scholarly journals Detection of Pitt–Hopkins Syndrome Based on Morphological Facial Features

2021 ◽  
Vol 11 (24) ◽  
pp. 12086
Author(s):  
Elena D’Amato ◽  
Constantino Carlos Reyes-Aldasoro ◽  
Arianna Consiglio ◽  
Gabriele D’Amato ◽  
Maria Felicia Faienza ◽  
...  
Keyword(s):  
Genetic Disorder ◽  
Recognition Rate ◽  
Initial Approximation ◽  
Gradient Boosting ◽  
Facial Features ◽  
Geometric Features ◽  
Relative Width ◽  
Healthy Controls ◽  
Histograms Of Oriented Gradients ◽  
Two Populations

This work describes a non-invasive, automated software framework to discriminate between individuals with a genetic disorder, Pitt–Hopkins syndrome (PTHS), and healthy individuals through the identification of morphological facial features. The input data consist of frontal facial photographs in which faces are located using histograms of oriented gradients feature descriptors. Pre-processing steps include color normalization and enhancement, scaling down, rotation, and cropping of pictures to produce a series of images of faces with consistent dimensions. Sixty-eight facial landmarks are automatically located on each face through a cascade of regression functions learnt via gradient boosting to estimate the shape from an initial approximation. The intensities of a sparse set of pixels indexed relative to this initial estimate are used to determine the landmarks. A set of carefully selected geometric features, for example, the relative width of the mouth or angle of the nose, is extracted from the landmarks. The features are used to investigate the statistical differences between the two populations of PTHS and healthy controls. The methodology was tested on 71 individuals with PTHS and 55 healthy controls. The software was able to classify individuals with an accuracy rate of 91%, while pediatricians achieved a recognition rate of 74%. Two geometric features related to the nose and mouth showed significant statistical difference between the two populations.

scholarly journals Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 187
Author(s):  
Arjun Benagatte Channegowda ◽  
H N Prakash
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition System ◽  
Multimodal Biometrics ◽  
Histograms Of Oriented Gradients ◽  
Multi Level ◽  
Deep Learning Neural Network ◽  
Hidden Neurons

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

scholarly journals Cantu syndrome and hypopituitarism: implications for endocrine monitoring

2019 ◽  
Vol 2019 ◽  
Author(s):  
Nicholas J Theis ◽  
Toby Calvert ◽  
Peter McIntyre ◽  
Stephen P Robertson ◽  
Benjamin J Wheeler
Keyword(s):  
Anterior Pituitary ◽  
Autosomal Dominant ◽  
Genetic Disorder ◽  
Facial Features ◽  
Pituitary Hormone ◽  
List Type ◽  
Pituitary Dysfunction ◽  
Pathogenic Variants ◽  
Clinical Surveillance ◽  
Learning Points

Summary Cantu syndrome, or hypertrichotic osteochondrodysplasia, is a rare, autosomal dominant genetically heterogeneous disorder. It is characterized by hypertrichosis, cardiac and skeletal anomalies and distinctive coarse facial features. We report a case where slowed growth velocity at 13 years led to identification of multiple pituitary hormone deficiencies. This adds to other reports of pituitary abnormalities in this condition and supports inclusion of endocrine monitoring in the clinical surveillance of patients with Cantu syndrome. Learning points: Cantu syndrome is a rare genetic disorder caused by pathogenic variants in the ABCC9 and KCNJ8 genes, which result in gain of function of the SUR2 or Kir6.1 subunits of widely expressed KATP channels. The main manifestations of the syndrome are varied, but most commonly include hypertrichosis, macrosomia, macrocephaly, coarse ‘acromegaloid’ facies, and a range of cardiac defects. Anterior pituitary dysfunction may be implicated in this disorder, and we propose that routine screening should be included in the clinical and biochemical surveillance of patients with Cantu syndrome.

scholarly journals Event-related potential examination of facial affect processing in bipolar disorder and schizophrenia

2012 ◽  
Vol 43 (1) ◽  
pp. 109-117 ◽  
Author(s):  
J. K. Wynn ◽  
C. Jahshan ◽  
L. L. Altshuler ◽  
D. C. Glahn ◽  
M. F. Green
Keyword(s):  
Bipolar Disorder ◽  
Event Related Potentials ◽  
Facial Affect ◽  
Event Related Potential ◽  
Facial Features ◽  
Healthy Controls ◽  
Related Potentials ◽  
Patient Groups ◽  
Bipolar Patients ◽  
Affect Processing

BackgroundPatients with bipolar disorder exhibit consistent deficits in facial affect identification at both behavioral and neural levels. However, little is known about which stages of facial affect processing are dysfunctional.MethodEvent-related potentials (ERPs), including amplitude and latency, were used to evaluate two stages of facial affect processing: N170 to examine structural encoding of facial features and N250 to examine decoding of facial features in 57 bipolar disorder patients, 30 schizophrenia patients and 30 healthy controls. Three conditions were administered: participants were asked to identify the emotion of a face, the gender of a face, or whether a building was one or two stories tall.ResultsSchizophrenia patients' emotion identification accuracy was lower than that of bipolar patients and healthy controls. N170 amplitude was significantly smaller in schizophrenia patients compared to bipolar patients and healthy controls, which did not differ from each other. Both patient groups had significantly longer N170 latency compared to healthy controls. For N250, both patient groups showed significantly smaller amplitudes compared with controls, but did not differ from each other. Bipolar patients showed longer N250 latency than healthy controls; patient groups did not differ from each other.ConclusionsBipolar disorder patients have relatively intact structural encoding of faces (N170) but are impaired when decoding facial features for complex judgments about faces (N250 latency and amplitude), such as identifying emotion or gender.

scholarly journals Alagille Syndrome: About Two Cases and Literature Review

2020 ◽  
pp. 1-6
Author(s):  
M. Akhrif ◽  
A. Radi ◽  
M. Kmari ◽  
A. Ourrai ◽  
A. Hassani ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Family History ◽  
Bile Ducts ◽  
Heart Diseases ◽  
Genetic Disorder ◽  
Alagille Syndrome ◽  
Facial Features ◽  
Clinical Expression ◽  
Intrahepatic Bile Ducts

Alagille syndrome is a multi-systemc genetic disorder with variable phenotypic penetrance that was first described in 1969 by Daniel Alagille.It is  characterized by anomalies of the intrahepatic bile ducts, heart, eye and skeleton, which are associated with facial features . The prognosis depends on the severity of the liver and heart diseases.  The authors reported  two  cases characterized by the  variability of clinical expression and evolution. The study concerned two girls aged  of 2 and 4 months  with no family history, who developed cholestatic jaundice evolving from the first month of life. The aim of this work is to remind the different clinical expressivity and the differentmodalities to manage the patients in order to ensure a best quality of life.

Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14069-e14069
Author(s):  
Oguz Akbilgic ◽  
Ibrahim Karabayir ◽  
Hakan Gunturkun ◽  
Joseph F Pierre ◽  
Ashley C Rashe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Test Data ◽  
Gut Microbiome ◽  
Classification Model ◽  
Outcome Variable ◽  
Gradient Boosting ◽  
Healthy Controls ◽  
Extreme Gradient Boosting ◽  
Significant Difference

e14069 Background: There is growing interest in the links between cancer and the gut microbiome. However, the effect of chemotherapy upon the gut microbiome remains unknown. We studied whether machine learning can: 1) accurately classify subjects with cancer vs healthy controls and 2) whether this classification model is affected by chemotherapy exposure status. Methods: We used the American Gut Project data to build a extreme gradient boosting (XGBoost) model to distinguish between subjects with cancer vs healthy controls using data on simple demographics and published microbiome. We then further explore the selected features for cancer subjects based on chemotherapy exposure. Results: The cohort included 7,685 subjects consisting of 561 subjects with cancer, 52.5% female, 87.3% White, and average age of 44.7 (SD 17.7). The binary outcome variable represents cancer status. Among 561 subjects with cancer, 94 of them were treated with chemotherapy agents before sampling of microbiomes. As predictors, there were four demographic variables (sex, race, age, BMI) and 1,812 operational taxonomic units (OTUs) each found in at least 2 subjects via RNA sequencing. We randomly split data into 80% training and 20% hidden test. We then built an XGBoost model with 5-fold cross-validation using only training data yielding an AUC (with 95% CI) of 0.79 (0.77, 0.80) and obtained the almost the same AUC on the hidden test data. Based on feature importance analysis, we identified 12 most important features (Age, BMI and 12 OTUs; 4C0d-2, Brachyspirae, Methanosphaera, Geodermatophilaceae, Bifidobacteriaceae, Slackia, Staphylococcus, Acidaminoccus, Devosia, Proteus) and rebuilt a model using only these features and obtained AUC of 0.80 (0.77, 0.83) on the hidden test data. The average predicted probabilities for controls, cancer patients who were exposed to chemotherapy, and cancer patients who were not were 0.071 (0.070,0.073), 0.125 (0.110, 0.140), 0.156 (0.148, 0.164), respectively. There was no statistically significant difference on levels of these 12 OTUs between cancer subjects treated with and without chemotherapy. Conclusions: Machine learning achieved a moderately high accuracy identifying patients’ cancer status based on microbiome. Despite the literature on microbiome and chemotherapy interaction, the levels of 12 OTUs used in our model were not significantly different for cancer patients with or without chemotherapy exposure. Testing this model on other large population databases is needed for broader validation.

