Reward-based Crowdfunding Success Prediction with Multimodal Data

As an increasing number of crowdfunding platforms recommend that entrepreneurs post multimodal data to improve data diversity and attract investors’ attention, it becomes necessary to study how functions of multimodal data take effect to predict fundraising outcomes (i.e., success or failure). There is a lack of research providing a comprehensive investigation of multimodal data in crowdfunding. Rooted in language and visual image metafunctional theories, we propose a framework to explore ideational, interpersonal, and textual metafunctions of multimodal data. We empirically examine the effectiveness of each metafunction, each modality, and their combination in predicting fundraising outcomes. The empirical evaluation shows the predictive utility of any metafunctions and metafunction combinations. The results also demonstrate that adding data modalities can help to improve the prediction performance

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-driven Approach

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes integer interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G

Proteomics analysis of differentially expressed proteins in chicken trachea and kidney after infection with the highly virulent and attenuated coronavirus infectious bronchitis virus in vivo

<p>Abstract</p> <p>Background</p> <p>Infectious bronchitis virus (IBV) is first to be discovered coronavirus which is probably endemic in all regions with intensive impact on poultry production. In this study, we used two-dimensional gel electrophoresis (2-DE) and two-dimensional fluorescence difference gel electrophoresis (2-DIGE), coupled with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF-MS), to explore the global proteome profiles of trachea and kidney tissues from chicken at different stages infected <it>in vivo </it>with the highly virulent ck/CH/LDL/97I P₅strain of infectious bronchitis virus (IBV) and the embryo-passaged, attenuated ck/CH/LDL/97I P₁₁₅strain.</p> <p>Results</p> <p>Fifty-eight differentially expressed proteins were identified. Results demonstrated that some proteins which had functions in cytoskeleton organization, anti-oxidative stress, and stress response, showed different change patterns in abundance from chicken infected with the highly virulent ck/CH/LDL/97I P₅strain and those given the embryo-passaged, attenuated P₁₁₅stain. In addition, the dynamic transcriptional alterations of 12 selected proteins were analyzed by the real-time RT-PCR, and western blot analysis confirmed the change in abundance of heat shock proteins (HSP) beta-1, annexin A2, and annexin A5.</p> <p>Conclusions</p> <p>The proteomic alterations described here may suggest that these changes to protein expression correlate with IBV virus' virulence in chicken, hence provides valuable insights into the interactions of IBV with its host and may also assist with investigations of the pathogenesis of IBV and other coronavirus infections.</p

Identification of a novel linear B-cell epitope in the UL26 and UL26.5 proteins of Duck Enteritis Virus

BACKGROUND: The Unique Long 26 (UL26) and UL26.5 proteins of herpes simplex virus are known to function during the assembly of the viruses. However, for duck enteritis virus (DEV), which is an unassigned member of the family Herpesviridae, little information is available about the function of the two proteins. In this study, the C-terminus of DEV UL26 protein (designated UL26c), which contains the whole of UL26.5, was expressed, and the recombinant UL26c protein was used to immunize BALB/c mice to generate monoclonal antibodies (mAb). The mAb 1C8 was generated against DEV UL26 and UL26.5 proteins and used subsequently to map the epitope in this region. Both the mAb and its defined epitope will provide potential tools for further study of DEV. RESULTS: A mAb (designated 1C8) was generated against the DEV UL26c protein, and a series of 17 partially overlapping fragments that spanned the DEV UL26c were expressed with GST tags. These peptides were subjected to enzyme-linked immunosorbent assay (ELISA) and western blotting analysis using mAb 1C8 to identify the epitope. A linear motif, (520)IYYPGE(525), which was located at the C-terminus of the DEV UL26 and UL26.5 proteins, was identified by mAb 1C8. The result of the ELISA showed that this epitope could be recognized by DEV-positive serum from mice. The (520)IYYPGE(525 )motif was the minimal requirement for reactivity, as demonstrated by analysis of the reactivity of 1C8 with several truncated peptides derived from the motif. Alignment and comparison of the 1C8-defined epitope sequence with those of other alphaherpesviruses indicated that the motif (521)YYPGE(525 )in the epitope sequence was conserved among the alphaherpesviruses. CONCLUSION: A mAb, 1C8, was generated against DEV UL26c and the epitope-defined minimal sequence obtained using mAb 1C8 was (520)IYYPGE(525). The mAb and the identified epitope may be useful for further study of the design of diagnostic reagents for DEV

Different linkages in the long and short regions of the genomes of duck enteritis virus Clone-03 and VAC Strains

