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. Recently, emerging studies have shown that metabolic regulation can specifically target tumor-infiltrating immune cells, and lipid accumulation in TME is associated with immunosuppression. Nevertheless, how Tregs actively regulate metabolic reprogramming to outcompete effector T cells (Teffs), and how lipid metabolic reprogramming contributes to the immunomodulatory capacity of Tregs have not been fully discussed. This review will discuss the physiological processes by which lipid accumulation confers a metabolic advantage to tumor-infiltrating Tregs (TI-Tregs) and amplifies their immunosuppressive functions. Furthermore, we will provide a summary of the driving effects of various metabolic regulators on the metabolic reprogramming of Tregs. Finally, we propose that targeting the lipid metabolism of TI-Tregs could be efficacious either alone or in conjunction with immune checkpoint therapy.

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. The high expression of estrogen receptors in GBC enables ADA@MOF-EPL to quickly enter and accumulate near the cell nucleus through estrone-mediated endocytosis and release ADA to inhibit WEE1 upon entering the acidic tumor microenvironment. Ultrasound irradiation induces ADA@MOF-EPL to generate reactive oxygen species (ROS), which leads to a further increase in DNA damage, resulting in a higher sensitivity of p53-mutated cancer cells to WEE1 inhibitor and promoting cell death via conditional synthetic lethality. The conditional factor induced by ADA@MOF-EPL further enhances the antitumor efficacy while significantly reducing systemic toxicity. Moreover, ADA@MOF-EPL demonstrates similar antitumor abilities in other p53-mutated solid tumors, revealing its potential as a broad-spectrum antitumor drug.

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. Thus, this paper presents a robust high-order flux tensor (RHO-FT) to characterize the small underwater moving targets against high-level fluctuating background. According to the dynamic behavior of active clutter from real-world harbor environment, we first classify it into two main types: (1) dynamic clutter but with relatively consistent spatial-temporal variation in a certain neighborhood; (2) sparkle clutter presenting completely random flashing. Then starting from the classical flux tensor, we develop a statistical high-order computation to handle the former followed by a spatial-temporal connected component to suppress the latter to achieve higher robustness. Experiments on a set of real-world harbor datasets demonstrate the effectiveness of our RHO-FT.

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. Hence, this paper proposes a HOT-Lac aided track scoring mechanism to solve the ambiguity of data association within the framework of Multiple Hypotheses Tracking (MHT). Specifically, the trajectory consistency of potential targets is captured by a momentum accumulation of the HOT Lac feature, which can inherit the historical information for the whole track. Meanwhile, due to the separability of the distribution of target and clutter in the HOT-Lac feature space, the probabilities of the target hypothesis and null hypothesis are modeled by the online computation of the HOT-Lac feature. Finally, the cumulative likelihood ratio based on HOT-Lac is integrated into MHT to score the potential tracks. Experiments in several real-world harbor scenarios demonstrate that the proposed HOT-Lac feature-aided tracker can suppress false tracks accurately and quickly.

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. The first challenge is that the echo intensity fluctuation breaks its potential assumption of brightness consistency in the pre-processing stage. The second challenge is that the small size of the blob targets could be smoothed out as outliers by its median filter-based post-processing. Hence starting from the classical optical flow equation, MAFF introduces a novel spatial-temporal connected component pre-processing and a novel blob shape segmentation refinement post-processing. Experiments on a set of real-world harbor datasets demonstrate the efficacy of our MAFF calculation framework.

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. To reduce the computational complexity of MDA, a new coarse gating strategy is proposed. This is realized by comparing the Mahalanobis distance between the current estimate and initial estimate in an iterative process for the maximum likelihood estimation of the target position with a certain threshold to eliminate potential infeasible associations. When the Mahalanobis distance is less than the threshold, the iteration will exit in advance so as to avoid the expensive computational costs caused by invalid iteration. Furthermore, the proposed strategy is combined with the two-stage multiple hypothesis tracking framework for bearings-only multisensor-multitarget tracking. Numerical experimental results verify its effectiveness.

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. Evaluated by a set of real-world harbor data, the proposed method is capable of computing HOT-Lac extremely rapidly while maintaining equivalent area under curve performances to its off-line counterpart, demonstrating its potential for real-time surveillance of the harbor.

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. Deep learning has been shown to perform very well for sophisticated visual inference tasks. With the composition of multiple processing layers, very complex high level representations that amplify the important aspects of input while suppressing irrelevant variations can be learned. Hence, DeepLofargram is proposed, composed of a deep convolutional neural network and its visualization counterpart. Plugging into specifically designed multi-task loss, an end-to-end training jointly learns to detect and recover the spatial location of potential lines. Leveraging on this deep architecture, performance limits of low SNR can be achieved as low as -24 dB on average and -26 dB for some. This is far beyond the perception of human vision and significantly improves the state-of-the-art.

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). The potential of HOT-Lac is demonstrated using a series of active sonar echographs with diverse cooperative targets detected in real-world harbor environments in the South China Sea. Specifically, it is shown how HOT-Lac can effectively distinguish different moving small targets from high-level background clutter, and how this ability can be exploited to highlight an invasion target in harbor security.

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. METHODS: We retrospectively extracted clinical data from electronic health records. Study outcomes were mortality, hospital length of stay, healthcare costs, and readmission rates. The impact of pressure injuries on death and readmission was analyzed via multiple logistic regression, hospital deaths within 30 days were analyzed using the survival analysis and Cox proportional hazards regression, and impact on the length of hospitalization and medical costs were analyzed through a multiple linear regression. RESULTS: Developing a pressure injury was significantly associated with an increased risk of in-hospital mortality (odds ratio [OR], 3.94; 95% confidence interval [CI], 2.91-5.33), 30-days in-hospital mortality (OR, 2.18; 95% CI, 1.59-3.00), and healthcare cost (ß = 11,937,333; P < .001). Pressure injuries were significantly associated with an extended length of hospitalization (ß = 20.84; P < .001) and length of intensive care unit (ICU) stay (ß = 8.16; P < .001). Having a pressure injury was significantly associated with an increased risk of not being discharged home (OR, 5.55; 95% CI, 4.35-7.08), along with increased risks of readmission (OR, 1.30; 95% CI, 1.05-1.62) and emergency department visits after discharge (OR, 1.70; 95% CI, 1.29-2.23). CONCLUSIONS: Development of pressure injuries influenced mortality, healthcare costs, ICU and hospital length of stay, and healthcare utilization following discharge (ie, readmission or emergency department visits). Hospital-level efforts and interdisciplinary approaches should be prioritized to develop interventions and protocols for pressure injury prevention.

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. Unfortunately, this ``1-D'' vector ℓp-norm hardly exploits potentially useful information on how the base kernels ``interact.'' To allow for higher order kernel-pair relationships, we extend the ``1-D'' vector ℓp-MKL to the ``2-D'' matrix (r,p) norms (1 ≤ r,p < ∞). We develop a new formulation and an efficient optimization strategy for (r,p)-MKL with guaranteed convergence. A theoretical analysis and experiments on seven UCI data sets shed light on the superiority of (r,p)-MKL over ℓp-MKL in various scenarios.

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. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.

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. Further comparison of these bounds helps to qualitatively reveal the performance differences produced by these regularization methods, that is, matrix-regularized LMKL achieves superior performance, followed by vector -norm LMKL and samplewise -norm LMKL. Finally, a set of experimental results on ten benchmark machine learning UCI data sets is reported and shown to empirically support our theoretical analysis.

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.

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. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

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. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.