Pancreatic ß-cells package double C2-like domain beta protein into extracellular vesicles via tandem C2 domains.

Introduction: Double C2-like domain beta (DOC2B) is a vesicle priming protein critical for glucose-stimulated insulin secretion in ß-cells. Individuals with type 1 diabetes (T1D) have lower levels of DOC2B in their residual functional ß-cell mass and platelets, a phenotype also observed in a mouse model of T1D. Thus, DOC2B levels could provide important information on ß-cell dys(function). Objective: Our objective was to evaluate the DOC2B secretome of ß-cells. In addition to soluble extracellular protein, we assessed DOC2B localized within membrane-delimited nanoparticles - extracellular vesicles (EVs). Moreover, in rat clonal ß-cells, we probed domains required for DOC2B sorting into EVs. Method: Using Single Extracellular VEsicle Nanoscopy, we quantified EVs derived from clonal ß-cells (human EndoC-ßH1, rat INS-1 832/13, and mouse MIN6); two other cell types known to regulate glucose homeostasis and functionally utilize DOC2B (skeletal muscle rat myotube L6-GLUT4myc and human neuronal-like SH-SY5Y cells); and human islets sourced from individuals with no diabetes (ND). EVs derived from ND human plasma, ND human islets, and cell lines were isolated with either size exclusion chromatography or differential centrifugation. Isolated EVs were comprehensively characterized using dotblots, transmission electron microscopy, nanoparticle tracking analysis, and immunoblotting. Results: DOC2B was present within EVs derived from ND human plasma, ND human islets, and INS-1 832/13 ß-cells. Compared to neuronal-like SH-SY5Y cells and L6-GLUT4myc myotubes, clonal ß-cells (EndoC-ßH1, INS-1 832/13, and MIN6) produced significantly more EVs. DOC2B levels in EVs (over whole cell lysates) were higher in INS-1 832/13 ß-cells compared to L6-GLUT4myc myotubes; SH-SY5Y neuronal-like cells did not release appreciable DOC2B. Mechanistically, we show that DOC2B was localized to the EV lumen; the tandem C2 domains were sufficient to confer sorting to INS-1 832/13 ß-cell EVs. Discussion: Clonal ß-cells and ND human islets produce abundant EVs. In cell culture, appreciable DOC2B can be packaged into EVs, and a small fraction is excreted as a soluble protein. While DOC2B-laden EVs and soluble protein are present in ND plasma, further studies will be necessary to determine if DOC2B originating from ß-cells significantly contributes to the plasma secretome.

LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA-miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA-miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA-miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA-miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.

Disease-linked mutations in Munc18-1 deplete synaptic Doc2.

Heterozygous de novo mutations in the neuronal protein Munc18-1/STXBP1 cause syndromic neurological symptoms, including severe epilepsy, intellectual disability, developmental delay, ataxia and tremor, summarized as STXBP1 encephalopathies. Although haploinsufficiency is the prevailing disease mechanism, it remains unclear how the reduction in Munc18-1 levels causes synaptic dysfunction in disease as well as how haploinsufficiency alone can account for the significant heterogeneity among patients in terms of the presence, onset and severity of different symptoms. Using biochemical and cell biological readouts on mouse brains, cultured mouse neurons and heterologous cells, we found that the synaptic Munc18-1 interactors Doc2A and Doc2B are unstable in the absence of Munc18-1 and aggregate in the presence of disease-causing Munc18-1 mutants. In haploinsufficiency-mimicking heterozygous knockout neurons, we found a reduction in Doc2A/B levels that is further aggravated by the presence of the disease-causing Munc18-1 mutation G544D as well as an impairment in Doc2A/B synaptic targeting in both genotypes. We also demonstrated that overexpression of Doc2A/B partially rescues synaptic dysfunction in heterozygous knockout neurons but not heterozygous knockout neurons expressing G544D Munc18-1. Our data demonstrate that STXBP1 encephalopathies are not only characterized by the dysfunction of Munc18-1 but also by the dysfunction of the Munc18-1 binding partners Doc2A and Doc2B, and that this dysfunction is exacerbated by the presence of a Munc18-1 missense mutant. These findings may offer a novel explanation for the significant heterogeneity in symptoms observed among STXBP1 encephalopathy patients.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features.

