99 research outputs found
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
Many gait recognition methods first partition the human gait into N-parts and
then combine them to establish part-based feature representations. Their gait
recognition performance is often affected by partitioning strategies, which are
empirically chosen in different datasets. However, we observe that strips as
the basic component of parts are agnostic against different partitioning
strategies. Motivated by this observation, we present a strip-based multi-level
gait recognition network, named GaitStrip, to extract comprehensive gait
information at different levels. To be specific, our high-level branch explores
the context of gait sequences and our low-level one focuses on detailed posture
changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the
strip-based feature representations by directly taking each strip of the human
body as the basic unit. Moreover, we propose a novel multi-branch structure,
called Enhanced Convolution Module (ECM), to extract different representations
of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the
Frame-Level feature extractor (FL) and SPB, and has two obvious advantages:
First, each branch focuses on a specific representation, which can be used to
improve the robustness of the network. Specifically, ST aims to extract
spatial-temporal features of gait sequences, while FL is used to generate the
feature representation of each frame. Second, the parameters of the ECM can be
reduced in test by introducing a structural re-parameterization technique.
Extensive experimental results demonstrate that our GaitStrip achieves
state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202
DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
Gait recognition is a biometric technology that recognizes the identity of
humans through their walking patterns. Compared with other biometric
technologies, gait recognition is more difficult to disguise and can be applied
to the condition of long-distance without the cooperation of subjects. Thus, it
has unique potential and wide application for crime prevention and social
security. At present, most gait recognition methods directly extract features
from the video frames to establish representations. However, these
architectures learn representations from different features equally but do not
pay enough attention to dynamic features, which refers to a representation of
dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the
human body are more informative than other parts (e.g. bags) during walking, in
this paper, we propose a novel and high-performance framework named DyGait.
This is the first framework on gait recognition that is designed to focus on
the extraction of dynamic features. Specifically, to take full advantage of the
dynamic information, we propose a Dynamic Augmentation Module (DAM), which can
automatically establish spatial-temporal feature representations of the dynamic
parts of the human body. The experimental results show that our DyGait network
outperforms other state-of-the-art gait recognition methods. It achieves an
average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D
dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset
Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis
Background: Periodontitis is a chronic immuno-inflammatory disease characterized
by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a
dysregulated local host immune response that is ineffective in combating microbial
challenges. An integrated investigation of genes involved in mediating immune response
suppression in periodontitis, based on multiple studies, can reveal genes pivotal to
periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based
autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by
integrating multiples omics datasets.
Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and
GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas
were included. Immunosuppression genes related to periodontitis in GSE16134
were used as input to build an AE, to identify the top disease-representative
immunosuppression gene features. Using K-means clustering and ANOVA, immune
subtype labels were assigned to disease samples and a support vector machine
(SVM) classifier was constructed. This classifier was applied to a validation set
(Immunosuppression genes related to periodontitis in GSE10334) for predicting
sample labels, evaluating the accuracy of the AE. In addition, differentially expressed
genes (DEGs), signaling pathways, and transcription factors (TFs) involved in
immunosuppression and periodontitis were determined with an array of bioinformatics
analysis. Shared DEGs common to DEGs differentiating periodontitis from controls
and those differentiating the immune subtypes were considered as the key
immunosuppression genes in periodontitis.
Results: We produced representative molecular features and identified two immune
subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation
set with the SVM classifier. Three “master” immunosuppression genes, PECAM1,
FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive
mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A,
STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network.
The two immune subtypes were distinct in terms of their regulating pathways.
Conclusion: This study applied a DL-based AE for the first time to identify immune
subtypes of periodontitis and pivotal immunosuppression genes that discriminated
periodontitis from the healthy. Key signaling pathways and TF-target DEGs that
putatively mediate immune suppression in periodontitis were identified. PECAM1,
FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic
targets for periodontitis
Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis
Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.
Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.
Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.
Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis
miR-26a mediates LC-PUFA biosynthesis by targeting the Lxrα-Srebp1 pathway in the marine teleost Siganus canaliculatus
MicroRNAs (miRNAs) have been recently shown to be important regulators of lipid metabolism. However, the mechanisms of miRNA-mediated regulation of long-chain polyunsaturated fatty acids (LC-PUFA) biosynthesis in vertebrates remain largely unknown. Herein, we for the first time addressed the role of miR-26a in LC-PUFA biosynthesis in the marine rabbitfish Siganus canaliculatus. The results showed that miR-26a was significantly down-regulated in liver of rabbitfish reared in seawater and in S. canaliculatus hepatocyte line (SCHL) incubated with the LC-PUFA precursor α-linolenic acid (ALA), suggesting that miR-26a may be involved in LC-PUFA biosynthesis due to its abundance being regulated by factors affecting LC-PUFA biosynthesis. Opposite patterns were observed in the expression of liver X receptor α (lxrα) and sterol regulatory element-binding protein-1 (srebp1), as well as the LC-PUFA biosynthesis related genes (Δ4 fads2, Δ6Δ5 fads2 and elovl5) in SCHL cells incubated with ALA. Luciferase reporter assays revealed rabbitfish lxrα as a target of miR-26a, and overexpression of miR-26a in SCHL cells markedly reduced protein levels of Lxrα, Srebp1 and Δ6Δ5 Fads2 induced by the agonist T0901317. Moreover, increasing endogenous Lxrα by knockdown of miR-26a facilitated Srebp1 activation and concomitant increased expression of genes involved in LC-PUFA biosynthesis, and consequently promoted LC-PUFA biosynthesis both in vitro and in vivo. These results indicate a critical role of miR-26a in regulating LC-PUFA biosynthesis through targeting the Lxrα-Srebp1 pathway and provide new insights into the regulatory network controlling LC-PUFA biosynthesis and accumulation in vertebrates
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Marine organic matter in the remote environment of the Cape Verde islands-an introduction and overview to the MarParCloud campaign
The project MarParCloud (Marine biological production, organic aerosol Particles and marine Clouds: a process chain) aims to improve our understanding of the genesis, modification and impact of marine organic matter (OM) from its biological production, to its export to marine aerosol particles and, finally, to its ability to act as ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). A field campaign at the Cape Verde Atmospheric Observatory (CVAO) in the tropics in September-October 2017 formed the core of this project that was jointly performed with the project MARSU (MARine atmospheric Science Unravelled). A suite of chemical, physical, biological and meteorological techniques was applied, and comprehensive measurements of bulk water, the sea surface microlayer (SML), cloud water and ambient aerosol particles collected at a ground-based and a mountain station took place. Key variables comprised the chemical characterization of the atmospherically relevant OM components in the ocean and the atmosphere as well as measurements of INPs and CCN. Moreover, bacterial cell counts, mercury species and trace gases were analyzed. To interpret the results, the measurements were accompanied by various auxiliary parameters such as air mass back-trajectory analysis, vertical atmospheric profile analysis, cloud observations and pigment measurements in seawater. Additional modeling studies supported the experimental analysis. During the campaign, the CVAO exhibited marine air masses with low and partly moderate dust influences. The marine boundary layer was well mixed as indicated by an almost uniform particle number size distribution within the boundary layer. Lipid biomarkers were present in the aerosol particles in typical concentrations of marine background conditions. Accumulation-and coarse-mode particles served as CCN and were efficiently transferred to the cloud water. The ascent of ocean-derived compounds, such as sea salt and sugar-like compounds, to the cloud level, as derived from chemical analysis and atmospheric transfer modeling results, denotes an influence of marine emissions on cloud formation. Organic nitrogen compounds (free amino acids) were enriched by several orders of magnitude in submicron aerosol particles and in cloud water compared to seawater. However, INP measurements also indicated a significant contribution of other non-marine sources to the local INP concentration, as (biologically active) INPs were mainly present in supermicron aerosol particles that are not suggested to undergo strong enrichment during ocean-atmosphere transfer. In addition, the number of CCN at the supersaturation of 0.30 % was about 2.5 times higher during dust periods compared to marine periods. Lipids, sugar-like compounds, UV-absorbing (UV: ultraviolet) humic-like substances and low-molecularweight neutral components were important organic compounds in the seawater, and highly surface-active lipids were enriched within the SML. The selective enrichment of specific organic compounds in the SML needs to be studied in further detail and implemented in an OM source function for emission modeling to better understand transfer patterns, the mechanisms of marine OM transformation in the atmosphere and the role of additional sources. In summary, when looking at particulate mass, we see oceanic compounds transferred to the atmospheric aerosol and to the cloud level, while from a perspective of particle number concentrations, sea spray aerosol (i.e., primary marine aerosol) contributions to both CCN and INPs are rather limited. © Author(s) 2020
Marine organic matter in the remote environment of the Cape Verde islands – an introduction and overview to the MarParCloud campaign
The project MarParCloud (Marine biological production, organic aerosol Particles and marine Clouds: a process chain) aims to improve our understanding of the genesis, modification and impact of marine organic matter (OM) from its biological production, to its export to marine aerosol particles and, finally, to its ability to act as ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). A field campaign at the Cape Verde Atmospheric Observatory (CVAO) in the tropics in September–October 2017 formed the core of this project that was jointly performed with the project MARSU (MARine atmospheric Science Unravelled). A suite of chemical, physical, biological and meteorological techniques was applied, and comprehensive measurements of bulk water, the sea surface microlayer (SML), cloud water and ambient aerosol particles collected at a ground-based and a mountain station took place.
