Upchurchlevesque1904

Deep convolutional neural networks have significantly boosted the performance of fundus image segmentation when test datasets have the same distribution as the training datasets. However, in clinical practice, medical images often exhibit variations in appearance for various reasons, e.g., different scanner vendors and image quality. These distribution discrepancies could lead the deep networks to over-fit on the training datasets and lack generalization ability on the unseen test datasets. To alleviate this issue, we present a novel Domain-oriented Feature Embedding (DoFE) framework to improve the generalization ability of CNNs on unseen target domains by exploring the knowledge from multiple source domains. Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative. Specifically, we introduce a Domain Knowledge Pool to learn and memorize the prior information extracted from multi-source domains. Then the original image features are augmented with domain-oriented aggregated features, which are induced from the knowledge pool based on the similarity between the input image and multi-source domain images. We further design a novel domain code prediction branch to infer this similarity and employ an attention-guided mechanism to dynamically combine the aggregated features with the semantic features. We comprehensively evaluate our DoFE framework on two fundus image segmentation tasks, including the optic cup and disc segmentation and vessel segmentation. Our DoFE framework generates satisfying segmentation results on unseen datasets and surpasses other domain generalization and network regularization methods.This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users. In our approach, privacy and discrimination are related to each other. Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task.

Insulin-induced hypoglycemia is recognized as a critical problem for diabetic patients, especially at night. To give glucose prediction and advance warning of hypoglycemia of at least 30 minutes, various glucose-insulin models have been proposed. Recognizing the complementary nature of the models, this research proposes a Glucose-Insulin Mixture (GIM) model to predict the glucose values for hypoglycemia detection, by optimally fusing different models with its adjusted parameters to address the inter- and intra-individual variability.

Two types of classic glucose-insulin models, the Ruan model, with single-compartment glucose kinetics, and the Hovorka model, with two-compartment glucose kinetics, are selected as two candidate models. Based on Bayesian inference, GIM is introduced with quantified contributions from the models with the associated parameters. GIM is then applied to predict the glucose values and hypoglycemia events.

The proposed model is validated by the nocturnal glucose data collected from 12 participants with type 1 diabetes. The GIM model has promising fitting of RMSE within 0.3465mmol/L and predicting of RMSE within 0.5571mmol/L. According to the literature, the hypoglycemia is defined as 3.9mmol/L, and the GIM model shows good short-term hypoglycemia prediction performance with the data collected within the last hour (accuracy 95.97%, precision 91.77%, recall 95.60%). In addition, the probability of hypoglycemia event in 30 minutes is inferred.

GIM, by fusing various glucose-insulin models via Bayesian inference, has the promise to capture glucose dynamics and predict hypoglycemia.

GIM based short-term hypoglycemia prediction has potential clinical utility for timely intervention.

GIM based short-term hypoglycemia prediction has potential clinical utility for timely intervention.The title of [1] contains a typo. "Desire of Use A Hierarchical Decomposition of Activities and Its Application on Mobility of Blind and Low-Vision Individuals" is the correct title.In preclinical studies, fructooligosaccharide (FOS) showed beneficial skeletal effects but its effect on peak bone mass (PBM) and bone loss caused by estrogen (E2) deficiency has not been studied, and we set out to study these effects in rats. Short-chain (sc)-FOS had no effect on body weight, body composition, and energy metabolism of ovary intact (sham) and ovariectomized (OVX) rats. scFOS did not affect serum and urinary calcium and phosphorus levels, and on calcium absorption, although an increasing trend was noted in the sham group. Sham and OVX rats given scFOS had better skeletal parameters than their respective controls. UBCS039 activator scFOS treatment resulted in a higher bone anabolic response but had no effect on the catabolic parameters. scFOS increased serum levels of a short-chain fatty acid, butyrate which is known to have osteogenic effect. Our study for the first time demonstrates that in rats scFOS at the human equivalent dose enhances PBM and protects against E2 deficiency-induced bone loss by selective enhancement of new bone formation, and implicates butyrate in this process.