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

Expression Pattern of DAB Adaptor Protein 2 in Left- and Right-Side Colorectal Carcinoma.

Left-sided and right-sided colorectal cancer (L-CRC and R-CRC) have relatively different clinical pictures and pathophysiological backgrounds. The aim of this study was to investigate the presence of DAB adapter protein 2 (DAB2) as a potential molecular mechanism that contributes to this diversity in terms of malignancy and responses to therapy. The expression of the suppressor gene DAB2 in colon cancer has already been analyzed, but its significance has not been fully elucidated. Archived samples from 34 patients who underwent colon cancer surgery were included in this study, with 13 patients with low-grade CRC and 21 with high-grade CRC. Twenty of the tumors were R-CRC, while 14 were L-CRC. DAB2 expression was analyzed immunohistochemically in the tumor tissue and the colon resection margin was used as a control. Tumors were divided into L-CRC and R-CRC, with splenic flexure as the cutoff point for each side. The results showed that R-CRC had lower DAB2 protein expression compared to L-CRC (p = 0.01). High-grade tumors had reduced DAB2 expression compared to low-grade tumors (p = 0.02). These results are consistent with the analysis of DAB2 gene expression data that we exported from the TCGA Colon and Rectal Cancer Study (COADREAD). In 736 samples of colon cancer, lower DAB2 gene expression was found in R-CRC compared to L-CRC (p < 0.0001). DAB2 gene expression was significantly higher in the sigmoid colon than in the cecum and ascending colon (p < 0.01). The analysis confirmed a lower expression of the DAB2 in tumors with positive microsatellite instability (p < 0.001). In conclusion, DAB2 has a role in the biological differences between R-CRC and L-CRC and its therapeutic and diagnostic potential needs to be further examined.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

16p11.2 CNV gene Doc2α functions in neurodevelopment and social behaviors through interaction with Secretagogin.

Copy-number variations (CNVs) of the human 16p11.2 genetic locus are associated with neurodevelopmental disorders, including autism spectrum disorders (ASDs) and schizophrenia. However, it remains largely unclear how this locus is involved in the disease pathogenesis. Doc2α is localized within this locus. Here, using in vivo and ex vivo electrophysiological and morphological approaches, we show that Doc2α-deficient mice have neuronal morphological abnormalities and defects in neural activity. Moreover, the Doc2α-deficient mice exhibit social and repetitive behavioral deficits. Furthermore, we demonstrate that Doc2α functions in behavioral and neural phenotypes through interaction with Secretagogin (SCGN). Finally, we demonstrate that SCGN functions in social/repetitive behaviors, glutamate release, and neuronal morphology of the mice through its Doc2α-interacting activity. Therefore, Doc2α likely contributes to neurodevelopmental disorders through its interaction with SCGN.

Double C-2 like domain beta (DOC2B) induces calcium dependent oxidative stress to promote lipotoxicity and mitochondrial dysfunction for its tumor suppressive function.

Mitochondria are biosynthetic and bioenergetic organelles that regulate many biological processes, including metabolism, oxidative stress, and cell death. Cervical cancer (CC) cells show impairments in mitochondrial structure and function and are linked with cancer progression. DOC2B is a tumor suppressor with anti-proliferative, anti-migratory, anti-invasive, and anti-metastatic function in CC. For the first time, we demonstrated the role of the DOC2B-mitochondrial axis with tumor growth regulatory functions in CC. We used DOC2B overexpression and knockdown model systems to show that DOC2B is localized to mitochondria and induces Ca2+-mediated lipotoxicity. DOC2B expression induced mitochondrial morphological changes with the subsequent reduction in mitochondrial DNA copy number, mitochondrial mass, and mitochondrial membrane potential. Intracellular and mitochondrial Ca2+, intracellular O.-2, and ATP levels were substantially elevated in the presence of DOC2B. DOC2B manipulation reduced glucose uptake, lactate production, and mitochondrial complex-IV activity. The presence of DOC2B significantly reduced the proteins associated with mitochondrial structure and biogenesis with the concomitant activation of AMPK signaling. Augmented lipid peroxidation (LPO) in the presence of DOC2B was a Ca2+-dependent process. Our findings demonstrated that DOC2B promotes lipid accumulation, oxidative stress, and LPO through intracellular Ca2+ overload, which may contribute to mitochondrial dysfunction and tumor-suppressive properties of DOC2B. We propose that the DOC2B-Ca2+-oxidative stress-LPO-mitochondrial axis could be targeted for confining CC. Further, the induction of lipotoxicity in tumor cells by activating DOC2B could serve as a novel therapeutic approach in CC.

Searching for associations between social media trending topics and organizations.

Trending topics are the most discussed topics at the moment on social media platforms, particularly on Twitter and Facebook. While the access to trending topics are free and available to everyone, marketing specialists and specific software are more expensive, therefore there are companies that do not have the budget to support those costs. The main goal of this work is to search for associations between trending topics and companies on social media platforms and HotRivers prototype was developed to fill this gap. This approach was applied to Twitter and used text mining techniques to process tweets, train personalized models of companies and deliver a list of the matched trending topics of the target company. So, in this work were tested different pre-processing text techniques and a method to select tweets called Centroid Strategy used on trending topics to avoid unwanted tweets. Also, were tested three models, an embedding vectors approach with Doc2Vec model, a probabilistic model with Latent Dirichlet Allocation, and a classification task approach with a Convolutional Neural Network used on the final architecture. The approach was validated with real cases like Adidas, Nike and Portsmouth Hospitals University. In the results stand out that trending topic Nike has an association with the company Nike and #WorldPatientSafetyDay has an association with Portsmouth Hospitals University. This prototype, HotRivers, can be a new marketing tool that points the direction to the next campaign.

A Novel Role for DOC2B in Ameliorating Palmitate-Induced Glucose Uptake Dysfunction in Skeletal Muscle Cells via a Mechanism Involving ß-AR Agonism and Cofilin.

Diet-related lipotoxic stress is a significant driver of skeletal muscle insulin resistance (IR) and type 2 diabetes (T2D) onset. ß2-adrenergic receptor (ß-AR) agonism promotes insulin sensitivity in vivo under lipotoxic stress conditions. Here, we established an in vitro paradigm of lipotoxic stress using palmitate (Palm) in rat skeletal muscle cells to determine if ß-AR agonism could cooperate with double C-2-like domain beta (DOC2B) enrichment to promote skeletal muscle insulin sensitivity under Palm-stress conditions. Previously, human T2D skeletal muscles were shown to be deficient for DOC2B, and DOC2B enrichment resisted IR in vivo. Our Palm-stress paradigm induced IR and ß-AR resistance, reduced DOC2B protein levels, triggered cytoskeletal cofilin phosphorylation, and reduced GLUT4 translocation to the plasma membrane (PM). By enhancing DOC2B levels in rat skeletal muscle, we showed that the deleterious effects of palmitate exposure upon cofilin, insulin, and ß-AR-stimulated GLUT4 trafficking to the PM and glucose uptake were preventable. In conclusion, we revealed a useful in vitro paradigm of Palm-induced stress to test for factors that can prevent/reverse skeletal muscle dysfunctions related to obesity/pre-T2D. Discerning strategies to enrich DOC2B and promote ß-AR agonism can resist skeletal muscle IR and halt progression to T2D.

Interdisciplinarity in Cognitive Science: A Document Similarity Analysis.

Cognitive science was established as an interdisciplinary domain of research in the 1970s. Since then, the domain has flourished, despite disputes concerning its interdisciplinarity. Multiple methods exist for the assessment of interdisciplinary research. The present study proposes a methodology for quantifying interdisciplinary aspects of research in cognitive science. We propose models for text similarity analysis that provide helpful information about the relationship between publications and their specific research fields, showing potential as a robust measure of interdisciplinarity. We designed and developed models utilizing the Doc2Vec method for analyzing cognitive science and related fields. Our findings reveal that cognitive science collaborates closely with most constituent disciplines. For instance, we found a balanced engagement between several constituent fields-including psychology, philosophy, linguistics, and computer science-that contribute significantly to cognitive science. On the other hand, anthropology and neuroscience have made limited contributions. In our analysis, we find that the scholarly domain of cognitive science has been exhibiting overt interdisciplinary for the past several decades.

Membrane Clustering of Coronavirus Variants Using Document Similarity.

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.

Down-Regulation of Double C2 Domain Alpha Promotes the Formation of Hyperplastic Nerve Fibers in Aganglionic Segments of Hirschsprung's Disease.

Hirschsprung's disease (HSCR) is a common developmental anomaly of the gastrointestinal tract in children. The most significant characteristics of aganglionic segments in HSCR are hyperplastic extrinsic nerve fibers and the absence of endogenous ganglion plexus. Double C2 domain alpha (DOC2A) is mainly located in the nucleus and is involved in Ca2+-dependent neurotransmitter release. The loss function of DOC2A influences postsynaptic protein synthesis, dendrite morphology, postsynaptic receptor density and synaptic plasticity. It is still unknown why hyperplastic extrinsic nerve fibers grow into aganglionic segments in HSCR. We detected the expression of DOC2A in HSCR aganglionic segment colons and established three DOC2A-knockdown models in the Neuro-2a cell line, neural spheres and zebrafish separately. First, we detected the protein and mRNA expression of DOC2A and found that DOC2A was negatively correlated with AChE+ grades. Second, in the Neuro-2a cell lines, we found that the amount of neurite outgrowth and mean area per cell were significantly increased, which suggested that the inhibition of DOC2A promotes nerve fiber formation and the neuron's polarity. In the neural spheres, we found that the DOC2A knockdown was manifested by a more obvious connection of nerve fibers in neural spheres. Then, we knocked down Doc2a in zebrafish and found that the down-regulation of Doc2a accelerates the formation of hyperplastic nerve fibers in aganglionic segments in zebrafish. Finally, we detected the expression of MUNC13-2 (UNC13B), which was obviously up-regulated in Grade3/4 (lower DOC2A expression) compared with Grade1/2 (higher DOC2A expression) in the circular muscle layer and longitudinal muscle layer. The expression of UNC13B was up-regulated with the knocking down of DOC2A, and there were protein interactions between DOC2A and UNC13B. The down-regulation of DOC2A may be an important factor leading to hyperplastic nerve fibers in aganglionic segments of HSCR. UNC13B seems to be a downstream molecule to DOC2A, which may participate in the spasm of aganglionic segments of HSCR patient colons.

Assessing Banks' Distress Using News and Regular Financial Data.

In this paper, we focus our attention on leveraging the information contained in financial news to enhance the performance of a bank distress classifier. The news information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with the issues related to Natural Language interpretation and to the analysis of news media. Among the different models proposed for such purpose, we investigate a deep learning approach. The methodology is based on a distributed representation of textual data obtained from a model (Doc2Vec) that maps the documents and the words contained within a text onto a reduced latent semantic space. Afterwards, a second supervised feed forward fully connected neural network is trained combining news data distributed representations with standard financial figures in input. The goal of the model is to classify the corresponding banks in distressed or tranquil state. The final aim is to comprehend both the improvement of the predictive performance of the classifier and to assess the importance of news data in the classification process. This to understand if news data really bring useful information not contained in standard financial variables.

Proteome-wide prediction and analysis of the Cryptosporidium parvum protein-protein interaction network through integrative methods.

As one of the most studied Apicomplexan parasite Cryptosporidium, Cryptosporidium parvum (C. parvum) causes worldwide serious diarrhea disease cryptosporidiosis, which can be deadly to immunodeficiency individuals, newly born children, and animals. Proteome-wide identification of protein-protein interactions (PPIs) has proven valuable in the systematic understanding of the genome-phenome relationship. However, the PPIs of C. parvum are largely unknown because of the limited experimental studies carried out. Therefore, we took full advantage of three bioinformatics methods, i.e., interolog mapping (IM), domain-domain interaction (DDI)-based inference, and machine learning (ML) method, to jointly predict PPIs of C. parvum. Due to the lack of experimental PPIs of C. parvum, we used the PPI data of Plasmodium falciparum (P. falciparum), which owned the largest number of PPIs in Apicomplexa, to train an ML model to infer C. parvum PPIs. We utilized consistent results of these three methods as the predicted high-confidence PPI network, which contains 4,578 PPIs covering 554 proteins. To further explore the biological significance of the constructed PPI network, we also conducted essential network and protein functional analysis, mainly focusing on hub proteins and functional modules. We anticipate the constructed PPI network can become an important data resource to accelerate the functional genomics studies of C. parvum as well as offer new hints to the target discovery in developing drugs/vaccines.

DOC2B is a negative regulator of Wnt/ß-catenin signaling pathway in cervical cancer.

DOC2B is a ubiquitously expressed isoform of the double C-2 protein family that requires Ca2+ for most of its physiological functions. Initial findings have indicated that DOC2B participates in exocytosis, vesicular transport, insulin secretion and regulation, glucose homeostasis, and neurotransmitter release. Aberrant expression of DOC2B has been reported in diabetes, leukemia, and cervical cancer (CC). Our earlier studies have demonstrated the inhibitory effects of DOC2B on CC cell proliferation, migration, invasion, and EMT and suggested the possible role of DOC2B in Wnt signaling inhibition. However, the association between DOC2B downregulation and Wnt/ß-catenin signaling activation and the underlying molecular mechanism remain elusive. Herein, we found that DOC2B inhibited Wnt/ß-catenin pathway by enhancing the expression of the components of the CTNNB1 destruction complex and by fostering proteasomal degradation of CTNNB1. The translocation of CTNNB1 to the nucleus and its interaction with TCF/LEF family transcription factors was perturbed in the presence of DOC2B in a GSK3ß independent manner. Further, we have identified DKK1 as one of the upregulated genes in the presence of DOC2B. DKK1 inhibition in DOC2B expressing cells by WAY262611 reactivated Wnt/ß-catenin signaling, relieved DOC2B induced senescence, and alleviated the inhibitory effects of DOC2B on the aforementioned malignant behaviors. We have provided evidence for DOC2B-DKK1-senescence-Wnt/ß-catenin-EMT signaling crosstalk to have tumor growth regulatory functions in CC. Thus, targeting DOC2B-DKK1-senescence-Wnt/ß-catenin-EMT signaling crosstalk via activation of DOC2B may offer a novel approach to restraint malignant behaviors in CC.

Metastatic suppression by DOC2B is mediated by inhibition of epithelial-mesenchymal transition and induction of senescence.

Senescence induction and epithelial-mesenchymal transition (EMT) events are the opposite sides of the spectrum of cancer phenotypes. The key molecules involved in these processes may get influenced or altered by genetic and epigenetic changes during tumor progression. Double C2-like domain beta (DOC2B), an intracellular vesicle trafficking protein of the double C2 protein family, plays a critical role in exocytosis, neurotransmitter release, and intracellular vesicle trafficking. DOC2B is repressed by DNA promoter hypermethylation and functions as a tumor growth regulator in cervical cancer. To date, the molecular mechanisms of DOC2B in cervical cancer progression and metastasis is elusive. Herein, the biological functions and molecular mechanisms regulated by DOC2B and its impact on senescence and EMT are described. DOC2B inhibition promotes proliferation, growth, and migration by relieving G0/G1-S arrest, actin remodeling, and anoikis resistance in Cal27 cells. It enhanced tumor growth and liver metastasis in nude mice with the concomitant increase in metastasis-associated CD55 and CD61 expression. Inhibition of EMT and promotion of senescence by DOC2B is a calcium-dependent process and accompanied by calcium-mediated interaction between DOC2B and CDH1. In addition, we have identified several EMT and senescence regulators as targets of DOC2B. We show that DOC2B may act as a metastatic suppressor by inhibiting EMT through induction of senescence via DOC2B-calcium-EMT-senescence axis.

