bluuebunny/crossref_metadata_2025_split huggingface dataset:
Further analysis of determinants of pentavalent and measles immunizations dropouts among children under five years of age in Ethiopia from Mini-EDHS 2019
Authors: Tolera Moti , Birhanu Abdi , Regassa Lemma Demissie , Getachew Tamirat , Negash Abraham , Jibro Usmael , Deressa Alemayehu , Abdurahman Dureti , Motuma Aboma , Gamachu Mulugeta , Mohammed Fethia , Balis Bikila , Mussa Ibsa 
Published: June 2024

Abstract

Background: Globally, immunization prevents an estimated 2–3 million deaths from illnesses that can be prevented by vaccination. The predictors of Pentavalent and Measles immunization dropout were not investigated sufficiently. Therefore, this analysis was meant to determine the coverage, burden, and predictors of Pentavalent and Measles immunizations in Ethiopia.
Materials and methods: this study was a further analysis of the Mini Ethiopian Demographic Health Survey (EDHS) 2019 which was conducted with a nationally representative sample. In this study, immunization data was collected from a total sample of 3208, and 2004 (34.8%) children who received pentavalent 1 were considered for this study. Univariate, bi-variate, and multilevel mixed effect analysis was done using STATA version 17 Software. The mixed-effect model with the lowest AIC and BIC (information criteria) was chosen and a factor was designated as a significant predictor of immunization dropout if its p-value was less than 0.05 at 95% CI. The best-fitting model among the fitted models was ultimately chosen using the Akaike and Bayesian Information Criteria (AIC and BIC).
Result: The mean age of the head of the household was 37.6. The majority (68.9%) of the residents were from rural areas. of the total population, 64.9% have no formal education. Being residents of Afar increased the odds of dropping out by 3.28 (AOR = 3.28; 95% CI: 1.12, 9.56), and being residents of Addis Ababa reduced their odds of dropping out by 68.1% (AOR = 0.319; 95% CI: 0.122, 0.833) compared to their Tigray counterparts. The dropout rate of Pentavalent 1 to measles 1 vaccination was 4.33 times higher among residents of Afar (AOR = 4.33; 95% CI: 1.38, 13.56). As the level of wealth increases, the quintile increases from poorer to richer compared to those in the poorest wealth quantile category, and marital status also affects the immunization dropout rate of their children.
Conclusions: Immunization coverage among Ethiopian children was low and the dropout from pentavalent 1 and measles vaccination was high compared to the national and international targets to improve immunization coverage and reduce dropout. So different cost-effective interventions like Education provision for the community, decision-making facilitation, behavioral change support, and multi-system participation were required to increase vaccination coverage and reduce the vaccine dropout among children in Ethiopia.
Multiomics analyses reveal interactions between the gut microbiota and metabolites in people living with human immunodeficiency virus before and during SARS-CoV-2 infection
Authors: Tian Xuebin , Xie Yiwen , Zhao Yulong , Jin Changzhong , Yao Pen , Dong Mingqing , Lu Xiangyun , Wu Nanping 
Published: June 2024

Abstract

Background
People living with human immunodeficiency virus (HIV) (PLWH) might have an increased risk of developing coronavirus disease 2019 (COVID-19); however, the impact of their gut microbiota and metabolites on the progress of COVID-19 is unknown. Herein, we analyzed the temporal changes in the gut microbiota composition and metabolites of PLWH at baseline and during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Methods
We analyzed the gut microbiota and metabolites by integrating 16S rRNA gene sequencing and liquid chromatography-mass spectrometry untargeted metabolomics of 36 PLWH at baseline and during SARS-CoV-2 infection periods.
Results
Significant changes in the composition of the gut microbiota and an increased ratio of Firmicutes/Bacteroidetes during SARS-CoV-2 infection, relative to the baseline, were observed. At the genus level, the abundances of g_Lactobacillus and g_Lactiplantibacillus increased significantly. Furthermore, seven differentially enriched human metabolic pathways (including protein digestion and absorption, central carbon metabolism in cancer, aminoacyl-tRNA biosynthesis, mineral absorption, ABC transporters, arginine and proline metabolism, and phenylalanine metabolism) were identified by comparing the fecal metabolites at baseline and during SARS-CoV-2 infection. Spearman correlation analysis revealed close relationships between the two differentially abundant microbiota members and the five differentially abundant fecal metabolites that might affect specific human metabolic pathways.
Conclusion
This study is the first to characterize the gut microbiota and metabolites in PLWH at baseline and during SARS-CoV-2 infection. The key microbiota and metabolites in the infection process were identified, providing new ideas for treatment.
Altered Neutrophil Responses to Dengue Virus Serotype Three: Delayed Apoptosis is Regulated by Stabilisation of Mcl-1
Authors: Kamsom Chatcharin , Edwards Steven W. , Thaosing Jiraphon , Papalee Saitharn , Pientong Chamsai , Kurosu Takeshi , Phanthanawiboon Supranee 
Published: June 2024

Abstract

Dengue is a global health concern, and the host-viral interactions that regulate disease severity are largely unknown. Detrimental effects of neutrophils in this disease have been reported, but the precise mechanisms and functional properties of dengue -activated neutrophils are not fully characterised. Here, we measured the effects of dengue virus serotype 3 (DV3) on neutrophil lifespan and functions. We show that DV3 extends neutrophil survival with a significant proportion of cells surviving for 72h post-incubation. These effects on neutrophil survival were greater than those observed by adding GM-CSF and TNF-α alone, but these cytokines enhanced survival induced by the virus. Enhanced reactive oxygen species (ROS) generation was observed following incubation with DV3 activation and this ROS production was enhanced by co-incubation with priming agents. In addition, DV triggered the enhanced IL-8 expression by the majority of neutrophils and a low percentage of cells were activated to express MCP-1 (CCL2). A low number of neutrophils showed increased co-expression of the migratory markers, CCR7 and CXCR4 which could promote their migration towards lymph nodes. DV3 significantly upregulated the BCL-XL gene at 3, 12, and 24h, and the Mcl-1 gene at 12h, following treatment. We also show that DV3 induces the Mcl-1 protein stabilization similar to GM-CSF. This report sheds new light on the mechanisms by which neutrophils may contribute to the pathology of dengue disease via delayed apoptosis and generation of pro-inflammatory molecules, and raises the possibility that dengue-activated neutrophils may play a role in activating cells of adaptive immunity.
Advancing Healthcare Monitoring: Anomaly Detection through Hybrid Deep Learning for Enhanced Connectivity in Software Defined Networking
Authors: Antony Leo Prasanth Lourdu , Elangovan Uma 
Published: June 2024

Abstract

In the realm of healthcare, the continuous evolution of monitoring systems demands innovative solutions to ensure heightened reliability and accuracy. This paper introduces a pioneering approach to healthcare monitoring through a hybrid deep learning model that combines the advantages of recurrent neural networks (RNN) and deep neural networks (DNN). Focused on enhancing connectivity in Software Defined Networking (SDN), our framework places a significant emphasis on anomaly detection for improved predictive accuracy. The proposed Hybrid Deep Learning model is meticulously designed to harness the complementary features of DNN and RNN, enabling the system to capture both spatial and temporal dependencies in healthcare data. This integration enhances the precision of anomaly detection, allowing for the identification of subtle deviations from normal patterns with unprecedented accuracy. Key to our methodology is the adaptability of Software Defined Networking, providing a flexible and programmable infrastructure. The Hybrid Deep Learning model operates seamlessly within this SDN framework, dynamically optimizing resource allocation and traffic patterns to accommodate the unique demands of healthcare monitoring. Through extensive experimentation and validation, our framework demonstrates remarkable predictive accuracy in identifying anomalies within healthcare data streams. Comparative analyses against traditional anomaly detection methods underscore the superiority of our approach, showcasing its efficacy in real-world healthcare scenarios. In conclusion, our research contributes to the advancement of healthcare monitoring by introducing a Hybrid Deep Learning model, combining DNN and RNN architectures, within the context of Software Defined Networking. The achieved high prediction accuracy in anomaly detection signifies a significant leap forward in the reliability and precision of healthcare monitoring systems, paving the way for more robust and responsive healthcare networks.
Oriented Two-dimensional Semiconducting Crystals on Amorphous Dielectrics by Selective Artificial Epitaxy
Authors: Groven Benjamin , Kandybka Iryna , Voronenkov Vladislav , Vranckx Dries , Medina Henry , Nijs Sebastiaan , Kumar Pawan , Moussa Alain , Saib Mohamed , Beggiato Matteo , Driessen Stijn , Yekefalah Fateme , Mehta Ankit , Nuytten Thomas , Banerjee Kaustuv , Smets Quentin , Kruv Anastasiia , Schram Tom , Favia Paola , Opstal Tinneke Van , Brems Steven , Pourtois Geoffrey , De Gendt Stefan , Delabie Annelies , Rosa Cesar Javier Lockhart de la , Asselberghs Inge , Morin Pierre , Kar Gouri 
Published: June 2024

