Accumulated evidence highlights that exercise can modulate multiple cytokines, influencing transcriptional pathways, and reprogramming certain metabolic processes, ultimately promoting antitumor immunity and enhancing the efficacy of immune checkpoint inhibitors in cancer patients. Exploring the mechanisms behind this will, for one thing, help us uncover key factors and pathways in exercise-assisted cancer immunotherapy, offering more possibilities for future treatment methods. For another, it will support the development of more personalized and effective exercise prescriptions, thereby improving the prognosis of cancer patients.

Combined hyperlipidemia (CHL) manifests as elevated cholesterol and triglycerides, associated with fatty liver and cardiovascular diseases. Emerging evidence underscores the crucial role of the intestinal microbiota in metabolic disorders. However, the potential therapeutic viability of remodeling the intestinal microbiota in CHL remains uncertain. In this study, CHL was induced in low-density lipoprotein receptor-deficient (LDLR^-/-) hamsters through an 8-week high-fat and high-cholesterol (HFHC) diet or a 4-month high-cholesterol (HC) diet. Placebo or antibiotics were administered through separate or cohousing approaches. Analysis through 16S rDNA sequencing revealed that intermittent antibiotic treatment and the cohousing approach effectively modulated the gut microbiota community without impacting its overall abundance in LDLR^-/- hamsters exhibiting severe CHL. Antibiotic treatment mitigated HFHC diet-induced obesity, hyperglycemia, and hyperlipidemia, enhancing thermogenesis and alleviating nonalcoholic steatohepatitis (NASH), concurrently reducing atherosclerotic lesions in LDLR^-/- hamsters. Metabolomic analysis revealed a favorable liver lipid metabolism profile. Increased levels of microbiota-derived metabolites, notably butyrate and glycylglycine, also ameliorated NASH and atherosclerosis in HFHC diet-fed LDLR^-/- hamsters. Notably, antibiotics, butyrate, and glycylglycine treatment exhibited protective effects in LDLR^-/- hamsters on an HC diet, aligning with outcomes observed in the HFHC diet scenario. Our findings highlight the efficacy of remodeling gut microbiota through antibiotic treatment and cohousing in improving obesity, NASH, and atherosclerosis associated with refractory CHL. Increased levels of beneficial microbiota-derived metabolites suggest a potential avenue for microbiome-mediated therapies in addressing CHL-associated diseases.

Neutrophils are primed for neutrophil extracellular trap (NET) formation during diabetes, and excessive NET formation from primed neutrophils compromises wound healing in patients with diabetes. Here, we demonstrate that trained immunity mediates diabetes-induced NET priming in neutrophils. Under diabetic conditions, neutrophils exhibit robust metabolic reprogramming comprising enhanced glycolysis via the pentose phosphate pathway and fatty acid oxidation, which result in the accumulation of acetyl-coenzyme A. Adenosine 5′-triphosphate-citrate lyase-mediated accumulation of acetyl-coenzyme A and histone acetyltransferases further induce the acetylation of lysine residues on histone 3 (AcH3K9, AcH3K14, and AcH3K27) and histone 4 (AcH4K8). The pharmacological inhibition of adenosine 5′-triphosphate-citrate lyase and histone acetyltransferases completely inhibited high-glucose-induced NET priming. The trained immunity of neutrophils was further confirmed in neutrophils isolated from patients with diabetes. Our findings suggest that trained immunity mediates functional changes in neutrophils in diabetic environments, and targeting neutrophil-trained immunity may be a potential therapeutic target for controlling inflammatory complications of diabetes.

Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC. This research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures, i.e., SC multiplexer multiply-accumulate, multiply-accumulate function generator, and binary rectified linear unit, and successfully scaled and implemented a fully parallel CNN model on Kintex7 FPGA. The proposed SC hardware only compromises 0.14% accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72% energy saving per image feedforward and 31× more data throughput than modern hardware. Unique to SC, early decision termination pushed the performance baseline exponentially with minimum accuracy loss, making SC CNN extremely lucrative for AI edge computing but limited to classification tasks. The SC’s inherent noise heavily penalizes CNN regression performance, rendering SC unsuitable for regression tasks.

Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.

Pixel-level structure segmentations have attracted considerable attention, playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine vision. However, current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space, thereby limiting the capability of light field transmission for visual knowledge. In this paper, we propose a general light field modeling method for pixel-level structure segmentation, comprising a generative light field prompting encoder (LF-GPE) and a prompt-based masked light field pretraining (LF-PMP) network. Our LF-GPE, serving as a light field backbone, can extract both appearance and geometric structural cues simultaneously. It aligns these features into a unified visual space, facilitating semantic interaction. Meanwhile, our LF-PMP, during the pretraining phase, integrates a mixed light field and a multi-view light field reconstruction. It prioritizes considering the geometric structural properties of the light field, enabling the light field backbone to accumulate a wealth of prior knowledge. We evaluate our pretrained LF-GPE on two downstream tasks: light field salient object detection and semantic segmentation. Experimental results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

