bluuebunny/crossref_metadata_2025_split huggingface dataset:
Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection
Authors: Karthikeyan M. , Manimegalai D. , RajaGopal Karthikeyan 
Published: January 2024

AbstractA Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.
Conditional generative learning for medical image imputation
Authors: Raad Ragheb , Ray Deep , Varghese Bino , Hwang Darryl , Gill Inderbir , Duddalwar Vinay , Oberai Assad A. 
Published: January 2024

AbstractImage imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application is considered. It is derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys: given an incomplete sequence of three CECT images, we are required to impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the “best guess” of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown that the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.
Contribution of uniparental disomy to fetal growth restriction: a whole-exome sequencing series in a prenatal setting
Authors: Li Mengmeng , Hao Na , Jiang Yulin , Xue Huili , Dai Yifang , Wang Mingming , Bai Junjie , Lv Yan , Qi Qingwei , Zhou Xiya 
Published: January 2024

AbstractFetal growth restriction (FGR), a leading cause of perinatal morbidity and mortality, is caused by fetal, maternal, and placental factors. Uniparental disomy (UPD) is a rare condition that leads to imprinting effects, low-level mosaic aneuploidies and homozygosity for pathogenic variants. In the present study, UPD events were detected in 5 women with FGR by trio exome sequencing (trio-WES) of a cohort of 150 FGR cases. Furthermore, noninvasive prenatal testing results of the 5 patients revealed a high risk of rare autosomal trisomy. Trio-WES showed no copy-number variations (CNVs) or nondisease-causing mutations associated with FGR. Among the 5 women with FGR, two showed gene imprinting, and two exhibited confined placental mosaicism (CPM) by copy number variant sequencing (CNV-seq). The present study showed that in FGR patients with UPD, the detection of imprinted genes and CPM could enhance the genetic diagnosis of FGR.
Research on the synergistic effects of urbanization and ecological environment in the Chengdu–Chongqing urban agglomeration based on the Haken model
Authors: Wu Weilong , Huang Ying , Zhang Yuzhou , Zhou Bo 
Published: January 2024

AbstractThe development of urbanization and the protection of the ecological environment are important aspects of sustainable development in urban agglomerations; thus, their coordination and synergy are crucial. Scholars in both China and other countries have conducted extensive research on the relationship between urbanization and the ecological environment from the perspective of coordinated development. However, there is a lack of research on the synergistic effects of urbanization and the ecological environment. Taking the Chengdu–Chongqing urban agglomeration as an example, this study uses the Haken model to analyze the synergistic evolution mechanism, development patterns, and future trends of urbanization and the ecological environment. This study draws the following conclusions: (1) while a synergistic development mechanism has been established between urbanization and the ecological environment, the current system is still in a stage of low-level synergy, and the control system’s parameter for the direction and path of evolution is urbanization. (2) From 2001 to 2020, the level of synergistic development between urbanization and the ecological environment was relatively low, mainly moving from low-level synergy to medium–low-level synergy to medium-level synergy. (3) There is a significant spatial disparity in the levels of synergistic development, for which the leading region centers on Chengdu and Chongqing in the northwest and southeast, respectively, and the lagging region comprises the eastern part of the urban agglomeration, mainly represented by Dazhou, Kaizhou, and Yunyang. (4) The future trend of synergistic development between urbanization and the ecological environment in the Chengdu–Chongqing urban agglomeration is promising but requires increased infrastructure construction in mountainous urban areas, enhanced cooperation and circulation in transportation, logistics, information and other aspects, and balanced improvements in the level of synergistic development between urbanization and the ecological environment. The study of the synergistic development effect of urbanization and the ecological environment in urban agglomerations is of great significance for reducing the negative impact of urbanization on the ecological environment, increasing the positive interaction between the two, and promoting sustainable development in urban agglomerations.
Modelling bronchial epithelial-fibroblast cross-talk in idiopathic pulmonary fibrosis (IPF) using a human-derived in vitro air liquid interface (ALI) culture
Authors: Barron Sarah L. , Wyatt Owen , O’Connor Andy , Mansfield David , Suzanne Cohen E. , Witkos Tomasz M. , Strickson Sam , Owens Róisín M. 
Published: January 2024

AbstractIdiopathic Pulmonary Fibrosis (IPF) is a devastating form of respiratory disease with a life expectancy of 3–4 years. Inflammation, epithelial injury and myofibroblast proliferation have been implicated in disease initiation and, recently, epithelial-fibroblastic crosstalk has been identified as a central driver. However, the ability to interrogate this crosstalk is limited due to the absence of in vitro models that mimic physiological conditions. To investigate IPF dysregulated cross-talk, primary normal human bronchial epithelial (NHBE) cells and primary normal human lung fibroblasts (NHLF) or diseased human lung fibroblasts (DHLF) from IPF patients, were co-cultured in direct contact at the air–liquid interface (ALI). Intercellular crosstalk was assessed by comparing cellular phenotypes of co-cultures to respective monocultures, through optical, biomolecular and electrical methods. A co-culture-dependent decrease in epithelium thickness, basal cell mRNA (P63, KRT5) and an increase in transepithelial electrical resistance (TEER) was observed. This effect was significantly enhanced in DHLF co-cultures and lead to the induction of epithelial to mesenchymal transition (EMT) and increased mRNA expression of TGFβ-2, ZO-1 and DN12. When stimulated with exogenous TGFβ, NHBE and NHLF monocultures showed a significant upregulation of EMT (COL1A1, FN1, VIM, ASMA) and senescence (P21) markers, respectively. In contrast, direct NHLF/NHBE co-culture indicated a protective role of epithelial-fibroblastic cross-talk against TGFβ-induced EMT, fibroblast-to-myofibroblast transition (FMT) and inflammatory cytokine release (IL-6, IL-8, IL-13, IL-1β, TNF-α). DHLF co-cultures showed no significant phenotypic transition upon stimulation, likely due to the constitutively high expression of TGFβ isoforms prior to any exogenous stimulation. The model developed provides an alternative method to generate IPF-related bronchial epithelial phenotypes in vitro, through the direct co-culture of human lung fibroblasts with NHBEs. These findings highlight the importance of fibroblast TGFβ signaling in EMT but that monocultures give rise to differential responses compared to co-cultures, when exposed to this pro-inflammatory stimulus. This holds implications for any translation conclusions drawn from monoculture studies and is an important step in development of more biomimetic models of IPF. In summary, we believe this in vitro system to study fibroblast-epithelial crosstalk, within the context of IPF, provides a platform which will aid in the identification and validation of novel targets.
On the robustness of democratic electoral processes to computational propaganda
Authors: Givi Glory M. , Delabays Robin , Jacquemet Matthieu , Jacquod Philippe 
Published: January 2024

AbstractThere is growing evidence of systematic attempts to influence democratic elections by controlled and digitally organized dissemination of fake news. This raises the question of the intrinsic robustness of democratic electoral processes against external influences. Particularly interesting is to identify the social characteristics of a voter population that renders it more resilient against opinion manipulation. Equally important is to determine which of the existing democratic electoral systems is more robust to external influences. Here we construct a mathematical electoral model to address these two questions. We find that, not unexpectedly, biased electorates with clear-cut elections are overall quite resilient against opinion manipulations, because inverting the election outcome requires to change the opinion of many voters. More interesting are unbiased or weakly biased electorates with close elections. We find that such populations are more resilient against opinion manipulations (i) if they are less polarized and (ii) when voters interact more with each other, regardless of their opinion differences, and that (iii) electoral systems based on proportional representation are generally the most robust. Our model qualitatively captures the volatility of the US House of Representatives elections. We take this as a solid validation of our approach.
Numerical analysis of the porous structure of spherical activated carbons obtained from ion-exchange resins
Authors: Kwiatkowski Mirosław , He Ping , Valtchev Valentin 
Published: January 2024

AbstractThis paper presents the results of an analysis of the porous structure of spherical activated carbons obtained from cation-exchange resin beads subjected to ion exchange prior to activation. The study investigated the effects of the type of cation exchange resin, the concentration of potassium cations in the resin beads and the temperature of the activation process on the adsorption properties of the resulting spherical activated carbons. The numerical clustering-based adsorption analysis method and the quenched solid density functional theory were used to analyse the porous structure of spherical activated carbons. Based on original calculations and unique analyses, complex relationships between preparation conditions and the porous structure properties of the obtained spherical activated carbons were demonstrated. The results of the study indicated the need for simultaneous analyses using advanced methods for the analysis of porous structures, i.e., the numerical clustering-based adsorption analysis method and the quenched solid density functional theory. This approach allows a reliable and precise determination of the adsorption properties of the materials analysed, including, among other things, surface heterogeneities, and thus an appropriate selection of production conditions to obtain materials with the expected adsorption properties required for a given industrial process.
Semantic and episodic processes differently predict false memories in the DRM task
Authors: Gatti Daniele , Rinaldi Luca , Mazzoni Giuliana , Vecchi Tomaso 
Published: January 2024

AbstractThere is a fervent debate about the processes underpinning false memories formation. Seminal theories have suggested that semantic memory would be involved in false memories production, while episodic memory would counter their formation. Yet, direct evidence corroborating such view is still lacking. Here, we tested this possibility by asking participants to perform the Deese–Roediger–McDermott (DRM) task, a typical false memory paradigm, in which they had to study lists of words and subsequently to recognize and distinguish them from new words (i.e., the false memory items). The same participants were also required to perform a semantic task and an episodic-source memory task. Our results showed that a higher number of false memories in the DRM task occurred for those participants with better semantic memory abilities, while a lower number of false memories occurred for participants with better episodic abilities. These findings support a key role of semantic processes in false memory formation and, more generally, help clarify the specific contribution of different memory systems to false recognitions.
Identification of key pyroptosis-related genes and microimmune environment among peripheral arterial beds in atherosclerotic arteries
Authors: Liu Jing-Wen , Zhang Zhao-Hua , Lv Xiao-Shuo , Xu Ming-Yuan , Ni Bin , He Bin , Wang Feng , Chen Jie , Zhang Jian-Bin , Ye Zhi-Dong , Liu Peng , Wen Jian-Yan 
Published: January 2024

AbstractAtherosclerosis is a chronic inflammatory disease characterized with innate and adaptive immunity but also involves pyroptosis. Few studies have explored the role of pyroptosis in advanced atherosclerotic plaques from different vascular beds. Here we try to identify the different underlying function of pyroptosis in the progression of atherosclerosis between carotid arteries and femoral. arteries. We extracted gene expression levels from 55 advanced carotid or femoral atherosclerotic plaques. The pyroptosis score of each sample was calculated by single-sample-gene-set enrichment analysis (ssGSEA). We then divided the samples into two clusters: high pyroptosis scores cluster (PyroptosisScoreH cluster) and low pyroptosis scores cluster (PyroptosisScoreL cluster), and assessed functional enrichment and immune cell infiltration in the two clusters. Key pyroptosis related genes were identified by the intersection between results of Cytoscape and LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Finally, all key pyroptosis related genes were validated in vitro. We found all but one of the 29 carotid plaque samples belonged to the PyroptosisScoreH cluster and the majority (19 out of 26) of femoral plaques were part of the PyroptosisScoreL cluster. Atheromatous plaque samples in the PyroptosisScoreL cluster had higher proportions of gamma delta T cells, M2 macrophages, myeloid dendritic cells (DCs), and cytotoxic lymphocytes (CTLs), but lower proportions of endothelial cells (ECs). Immune full-activation pathways (e.g., NOD-like receptor signaling pathway and NF-kappa B signaling pathway) were highly enriched in the PyroptosisScoreH cluster. The key pyroptosis related genes GSDMD, CASP1, NLRC4, AIM2, and IL18 were upregulated in advanced carotid atherosclerotic plaques. We concluded that compared to advanced femoral atheromatous plaques, advanced carotid atheromatous plaques were of higher grade of pyroptosis. GSDMD, CASP1, NLRC4, AIM2, and IL18 were the key pyroptosis related genes, which might provide a new sight in the prevention of fatal strokes in advanced carotid atherosclerosis.
A robust deep learning detector for sleep spindles and K-complexes: towards population norms
Authors: Tapia-Rivas Nicolás I. , Estévez Pablo A. , Cortes-Briones José A. 
Published: January 2024

AbstractSleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.
Potential diagnostic application of a novel deep learning- based approach for COVID-19
Authors: Sadeghi Alireza , Sadeghi Mahdieh , Sharifpour Ali , Fakhar Mahdi , Zakariaei Zakaria , Sadeghi Mohammadreza , Rokni Mojtaba , Zakariaei Atousa , Banimostafavi Elham Sadat , Hajati Farshid 
Published: January 2024

AbstractCOVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available athttps://github.com/alrzsdgh/COVID. The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients’ CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.
MAD2L2, a key regulator in ovarian cancer and promoting tumor progression
Authors: Xu Kejun , Zheng Xiaojiao , Shi Hongyan , Ou Jilan , Ding Huiqing 
Published: January 2024

AbstractOvarian cancer (OVCA), a prevalent gynecological malignancy, ranks as the fourth most common cancer among women. Mitotic Arrest Deficient 2 Like 2 (MAD2L2), a chromatin-binding protein and a component of DNA polymerase ζ, has been previously identified as an inhibitor of tumor growth in colorectal cancer. However, the roles of MAD2L2 in OVCA, including its expression, impact, and prognostic significance, remain unclear. We employed bioinformatics tools, Cox Regression analysis, and in vitro cell experiments to investigate its biological functions. Our findings reveal that MAD2L2 typically undergoes genomic alterations, such as amplifications and deep deletions. Moreover, we observed an overexpression of MAD2L2 mRNA in OVCA patients, correlating with reduced survival rates, particularly in those with Grade IV tumors. Furthermore, analysis of mRNA biofunctions indicated that MAD2L2 is predominantly localized in the organellar ribosome, engaging mainly in NADH dehydrogenase activity. This was deduced from the results of gene ontology enrichment analysis, which also identified its role as a structural constituent in mitochondrial translation elongation. These findings were corroborated by KEGG pathway analysis, further revealing MAD2L2’s involvement in tumor metabolism and the cell death process. Notably, MAD2L2 protein expression showed significant associations with various immune cells, including CD4+T cells, CD8+T cells, B cells, natural killer cells, and Myeloid dendritic cells. Additionally, elevated levels of MAD2L2 were found to enhance cell proliferation and migration in OVCA cells. The upregulation of MAD2L2 also appears to inhibit the ferroptosis process, coinciding with increased mTOR signaling activity in these cells. Our study identifies MAD2L2 as a novel regulator in ovarian tumor progression and offers new insights for treating OVCA.