Integrated facial analysis and targeted sequencing identifies a novel KDM6A pathogenic variant resulting in Kabuki syndrome

Kabuki syndrome(KS)is a rare congenital mental retardation condition characterized by facial dysmorphia,visceral and skeletal malformations,and developmental delay.The integrated phenotype and genotype-based prioritization is critical for diagnoses of genetic diseases.In this study,a Chinese woman,presenting with characteristic facial features of KS,came for pre-pregnancy consultation.We aimed to clarify the diagnosis and provide pre-pregnancy genetic counseling.Facial dysmorphology analysis and next-generation sequencing-based multigene panel approach were used to identify candidate syndromes and causative variants,respectively.The candidate variant was verified by Sanger sequencing.We identified a novel de novo KDM6A pathogenic variant(c.3521G>A)in the woman,which was in line with the Face2Gene analysis result.Peripheral blood RNA assay showed that the variant transcript underwent the nonsense-mediated mRNA decay and led to subsequent haploinsufficiency of KDM6A.Our study provides the genetic diagnosis method for KS type 2 and identifies the first KDM6A point variant in Chinese patient.

A Facial Size Automatic Measurement and Analysis Technology

Facial measurement and analysis is an important part of anthropometry,which provides data support for the design of facial protective equipment.To overcome the inconveniences,low efficiency and poor measurement accuracy of facial size parameters measured and analyzed by manual contact,a method of automatic measurement and analysis of face size parameters is proposed.First,the automatic marking method of faces based on deep learning can improve the efficiency ofmeasuring facial parameters.Then,facial parameters,including nose middle width,nose width,face width and eye width,can be measured.Finally,the data set of face size parameters is classified and counted based on fuzzy clustering analysis.Sixty-five groups of Han youth facial data are collected formeasurement and analysis,and compared with the existing algorithms,the facial morphology analysis system presented in this paper has higher measurement accuracy.

Expression Analysis Based on Face Regions in Real-world Conditions

Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challenges in real-world conditions,i.e.,illumination changes,large pose variations and partial or full occlusions.Those challenges lead to different face areas with different degrees of sharpness and completeness.Inspired by this fact,we focus on the authenticity of predictions generated by different<emotion,region>pairs.For example,if only the mouth areas are available and the emotion classifier predicts happiness,then there is a question of how to judge the authenticity of predictions.This problem can be converted into the contribution of different face areas to different emotions.In this paper,we divide the whole face into six areas:nose areas,mouth areas,eyes areas,nose to mouth areas,nose to eyes areas and mouth to eyes areas.To obtain more convincing results,our experiments are conducted on three different databases:facial expression recognition+(FER+),real-world affective faces database(RAF-DB)and expression in-the-wild(ExpW)dataset.Through analysis of the classification accuracy,the confusion matrix and the class activation map(CAM),we can establish convincing results.To sum up,the contributions of this paper lie in two areas:1)We visualize concerned areas of human faces in emotion recognition;2)We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.