Lorentsenhaaning2185

Here, we explore the current understandings of asthma endotypes and review their associated phenotypes. We provide practical and evidence-based guidance for clinicians considering a biologic for asthma add-on maintenance therapy.

To describe progressive corneal microcyst-like epithelial changes (MECs) that developed in patients treated with the investigational drug belantamab mafodotin (belamaf) for refractory multiple myeloma (MM).

This is a single center case series of patients with MM receiving the investigational drug belamaf.

All 12 patients included in this analysis who were treated with belamaf developed MECs that initially appeared in the peripheral cornea and progressed centrally with time. Cessation of therapy resulted in regression of the MECs first in the periphery then centrally. Microcyst-like epithelial changes recurred in all patients on retreatment. With prolonged therapy, eight patients developed corneal staining patterns suggestive of limbal stem cell dysfunction (LSCD).

We describe MECs and LSCD associated with systemic administration of belamaf. Further study is needed to determine the etiology and composition of the MECs and the mechanism of limbal stem cell involvement.

We describe MECs and LSCD associated with systemic administration of belamaf. Further study is needed to determine the etiology and composition of the MECs and the mechanism of limbal stem cell involvement.A number of coronavirus disease 2019 (COVID-19) vaccine candidates have shown promising results, but substantial uncertainty remains regarding their effectiveness and global rollout. Boosting innate immunity with bacillus Calmette Guérin (BCG) or other live attenuated vaccines may also play a role in the fight against the COVID-19 pandemic. BCG has long been known for its nonspecific beneficial effects that are most likely explained by epigenetic and metabolic reprogramming of innate immune cells, termed trained immunity. In this issue of the JCI, Rivas et al. add to these arguments by showing that BCG-vaccinated health care providers from a Los Angeles health care organization had lower rates of COVID-19 diagnoses and seropositivity compared with unvaccinated individuals. Prospective clinical trials are thus warranted to explore the effects of BCG vaccination in COVID-19. selleck compound We posit that beyond COVID-19, vaccines such as BCG that elicit trained immunity may mitigate the impact of emerging pathogens in future pandemics.[This corrects the article DOI 10.2196/18858.].The stability analysis problem is considered for multiarea load frequency control (LFC) systems with electric vehicles (EVs) and time delays. The novel linear operator inequality approach is proposed and a less conservative delay-dependent stability condition is achieved. First, the model of the multiarea LFC system with EVs and delays is expressed by the partial integral equation (PIE) at the first time. Then, a complete quadratic Lyapunov-Krasovskii functional is built in the form of an inner product of the linear partial integral (PI) operator. The novel stability criteria with less conservatism are proposed in the form of linear operator inequality. Moreover, the relationships between the delay margins and the controller parameters are shown. Finally, the simulations are conducted on both one-area and two-area LFC systems to show the effectiveness of the proposed approach.This article concentrates on the adaptive neural control for switched nonlinear systems interconnected with unmodeled dynamics. The investigated model consists of two dynamic processes, namely, the x-system and the unmodeled z-dynamics. In this article, we focus on a scenario that the unmodeled z-dynamics do not contain input-to-state practically stable (ISpS) modes, that is, all modes are not ISpS (non-ISpS). First, we design an adaptive neural controller such that each mode of the closed-loop x-system is ISpS with respect to the state of dynamic uncertainties. Then, fast average dwell time (fast ADT) and slow average dwell time (slow ADT) are simultaneously used to limit the switching law. In this way, both the closed-loop x-system and the unmodeled z-dynamics are ISpS under switching. By assigning the ISpS gains with small-gain theorem, we can guarantee that the whole closed-loop system is semiglobal uniformly ultimately bounded (SGUUB), and meanwhile, the system output is steered to a small region of zero. Finally, simulation examples are used to verify the effectiveness of the proposed control scheme.Automated emotion recognition in the wild from facial images remains a challenging problem. Although recent advances in deep learning have assumed a significant breakthrough in this topic, strong changes in pose, orientation, and point of view severely harm current approaches. In addition, the acquisition of labeled datasets is costly and the current state-of-the-art deep learning algorithms cannot model all the aforementioned difficulties. In this article, we propose applying a multitask learning loss function to share a common feature representation with other related