<p>Abstract</p> <p>Background</p> <p>Duck enteritis virus (DEV) is an unassigned member in the family <it>Herpesviridae</it>. To demonstrate further the evolutionary position of DEV in the family <it>Herpesviridae</it>, we have described a 42,897-bp fragment. We demonstrated novel genomic organization at one end of the long (L) region and in the entire short (S) region in the Clone-03 strain of DEV.</p> <p>Results</p> <p>A 42,897-bp fragment located downstream of the <it>LOFR11 </it>gene was amplified from the Clone-03 strain of DEV by using 'targeted gene walking PCR'. Twenty-two open reading frames (ORFs) were predicted and determined in the following order: 5'<it>-LORF11-RLORF1</it>-<it>ORF1</it>-<it>ICP4</it>-<it>S1-S2-US1-US10-SORF3-US2-MDV091.5-like-US3-US4-US5-US6-US7-US8-ORFx-US1-S2-S1-ICP4 </it>-3'. This was different from that of the published VAC strain, both in the linkage of the L region and S region, and in the length of the US10 and US7 proteins. The <it>MDV091.5-like </it>gene, <it>ORFx </it>gene, <it>S1 </it>gene and <it>S2 </it>gene were first observed in the DEV genome. The lengths of DEV US10 and US7 were determined to be 311 and 371 amino acids, respectively, in the Clone-03 strain of DEV, and these were different from those of other strains. The comparison of genomic organization in the fragment studied herein with those of other herpesviruses showed that DEV possesses some unique characteristics, such as the duplicated US1 at each end of the US region, and the US5, which showed no homology with those of other herpesviruses. In addition, the results of phylogenetic analysis of ORFs in the represented fragment indicated that DEV is closest to its counterparts VZV (<it>Varicellovirus</it>) and other avian herpesviruses.</p> <p>Conclusion</p> <p>The molecular characteristics of the 42,897-bp fragment of Clone-03 have been found to be different from those of the VAC strain. The phylogenetic analysis of genes in this region showed that DEV should be a separate member of the subfamily <it>Alphaherpesvirinae</it>.</p

Comparative analysis of the genes UL1 through UL7 of the duck enteritis virus and other herpesviruses of the subfamily Alphaherpesvirinae

The nucleotide sequences of eight open reading frames (ORFs) located at the 5' end of the unique long region of the duck enteritis virus (DEV) Clone-03 strain were determined. The genes identified were designated UL1, UL2, UL3, UL4, UL5, UL6 and UL7 homologues of the herpes simplex virus 1 (HSV-1). The DEV UL3.5 located between UL3 and UL4 had no homologue in the HSV-1. The arrangement and transcription orientation of the eight genes were collinear with their homologues in the HSV-1. Phylogenetic trees were constructed based on the alignments of the deduced amino acids of eight proteins with their homologues in 12 alpha-herpesviruses. In the UL1, UL3, UL3.5, UL5 and UL7 proteins trees, the branches were more closely related to the genus Mardivirus. However, the UL2, UL4, and UL6 proteins phylogenetic trees indicated a large distance from Mardivirus, indicating that the DEV evolved differently from other viruses in the subfamily Alphaherpesvirinae and formed a single branch within this subfamily

Three Essays on Artificial Intelligence in Business and Healthcare

The big data era has provided researchers with challenges and opportunities for data-centric research. On the one hand, recent developments in AI technology have allowed advanced techniques to process text/image/audio/video and graph-structured data, providing new opportunities to employ big data for explanatory and predictive analytics in information systems research. On the other hand, the field requires a new level of artificial intelligence–transparent, robust, and ethical AI–to facilitate reliable business decision-making. My three dissertation essays apply, develop, and enhance state-of-the-art AI methods, leveraging various data sources as well as domain knowledge synthesis, to deal with issues in business and healthcare fields. In Essay 1, I investigate the possibility of using deep learning models for Computed Tomography (CT) localizer image reconstruction. CT has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes. I investigate the possibility of using deep learning models to reconstruct one localization scan image from the other, thus reducing the patient dose and simplifying the clinical workflow. I propose a modified encoder-decoder network and a scaled mixture loss function specifically for the focal task. Experiment results indicate that although the reconstructed abdominal CT localization images may lack some details on the internal organ structures, they could be used effectively for tube current modulation calculation and patient positioning purposes, leading to a reduction of radiation dose and scan time in clinical CT exams. In Essay 2, I propose a robust meta-graph learning method for multimodal time series prediction. Multimodal time series prediction is a difficult problem given the intricate feature interrelationships. I explore interrelationships of multilevel features in multimodal time series data and disentangle the intricate interrelationships with a robust meta-graph learning method named RMGL. The design of RMGL is rooted in theoretical foundations regarding graph convolutional networks and a novel graph attention mechanism. The core of RMGL is a meta-graph composed of three hierarchically interconnected graphs, representing feature-wise, modality-wise, and time-step-wise interrelationships, respectively. The interconnections across the graphs allow feature representations to propagate simultaneously, thereby quantifying multilevel feature interrelationships with graph structures synchronously and efficiently. Furthermore, RMGL introduces a novel weight regularization scheme to effectively learn the meta-graph for prediction based on the low-pass nature of graph convolutional filters. RMGL outperformed state-of-the-art alternatives in an empirical evaluation with a financial risk prediction task. Ablation experiments and further analyses indicated the effectiveness of RMGL. In Essay 3, I propose a knowledge-enhanced, transformer-based text categorization model to detect employee trust indices from employee reviews. The indices of Employee Trust Model (ETM) are intangibles. Extant measurement options that require members of an organization to complete surveys make it difficult to collect data from large samples of firms across times. The use of small samples has led to conflicting results in managerial and finance research and made findings less appealing to practitioners. Furthermore, the absence of data in the time dimension has restricted analytical methods in use and limited the application of theoretical frameworks. I propose DeepEmployee, a novel design artifact based on automated text classification, to detect ETM indices from employee-generated reviews. DeepEmployee stems from design science research and includes three cohesive and complementary parts: (1) domain-specific knowledge construction based on theoretical frameworks in the management field, (2) a state-of-the-art deep learning design artifact that incorporates domain-specific knowledge to improve performance, and (3) a rigorous two-part evaluation of improvements in ETM detection and increased explanatory and predictive power in downstream tasks

Hydrogen Gas Improves Seed Germination in Cucumber by Regulating Sugar and Starch Metabolisms

Hydrogen gas (H2), an important gaseous regulator, is involved in various plant growth and development processes. However, there have been few studies on the role of H2 in seed germination. In this study, the role and underlying mechanisms of H2 in enhancing seed germination were investigated in cucumber (Cucumis sativus L.). The results revealed that the germination rate, germ length, germination index, and vitality index of cucumber exhibited a dose-dependent relationship with the increase in concentrations of hydrogen-rich water (HRW, a H2 donor; 0, 1, 10, 25, 50, 75, and 100%), attaining the maximum values with 75% HRW treatment. Treatment with 75% HRW resulted in higher contents of soluble sugar, soluble protein, and starch than the control. Additionally, the activity of α-amylase, β-amylase, and total amylase was significantly improved by 75% HRW treatment compared to the control, reaching the maximum values at 36 h. Moreover, the expression levels of starch-related genes AMY and BMY and sugar-related genes SS4 and SS3 were significantly upregulated by 75% HRW treatment during germination, particularly at 36 h. These results suggest that H2 might promote cucumber seed germination by increasing sugar and starch metabolisms

Proteomic analysis of chicken embryonic trachea and kidney tissues after infection <it>in ovo </it>by avian infectious bronchitis coronavirus

<p>Abstract</p> <p>Background</p> <p>Avian infectious bronchitis (IB) is one of the most serious diseases of economic importance in chickens; it is caused by the avian infectious coronavirus (IBV). Information remains limited about the comparative protein expression profiles of chicken embryonic tissues in response to IBV infection <it>in ovo</it>. In this study, we analyzed the changes of protein expression in trachea and kidney tissues from chicken embryos, following IBV infection <it>in ovo</it>, using two-dimensional gel electrophoresis (2-DE) coupled with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF-TOF MS).</p> <p>Results</p> <p>17 differentially expressed proteins from tracheal tissues and 19 differentially expressed proteins from kidney tissues were identified. These proteins mostly related to the cytoskeleton, binding of calcium ions, the stress response, anti-oxidative, and macromolecular metabolism. Some of these altered proteins were confirmed further at the mRNA level using real-time RT-PCR. Moreover, western blotting analysis further confirmed the changes of annexin A5 and HSPB1 during IBV infection.</p> <p>Conclusions</p> <p>To the best of our knowledge, we have performed the first analysis of the proteomic changes in chicken embryonic trachea and kidney tissues during IBV infection <it>in ovo</it>. The data obtained should facilitate a better understanding of the pathogenesis of IBV infection.</p