Integrated 2-dimensional (2D) photonic devices such as monolayer waveguide has generated exceptional interest because of their ultimate thinness. In particular, they potentially permit stereo photonic architecture through bond-free van der Waals integration. However, little is known about the coupling and controlling of the single-atom guided wave to its photonic environment, which governs the design and application of integrated system. Here, we report the optical coupling of atomically guided waves to other photonic modes. We directly probe the mode beating between evanescent waves in a monolayer 2D waveguide and a silicon photonic waveguide, which constitutes a vertically integrated interferometer. The mode-coupling measures the dispersion relation of the guided wave inside the atomic waveguide and unveils it strongly modifies matter’s electronic states, manifesting by the formation of a propagating polariton. We also demonstrated light modulating and spectral detecting in this compact nonplanar interferometer. These findings provide a generalizable and versatile platform toward monolithic 3-dimensional integrated photonics.

The presence of endotoxemia is strongly linked to the development of endothelial dysfunction and disruption of myocardial microvascular reactivity. These factors play a crucial role in the progression of endotoxemic cardiomyopathy. Sepsis-related multiorgan damage involves the participation of the catalytic subunit of DNA-dependent protein kinase (DNA-PKcs). However, whether DNA-PKcs contributes to endothelial dysfunction and myocardial microvascular dysfunction during endotoxemia remains unclear. Hence, we conducted experiments in mice subjected to lipopolysaccharide (LPS)-induced endotoxemic cardiomyopathy, as well as assays in primary mouse cardiac microvascular endothelial cells. Results showed that endothelial-cell-specific DNA-PKcs ablation markedly attenuated DNA damage, sustained microvessel perfusion, improved endothelial barrier function, inhibited capillary inflammation, restored endothelium-dependent vasodilation, and improved heart function under endotoxemic conditions. Furthermore, we show that upon LPS stress, DNA-PKcs recognizes a TQ motif in cofilin2 and consequently induces its phosphorylation at Thr²⁵. Phosphorylated cofilin2 shows increased affinity for F-actin and promotes F-actin depolymerization, resulting into disruption of the endothelial barrier integrity, microvascular inflammation, and defective eNOS-dependent vasodilation. Accordingly, cofilin2-knockin mice expressing a phospho-defective (T25A) cofilin2 mutant protein showed improved endothelial integrity and myocardial microvascular function upon induction of endotoxemic cardiomyopathy. These findings highlight a novel mechanism whereby DNA-PKcs mediates cofilin2^Thr25 phosphorylation and subsequent F-actin depolymerization to contribute to endotoxemia-related cardiac microvascular dysfunction.

2,2′,7,7′-Tetrakis(N,N-di-p-methoxyphenyl)-amine-9,9′-spirobifluorene (Spiro-OMeTAD) represents the state-of-the-art hole-transporting material (HTM) in n-i-p perovskite solar cells (PSCs). However, its susceptibility to stability issues has been a long-standing concern. In this study, we embark on a comprehensive exploration of the untapped potential within the family of spiro-type HTMs using an innovative anisotropic regulation strategy. Diverging from conventional approaches that can only modify spirobifluorene with single functional group, this approach allows us to independently tailor the two orthogonal components of the spiro-skeleton at the molecular level. The newly designed HTM, SF-MPA-MCz, features enhanced thermal stability, precise energy level alignment, superior film morphology, and optimized interfacial properties when compared to Spiro-OMeTAD, which contribute to a remarkable power conversion efficiency (PCE) of 24.53% for PSCs employing SF-MPA-MCz with substantially improved thermal stability and operational stability. Note that the optimal concentration for SF-MPA-MCz solution is only 30 mg/ml, significantly lower than Spiro-OMeTAD (>70 mg/ml), which could remarkably reduce the cost especially for large-area processing in future commercialization. This work presents a promising avenue for the versatile design of multifunctional HTMs, offering a blueprint for achieving efficient and stable PSCs.

Haptic interactions between human and machines are essential for information acquisition and object manipulation. In virtual reality (VR) system, the haptic sensing device can gather information to construct virtual elements, while the haptic feedback part can transfer feedbacks to human with virtual tactile sensation. Therefore, exploring high-performance haptic sensing and feedback interface imparts closed-loop haptic interaction to VR system. This review summarizes state-of-the-art VR-related haptic sensing and feedback techniques based on the hardware parts. For the haptic sensor, we focus on mechanism scope (piezoresistive, capacitive, piezoelectric, and triboelectric) and introduce force sensor, gesture translation, and touch identification in the functional view. In terms of the haptic feedbacks, methodologies including mechanical, electrical, and elastic actuators are surveyed. In addition, the interactive application of virtual control, immersive entertainment, and medical rehabilitation is also summarized. The challenges of virtual haptic interactions are given including the accuracy, durability, and technical conflicts of the sensing devices, bottlenecks of various feedbacks, as well as the closed-loop interaction system. Besides, the prospects are outlined in artificial intelligence of things, wise information technology of medicine, and multimedia VR areas.