Correlation between relative amounts of lipid species and number of days since the onset of symptoms among COVID-19 cases. by Spela Kokelj (8356644)

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Correlation between protein abundances and R_5-20 z-score among cases at baseline. Protein abundances, normalized to mean 0 and variance 1, are displayed on the y-axis. (... by Spela Kokelj (8356644)

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General characteristics and clinical data of the subjects included in the study. by Spela Kokelj (8356644)

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Distribution of average relative amounts of lipid classes. by Spela Kokelj (8356644)

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Flow chart on the inclusion and drop out of patients. by Spela Kokelj (8356644)

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Differentially abundant lipid species in COVID-19 cases at baseline and at follow-up as compared to healthy controls. by Spela Kokelj (8356644)

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Symptoms reported by COVID-19 cases at baseline and at follow-up. by Spela Kokelj (8356644)

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The catalase enzyme response of <i>A</i>. <i>albopictus</i> larvae decreased (1.5-fold) in response to <i>M</i>. <i>robertsii</i>-derived copper nanoparticles. by Perumal Vivekanandhan (4863484)

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The optimal concentration of PA modulates the mitochondrial distribution but not mitochondrial membrane potential in COCs challenged with MGO during IVM. by Shahrzad Ronasi (20933556)

The optimal concentration of PA restores the impaired cell lineage allocation in blastocysts developed from MGO-challenged COCs. by Shahrzad Ronasi (20933556)

The optimal concentration of PA modulates the mitochondrial distribution but not mitochondrial membrane potential in DOs challenged with MGO during IVM. by Shahrzad Ronasi (20933556)

TSLPR deficient mice show decreased AHR upon HDM exposure. by Latifa Koussih (326043)

Centrifuged FMT products consistently exhibit the lowest median log₁₀ drop of CFU/g following freezing and lyophilization. In contrast, unprocessed feces exhibited the g... by Nina K. Randolph (21030732)

Architecture of Swin-T model. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Model the experimental results curve. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Results of comparison experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Token merging module. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Comparative results of the ablation experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”