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A portable electrochemical aptasensor integrated with machine learning was developed for rapid and on-site detection of Staphylococcus aureus (S. aureus) in food and beverage samples. The aptasensor was fabricated using screen-printed carbon electrodes (SPCEs) modified with gold nanoparticles (AuNPs) and functionalized with an Iron-regulated Surface Determinant Protein A (IsdA)-specific aptamer for the detection of S. aureus. Approximately 2,000 cyclic voltammetry (CV) data points were collected for six different food and beverage matrices spiked with varying concentrations of S. aureus (1, 10, 500, and 1000 colony-forming unit (CFU)/mL). Each CV scan was repeated 10 times, linearly averaged, and baseline corrected before model input. Noise filtering and normalization were performed to ensure consistent feature representation across training and testing datasets. Machine learning models, including Convolutional Neural Networks (CNNs) and Transformer architectures, were applied to classify the samples. The CNN model demonstrated superior performance, with a test loss of 0.0402 and a test accuracy of 99.21%. In contrast, the Transformer model achieved a test loss of 0.2014 and an accuracy of 94.21%. To enhance usability, an Android application was developed using the Network Enabled Technologies (NET) framework, enabling real-time inference of bacterial concentration directly from CV data on mobile devices (e.g. smartphones). This system demonstrates potential for a rapid, accurate, and scalable solution for real-world food safety monitoring. 

ABSTRACT In metazoans, autophagosomes fuse with late endosomes (LEs)/multivesicular bodies (MVBs) to form a hybrid organelle known as an amphisome. Subsequently upon fusion with lysosomes the contents of amphisomes are degraded. While the formation of metazoan amphisomes has been well established, it has remained an open question whether amphisomes form and deliver their cargo to the central vacuole for degradation in plant cells. In this mini review, we provide an update on recent discoveries in the field of plant autophagy that demonstrate the formation of amphisome-like organelles that are generated through several distinct autophagosome/MVB fusion pathways. 

Treadmill running is a common workout for individuals across fitness levels. In this paper, we propose mm-RunAssist, a first-of-its-kind mmWave-based system that enhances treadmill workouts by monitoring respiration waveforms, running rhythm (i.e., coordination between breathing and strides), and detecting fall-off events. Extracting respiration from moving subjects using RF signals is challenging due to dominant motion artifacts. While prior deep learning efforts use adversarial or contrastive learning to mitigate such artifacts, they have been evaluated primarily under low-intensity activities like walking. To address this gap, mm-RunAssist introduces a Dual-task Variational U-Net that shares latent representations between respiration and upper-body movement tracking. This dual-task setup, guided by belt and depth sensors during training, improves reconstruction under intense body motion. Our system not only recovers fine-grained respiratory patterns during running but also supports cadence analysis through arm swing tracking. Extensive experiments with three state-of-the-art baselines under various conditions demonstrate mm-RunAssist's robustness and accuracy in treadmill running scenarios. Results show that mm-RunAssist advances RF sensing by effectively extracting vital signs even during vigorous body movements, offering new capabilities for fitness monitoring and non-intrusive health assessment. 

CO₂capture from post-combustion flue gas originating from coal or natural gas power plants, or even from the ambient atmosphere, is a promising strategy to reduce the atmospheric CO₂concentration and achieve global decarbonization goals. However, the co-existence of water vapor in these sources presents a significant challenge, as water often competes with CO₂for adsorption sites, thereby diminishing the performance of adsorbent materials. Selectively capturing CO₂in the presence of moisture is a key goal, as there is a growing demand for materials capable of selectively adsorbing CO₂under humid conditions. Among these, metal–organic frameworks (MOFs), a class of porous, highly tunable materials, have attracted extensive interest for gas capture, storage, and separation applications. The numerous combinations of secondary building units and organic linkers offer abundant opportunities for designing systems with enhanced CO₂selectivity. Interestingly, some recent studies have demonstrated that interactions between water and CO₂within the confined pore space of MOFs can enhance CO₂uptake, flipping the traditionally detrimental role of moisture into a beneficial one. These findings introduce a new paradigm: water-enhanced CO₂capture in MOFs. In this review, we summarize these recent discoveries, highlighting examples of MOFs that exhibit enhanced CO₂adsorption under humid conditions compared to dry conditions. We discuss the underlying mechanisms, design strategies, and structural features that enable this behavior. Finally, we offer a brief perspective on future directions for MOF development in the context of water-enhanced CO₂capture.