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8%). Diazoxide was commenced in 28 patients (82.3%); all responded. HH resolved in 20 patients (58.8%) following a median of 0.9 years (interquartile range (IQR) 0.2-6.8). Nine patients (n = 9, 26.5%) had developmental delay. Two patients developed Fanconi syndrome (p.Arg63Trp, HNF4A) and four had other renal or hepatic findings. Five (14.7%) developed MODY at a median of 11.0 years (IQR 9.0-13.9). Of patients with inherited mutations (n = 25, 73.5%), a family history of diabetes was present in 22 (88.0%).

We build on the knowledge of the natural history and pancreatic and extra-pancreatic phenotypes of HNF4A/HNF1Amutations and illustrate the heterogeneity of this condition.

We build on the knowledge of the natural history and pancreatic and extra-pancreatic phenotypes of HNF4A/HNF1Amutations and illustrate the heterogeneity of this condition.The disruption of traditional, in-person learning due to the COVID-19 pandemic necessitated the rapid development and use of revised and novel learning opportunities using a variety of remote instructional methodologies. This viewpoint describes the process used by an undergraduate Public Health program to transition a traditional, in-person, semester-long, 480-hour internship to a virtual-only learning experience guided by the existing student learning outcomes. Working closely with public health professionals at existing internship agencies, alumni from the program, student interns, and program faculty developed a modified virtual internship composed of 6 components. The development of this modified virtual internship model was guided by previous research on the components of successful internships and the elements of high-impact learning practices.The COVID-19 pandemic has revealed deeply entrenched structural inequalities that resulted in an excess of mortality and morbidity in certain racial and ethnic groups in the United States. Therefore, this paper examines from the US perspective how structural racism and defective data collection on racial and ethnic minorities can negatively influence the development of precision public health (PPH) approaches to tackle the ongoing COVID-19 pandemic. Importantly, the effects of structural and data racism on the development of fair and inclusive data-driven components of PPH interventions are discussed, such as with the use of machine learning algorithms to predict public health risks. The objective of this viewpoint is thus to inform public health policymaking with regard to the development of ethically sound PPH interventions against COVID-19. Particular attention is given to components of structural racism (eg, hospital segregation, implicit and organizational bias, digital divide, and sociopolitical influences) that are likely to hinder such approaches from achieving their social justice and health equity goals.Spiking neural networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, and event-driven processing of binary time series. Most existing learning algorithms for SNNs are based on deterministic neuronal models, such as leaky integrate-and-fire, and rely on heuristic approximations of backpropagation through time that enforces constraints such as locality. In contrast, probabilistic SNN models can be trained directly via principled online, local, and update rules that have proven to be particularly effective for resource-constrained systems. This article investigates another advantage of probabilistic SNNs, namely, their capacity to generate independent outputs when queried over the same input. It is shown that the multiple generated output samples can be used during inference to robustify decisions and to quantify uncertainty-a feature that deterministic SNN models cannot provide. Furthermore, they can be leveraged for training in order to obtain more accurate statistical estimates of the log-loss training criterion and its gradient. Specifically, this article introduces an online learning rule based on generalized expectation-maximization (GEM) that follows a three-factor form with global learning signals and is referred to as GEM-SNN. Experimental results on structured output memorization and classification on a standard neuromorphic dataset demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of samples used for inference and training.In this article, a novel value iteration scheme is developed with convergence and stability discussions. A relaxation factor is introduced to adjust the convergence rate of the value function sequence. The convergence conditions with respect to the relaxation factor are given. The stability of the closed-loop system using the control policies generated by the present VI algorithm is investigated. Moreover, an integrated VI approach is developed to accelerate and guarantee the convergence by combining the advantages of the present and traditional value iterations. Also, a relaxation function is designed to adaptively make the developed value iteration scheme possess fast convergence property. Finally, the theoretical results and the effectiveness of the present algorithm are validated by numerical examples.This