WITHDRAWN:Identification of bovine uterine transcriptomic hub genes using Bayesian network algorithms.

Ahead of Print article withdrawn by publisher.

A structural approach to detecting opinion leaders in Twitter by random matrix theory.

This paper presents a novel approach leveraging Random Matrix Theory (RMT) to identify influential users and uncover the underlying dynamics within social media discourse networks. Focusing on the retweet network associated with the 2021 Iranian presidential election, our study reveals intriguing findings. RMT analysis unveils that power dynamics within both poles of the network do not conform to a "one-to-many" pattern, highlighting a select group of users wielding significant influence within their clusters and across the entire network. By harnessing Random Matrix Theory (RMT) and complementary methodologies, we gain a profound understanding of the network's structure and, in turn, unveil the intricate dynamics of the discussion extending beyond mere structural analysis. In sum, our findings underscore the potential of RMT as a tool to gain deeper insights into network dynamics, particularly within popular discussions. This approach holds promise for investigating opinion leaders in diverse political and non-political dialogues.

Opinion manipulation on Farsi Twitter.

For Iranians and the Iranian diaspora, the Farsi Twittersphere provides an important alternative to state media and an outlet for political discourse. But this understudied online space has become an opinion manipulation battleground, with diverse actors using inauthentic accounts to advance their goals and shape online narratives. Examining trending discussions crossing social cleavages in Iran, we explore how the dynamics of opinion manipulation differ across diverse issue areas. Our analysis suggests that opinion manipulation by inauthentic accounts is more prevalent in divisive political discussions than non-divisive or apolitical discussions. We show how Twitter's network structures help to reinforce the content propagated by clusters of inauthentic accounts in divisive political discussions. Analyzing both the content and structure of online discussions in the Iranian Twittersphere, this work contributes to a growing body of literature exploring the dynamics of online opinion manipulation, while improving our understanding of how information is controlled in the digital age.

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering.

Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66% on PASD, 8.42% on PerCQA, and 3.08% on WikiFA datasets in terms of MRR.

The footprint of campaign strategies in Farsi Twitter: A case for 2021 Iranian presidential election.

The rise of social media accompanied by the Covid-19 Pandemic has instigated a shift in paradigm in the presidential campaigns in Iran from the real world to social media. Unlike previous presidential elections, there was a decrease in physical events and advertisements for the candidates; in turn, the online presence of presidential candidates is significantly increased. Farsi Twitter played a specific role in this matter, as it became the platform for creating political content. In this study, we found traces of organizational activities in Farsi Twitter, and our investigations reveal that the discussion network of the 2021 election is heterogeneous and highly polarized. However, unlike many other documented election cases in Iran and around the globe, communities of candidates' supporters are very close in one pole, and the other pole is for "Anti-voters" who endorse boycotting the election. With almost no reciprocal ties, these two poles form two echo chambers, one favoring the election and the other for voter suppression. Furthermore, a high presence of bot activity is observed among the most influential users in all of the involved communities.

Automatic Artificial Pancreas Systems Using an Intelligent Multiple-Model PID Strategy.

In this paper, an individualized intelligent multiple-model technique is proposed to design automatic artificial pancreas (AP) systems for the glycemic regulation of type 1 diabetic patients. At first, using the multiple-model concept, the insulin-glucose regulatory system is mathematically identified by constructing some local models. In this step, trade-offs between the number of local models and the complexity of the overall closed-loop system are made by defining and solving a bi-objective optimization problem. Then, optimal AP systems are designed by tuning a bank of proportional-integral-derivative (PID) controllers via the genetic algorithm (GA). A fuzzy gain scheduling strategy is employed to determine the participation percentages of the PID controllers in the control action. Finally, two safety mechanisms, called insulin on board (IOB) constraint and pump shut-off, are installed in the AP systems to enhance their performance. To assess the proposed AP systems, in silico experiments are performed on virtual patients of the UVA/Padova metabolic simulator. The obtained results reveal that the proposed intelligent multiple-model methodology leads to AP systems with limited hyperglycemia and no severe hypoglycemia.

Graph-based relevancy-redundancy gene selection method for cancer diagnosis.

Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority.

Constraint-Based, Score-Based and Hybrid Algorithms to Construct Bayesian Gene Networks in the Bovine Transcriptome.

Bayesian gene networks are powerful for modelling causal relationships and incorporating prior knowledge for making inferences about relationships. We used three algorithms to construct Bayesian gene networks around genes expressed in the bovine uterus and compared the efficacies of the algorithms. Dataset GSE33030 from the Gene Expression Omnibus (GEO) repository was analyzed using different algorithms for hub gene expression due to the effect of progesterone on bovine endometrial tissue following conception. Six different algorithms (grow-shrink, max-min parent children, tabu search, hill-climbing, max-min hill-climbing and restricted maximum) were compared in three higher categories, including constraint-based, score-based and hybrid algorithms. Gene network parameters were estimated using the bnlearn bundle, which is a Bayesian network structure learning toolbox implemented in R. The results obtained indicated the tabu search algorithm identified the highest degree between genes (390), Markov blankets (25.64), neighborhood sizes (8.76) and branching factors (4.38). The results showed that the highest number of shared hub genes (e.g., proline dehydrogenase 1 (PRODH), Sam-pointed domain containing Ets transcription factor (SPDEF), monocyte-to-macrophage differentiation associated 2 (MMD2), semaphorin 3E (SEMA3E), solute carrier family 27 member 6 (SLC27A6) and actin gamma 2 (ACTG2)) was seen between the hybrid and the constraint-based algorithms, and these genes could be recommended as central to the GSE33030 data series. Functional annotation of the hub genes in uterine tissue during progesterone treatment in the pregnancy period showed that the predicted hub genes were involved in extracellular pathways, lipid and protein metabolism, protein structure and post-translational processes. The identified hub genes obtained by the score-based algorithms had a role in 2-arachidonoylglycerol and enzyme modulation. In conclusion, different algorithms and subsequent topological parameters were used to identify hub genes to better illuminate pathways acting in response to progesterone treatment in the bovine uterus, which should help with our understanding of gene regulatory networks in complex trait expression.

Ghrelin modulates morphine-nicotine interaction in avoidance memory: Involvement of CA1 nicotinic receptors.

Ghrelin is a stomach-derived hormone which regulates appetite and energy balance in the body. Recent studies show that ghrelin has been linked to the learning and memory process. Ghrelin also modulates reward properties of addictive drugs. However, the involvement of ghrelin in cognitive effects of addictive drugs has not been examined yet. The goal of present study is to examine the effect of intra-CA1 administration of ghrelin on morphine response for avoidance task alone or in combination with nicotine. Here, we also investigated the role of hippocampal nicotinic cholinergic receptors in possible interaction of the drugs in adult male Wistar rats. Results showed that subcutaneous administration of morphine immediately after training impaired memory in the test day and induced amnesia, while intra-CA1 pre-injection of ghrelin prevented amnesic effect of morphine and improved memory. Also, systemic administration of nicotine five min prior to morphine administration dose-dependently inhibited morphine-induced amnesia. The results showed that intra-CA1 injection of an ineffective dose of ghrelin (0.03 nmol/µl) potentiated the nicotine (0.2 mg/kg, s.c.) response on amnesia induced by morphine. This stimulatory effect was inhibited by mechamylamine, a non-competitive nicotinic receptor antagonist. Moreover, post-training administration of drugs (ghrelin, nicotine and mecamylamine) alone had no effect on memory consolidation. In conclusion, present study suggests the significant role of ghrelin in morphine-related memory and its interactive effect with nicotine in avoidance task via CA1 nicotinic receptors.