tasks. Particularly, we show that emotion recognition benefits from jointly learning a model with a detector of facial action units (collective muscle movements). The proposed loss function addresses the problem of learning multiple tasks with heterogeneously labeled data, improving previous multitask approaches. We validate the proposal using three datasets acquired in noncontrolled environments, and an application to predict compound facial emotion expressions.In this article, the problem of event-based adaptive fuzzy fixed-time tracking control for a class of uncertain nonlinear systems with unknown virtual control coefficients (UVCCs) is considered. The unknown nonlinear functions of the considered systems are approximated by fuzzy-logic systems (FLSs). Moreover, a novel Lyapunov function is designed to remove the requirement of lower bounds of the UVCC in control laws. In addition, an event-triggered control method is developed by using the backstepping technique to save the network resources. Through theoretical analysis, the event-based fixed-time controller was proposed, which can guarantee that all signals of the controlled system are bounded and the tracking error can converge to a small neighborhood of the origin in a fixed time. Meanwhile, the convergence time is independent of the initial states. Two numerical examples are presented to demonstrate the effectiveness of the proposed approach.This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of brain regions, and these regions are traditionally parcellated with a particular brain atlas. Most existing studies have adopted a predefined brain atlas for all subjects. However, the constructed FC networks inevitably ignore the potentially important subject-specific information, particularly, the subject-specific brain parcellation. Similar to the drawback of the ``single view (versus the ``multiview learning) in medical image-based classification, FC networks constructed based on a single atlas may not be sufficient to reveal the underlying complicated differences between normal controls and disease-affected patients due to the potential bias from that partimise in the brain connectome-based individualized diagnosis of brain diseases.The strong age dependency of many deleterious health outcomes likely reflects the cumulative effects from a variety of risk and protective factors that occur over one's life course. This notion has become increasingly explored in the etiology of chronic disease and associated comorbidities in aging. Our recent work has shown the robust classification of individuals at risk for cardiovascular pathophysiology using CT-based soft tissue radiodensity parameters obtained from nonlinear trimodal regression analysis (NTRA). Past and present lifestyle influences the incidence of comorbidities like hypertension (HTN), diabetes (DM) and cardiac diseases. 2,943 elderly subjects from the AGES-Reykjavik study were sorted into a three-level binary-tree structure defined by 1) lifestyle factors (smoking and self-reported physical activity level), 2) comorbid HTN or DM, and 3) cardiac pathophysiology. NTRA parameters were extracted from mid-thigh CT cross-sections to quantify radiodensitometric changes in three tissue types lean muscle, fat, and loose-connective tissue. Between-group differences were assessed at each binary-tree level, which were then used in tree-based machine learning (ML) models to classify subjects with DM or HTN. Classification scores for detecting HTN or DM based on lifestyle factors were excellent (AUCROC 0.978 and 0.990, respectively). Finally, tissue importance analysis underlined the comparatively-high significance of connective tissue parameters in ML classification, while predictive models of DM onset from five-year longitudinal data gave a classification accuracy of 94.9%. Altogether, this work serves as an important milestone toward the construction of predictive tools for assessing the impact of lifestyle factors and healthy aging based on a single image.The Nash equilibrium is an important concept in game theory. It describes the least exploitability of one player from any opponents. We combine game theory, dynamic programming, and recent deep reinforcement learning (DRL) techniques to online learn the Nash equilibrium policy for two-player zero-sum Markov games (TZMGs). The problem is first formulated as a Bellman minimax equation, and generalized policy iteration (GPI) provides a double-loop iterative way to find the equilibrium. Then, neural networks are introduced to approximate Q functions for large-scale problems. An online minimax Q network learning algorithm is proposed to train the network with observations. Experience replay, dueling network, and double Q-learning are applied to improve the learning process. The contributions are twofold 1) DRL techniques are combined with GPI to find the TZMG Nash equilibrium for the first time and 2) the convergence of the online learning algorithm with a lookup table and experience replay is proven, whose proof is not only useful for TZMGs but also instructive for single-agent Markov decision problems.