Lipid metabolism in tumor-infiltrating regulatory T cells: perspective to precision immunotherapy.

Regulatory T cells (Tregs) are essential to the negative regulation of the immune system, as they avoid excessive inflammation and mediate tumor development. The abundance of Tregs in tumor tissues suggests that Tregs may be eliminated or functionally inhibited to stimulate antitumor immunity. However, immunotherapy targeting Tregs has been severely hampered by autoimmune diseases due to the systemic elimination of Tregs.

Adavosertib-encapsulated metal-organic frameworks for p53-mutated gallbladder cancer treatment via synthetic lethality.

Adavosertib (ADA) is a WEE1 inhibitor that exhibits a synthetic lethal effect on p53-mutated gallbladder cancer (GBC). However, drug resistance due to DNA damage response compensation pathways and high toxicity limits further applications. Herein, estrone-targeted ADA-encapsulated metal-organic frameworks (ADA@MOF-EPL) for GBC synthetic lethal treatment by inducing conditional factors are developed.

Detecting moving targets in active sonar echograph of harbor environment using robust high-order flux tensor.

An important goal of an active sonar system is to detect and track underwater intruders such as frogmen, unmanned underwater vehicles, etc. Unfortunately, the intruders appear visually as a small fluctuating "blob" against the high-level fluctuating background caused by multipath propagation and reverberation in the harbor environment, making it difficult to be distinguished. Classical motion features well developed in computer vision cannot cope with an underwater environment.

High-order time lacunarity feature-aided multiple hypotheses tracking for underwater active small targets in high-clutter harbor environment.

Active tracking of underwater small targets is a great challenge with kinematic information alone. This is because the active sonar often encounters multipath propagation and the induced clutter can even mask target echoes. Recently, high-order time lacunarity (HOT-Lac) has shown its ability in effectively highlighting "blob" targets from high clutter harbor environments.

Motion acoustic flow field: Underwater small target motion characterization for active sonar echograph of harbor environment.

Spatial-temporal variations of active sonar echo intensity can provide effective motion information for characterizing intruding small targets and play a key role in follow-up tracking, behavioral analysis, and recognition, etc. Inspired by the idea of optical flow, which can be used to calculate subtle spatial-temporal variations of each pixel in image sequences, a different motion acoustic flow field (MAFF) is proposed for estimating the motion of underwater small targets in successive active sonar echographs from harbor environments. This is because directly applying current calculation framework for optical flow presents two challenges in this case.

A New Coarse Gating Strategy Driven Multidimensional Assignment for Two-Stage MHT of Bearings-Only Multisensor-Multitarget Tracking.

The problem of two-dimensional bearings-only multisensor-multitarget tracking is addressed in this work. For this type of target tracking problem, the multidimensional assignment (MDA) is crucial for identifying measurements originating from the same targets. However, the computation of the assignment cost of all possible associations is extremely high.

Fast online high-order time lacunarity for characterizing active sonar echographs of harbor environment.

High-order time lacunarity (HOT-Lac) is an effective feature for characterizing active sonar echographs of harbor environments. However, it involves high computational complexity of loop summations. Motivated by the idea of integral image, this Letter extends an echo-intensity integral sequence, a representation of filtering with time domain recursion, permitting fast and online updates of HOT-Lac in a constant number of operations.

DeepLofargram: A deep learning based fluctuating dim frequency line detection and recovery.

This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, long enough time integration can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines.

Detecting moving targets in active sonar echograph of harbor environment using high-order time lacunarity.

This paper presents a feature for detecting potential targets from high level littoral clutters in active sonar echographs. Based on lacunarity, which describes the image texture statistics, an extension to the time domain is made in order to measure the dynamic behavior of target echoes and background clutters. Moreover, as high-order moments have been shown to well characterize the non-Rayleigh tails of littoral clutter, high-order computation is incorporated in the proposed high-order time lacunarity (HOT-Lac).

Impact of Pressure Injuries on Patient Outcomes in a Korean Hospital: A Case-Control Study.

Purpose: The purpose of this study was to compare the effect of pressure injuries on mortality, hospital length of stay, healthcare costs, and readmission rates in hospitalized patients.

Design: A case-control study.

Subjects And Setting: The sample comprised 5000 patients admitted to a tertiary hospital located in Seoul Korea; 1000 patients with pressure injuries (cases) were compared to 4000 patients who acted as controls.

Matrix-Regularized Multiple Kernel Learning via (r,p) Norms.

This paper examines a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r,p) norms. For the problem of learning a linear combination in the support vector machine-based framework, model complexity is typically controlled using various regularization strategies on the combined kernel weights. Recent research has developed a generalized ℓp-norm MKL framework with tunable variable p(p≥1) to support controlled intrinsic sparsity.

Usefulness of the Braden Scale in Intensive Care Units: A Study Based on Electronic Health Record Data.

Nurses working in intensive care units have expressed concern that some categories of the Braden scale such as activity and nutrition are not suitable for intensive care unit patients. Upon examining the validity of the Braden scale using the electronic health data, we found relatively low predictability of the tool. Risk factors from the sensory perception and activity categories were not associated with risk of pressure ulcers.

On the Impact of Regularization Variation on Localized Multiple Kernel Learning.

This brief analyzes the effects of regularization variations in the localized kernel weights on the hypothesis generated by localized multiple kernel learning (LMKL) algorithms. Recent research on LMKL includes imposing different regularizations on the localized kernel weights and has led to varying formulations and solution strategies. Following the stability analysis theory as presented by Bousquet and Elisseeff, we give stability bounds based on the norm of the variation of localized kernel weights for three LMKL methods cast in the support vector machine classification framework, including vector -norm LMKL, matrix-regularized -norm LMKL, and samplewise -norm LMKL.

Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization.

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem.

Probability-confidence-kernel-based localized multiple kernel learning with lp norm.

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm.