Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitigation strategies across multiple automated facial analysis tasks, as it allows to analyse, characterise and synthesise human facial diversity. In this paper, we propose a novel GAN framework to enable fine-grained control over individual race-related phenotype attributes of the facial images. Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data. Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training. Unlike prior work, our framework only relies upon 2D imagery and related parameters to achieve state-of-the-art individual control over race-related phenotype attributes with improved photo-realistic output

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time bject detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimuli - as both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. On-screen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects.Comment: Accepted as a full paper at the 2019 International Conference on Robotics and Automation (ICRA

French translation, cultural adaptation and validation of the BDDQ-AS for rhinoplasty patients

The Body Dysmorphic Disorder Questionnaire-Aesthetic Surgery (BDDQ-AS) is a validated questionnaire that is used as a screening tool for body dysmorphic disorder (BDD) in aesthetic rhinoplasty patients. The BDDQ-AS questionnaire was translated from English to French according to international guidelines. Ten French-speaking rhinoplasty patients were interviewed in order to evaluate the understandability and acceptability of the translation and produce a final version. It was then administered to 165 consecutive patients. Psychometric properties were evaluated using item-reponse theory (IRT). Internal consistency was high, with Cronbach's alpha of 0.90 (95% lower CL 0.88). While the discrimination abilities of all the items were good (over 2.0), their difficulty parameters were shifted towards greater severity of symptoms. That shift could also be observed in information function graph for the entire scale. In other words, the BDDQ-AS performed better in patients with more severe body dysmorphic symptoms. In conclusion, the BDDQ-AS was translated, adapted, and psychometrically validated for use in a French-speaking population

Minimal Clinically Important Difference of the Standardized Cosmesis and Health Nasal Outcomes Survey

Background: The minimal clinically important difference (MCID) for the Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) has not been determined.Objectives: The authors sought to define the MCID for both domains of the SCHNOS questionnaire.Methods: This prospective cohort study included patients who underwent functional, cosmetic, or combined rhinoplasty operation from June 2017 to June 2018 at a tertiary referral center. The average preoperative, postoperative, and change in scores were calculated for the nasal obstruction symptom evaluation scale (NOSE) and SCHNOS. Anchor-based MCIDs were estimated for both SCHNOS subscales to define change in obstruction and cosmesis perceived after the rhinoplasty.Results: Eighty-seven patients (69% women, 31% males) with a mean age (standard deviation [SD]) of 38 years (14.7) at the time of surgery were included. The mean postoperative follow-up period (SD) was 145 days (117). The mean preoperative score (SD) for the NOSE was 52 (32), SCHNOS for nasal obstruction (SCHNOS-O) score was 55 (33), and SCHNOS for nasal cosmesis (SCHNOS-C) score was 50 (26) points. Postoperatively, the NOSE score was 23 (22), SCHNOS-O score was 24 (23), and SCHNOS-C score was 13 (18) points. The mean change in scores (SD) for NOSE, SCHNOS-O, and SCHNOS-C was -29 (37), -31 (38), and -37 (28), respectively. The calculated MCID for SCHNOS-O was 26 (16) and for SCHNOS-C was 22 (15) points. The MCID for NOSE was 24 (13) points. A sensitivity test for the patients with a follow-up >= 3 months showed only slightly different MCID estimates: 28 (17) for SCHNOS-O, 18 (13) for SCHNOS-C, and 24 (15) points for NOSE.Conclusions: For the obstruction domain SCHNOS-O, the MCID was 28 points. For the cosmetic domain SCHNOS-C, the MCID was 18 points.</div

Exploring Emotion Representation to Support Dialogue in Police Training on Child Interviewing

Police officers when dealing with interviewing children have to cope with a complex set of emotions from a vulnerable witness. Triggers for recognising those emotions and how to build rapport are often the basis of learning exercises. However, current training pulls together the full complexity of emotions during role-playing which can be over-whelming and reduce appropriate learning focus. Interestingly a serious game’s interface can provide valuable training not because it represents full complex, multimedia interactions but because it can restrict emotional complexity and increase focus during the interactions on key factors for emotional recognition. The focus of this paper is to report on a specific aspect that was explored during the development of a serious game that aims to address the current police-training needs of child interviewing techniques, where the recognition of emotions plays an important role in understanding how to build rapport with children. The review of literature reveals that emotion recognition, through facial expressions, can contribute significantly to the perceived quality of communication. For this study an ‘emotions map’ was created and tested by 41 participants to be used in the development of a targeted interface design to support the different levels of emotion recognition. The emotions identified were validated with a 70 % agreement across experts and non-experts highlighting the innate role of emotion recognition. A discussion is made around the role of emotions and game-based systems to support their identification for work-based training. As part of the graphical development of the Child Interview Stimulator (CIS) we examined different levels of emotional recognition that can be used to support the in-game graphical representation of a child’s response during a police interview

Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more “traditional” text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them

Collaborative denoising autoencoder for high glycated haemoglobin prediction.

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Type-2 Diabetes Mellitus (T2DM). This will save healthcare providers a major cost associated with the administration and assessment of clinical tests for HbA1c. A novel collaborative denoising autoencoder framework is used to address this challenge. The framework builds an independent denoising autoencoder model for the high and low HbA1c level, which extracts feature representations in the latent space. A baseline model using just three features: patient age together with triglycerides and glucose level achieves 76% F1-score with an SVM classifier. The collaborative denoising autoencoder uses 78 features and can predict HbA1c level with 81% F1-score

Evaluating the role of human papillomaviruses in conjunctival neoplasia

Mucosal, cutaneous and Epidermodysplasia verruciformis (EV)-related human papillomaviruses (HPVs) were searched by broad-spectrum PCR in 86 conjunctival neoplasia biopsies and 63 conjunctival non-neoplastic control tissue from Ugandan subjects. Seven different EV-related HPV types, including a putative new HPV, and two mucosal HPVs were detected in 25% (14 out of 56) of HIV-positive, in 10% (three out of 30) of HIV-negative conjunctival neoplasia samples, and rarely (0–1.6%) in control subjects. The absence of high-risk HPVs and the low detection frequency of EV-related HPV types in more advanced tumour stages (10%) raise doubts about their role in conjunctival carcinomas

Female genital schistosomiasis as an evidence of a neglected cause for reproductive ill-health: a retrospective histopathological study from Tanzania

BACKGROUND: Schistosomiasis affects the reproductive health of women. Described sequelae are ectopic pregnancy, infertility, abortion, and cervical lesions and symptoms mimicking cervical cancer and STIs. There are indications that cervical schistosomiasis lesions could become co-factors for viral infection such as HIV and HPV. METHODS: In a retrospective descriptive histopathological study clinical specimens sent between 1999 and 2005 to the pathology department of a consultant hospital in Tanzania were reviewed to analyse the occurrence and features of schistosomiasis in female genital organs. RESULTS: During the study period, schistosomiasis was histopathologically diagnosed in 423 specimens from different organs (0.7% of all specimens examined in the study period), out of those 40% were specimens from female and male organs. The specimens were sent from 24 hospitals in 13 regions of mainland Tanzania. Female genital schistosomiasis was diagnosed in 125 specimens from 111 patients. The main symptoms reported were bleeding disorders (48%), ulcer (17%), tumor (20%), lower abdominal pain (11%) and infertility (7%). The majority of cases with genital schistosomiasis were diagnosed in cervical tissue (71 cases). The confirmation of cervical cancer was specifically requested for 53 women, but the diagnosis could only be verified for 13 patients (25%), in 40 cases only severe cervical schistosomiasis was diagnosed. Vulval/labial schistosomiasis was seen in specimens from young women. Infertility was reported in four patients with schistosomiasis of the Fallopian tubes. CONCLUSION: Genital schistosomiasis adds to the disease burden of women in all age groups. Pathological consequences due to the involvement of different genital organs can be damaging for the affected women. Clinical unawareness of genital schistosomiasis can lead to misdiagnosis and therefore false and ineffective therapy. In endemic areas cervical schistosomiasis should be considered as differential diagnosis of cancer

HIV and HPV infections and ocular surface squamous neoplasia: systematic review and meta-analysis.

BACKGROUND: The frequency of ocular surface squamous neoplasias (OSSNs) has been increasing in populations with a high prevalence of infection with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and infection with human papillomavirus (HPV). We aimed to quantify the association between HIV/AIDS and HPV infection and OSSN, through systematic review and meta-analysis. METHODS: The articles providing data on the association between HIV/AIDS and/or HPV infection and OSSN were identified in MEDLINE, SCOPUS and EMBASE searched up to May 2013, and through backward citation tracking. The DerSimonian and Laird method was used to compute summary relative risk (RR) estimates and 95% confidence intervals (95% CI). Heterogeneity was quantified with the I(2) statistic. RESULTS: HIV/AIDS was strongly associated with an increased risk of OSSN (summary RR=8.06, 95% CI: 5.29-12.30, I(2)=56.0%, 12 studies). The summary RR estimate for the infection with mucosal HPV subtypes was 3.13 (95% CI: 1.72-5.71, I(2)=45.6%, 16 studies). Four studies addressed the association between both cutaneous and mucosal HPV subtypes and OSSN; the summary RR estimates were 3.52 (95% CI: 1.23-10.08, I(2)=21.8%) and 1.08 (95% CI: 0.57-2.05, I(2)=0.0%), respectively. CONCLUSION: Human immunodeficiency virus infection increases the risk of OSSN by nearly eight-fold. Regarding HPV infection, only the cutaneous subtypes seem to be a risk factor