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The rapid expansion of edge devices and Internet of Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusion based GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GOn well-suited for real-time communications and downstream applications. 

As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient (aphy), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA’s hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving aphy from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of aphy based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for aphy prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating aphy as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in aphy within an optically complex estuarine environment. 

The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusionbased GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing highquality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO n well-suited for real-time communications and downstream applications. 

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE. 

Abstract In search of effective therapeutics for breast cancers, establishing physiologically relevant in vitro models is of great benefit to facilitate the clinical translation. Despite extensive progresses, it remains to develop the tumor models maximally recapturing the key pathophysiological attributes of their native counterparts. Therefore, the current study aimed to develop a microsphere‐enabled modular approach toward the formation of in vitro breast tumor models with the capability of incorporating various selected cells while retaining spatial organization. Poly (lactic‐co‐glycolic acid) microspheres (150‐200 mm) with tailorable pore size and surface topography are fabricated and used as carriers to respectively lade with breast tumor‐associated cells. Culture of cell‐laden microspheres assembled within a customized microfluidic chamber allowed to form 3D tumor models with spatially controlled cell distribution. The introduction of endothelial cell‐laden microspheres into cancer‐cell laden microspheres at different ratios would induce angiogenesis within the culture to yield vascularized tumor. Evaluation of anticancer drugs such as doxorubicin and Cediranib on the tumor models do demonstrate corresponding physiological responses. Clearly, with the ability to modulate microsphere morphology, cell composition and spatial distribution, microsphere‐enabled 3D tumor tissue formation offers a high flexibility to satisfy the needs for pathophysiological study, anticancer drug screening or design of personalized treatment. 

Abstract Ubiquinone mimics known as quinone outside inhibitors (QoIs) are one of the most prominent fungicides used to protect crops in the agricultural industry. Due to chemotype similarities with known QoIs, peniciaculin A, a triaryl natural product, was proposed to exhibit similar broad spectrum antifungal activity against phytopathogens. Instability of the tertiary alcohol and phenol motif, however, prompted exploration of the antifungal properties of simplified analogues to probe possible overlap in mechanism of action between the natural product and QoIs. Peniciaculin A inspired analogues mimicking known QoI scaffolds displayed broad spectrum antifungal activity while those containing scaffolds dissimilar to QoIs possessed negligible bioactivity. These activity profiles suggest peniciaculin A is likely acting as a QoI. 

Abstract Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height–diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.