Abstract

Defective grain boundaries form in semiconductors when deposition approaches do not control crystal grain orientation. This poses existential limitations to fabricating highly performing semiconductor devices with two-dimensional semiconductors for industry’s future Angstrom technology nodes. Today’s monolithic or bottom-up deposition methods do not control crystal grain orientation on industry-standard substrates covered with amorphous dielectrics due to lack of crystallographic symmetry provided by the substrate. Here, we report selective artificial chemical vapor epitaxy of tungsten disulfide (WS2), an approach to orient two-dimensional crystals on amorphous substrates with nanopatterns. Amorphous nanopatterns with axial symmetry that mimics the trigonal crystallographic symmetry of the WS2 crystal lattice guide WS2 crystal orientation as well as location. We show a proof of concept in a 300 mm industrial pilot-line at back-end-of-line compatible deposition temperature. This monolithic concept opens possibilities to eliminate grain boundaries and deposit single-crystalline two-dimensional materials directly on conventional amorphous dielectrics at feature scales relevant for most advanced device architectures.
Product-based topological Lagrangian neural network for learning articulated 2D multi-rigid-body system
Authors: Liu Xiao-Feng , Ai Zhi-Hao , Wang Kang-Hao , Zhang Hui-Bo , Cai Guo-Ping 
Published: June 2024

Abstract

Lagrangian neural networks (LNN) is a physical-informed data-driven framework for learning the dynamics of physical systems. The incorporation of strong inductive biases enables LNN to outperform purely data-driven methods. However, its application has predominantly been confined to simple systems like pendulums, springs, or single rigid bodies such as gyroscopes or rigid rotors. In this paper, we present a so-called product-based topological Lagrangian neural network (PTLNN) that can learn the dynamics of articulated multi-rigid-body system by exploiting the coupling nonlinearity and the topological relation of system. Compared to other improved Lagrangian neural networks, such as Lagrangian graph neural network and Constraint Lagrangian neural network, the additional prior-knowledges in PTLNN do not need to be measured, which make PTLNNs easier to use. We demonstrate the performance of PTLNN by learning the dynamics of articulated systems with different degrees of freedom. The testing results illustrates that PTLNN outperforms other physical-informed neural networks such as LNN, HNN and NODE, which only encode prior knowledge without measurement.
In silico analysis reveals inhibitory potential of phytocompounds Taraxerol (Coccinia indica) and Epoxywithanolide-1(Withania coagulans) on α-Amylase : A possible way to control postprandial hyperglycemia-induced endothelial dysfunction and cardiovascular events
Authors: Ravi Lokesh , Sadhana Venkatesh , R Shree Kumari G , V Mohanasrinivasan , Manjunathan Reji , K Ajith Kumar , Kesavan Mookkandi Palsamy 
Published: June 2024

Abstract

Postprandial hyperglycemia (PPG) exacerbates endothelial dysfunction and impairs vascular function in diabetes as well in healthy people. Though there are drugs available to regulate PPG, synthetic drugs' severe gastrointestinal side effects warranted the search for alternative treatments. Recently, some phytochemicals captured the attention because of their inhibitory effects on α-amylase to control diabetes. The aim of this study was to investigate and identify potential alpha-amylase inhibitors in C. indica and W. coagulans. This study also aims to understand one of the possible mechanisms of action of plants for their anti-diabetic activity. A total of 36 phytochemical ligands were subjected for protein-ligand docking analysis. Among the phytochemicals, Taraxerol and Epoxywithanolide-I demonstrated significant binding free energy of -10.2 Kcal/mol and − 11.9 Kcal/mol respectively, which was higher than the reference acarbose with − 8.6 Kcal/mol. These molecules were subjected for Molecular Dynamics Simulation (MDS) analysis with alpha-amylase protein for a duration of 150 ns. Among the three complexes, Taraxerol and Epoxywithanolide-I complexes demonstrates strong potential as inhibitors of the target protein. MDS results were analyzed via Root Mean Square Deviation (RMSD), Fluctuation of Residues, Potential Energy, Radii of Gyration and Solvent Access Surface Area analysis. Taraxerol demonstrated a significantly low potential energy of -1924605.25 Kj/mol, and Epoxywithanolide-I demonstrated − 1964113.3 Kj/mol of potential energy. RMSD plot shows that Epoxywithanolide-I has much higher stability than the other MDS complexes. Drugability and toxicity studies show that the test ligands are demonstrating strong potential as drug like molecules. The results of the study conclude that, Taraxerol of C. indica and Epoxywithanolide-I of W. coagulans are strong inhibitors of alpha-amylase enzyme and that, this is one of the possible mechanisms of action of the plants for their reported anti-diabetic activities. Further in-vitro analysis is in demand to prove the observed results.
ESGWKNN: Research on Indoor Localization Algorithm Based on Wi-Fi
Authors: Ye Run , Pan Shun , Yan Bin , Zhang Cheng , Zhou Xiaojia 
Published: June 2024

Abstract

Aiming at the problem that the accuracy of the traditional WiFi-based K-Nearest Neighbor algorithm (KNN) indoor positioning algorithm cannot meet the requirements of precise positioning, this paper proposes a K-nearest neighbor indoor positioning algorithm based on location range limitation, namely the algorithm combining echo state network and Gaussian weighted K-nearest neighbors (ESGWKNN). The ESGWKNN algorithm first uses the echo state network to estimate the range to obtain the local spatial range information, and then uses the GWKNN algorithm to accurately locate within the local spatial range to reduce the positioning accuracy problem caused by excessive space. The experimental results show that compared with the traditional KNN algorithm, the ESGWKNN algorithm has higher positioning accuracy.
Deformation-aware image restoration from atmospheric turbulence based on quasiconformal geometry and pulse-coupled neural network
Authors: Yin Jie , Sun Tao , Zhang Guorong , Wu Yuhao , Zhang Xiao 
Published: June 2024

Abstract

Atmospheric turbulence is a major factor in image degradation issues such as blurring, distortion and intensity fluctuations when monitoring long-range targets. The randomness, spatiotemporal variation and perturbations of turbulence make it challenging to restore vision-friendly and credible images from degraded image sequences. In this work, we address the problem by proposing a deformation-aware image restoration algorithm based on quasiconformal geometry and pulse-coupled neural network (PCNN). To accurately measure the magnitude of geometric deformation caused by turbulence, the deformation within degraded images is specified in a non-conformal distortion that disrupts local geometry. The Beltrami coefficient uniquely associated with the quasiconformal maps is applied to quantify the average distortion degree. The deformation-aware measurement minimizes registration errors in aligning degraded images by more reliable reconstruction of reference frames. Additionally, an improved PCNN model inspired by the primary visual cortex is developed to boost the perceptual quality of the restored image with lucky image fusion. The absence of manual parameter tuning and the ability to simultaneously process image sequences in the PCNN model enhance the robustness of the restoration algorithm. The performance of our algorithm is validated by experiments on physically simulated and real data, which contain 220 sequences with 22928 frames. The results show that our algorithm can yield a superior restoration through atmospheric turbulence compared with several state-of-the-art methods. The code is available at https://github.com/whuluojia/ImTurb.
Parametric excitation of a cold atom in an optical polarization-modulated magneto-optical trap
Authors: Lee Min-Hwan , Park Sanghyun , Baek Jaeuk , Noh Heung-Ryoul , Moon Geol 
Published: June 2024

Abstract

This paper presents a novel method for achieving parametric excitation in an optical polarization-modulated magneto-optical trap for a two-level atom. The proposed method has a unique mechanism that is different from that of the existing methods, which modulate the laser intensity, magnetic field gradient, or laser detuning, because the excitation realized through the polarization modulation originates from the competition between the repulsive and attractive forces on a two-level atom. The theoretical calculation is based on the simple Doppler cooling theory, and we derive atomic motion in a rotating frame and coupled equation for motional amplitude and phase, in detail.
PACS numbers: 37.10.Vz, 37.10.De, 37.10.Gh
IMmuneCite: an open-source workflow for processing multiplex images and analysis of immune enriched spatial proteomic data
Authors: Barbetta Arianna , Bangerth Sarah , Lee Jason T.C. , Rocque Brittany , Torres Evanthia T Roussos , Kohli Rohit , Akbari Omid , Emamaullee Juliet 
Published: June 2024

Abstract

Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of > 30 immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel by applying IMmuneCite to data from murine liver tissue. In conclusion, we demonstrated that IMmuneCite is an open source, user-friendly computational tool that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.
Mechanism of Preventing Recurrence of Stage II-III Colorectal Cancer Metastasis
with Immuno-inflammatory and Hypoxic Microenvironment by a Four Ingredients
Chinese Herbal Formula: A Bioinformatics and Network Pharmacology Analysis
Authors: Shi Chuan , Liu Xing , Han Su-Su , Tang Yu-Fei , Zeng Hai-Lun , Du Mei-Lu , Yang Yi , Jia Jia-Ning , Shi Qi , Hou Feng-Gang 
Published: June 2024

Background:
Colorectal Cancer (CRC) is one of the top three malignancies with the highest incidence
and mortality.

Objective:
The study aimed to identify the effect of Traditional Chinese Medicine (TCM) on postoperative
patients with stage II-III CRC and explore the core herb combination and its mechanism.

Methods:
An observational cohort study was conducted on patients diagnosed with stage II-III CRC from
January 2016 to January 2021. The primary outcome was disease-free survival, which was compared between
the patients who received TCM or not, and the secondary outcome was the hazard ratio. The relevance principle
was used to obtain the candidate herb combinations, and the core combination was evaluated through an
assessment of efficacy and representativeness. Then, biological processes and signaling pathways associated
with CRC were obtained by Gene Ontology function, Kyoto Encyclopedia of Gene and Genomes pathway,
and Wikipathway. Furthermore, hub genes were screened by the Kaplan-Meier estimator, and molecular
docking was employed to predict the binding sites of key ingredients to hub genes. The correlation analysis
was employed for the correlations between the hub genes and tumor-infiltrating immune cells and hypoxiarelated
genes. Ultimately, a quantitative polymerase chain reaction was performed to verify the regulation of
hub genes by their major ingredients.

Results:
A total of 707 patients were included. TCM could decrease the metastatic recurrence associated with
stage II-III CRC (HR: 0.61, log-rank P < 0.05). Among those patients in the TCM group, the core combination
was Baizhu → Yinchen, Chenpi, and Fuling (C combination), and its antitumor mechanism was most
likely related to the regulation of BCL2L1, XIAP, and TOP1 by its key ingredients, quercetin and tangeretin.
The expression of these genes was significantly correlated with both tumor-infiltrating immune cells and hypoxia-
related genes. In addition, quercetin and tangeretin down-regulated the mRNA levels of BCL2L1, XIAP,
and TOP1, thereby inhibiting the growth of HCT116 cells.

Conclusion:
Overall, a combination of four herbs, Baizhu → Yinchen, Chenpi, and Fuling, could reduce metastatic
recurrence in postoperative patients with stage II-III CRC. The mechanism may be related to the regulation
of BCL2L1, XIAP, and TOP1 by its key ingredients quercetin and tangeretin.