Key variables comprised the chemical characterization of the atmospherically relevant OM components in the ocean and the atmosphere as well as measurements of INPs and CCN. Moreover, bacterial cell counts, mercury species and trace gases were analyzed. To interpret the results, the measurements were accompanied by various auxiliary parameters such as air mass back-trajectory analysis, vertical atmospheric profile analysis, cloud observations and pigment measurements in seawater. Additional modeling studies supported the experimental analysis.
During the campaign, the CVAO exhibited marine air masses with low and partly moderate dust influences. The marine boundary layer was well mixed as indicated by an almost uniform particle number size distribution within the boundary layer. Lipid biomarkers were present in the aerosol particles in typical concentrations of marine background conditions. Accumulation- and coarse-mode particles served as CCN and were efficiently transferred to the cloud water. The ascent of ocean-derived compounds, such as sea salt and sugar-like compounds, to the cloud level, as derived from chemical analysis and atmospheric transfer modeling results, denotes an influence of marine emissions on cloud formation. Organic nitrogen compounds (free amino acids) were enriched by several orders of magnitude in submicron aerosol particles and in cloud water compared to seawater. However, INP measurements also indicated a significant contribution of other non-marine sources to the local INP concentration, as (biologically active) INPs were mainly present in supermicron aerosol particles that are not suggested to undergo strong enrichment during ocean–atmosphere transfer. In addition, the number of CCN at the supersaturation of 0.30 % was about 2.5 times higher during dust periods compared to marine periods. Lipids, sugar-like compounds, UV-absorbing (UV: ultraviolet) humic-like substances and low-molecular-weight neutral components were important organic compounds in the seawater, and highly surface-active lipids were enriched within the SML. The selective enrichment of specific organic compounds in the SML needs to be studied in further detail and implemented in an OM source function for emission modeling to better understand transfer patterns, the mechanisms of marine OM transformation in the atmosphere and the role of additional sources.
In summary, when looking at particulate mass, we see oceanic compounds transferred to the atmospheric aerosol and to the cloud level, while from a perspective of particle number concentrations, sea spray aerosol (i.e., primary marine aerosol) contributions to both CCN and INPs are rather limited
Optimized deep learning networks for segmentation and classification of thyroid nodules in ultrasound images
In recent years, the incidence of thyroid nodules has been continuously increasing. With the increase in people's iodine intake and the growing environmental radioactive pollution, the risk of malignant transformation has also been increasing, posing a serious threat to human health. In the actual clinical diagnosis of thyroid nodules, doctors often use ultrasound imaging for detection. This study aims to accurately segment the thyroid nodule region and distinguish between benign and malignant nodules in ultrasound images using deep learning methods, thereby assisting doctors in improving diagnostic efficiency.
To accurately segment the nodular region, this study utilizes the Mask R-CNN network to perform segmentation on ultrasound images and optimizes the backbone network to enhance its feature extraction capabilities. A branch is added to the feature pyramid model of the original backbone network, and all the output feature maps are then fused to obtain fused features, which are subsequently output. This achieves multi-scale feature fusion and balances the information differences among the output feature maps.
To achieve accurate identification of benign and malignant thyroid nodules, this study applies the DenseNet network to detect ultrasound images and optimizes its network structure. Five branches are added to the original network to fuse the input and output feature maps, transmitting a large amount of low-level texture information. Attention mechanisms are also introduced between each Dense block and adjacent transition layers to retain effective image features and balance the weights of the fused feature maps, allowing the network to better utilize the global information from the feature maps.
The improved network models are tested using nodule images, the improved Mask R-CNN model exhibits high accuracy. The improved DenseNet can provide reference for the clinical diagnosis of thyroid nodular diseases and enhance the diagnostic efficiency of doctors
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