Search for: All records

Flowers are critical for successful reproduction and have been a major axis of diversification among angiosperms. As the frequency and severity of droughts are increasing globally, maintaining water balance of flowers is crucial for food security and other ecosystem services that rely on flowering. Yet remarkably little is known about the hydraulic strategies of flowers. We characterized hydraulic strategies of leaves and flowers of ten species by combining anatomical observations using light and scanning electron microscopy with measurements of hydraulic physiology (minimum diffusive conductance ( g min ) and pressure-volume (PV) curves parameters). We predicted that flowers would exhibit higher g min and higher hydraulic capacitance than leaves, which would be associated with differences in intervessel pit traits because of their different hydraulic strategies. We found that, compared to leaves, flowers exhibited: 1) higher g min , which was associated with higher hydraulic capacitance ( C T ); 2) lower variation in intervessel pit traits and differences in pit membrane area and pit aperture shape; and 3) independent coordination between intervessel pit traits and other anatomical and physiological traits; 4) independent evolution of most traits in flowers and leaves, resulting in 5) large differences in the regions of multivariate trait space occupied by flowers and leaves. Furthermore, across organs intervessel pit trait variation was orthogonal to variation in other anatomical and physiological traits, suggesting that pit traits represent an independent axis of variation that have as yet been unquantified in flowers. These results suggest that flowers, employ a drought-avoidant strategy of maintaining high capacitance that compensates for their higher g min to prevent excessive declines in water potentials. This drought-avoidant strategy may have relaxed selection on intervessel pit traits and allowed them to vary independently from other anatomical and physiological traits. Furthermore, the independent evolution of floral and foliar anatomical and physiological traits highlights their modular development despite being borne from the same apical meristem. 

Resistance to carbapenem β-lactams presents major clinical and economical challenges for the treatment of pathogen infections. The fast hydrolysis of carbapenems by carbapenemase-producing bacterial strains enables the effective deactivation of carbapenem antibiotics. In this study, we aim to unravel the structural features that distinguish the notable deacylation activity of carbapenemases. The deacylation reactions between imipenem (IPM) and the KPC-2 class A serine-based β-lactamases (ASβLs) are modeled with combined quantum mechanical/molecular mechanical (QM/MM) minimum energy pathway (MEP) calculations and interpretable machine-learning (ML) methods. We first applied a dual-level computational protocol to achieve fast sampling of QM/MM MEPs. A tree-based ensemble ML model was employed to learn the MEP activation barriers from the conformational features of the KPC-2/IPM active site. The barrier-predicting model was then unboxed using the Shapley additive explanation (SHAP) importance attribution methods to derive mechanistic insights, which were also verified by additional QM/MM wavefunction analysis. Essentially, we show that potential hydrogen bonding interactions of the general base and the tautomerization states of the carbapenem pyrroline ring could concertedly regulate the activation barrier of KPC-2/IPM deacylation. Nonetheless, we demonstrate the efficacy of interpretable ML to assist the analysis of QM/MM simulation data for robust extraction of human-interpretable mechanistic insights. 

Frequency modulated continuous wave laser ranging (FMCW LiDAR) enables distance mapping with simultaneous position and velocity information, is immune to stray light, can achieve long range, operate in the eye-safe region of 1550 nm and achieve high sensitivity. Despite its advantages, it is compounded by the simultaneous requirement of both narrow linewidth low noise lasers that can be precisely chirped. While integrated silicon-based lasers, compatible with wafer scale manufacturing in large volumes at low cost, have experienced major advances and are now employed on a commercial scale in data centers, and impressive progress has led to integrated lasers with (ultra) narrow sub-100 Hz-level intrinsic linewidth based on optical feedback from photonic circuits, these lasers presently lack fast nonthermal tuning, i.e. frequency agility as required for coherent ranging. Here, we demonstrate a hybrid photonic integrated laser that exhibits very narrow intrinsic linewidth of 25 Hz while offering linear, hysteresis-free, and mode-hop-free-tuning beyond 1 GHz with up to megahertz actuation bandwidth constituting 1.6 × 10¹⁵Hz/s tuning speed. Our approach uses foundry-based technologies - ultralow-loss (1 dB/m) Si₃N₄photonic microresonators, combined with aluminium nitride (AlN) or lead zirconium titanate (PZT) microelectromechanical systems (MEMS) based stress-optic actuation. Electrically driven low-phase-noise lasing is attained by self-injection locking of an Indium Phosphide (InP) laser chip and only limited by fundamental thermo-refractive noise at mid-range offsets. By utilizing difference-drive and apodization of the photonic chip to suppress mechanical vibrations of the chip, a flat actuation response up to 10 MHz is achieved. We leverage this capability to demonstrate a compact coherent LiDAR engine that can generate up to 800 kHz FMCW triangular optical chirp signals, requiring neither any active linearization nor predistortion compensation, and perform a 10 m optical ranging experiment, with a resolution of 12.5 cm. Our results constitute a photonic integrated laser system for scenarios where high compactness, fast frequency actuation, and high spectral purity are required.

 

Finding curable therapies for neurodegenerative disease (ND) is still a worldwide medical and clinical challenge. Recently, investigations have been made into the development of novel therapeutic techniques, and examples include the remote stimulation of nanocarriers to deliver neuroprotective drugs, genes, growth factors, and antibodies using a magnetic field and/or low-power lights. Among these potential nanocarriers, magneto-plasmonic nanoparticles possess obvious advantages, such as the functional restoration of ND models, due to their unique nanostructure and physiochemical properties. In this review, we provide an overview of the latest advances in magneto-plasmonic nanoparticles, and the associated therapeutic approaches to repair and restore brain tissues. We have reviewed their potential as smart nanocarriers, including their unique responsivity under remote magnetic and light stimulation for the controlled and sustained drug delivery for reversing neurodegenerations, as well as the utilization of brain organoids in studying the interaction between NPs and neuronal tissue. This review aims to provide a comprehensive summary of the current progress, opportunities, and challenges of using these smart nanocarriers for programmable therapeutics to treat ND, and predict the mechanism and future directions.