Phosphorylation Modulates the Subcellular Localization of SOX11

SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11

Table_1_Phosphorylation Modulates the Subcellular Localization of SOX11.XLSX

<p>SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11.</p

Image_2_Phosphorylation Modulates the Subcellular Localization of SOX11.TIF

<p>SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11.</p

Image_4_Phosphorylation Modulates the Subcellular Localization of SOX11.TIF

<p>SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11.</p

Table_2_Phosphorylation Modulates the Subcellular Localization of SOX11.XLSX

<p>SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11.</p

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic