12 reacted highly with B. melitensis (M5-90), B. abortus (S19, 104M, and 2308), and B. suis strain (S2). No cross-reactivity with Yersinia enterocolitica O:9, Salmonella spp., and Escherichia coli was found. By mapping Omp25 epitopes, mAb 6C12 was found as reacting with a semi-conformational epitope, and mAbs 4A12 and 8F3 as recognizing a different linear epitope, respectively. The paired mAbs were tested for detecting Brucella species, suggesting that 8F3 was suitable for solid phase capture and 6C12 or 4A12 was suitable for conjugation with HRP for detection of Brucella Omp25 in ELISA. The FCM was established by mAb 6C12 for detecting intracellular Brucellae-infected peripheral blood mononuclear cells (PBMCs) from brucellosis patients. In conclusion, mAbs against Omp25 are precious reagents for detection of Brucellae in clinical samples with various immunoassays. mAb 6C12-based FCM could be potentially used for the monitoring of therapeutic efficacy for brucellosis in clinical practice

Physiological and Biochemical Responses of Pepper (Capsicum annuum L.) Seedlings to Nickel Toxicity

Globally, heavy metal pollution of soil has remained a problem for food security and human health, having a significant impact on crop productivity. In agricultural environments, nickel (Ni) is becoming a hazardous element. The present study was performed to characterize the toxicity symptoms of Ni in pepper seedlings exposed to different concentrations of Ni. Four-week-old pepper seedlings were grown under hydroponic conditions using seven Ni concentrations (0, 10, 20, 30, 50, 75, and 100 mg L–1 NiCl2. 6H2O). The Ni toxicity showed symptoms, such as chlorosis of young leaves. Excess Ni reduced growth and biomass production, root morphology, gas exchange elements, pigment molecules, and photosystem function. The growth tolerance index (GTI) was reduced by 88-, 75-, 60-, 45-, 30-, and 19% in plants against 10, 20, 30, 50, 75, and 100 mg L–1 Ni, respectively. Higher Ni concentrations enhanced antioxidant enzyme activity, ROS accumulation, membrane integrity [malondialdehyde (MDA) and electrolyte leakage (EL)], and metabolites (proline, soluble sugars, total phenols, and flavonoids) in pepper leaves. Furthermore, increased Ni supply enhanced the Ni content in pepper’s leaves and roots, but declined nitrogen (N), potassium (K), and phosphorus (P) levels dramatically. The translocation of Ni from root to shoot increased from 0.339 to 0.715 after being treated with 10–100 mg L–1 Ni. The uptake of Ni in roots was reported to be higher than that in shoots. Generally, all Ni levels had a detrimental impact on enzyme activity and led to cell death in pepper seedlings. However, the present investigation revealed that Ni ≥ 30 mg L–1 lead to a deleterious impact on pepper seedlings. In the future, research is needed to further explore the mechanism and gene expression involved in cell death caused by Ni toxicity in pepper plants

Prime-boost vaccination of mice and rhesus macaques with two novel adenovirus vectored COVID-19 vaccine candidates.

ABSTRACTCOVID-19 vaccines are being developed urgently worldwide. Here, we constructed two adenovirus vectored COVID-19 vaccine candidates of Sad23L-nCoV-S and Ad49L-nCoV-S carrying the full-length gene of SARS-CoV-2 spike protein. The immunogenicity of two vaccines was individually evaluated in mice. Specific immune responses were observed by priming in a dose-dependent manner, and stronger responses were obtained by boosting. Furthermore, five rhesus macaques were primed with 5 × 109 PFU Sad23L-nCoV-S, followed by boosting with 5 × 109 PFU Ad49L-nCoV-S at 4-week interval. Both mice and macaques well tolerated the vaccine inoculations without detectable clinical or pathologic changes. In macaques, prime-boost regimen induced high titers of 103.16 anti-S, 102.75 anti-RBD binding antibody and 102.38 pseudovirus neutralizing antibody (pNAb) at 2 months, while pNAb decreased gradually to 101.45 at 7 months post-priming. Robust T-cell response of IFN-γ (712.6 SFCs/106 cells), IL-2 (334 SFCs/106 cells) and intracellular IFN-γ in CD4+/CD8+ T cell (0.39%/0.55%) to S peptides were detected in vaccinated macaques. It was concluded that prime-boost immunization with Sad23L-nCoV-S and Ad49L-nCoV-S can safely elicit strong immunity in animals in preparation of clinical phase 1/2 trials

Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness