Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202

Gene therapy in patient-specific stem cell lines and a preclinical model of retinitis pigmentosa with membrane frizzled-related protein defects

Defects in Membrane Frizzled-related Protein (MFRP) cause autosomal recessive retinitis pigmentosa (RP). MFRP codes for a retinal pigment epithelium (RPE)-specific membrane receptor of unknown function. In patient-specific induced pluripotent stem (iPS)-derived RPE cells, precise levels of MFRP, and its dicistronic partner CTRP5, are critical to the regulation of actin organization. Overexpression of CTRP5 in naive human RPE cells phenocopied behavior of MFRP-deficient patient RPE (iPS-RPE) cells. AAV8 (Y733F) vector expressing human MFRP rescued the actin disorganization phenotype and restored apical microvilli in patient-specific iPS-RPE cell lines. As a result, AAV-treated MFRP mutant iPS-RPE recovered pigmentation and transepithelial resistance. The efficacy of AAV-mediated gene therapy was also evaluated in Mfrp(rd6)/Mfrp(rd6) mice--an established preclinical model of RP--and long-term improvement in visual function was observed in AAV-Mfrp-treated mice. This report is the first to indicate the successful use of human iPS-RPE cells as a recipient for gene therapy. The observed favorable response to gene therapy in both patient-specific cell lines, and the Mfrp(rd6)/Mfrp(rd6) preclinical model suggests that this form of degeneration caused by MFRP mutations is a potential target for interventional trials

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical, and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using the area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical decision support tool to identify "at-risk" children for early intervention.info:eu-repo/semantics/publishedVersio

INVESTIGATING THE ANTI-INFLAMMATORY ACTIVITY OF AN ETHANOLIC EXTRACT FROM ARTOCARPUS TONKINENSIS LEAVES USING A COLLAGEN ANTIBODY-INDUCED ARTHRITIC MOUSE MODEL

The obtained results here demonstrate that the 70% ethanolic leaf extract of A. tonkinensis (AT2), traditionally used in Vietnamese folk medicine for treating arthritic symtoms, has beneficial effects on pro-inflammatory cytokine inhibition and in an experimental arthritic mouse model. LPS-stimulated RAW 264.7 macrophages treated with AT2 showed a significant decrease in the production of IL-6 and TNFa at concentrations of 12.5, 25 and 50 µg/mL (P0.05), indicating its potential anti-inflammatory properties. Treatment of CAIA mice with AT2 also led to diminish the incidence of arthritis at doses of 200 and 300 mg/kg body weight

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Highlights from the 2019 International Myopia Summit on 'controversies in myopia'.

Myopia is an emerging public health issue with potentially significant economic and social impact, especially in East Asia. However, many uncertainties about myopia and its clinical management remain. The International Myopia Summit workgroup was convened by the Singapore Eye Research Institute, the WHO Regional Office for the Western Pacific and the International Agency for the Prevention of Blindness in 2019. The aim of this workgroup was to summarise available evidence, identify gaps or unmet needs and provide consensus on future directions for clinical research in myopia. In this review, among the many 'controversies in myopia' discussed, we highlight three main areas of consensus. First, development of interventions for the prevention of axial elongation and pathologic myopia is needed, which may require a multifaceted approach targeting the Bruch's membrane, choroid and/or sclera. Second, clinical myopia management requires co-operation between optometrists and ophthalmologists to provide patients with holistic care and a tailored approach that balances risks and benefits of treatment by using optical and pharmacological interventions. Third, current diagnostic technologies to detect myopic complications may be improved through collaboration between clinicians, researchers and industry. There is an unmet need to develop new imaging modalities for both structural and functional analyses and to establish normative databases for myopic eyes. In conclusion, the workgroup's call to action advocated for a paradigm shift towards a collaborative approach in the holistic clinical management of myopia

The influence of human genetic variation on early transcriptional responses and protective immunity following immunization with Rotarix vaccine in infants in Ho Chi Minh City in Vietnam : a study protocol for an open single-arm interventional trial [awaiting peer review]

Background: Rotavirus (RoV) remains the leading cause of acute gastroenteritis in infants and children aged under five years in both high- and low-middle-income countries (LMICs). In LMICs, RoV infections are associated with substantial mortality. Two RoV vaccines (Rotarix and Rotateq) are widely available for use in infants, both of which have been shown to be highly efficacious in Europe and North America. However, for unknown reasons, these RoV vaccines have markedly lower efficacy in LMICs. We hypothesize that poor RoV vaccine efficacy across in certain regions may be associated with genetic heritability or gene expression in the human host. Methods/design: We designed an open-label single-arm interventional trial with the Rotarix RoV vaccine to identify genetic and transcriptomic markers associated with generating a protective immune response against RoV. Overall, 1,000 infants will be recruited prior to Expanded Program on Immunization (EPI) vaccinations at two months of age and vaccinated with oral Rotarix vaccine at two and three months, after which the infants will be followed-up for diarrheal disease until 18 months of age. Blood sampling for genetics, transcriptomics, and immunological analysis will be conducted before each Rotarix vaccination, 2-3 days post-vaccination, and at each follow-up visit (i.e. 6, 12 and 18 months of age). Stool samples will be collected during each diarrheal episode to identify RoV infection. The primary outcome will be Rotarix vaccine failure events (i.e. symptomatic RoV infection despite vaccination), secondary outcomes will be antibody responses and genotypic characterization of the infection virus in Rotarix failure events. Discussion: This study will be the largest and best powered study of its kind to be conducted to date in infants, and will be critical for our understanding of RoV immunity, human genetics in the Vietnam population, and mechanisms determining RoV vaccine-mediated protection. Registration: ClinicalTrials.gov, ID: NCT03587389. Registered on 16 July 2018