Thesaurus-based word embeddings for automated biomedical literature classification.

The special nature, volume and broadness of biomedical literature pose barriers for automated classification methods. On the other hand, manually indexing is time-consuming, costly and error prone. We argue that current word embedding algorithms can be efficiently used to support the task of biomedical text classification even in a multilabel setting, with many distinct labels. The ontology representation of Medical Subject Headings provides machine-readable labels and specifies the dimensionality of the problem space. Both deep- and shallow network approaches are implemented. Predictions are determined by the similarity between extracted features from contextualized representations of abstracts and headings. The addition of a separate classifier for transfer learning is also proposed and evaluated. Large datasets of biomedical citations are harvested for their metadata and used for training and testing. These automated approaches are still far from entirely substituting human experts, yet they can be useful as a mechanism for validation and recommendation. Dataset balancing, distributed processing and training parallelization in GPUs, all play an important part regarding the effectiveness and performance of proposed methods.

Inhibition of Histamine Release from RBL-2H3 Cells by Zoledronate Did Not Affect Rab27a/Doc2a Interaction.

Mast cell (MC) exocytosis is organized by prenylated protein, including Rab families. Among Rab proteins, Rab3a, Rab27a, and Rab11 are responsible for exocytosis arrangement. Rab3a and Rab27a are contributed to exocytosis by interacting with other exocytosis proteins. Zoledronate administration disrupted the Rab prenylation process that affected its interaction with other proteins, and finally, its function. The present study has investigated the effect of zoledronate on the histamine release (HR) from RBL-2H3 cells. The main focus is to answer the question of whether zoledronate affects Rab27a/Doc2a interaction. Histamine release on RBL-2H3 cells after zoledronate or clodronate administration was measured using HPLC-fluorometry. Dinitrophenylated bovine serum albumin (DNP-BSA) (20 ng/mL) or ionomycin (1 µM) are used as secretagogues. Calcium (Ca2+) influx observation was performed using Fura-2A/M. In situ proximity ligation assay (PLA) is used to investigate Rab27a/Doc2a interaction after bisphosphonates (BPs) treatment. Histamine concentration measurement with HPLC-fluorometry showed that zoledronate (30, 100 µM) inhibited HR from antigen-activated RBL-2H3 cells. Zoledronate showed less inhibition in cells activated with ionomycin. Intracellular Ca2+ concentration and Ca2+ flux rate from the extracellular compartment was not changed by zoledronate administration. No changes in Rab27a/Doc2a interaction after zoledronate treatment. Histamine release inhibition by zoledronate in DNP-BSA-activated RBL-2H3 cells is not related to the disruption of Rab27a/Doc2a interaction and is not involve the change in Ca2+ influx.

Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts.

Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddings in an unsupervised manner. To assess the algorithms' performance, we define sentiment metrics and use a semantic lexicon SentiWordNet (SWN) to establish the benchmark measures. Our empirical results are obtained on the Obesity data set from i2b2 clinical discharge summaries and the Reuters Science dataset. We use the Welch's test to analyze the obtained sentiment evaluation. On the Obesity data, the Welch's test found significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, the Word2Vec results support the SWN results, whereas the Doc2Vec results partially correspond to the Word2Vec and the SWN results. On the Reuters data, the Welch's test did not find significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, Word2Vec and Doc2Vec results only in part correspond to the SWN results. In unsupervised sentiment analysis of medical and scientific texts, the Word2Vec sentiment analysis has been more consistent with the SentiWordNet sentiment assessment than the Doc2Vec sentiment analysis. The Welch's test of the SentiWordNet results has been a strong indicator of future correspondence between Word2Vec and SentiWordNet results.