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Enhancing water security through integrated decision-making and selective withdrawal for sustainable reservoir management
The pyruvate dehydrogenase complex in concert with the DNA/RNA-binding protein YBX1 regulates cell senescence and tumorigenesis
Expanding the Inositol Pyrophosphate Toolbox: Stereoselective Synthesis and Application of PP‐InsP <sub>4</sub> Isomers in Plant Signaling
Abstract Inositol pyrophosphates (PP‐InsPs) are highly phosphorylated signaling molecules that regulate diverse cellular processes, including phosphate homeostasis and energy metabolism across species. Despite extensive research on well‐characterized exhaustively phosphorylated PP‐InsPs, such as 5‐PP‐InsP 5 (5‐InsP 7 ) and 1,5‐(PP) 2 ‐InsP 4 (1,5‐InsP 8 ), the functional relevance of less abundant not fully phosphorylated isomers, remains largely unknown. In this study, we synthesized all unsymmetric 5‐PP‐InsP 4 isomers in enantiopure form and assigned their structures using 31 P‐NMR analysis in combination with a chiral solvating agent. Additionally, we developed 18 O‐labeled PP‐InsP 4 standards for mass spectrometry in combination with capillary electrophoresis (CE‐MS), enabling the assignment of PP‐InsP 4 in Arabidopsis thaliana under phosphate starvation. Our findings show that the previously detected, phosphate starvation‐induced root‐specific PP‐InsP 4 isomer does not match any 5‐PP‐InsP 4 isomer, contrary to previous suggestions, thus indicating an alternative phosphorylation pattern. Enzyme assays further demonstrate that Arabidopsis ITPK1 selectively phosphorylates [6‐OH]‐InsP 5 and [3‐OH]‐InsP 5 at the 5‐position, while other InsP 5 isomers remain unchanged. This suggests that an unidentified enzymatic activity is involved in the formation of the elusive root PP‐InsP 4 species. Our study provides a comprehensive framework for the synthesis, analysis, and functional investigation of PP‐InsP 4 , providing an entry point for future studies on their biochemical activity and their physiological roles.
An innovative plastic boat utilizing macroscopic openings for efficient oil spill cleanup on water
Nuclear basket proteins Nup2 and Mlp1 drive heat shock–induced 3D genome restructuring downstream of transcriptional activation
Three-Dimensional gait biomechanics in patients with mild knee osteoarthritis
The regulation of lipid A biosynthesis
Prolonging Exciton Diffusion Length via Modulating Aggregation Structures for Binary Organic Photovoltaics Approaching 20% Certified Efficiency
Abstract The performance of flexible all‐polymer organic photovoltaics (OPVs) constrained by low short‐circuit current density ( J SC ) and fill factor (FF), resulting in diminished power conversion efficiency (PCE) and compromised mechanical stability. Enhancing the exciton diffusion length ( L D ) is pivotal for improving device parameters, including PCE. However, the underlying mechanisms governing exciton diffusion dynamics, influenced by the aggregation structure of conjugated polymers, remain insufficiently understood. This study employs molecular dynamics simulations to calculate the interchain free energy distribution [Δ G (r)] and strategically modulates the aggregation behavior of the polymer donor PM6 by controlling its molecular weight (MW). Medium‐MW PM6 demonstrates optimized aggregation behavior, leading to extended L D and precisely tuned fluid mechanics, which facilitate the formation of a pseudo‐planar heterojunction (PPHJ) active layer. These advancements enable PPHJ‐based all‐polymer flexible devices to achieve a PCE of 18.01%, with notable improvements in J SC and FF, and retain 90.4% of their initial efficiency after 2000 bending cycles. Encouraged by these advantages, a record‐breaking efficiency of 20.0% (certified 19.68%) was achieved for eco‐friendly, printed OPVs (PM6//L8‐BO) with small‐area devices (0.0621 cm 2 ), whereas large‐area modules (23.60 cm 2 ) reached an efficiency of 15.60%.
Explainable self-supervised learning for medical image diagnosis based on DINO V2 model and semantic search
Abstract Medical images have become indispensable for decision-making and significantly affect treatment planning. However, increasing medical imaging has widened the gap between medical images and available radiologists, leading to delays and diagnosis errors. Recent studies highlight the potential of deep learning (DL) in medical image diagnosis. However, their reliance on labelled data limits their applicability in various clinical settings. As a result, recent studies explore the role of self-supervised learning to overcome these challenges. Our study aims to address these challenges by examining the performance of self-supervised learning (SSL) in diverse medical image datasets and comparing it with traditional pre-trained supervised learning models. Unlike prior SSL methods that focus solely on classification, our framework leverages DINOv2’s embeddings to enable semantic search in medical databases (via Qdrant), allowing clinicians to retrieve similar cases efficiently. This addresses a critical gap in clinical workflows where rapid case The results affirmed SSL’s ability, especially DINO v2, to overcome the challenge associated with labelling data and provide an accurate diagnosis superior to traditional SL. DINO V2 provides 100%, 99%, 99%, 100 and 95% for classification accuracy of Lung cancer, brain tumour, leukaemia and Eye Retina Disease datasets, respectively. While existing SSL models (e.g., BYOL, SimCLR) lack interpretability, we uniquely combine DINOv2 with ViT-CX, a causal explanation method tailored for transformers. This provides clinically actionable heatmaps, revealing how the model localizes tumors/cellular patternsa feature absent in prior SSL medical imaging studies Furthermore, our research explores the impact of semantic search in the medical images domain and how it can revolutionize the querying process and provide semantic results alongside SSL and the Qudra Net dataset utilized to save the embedding of the developed model after the training process. Cosine similarity measures the distance between the image query and stored information in the embedding using cosine similarity. Our study aims to enhance the efficiency and accuracy of medical image analysis, ultimately improving the decision-making process.
Structural basis of transglucosylation in dextran dextrinase, a homolog of anomer-inverting GH15 glucoside hydrolases
Enzyme‐Assisted Confined Synthesis of Metal Nanoparticles in Covalent Organic Frameworks for Efficient Enzyme‐Metal Cascade Catalysis
Abstract The integration of enzymatic and metal catalysis in cascade reactions offers a highly efficient approach for producing high‐value chemicals, such as chiral pharmaceuticals. However, overcoming the inherent incompatibility between metal and enzyme catalysts and optimizing their stability and activity to achieve effective synergy, remains a significant challenge. Here, we present an enzyme‐assisted, confined synthesis of metal nanoparticles (MNPs) within the nanochannels of covalent organic frameworks (COFs), to construct efficient enzyme‐metal hybrid catalysts for cascade reactions. The COF nanochannels stabilize the enzyme during MNP formation and the catalytic process, and synergize with the enzyme to regulate the size, dispersion, and electronic state of the MNPs through surface amino acid residues, realizing the co‐encapsulation and dual‐optimization of both components. Using Candida antarctica lipase B (CALB) and Pd nanoparticles as a model system, Pd/CALB@COF exhibits an 8.2‐fold higher yield in the kinetic resolution (KR) of racemic 1‐phenylethylamine (1‐PEA), and a 2.7‐fold enhancement in racemization conversion, compared to counterparts without COF. Their synergy in dynamic kinetic resolution (DKR) delivers 91% yield, >98% enantiomeric excess (e.e.) value, and recyclability, with applicability to various chiral amines. This strategy has been validated across different metal‐enzyme systems, establishing a versatile platform for designing efficient enzyme‐metal cascade systems.