Optimizing Emotional Insight through Unimodal and Multimodal Long Short-term Memory Models

The field of multimodal emotion recognition is increasingly gaining popularity as a research area. It involves analyzing human emotions across multiple modalities, such as acoustic, visual, and language. Emotion recognition is more effective as a multimodal learning task than relying on a single modality. In this paper, we present an unimodal and multimodal long short-term memory model with a class weight parameter technique for emotion recognition on the CMU-Multimodal Opinion Sentiment and Emotion Intensity dataset. In addition, a critical challenge lies in selecting the most effective fusion method for integrating multiple modalities. To address this, we applied four different fusion techniques: Early fusion, late fusion, deep fusion, and tensor fusion. These fusion methods improved the performance of multimodal emotion recognition compared to unimodal approaches. With the highly imbalanced number of samples per emotion class in the MOSEI dataset, adding a class weight parameter technique leads our model to outperform the state of the art on all three modalities — acoustic, visual, and language — as well as on all the fusion models. The challenges of class imbalance, which can lead to biased model performance, and using an effective fusion method for integrating multiple modalities often result in decreased accuracy in recognizing less frequent emotion classes. Our proposed model shows 2–3% performance improvement in the unimodal and 2% in the multimodal over the state-of-the-art achieved results

Credit card fraud detection using AdaBoost and majority voting

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards

Parametrization of the Hybrid Potential for Pairs of Neutral Atoms

The hybrid form is a combination of the Rydberg potential and the London inverse-sixth-power energy. It is accurate at all relevant distance scales and simple enough for use in all-atom simulations of biomolecules. One may compute the parameters of the hybrid potential for the ground state of a pair of neutral atoms from their internuclear separation, the depth and curvature of their potential at its minimum, and from their van der Waals coefficient of dispersion.Comment: 7 pages, 11 figures, includes lithium, sodium, & potassium dimers, minor correction

ORegAnno: an open-access community-driven resource for regulatory annotation

ORegAnno is an open-source, open-access database and literature curation system for community-based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. The current release comprises 30 145 records curated from 922 publications and describing regulatory sequences for over 3853 genes and 465 transcription factors from 19 species. A new feature called the ‘publication queue’ allows users to input relevant papers from scientific literature as targets for annotation. The queue contains 4438 gene regulation papers entered by experts and another 54 351 identified by text-mining methods. Users can enter or ‘check out’ papers from the queue for manual curation using a series of user-friendly annotation pages. A typical record entry consists of species, sequence type, sequence, target gene, binding factor, experimental outcome and one or more lines of experimental evidence. An evidence ontology was developed to describe and categorize these experiments. Records are cross-referenced to Ensembl or Entrez gene identifiers, PubMed and dbSNP and can be visualized in the Ensembl or UCSC genome browsers. All data are freely available through search pages, XML data dumps or web services at: http://www.oreganno.org

Intracellular Invasion of Orientia tsutsugamushi Activates Inflammasome in ASC-Dependent Manner

Orientia tsutsugamushi, a causative agent of scrub typhus, is an obligate intracellular bacterium, which escapes from the endo/phagosome and replicates in the host cytoplasm. O. tsutsugamushi infection induces production of pro-inflammatory mediators including interleukin-1β (IL-1β), which is secreted mainly from macrophages upon cytosolic stimuli by activating cysteine protease caspase-1 within a complex called the inflammasome, and is a key player in initiating and maintaining the inflammatory response. However, the mechanism for IL-1β maturation upon O. tsutsugamushi infection has not been identified. In this study, we show that IL-1 receptor signaling is required for efficient host protection from O. tsutsugamushi infection. Live Orientia, but not heat- or UV-inactivated Orientia, activates the inflammasome through active bacterial uptake and endo/phagosomal maturation. Furthermore, Orientia-stimulated secretion of IL-1β and activation of caspase-1 are ASC- and caspase-1- dependent since IL-1β production was impaired in Asc- and caspase-1-deficient macrophages but not in Nlrp3-, Nlrc4- and Aim2-deficient macrophages. Therefore, live O. tsutsugamushi triggers ASC inflammasome activation leading to IL-1β production, which is a critical innate immune response for effective host defense

A20 (Tnfaip3) Deficiency in Myeloid Cells Protects against Influenza A Virus Infection

The innate immune response provides the first line of defense against viruses and other pathogens by responding to specific microbial molecules. Influenza A virus (IAV) produces double-stranded RNA as an intermediate during the replication life cycle, which activates the intracellular pathogen recognition receptor RIG-I and induces the production of proinflammatory cytokines and antiviral interferon. Understanding the mechanisms that regulate innate immune responses to IAV and other viruses is of key importance to develop novel therapeutic strategies. Here we used myeloid cell specific A20 knockout mice to examine the role of the ubiquitin-editing protein A20 in the response of myeloid cells to IAV infection. A20 deficient macrophages were hyperresponsive to double stranded RNA and IAV infection, as illustrated by enhanced NF-κB and IRF3 activation, concomitant with increased production of proinflammatory cytokines, chemokines and type I interferon. In vivo this was associated with an increased number of alveolar macrophages and neutrophils in the lungs of IAV infected mice. Surprisingly, myeloid cell specific A20 knockout mice are protected against lethal IAV infection. These results challenge the general belief that an excessive host proinflammatory response is associated with IAV-induced lethality, and suggest that under certain conditions inhibition of A20 might be of interest in the management of IAV infections