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Highly Selective Intermolecular Cross-Coupling Reactions via a Persistent Radical Effect on Ag(111)
Air-dried high-strength black MXene aerogel for spatiotemporal infrared information management
Longitudinal assessment of renal allograft function in donors and pediatric recipients by arterial spin labeling MRI perfusion quantification
Stiffening Organic Crystals through Polymerization Using Visible Light
Optical super-resolution histology of formalin-fixed paraffin-embedded tissue samples: challenges and opportunities
Abstract This review covers the advancements of optical super-resolution microscopy (SRM) on formalin-fixed paraffin-embedded (FFPE) histological samples. We cover the implementation of various SRM strategies in histology, including wide field methods such as structured illumination microscopy, single-molecule localization microscopy and fluorescence fluctuations-based SRM, as well as the point-scanning stimulated emission depletion microscopy. We also cover the recent developments in FFPE-based expansion microscopy. The review highlights the advantages and challenges of these SRM methods in FFPE histology, and provides insights into emerging optical and computational techniques that can potentially open avenues for understanding disease mechanisms, tailoring treatments, and advancing personalized medicine across disciplines. This review article is intended for a broad audience, including histopathologists, biologists, physiologists, and physicists.
Interlimb training improves motor function in partial-hand but not necessarily transradial simulated prosthesis use
Deep-Red to Near-Infrared Light-Driven Radical Generation from Organoboron Compounds via Ligand-Induced Direct Excitation Catalysis
A plastid carbohydrate carrier mediates ribose recycling from nucleotide catabolism and glucose export from starch degradation
Abstract In plants, nucleotide degradation releases ribose in the cytosol. An unidentified transporter then brings the ribose into the plastids for phosphorylation. This process of ribose recycling is particularly prominent in root nodules of soybean ( Glycine max ) and common bean ( Phaseolus vulgaris ) during symbiotic nitrogen fixation. In this biological context, we identified a plastid ribose transporter, which is an ortholog of the putative plastid glucose transporter (pGlcT) of Arabidopsis thaliana . We show that Arabidopsis mutants of At-pGlcT , but not of the related At - pGlcT2 , accumulate ribose and fructose constitutively, whereas glucose accumulates only at night. Uridine feeding experiments leading to cytosolic ribose release indicated that At -pGlcT transports ribose from the cytosol into the plastids. Uptake assays with complemented Escherichia coli sugar transport mutants directly demonstrated that At -pGlcT transports ribose, glucose, and fructose. Ribose and fructose accumulation were also observed in CRISPR-induced bean nodule mutants of Pv - pGlcT . Additionally, our data show that ribose recycling is important for producing allantoin, a nitrogen fixation product used for nitrogen export from nodules to shoots. We conclude that pGlcT is a plastid facilitator for the import of ribose from nucleotide catabolism, for the export of glucose from nocturnal starch breakdown, and for cytosol-plastid fructose exchange in vivo.
Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration
Abstract Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.
Dinickela-bicyclo[4,4,0]-decadiene Complex with a Linear Heterometallic Mg–Ni–Ni–Mg Chain
Trypanosoma brucei cattle infections contain cryptic transmission-adapted bloodstream forms at low parasitaemia
Abstract Tsetse-transmitted Trypanosoma parasites infect a wide host range and cause Human African Trypanosomiasis and Animal African Trypanosomosis. The dominant hosts of Trypanosoma brucei sensu lato are non-human mammals, including agriculturally important cattle. In rodent infections, T. brucei transitions from proliferative slender to tsetse-transmissible stumpy forms at high parasitaemia in a density-dependent quorum sensing-type process. However, chronic bovine infections are characterised by markedly lower blood parasitaemia levels; mostly substantially below the density assumed to trigger slender-to-stumpy differentiation. This challenges the current (rodent-based) assumptions and quantitative parameter estimations around stumpy form generation in the bloodstream. By combining scRNA-seq and microscopy we observe mixed populations of parasites with both slender- and stumpy-associated transcriptomes in cattle blood. The appearance of the latter coincides with fewer detectably dividing parasites and parasites with shortened flagellum indicative of differentiation, despite the absence of stumpy morphology or developmental marker protein expression. Comparisons with murine infections and in vitro culture demonstrates conserved transcriptomic signatures for both slender- and stumpy-like forms, as well as host specific differences, including a subpopulation of slender-like parasites upregulating pyruvate metabolism and TCA cycle transcripts in cattle samples. These similarities and differences are key to understanding parasite development and transmission in its natural host.
Thyme oil mitigates cadmium-induced oxidative and genotoxic stress in Vicia faba root meristem cells under in vitro conditions
Borane/Transition Metal–Catalyzed Allenylic and Allylic Alkylation of Unactivated 2-Alkylbenzoxazoles
LDBT instead of DBTL: combining machine learning and rapid cell-free testing
Correction: High-efficiency dual-band switched beam antenna with back lobe suppression using parasitic elements and patch etching for 5G
A 2D Double Perovskite Based on the Chiral Cystaminium Cation Exhibiting Multiple Switches in Quadratic Nonlinear Optical Response
Shifting dominant periods in extreme climate impacts under global warming
Abstract Spatio-temporal patterns of extreme climate events have been extensively studied, yet two questions remain underexplored: Do such events occur regularly, and how do regularity patterns change under global warming? We address these questions by investigating dominant periods in crop failure, heatwave, and wildfire data. Here, we show that under pre-industrial conditions dominant periods emerge in 28% of cropland exposed to crop failure and 10% of wildfire-affected areas, likely related to climatic oscillations such as the El Niño-Southern Oscillation, while heatwaves occur irregularly. The number of dominant periods increases by 2–13% during the transition from the pre-industrial era to the anthropocene. In the anthropocene, the occurrence of extreme events shifts towards monotonic growth, replacing previous natural regularity patterns. Linearly de-trended projections reveal an additional shift towards smaller dominant periods due to climate change. These shifts in regularity are crucial for adaptation planning, and our method offers an additional approach for studying extreme events.