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In network-constrained environments, distributed multi-agent systems—such as UGVs and UAVs—must communicate effectively to support computationally demanding scene perception tasks like semantic and instance segmentation. These tasks are challenging because they require high accuracy even when using low-quality images, and the network limitations restrict the amount of data that can be transmitted between agents. To overcome the above challenges, we propose TAVIC-DAS to perform a task and channel-aware variable-rate image compression to enable distributed task execution and minimize communication latency by transmitting compressed images. TAVIC-DAS proposes a novel image compression and decompression framework (distributed across agents) that integrates channel parameters such as RSSI and data rate into a task-specific "semantic segmentation" DNN to generate masks representing the object of interest in the scene (ROI maps) by determining a high pixel density needed to represent objects of interest and low density to represents surrounding pixels within an image. Additionally, to accommodate agents with limited computational resources, TAVIC-DAS incorporates resource-aware model quantization. We evaluated TAVIC-DAS on platforms such as ROSMaster X3 and Jetson Xavier, which communicated using a low-frequency proprietary Doodle radio operating at 915 MHz. The experimental results show that TAVIC-DAS achieves approximately 7.62% higher PSNR and is about 6.39% more resource efficient compared to state-of-the-art techniques. 

Robust communication is vital for multi-agent robotic systems involving heterogeneous agents like Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) operating in dynamic and contested environments. These agents often communicate to collaboratively execute critical tasks for perception awareness and are faced with different communication challenges: (a) The disparity in velocity between these agents results in rapidly changing distances, in turn affecting the physical channel parameters such as Received Signal Strength Indicator (RSSI), data rate (applicable for certain networks) and most importantly "reliable data transfer", (b) As these devices work in outdoor and network-deprived environments, they tend to use proprietary network technologies with low frequencies to communicate long range, which tremendously reduces the available bandwidth. This poses a challenge when sending large amounts of data for time-critical applications. To mitigate the above challenges, we propose DACC-Comm, an adaptive flow control and compression sensing framework to dynamically adjust the receiver window size and selectively sample the image pixels based on various network parameters such as latency, data rate, RSSI, and physiological factors such as the variation in movement speed between devices. DACC-Comm employs state-of-the-art DNN (TABNET) to optimize the payload and reduce the retransmissions in the network, in turn maintaining low latency. The multi-head transformer-based prediction model takes the network parameters and physiological factors as input and outputs (a) an optimal receiver window size for TCP, determining how many bytes can be sent without the sender waiting for an acknowledgment (ACK) from the receiver, (b) a compression ratio to sample a subset of pixels from an image. We propose a novel sampling strategy to select the image pixels, which are then encoded using a feature extractor. To optimize the amount of data sent across the network, the extracted feature is further quantized to INT8 with the help of post-training quantization. We evaluate DACC-Comm on an experimental testbed comprising Jackal and ROSMaster2 UGV devices that communicate image features using a proprietary radio (Doodle) in 915-MHz frequency. We demonstrate that DACC-Comm improves the retransmission rate by ≈17% and reduces the overall latency by ≈12%. The novel compression sensing strategy reduces the overall payload by ≈56%. 

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C–H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis. 

Artificial metalloenzymes (ArMs) can combine the unique features of both metal complexes and enzymes by incorporating a cofactor within a protein scaffold. Herein, we describe a panel of ArMs constructed by covalently linking Ir( iii ) polypyridyl complexes into a prolyl oligopeptidase scaffold. Spectroscopic methods were used to examine how properties of the resulting ArMs are influenced by structural variation of the cyclometalated ligands and the protein scaffold. Visible light photocatalysis by these hybrid catalysts was also examined, leading to the finding that they catalyze inter/intra-molecular [2 + 2] photocycloaddition in aqueous solution. Low but reproducible enantioselectivity was observed using a cofactor that undergoes partial kinetic resolution upon bioconjugation within the ArM active site, showing the importance of scaffold/cofactor interactions for enabling selective ArM photocatalysis. Further evidence of the importance of cofactor/scaffold interactions was provided by analyzing native POP peptidase catalysis by the ArMs. Together, these studies show how Ir( iii )-based ArMs constitute a promising starting point for ongoing studies to control the stereoselectivity of EnT reactions by engineering substrate binding/activation motifs in POP. 

The corrinoid protein MtaC, which is natively involved in methyl transferase catalysis, catalyzes N -alkylation of aniline using ethyl diazoacetate. Our results show how the native preference of B 12 scaffolds for radical versus polar chemistry translates to non-native catalysis, which could guide selection of B 12 -dependent proteins for biocatalysis. MtaC also has high thermal stability and organic solvent tolerance, remaining folded even in pure methanol. 

Abstract Despite the unique reactivity of vitamin B₁₂and its derivatives, B₁₂‐dependent enzymes remain underutilized in biocatalysis. In this study, we repurposed the B₁₂‐dependent transcription factor CarH to enable non‐native radical cyclization reactions. An engineered variant of this enzyme, CarH*, catalyzes the formation γ‐ and δ‐lactams through either redox‐neutral or reductive ring closure with marked enhancement of reactivity and selectivity relative to the free B₁₂cofactor. CarH* also catalyzes an unusual spirocyclization by dearomatization of pendant arenes to produce bicyclic 1,3‐diene products instead of 1,4‐dienes provided by existing methods. These results and associated mechanistic studies highlight the importance of protein scaffolds for controlling the reactivity of B₁₂and expanding the synthetic utility of B₁₂‐dependent enzymes. 

Visible light photocatalysis enables a broad range of organic transformations that proceed via single electron or energy transfer. Metal polypyridyl complexes are among the most commonly employed visible light photocatalysts. The photophysical properties of these complexes have been extensively studied and can be tuned by modifying the substituents on the pyridine ligands. On the other hand, ligand modifications that enable substrate binding to control reaction selectivity remain rare. Given the exquisite control that enzymes exert over electron and energy transfer processes in nature, we envisioned that artificial metalloenzymes (ArMs) created by incorporating Ru( ii ) polypyridyl complexes into a suitable protein scaffold could provide a means to control photocatalyst properties. This study describes approaches to create covalent and non-covalent ArMs from a variety of Ru( ii ) polypyridyl cofactors and a prolyl oligopeptidase scaffold. A panel of ArMs with enhanced photophysical properties were engineered, and the nature of the scaffold/cofactor interactions in these systems was investigated. These ArMs provided higher yields and rates than Ru(Bpy) 3 2+ for the reductive cyclization of dienones and the [2 + 2] photocycloaddition between C -cinnamoyl imidazole and 4-methoxystyrene, suggesting that protein scaffolds could provide a means to improve the efficiency of visible light photocatalysts. 

Abstract Halocyclization of alkenes is a powerful bond-forming tool in synthetic organic chemistry and a key step in natural product biosynthesis, but catalyzing halocyclization with high enantioselectivity remains a challenging task. Identifying suitable enzymes that catalyze enantioselective halocyclization of simple olefins would therefore have significant synthetic value. Flavin-dependent halogenases (FDHs) catalyze halogenation of arene and enol(ate) substrates. Herein, we reveal that FDHs engineered to catalyze site-selective aromatic halogenation also catalyze non-native bromolactonization of olefins with high enantioselectivity and near-native catalytic proficiency. Highly selective halocyclization is achieved by characterizing and mitigating the release of HOBr from the FDH active site using a combination of reaction optimization and protein engineering. The structural origins of improvements imparted by mutations responsible for the emergence of halocyclase activity are discussed. This expansion of FDH catalytic activity presages the development of a wide range of biocatalytic halogenation reactions.