brief considers constrained nonconvex stochastic finite-sum and online optimization in deep neural networks. Adaptive-learning-rate optimization algorithms (ALROAs), such as Adam, AMSGrad, and their variants, have widely been used for these optimizations because they are powerful and useful in theory and practice. Here, it is shown that the ALROAs are ε-approximations for these optimizations. We provide the learning rates, mini-batch sizes, number of iterations, and stochastic gradient complexity with which to achieve ε-approximations of the algorithms.Zero-shot learning casts light on lacking unseen class data by transferring knowledge from seen classes via a joint semantic space. However, the distributions of samples from seen and unseen classes are usually imbalanced. Many zero-shot learning methods fail to obtain satisfactory results in the generalized zero-shot learning task, where seen and unseen classes are all used for the test. Also, irregular structures of some classes may result in inappropriate mapping from visual features space to semantic attribute space. A novel generative mixup networks with semantic graph alignment is proposed in this article to mitigate such problems. To be specific, our model first attempts to synthesize samples conditioned with class-level semantic information as the prototype to recover the class-based feature distribution from the given semantic description. Second, the proposed model explores a mixup mechanism to augment training samples and improve the generalization ability of the model. Third, triplet gradient matching loss is developed to guarantee the class invariance to be more continuous in the latent space, and it can help the discriminator distinguish the real and fake samples. Finally, a similarity graph is constructed from semantic attributes to capture the intrinsic correlations and guides the feature generation process. Extensive experiments conducted on several zero-shot learning benchmarks from different tasks prove that the proposed model can achieve superior performance over the state-of-the-art generalized zero-shot learning.Land remote-sensing analysis is a crucial research in earth science. In this work, we focus on a challenging task of land analysis, i.e., automatic extraction of traffic roads from remote-sensing data, which has widespread applications in urban development and expansion estimation. Nevertheless, conventional methods either only utilized the limited information of aerial images, or simply fused multimodal information (e.g., vehicle trajectories), thus cannot well recognize unconstrained roads. To facilitate this problem, we introduce a novel neural network framework termed cross-modal message propagation network (CMMPNet), which fully benefits the complementary different modal data (i.e., aerial images and crowdsourced trajectories). selleck chemicals Specifically, CMMPNet is composed of two deep autoencoders for modality-specific representation learning and a tailor-designed dual enhancement module for cross-modal representation refinement. In particular, the complementary information of each modality is comprehensively extracted and dynamically propagated to enhance the representation of another modality. Extensive experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction benefiting from blending different modal data, either using image and trajectory data or image and light detection and ranging (LiDAR) data. From the experimental results, we observe that the proposed approach outperforms current state-of-the-art methods by large margins. Our source code is resealed on the project page http//lingboliu.com/multimodal_road_extraction.html.In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for n-degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.The purpose of this study was to assess 1) how treadmill slope variance affected external power output (PO) and propulsion technique reliability; and 2) how PO is associated with propulsion technique. Eighteen individuals with spinal cord injury performed two wheelchair treadmill exercise blocks (0% and 1% treadmill slope, standardized velocity) twice on two separate days. PO, velocity, and 14 propulsion technique variables were measured. In a follow-up study, N = 29 performed wheelchair treadmill drag tests. Target and actual slope were documented and PO, intraclass correlation coefficients (ICC) and smallest detectable differences (SDD) were calculated. Within and between visits, the reliability study ICCs were perfect for velocity (1.0), weak for PO (0.33-0.46), and acceptable (>0.70) for five (0% slope) and 10 (1% slope) propulsion technique variables, resulting in SDDs of 35-196%. Measured PO explained 56-90% of the variance in key propulsion technique variables. In the follow-up, PO ICCs were weak (0.43) and SDDs high. Bias between target and actual slope appeared random. In conclusion, PO variability accounts for 50-90% of the variability in propulsion technique variables when speed and wheelchair set-up are held constant. Therefore, small differences in PO between interventions could mask the effect of the interventions on propulsion technique.