A markerless 2D video of facial features recognition based artificial intelligence model to assist Parkinson’s disease diagnosis (Preprint)

2021 ◽  
Author(s):  
Yu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Diagnostic Performance ◽  
Feature Recognition ◽  
Texture Features ◽  
Facial Feature ◽  
Disease Diagnosis ◽  
Facial Features ◽  
Geometric Features

UNSTRUCTURED Background: Mask face is a characteristic clinical manifestation of Parkinson's disease (PD), but subjective evaluations from different clinicians often show low consistency owing to lacking accurate detection technology. With the objective of making monitoring easier and more accessible, we developed a markerless 2D video of facial features recognition based artificial intelligence (AI) model to assess facial features of PD patients and aimed to investigate how AI could help neurologists improve PD early diagnostic performance. Methods: We collected 140 videos of facial expressions of 70 PD patients and 70 healthy controls from three hospitals. We developed and tested the AI model that performs mask face recognition of PD patients based on the acquisition and evaluation of facial features including geometric features and texture features, using a single 2D video camera. The diagnostic performance of AI model was compared with 5 neurologists. Results: Experimental results show that our AI models can achieve feasible and effective facial feature recognition ability to assist PD diagnosis. The precision and F1 values of PD diagnosis can reach 83% and 86%, using geometric features and texture features, respectively. When these two features are combined, a F1 value of 88% can be reached. Further, the facial features of patients with PD were not affected by the motor and non-motor symptoms of PD. Conclusions: PD patients commonly exhibit facial features. Video of facial features recognition based AI model can provide a valuable tool to assist PD diagnosis and potential of realizing remote monitoring on patients’ condition especially on the COVID-19 pandemic.

Association of ACE, ACE2 and UTS2 Polymorphisms with Essential Hypertension in Han and Dongxiang Populations from North-western China

2006 ◽  
Vol 34 (3) ◽  
pp. 272-283 ◽  
Author(s):  
L Yi ◽  
YH Gu ◽  
XL Wang ◽  
LZ An ◽  
XD Xie ◽  
...  
Keyword(s):  
Essential Hypertension ◽  
Angiotensin Converting Enzyme ◽  
Western China ◽  
Healthy Controls ◽  
Converting Enzyme ◽  
Urotensin Ii ◽  
Single Nucleotide ◽  
Angiotensin Converting Enzyme 2 ◽  
Two Populations ◽  
North Western

To assess the significance of polymorphisms of the genes for angiotensin-converting enzyme ( ACE), angiotensin-converting enzyme 2 ( ACE2) and urotensin II (UTS2) as risk factors for essential hypertension in two populations from north-western China, we enrolled 198 patients with essential hypertension and 131 healthy controls from the Han population and 120 patients with essential hypertension and 102 healthy controls from the Dongxiang population. Polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism were used to analyse gene polymorphisms. The results provided evidence that genetic variants of UTS2 and ACE2 may play a role in the development of essential hypertension in these populations. Polymorphisms of ACE were not associated with essential hypertension in either population. This is the first report showing that the S89N single-nucleotide polymorphism of the UTS2 gene is associated with essential hypertension.

