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Sea ice regulates heat exchange between the ocean and atmosphere in Earth’s polar regions. The thermal conductivity of sea ice governs this exchange, and is a key parameter in climate modelling. However, it is challenging to measure and predict due to its sensitive dependence on temperature, salinity and brine microstructure. Moreover, as temperature increases, sea ice becomes permeable, and fluid can flow through the porous microstructure. While models for thermal diffusion through sea ice have been obtained, advective contributions to transport have not been considered theoretically. Here, we homogenize a multiscale advection–diffusion equation that models thermal transport through porous sea ice when fluid flow is present. We consider two-dimensional models of convective flow and use an integral representation to derive bounds on the thermal conductivity as a function of the Péclet number. These bounds guarantee enhancement in the thermal conductivity due to the added flow. Further, we relate the Péclet number to temperature, making these bounds useful for global climate models. Our analytic approach offers a mathematical theory which can not only improve predictions of atmosphere–ice–ocean heat exchanges in climate models, but can provide a theoretical framework for a range of problems involving advection–diffusion processes in various fields of application. 

Automatic video analysis tools are an indispensable component in imaging applications. Object detection, the first and the most important step for automatic video analysis, is implemented in many embedded cameras. The accuracy of object detection relies on the quality of images that are processed. This paper proposes a new image quality model for predicting the performance of object detection on embedded cameras. A video data set is constructed that considers different factors for quality degradation in the imaging process, such as reduced resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures. 

Although many advanced biosensing techniques have been proposed for cytokine profiling, there are no clinically available methods that integrate high-resolution immune cell monitoring and in situ multiplexed cytokine detection together in a biomimetic tissue microenvironment. The primary challenge arises due to the lack of suitable label-free sensing techniques and difficulty for sensor integration. In this work, we demonstrated a novel integration of a localized-surface plasmon resonance (LSPR)-based biosensor with a biomimetic microfluidic ‘adipose-tissue-on-chip’ platform for an in situ label-free, high-throughput and multiplexed cytokine secretion analysis of obese adipose tissue. Using our established adipose-tissue-on-chip platform, we were able to monitor the adipose tissue initiation, differentiation, and maturation and simulate the hallmark formation of crown-like structures (CLSs) during pro-inflammatory stimulation. With integrated antibody-conjugated LSPR barcode sensor arrays, our platform enables simultaneous multiplexed measurements of pro-inflammatory (IL-6 and TNF-α) and anti-inflammatory (IL-10 and IL-4) cytokines secreted by the adipocytes and macrophages. As a result, our adipose-tissue-on-chip platform is capable of identifying stage-specific cytokine secretion profiles from a complex milieu during obesity progression, highlighting its potential as a high-throughput preclinical readout for personalized obesity treatment strategies. 

Abstract Rapid and accurate immune monitoring plays a decisive role in effectively treating immune‐related diseases especially at point‐of‐care, where an immediate decision on treatment is needed upon precise determination of the patient immune status. Derived from the emerging clinical demands, there is an urgent need for a cytokine immunoassay that offers unprecedented sensor performance with high sensitivity, throughput, and multiplexing capability, as well as short turnaround time at low system complexity, manufacturability, and scalability. In this paper, a label‐free, high throughput cytokine immunoassay based on a magnet patterned Fe₃O₄/Au core–shell nanoparticle (FACSNP) sensing array is developed. By exploiting the unique superparamagnetic and plasmonic properties of the core–shell nanomaterials, a facile microarray patterning technique is established that allows the fabrication of a uniform, self‐assembled microarray on a large surface area with remarkable tunability and scalability. The sensing performance of the FACSNP microarray is validated by real‐time detection of four cytokines in complex biological samples, showing high sensitivity (≈20 pg mL⁻¹), selectivity and throughput with excellent statistical accuracy. The developed immunoassay is successfully applied for rapid determination of the functional immunophenotype of leukemia tumor‐associated macrophages, manifesting its potential clinical applications for real‐time immune monitoring, early cancer detection, and therapeutic drug stratification toward personalized medicine.