The diffusion of a mass genetic screening programme

1978 ◽  
Vol 10 (3) ◽  
pp. 287-297
Author(s):  
David V. McQueen
Keyword(s):  
Genetic Screening ◽  
Metropolitan Areas ◽  
Screening Programme ◽  
Genetic Disorder ◽  
Diffusion Of Knowledge ◽  
Tay Sachs Disease ◽  
Two Populations ◽  
Jewish Populations

SummaryThis paper reports the diffusion of knowledge of screening for a genetic disorder (Tay–Sachs disease) in two fairly well defined, urban, Jewish populations in the Baltimore and Washington metropolitan areas, and on some of the sociological variables which influence the diffusion. Differences between the two populations are shown.

Research on Facial Features of Major Chinese Nationalities

2014 ◽  
Vol 687-691 ◽  
pp. 837-840
Author(s):  
Zhi Jie Li ◽  
Xiao Dong Duan ◽  
Cun Rui Wang
Keyword(s):  
Face Recognition ◽  
Measurement Method ◽  
Scientific Basis ◽  
Facial Features ◽  
Geometric Features ◽  
Face Images ◽  
Similarities And Differences

This paper analyze the facial features of 6 main chinese nationalities using measurement method on face images. We select several measurement and calculation indices according to the facial geometric features of each group. It is found that Mongolia, Korean and Han nationalitis are similar in facial features, while Tibetans and Uighurs nationalities have larger differences. Analysis of the similarities and differences among groups can provide a scientific basis for face recognition of multiple nationalities.

scholarly journals Activated L-Arginine/Nitric Oxide Pathway in Pediatric Cystic Fibrosis and Its Association with Pancreatic Insufficiency, Liver Involvement and Nourishment: An Overview and New Results

2020 ◽  
Vol 9 (6) ◽  
pp. 2012
Author(s):  
Folke Brinkmann ◽  
Beatrice Hanusch ◽  
Manfred Ballmann ◽  
Sebene Mayorandan ◽  
Alexander Bollenbach ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Cystic Fibrosis ◽  
High Performance ◽  
Genetic Disorder ◽  
Pancreatic Insufficiency ◽  
Plasma Concentrations ◽  
Gas Chromatography Mass Spectrometry ◽  
No Production ◽  
Healthy Controls ◽  
Synthesis Inhibitor

Cystic fibrosis (CF; OMIM 219700) is a rare genetic disorder caused by a chloride channel defect, resulting in lung disease, pancreas insufficiency and liver impairment. Altered L-arginine (Arg)/nitric oxide (NO) metabolism has been observed in CF patients’ lungs and in connection with malnutrition. The aim of the present study was to investigate markers of the Arg/NO pathway in the plasma and urine of CF patients and to identify possible risk factors, especially associated with malnutrition. We measured the major NO metabolites nitrite and nitrate, Arg, a semi-essential amino acid and NO precursor, the NO synthesis inhibitor asymmetric dimethylarginine (ADMA) and its major urinary metabolite dimethylamine (DMA) in plasma and urine samples of 70 pediatric CF patients and 78 age-matched healthy controls. Biomarkers were determined by gas chromatography–mass spectrometry and high-performance liquid chromatography. We observed higher plasma Arg (90.3 vs. 75.6 µM, p < 0.0001), ADMA (0.62 vs. 0.57 µM, p = 0.03), Arg/ADMA ratio (148 vs. 135, p = 0.01), nitrite (2.07 vs. 1.95 µM, p = 0.03) and nitrate (43.3 vs. 33.1 µM, p < 0.001) concentrations, as well as higher urinary DMA (57.9 vs. 40.7 µM/mM creatinine, p < 0.001) and nitrate (159 vs. 115 µM/mM creatinine, p = 0.001) excretion rates in the CF patients compared to healthy controls. CF patients with pancreatic sufficiency showed plasma concentrations of the biomarkers comparable to those of healthy controls. Malnourished CF patients had lower Arg/ADMA ratios (p = 0.02), indicating a higher NO synthesis capacity in sufficiently nourished CF patients. We conclude that NO production, protein-arginine dimethylation, and ADMA metabolism is increased in pediatric CF patients. Pancreas and liver function influence Arg/NO metabolism. Good nutritional status is associated with higher NO synthesis capacity and lower protein-arginine dimethylation.

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