A microscale soft ionic power source modulates neuronal network activity.

Bio-integrated devices need power sources to operate1,2. Despite widely used technologies that can provide power to large-scale targets, such as wired energy supplies from batteries or wireless energy transduction3, a need to efficiently stimulate cells and tissues on the microscale is still pressing. The ideal miniaturized power source should be biocompatible, mechanically flexible and able to generate an ionic current for biological stimulation, instead of using electron flow as in conventional electronic devices4-6. One approach is to use soft power sources inspired by the electrical eel7,8; however, power sources that combine the required capabilities have not yet been produced, because it is challenging to obtain miniaturized units that both conserve contained energy before usage and are easily triggered to produce an energy output. Here we develop a miniaturized soft power source by depositing lipid-supported networks of nanolitre hydrogel droplets that use internal ion gradients to generate energy. Compared to the original eel-inspired design7, our approach can shrink the volume of a power unit by more than 105-fold and it can store energy for longer than 24 h, enabling operation on-demand with a 680-fold greater power density of about 1,300 W m-3. Our droplet device can serve as a biocompatible and biological ionic current source to modulate neuronal network activity in three-dimensional neural microtissues and in ex vivo mouse brain slices. Ultimately, our soft microscale ionotronic device might be integrated into living organisms.

Differential modulation of C. elegans motor behavior by NALCN and two-pore domain potassium channels.

Two-pore domain potassium channels (K2P) are a large family of "background" channels that allow outward "leak" of potassium ions. The NALCN/UNC80/UNC79 complex is a non-selective channel that allows inward flow of sodium and other cations. It is unclear how K2Ps and NALCN differentially modulate animal behavior. Here, we found that loss of function (lf) in the K2P gene twk-40 suppressed the reduced body curvatures of C. elegans NALCN(lf) mutants. twk-40(lf) caused a deep body curvature and extended backward locomotion, and these phenotypes appeared to be associated with neuron-specific expression of twk-40 and distinct twk-40 transcript isoforms. To survey the functions of other less studied K2P channels, we examined loss-of-function mutants of 13 additional twk genes expressed in the motor circuit and detected defective body curvature and/or locomotion in mutants of twk-2, twk-17, twk-30, twk-48, unc-58, and the previously reported twk-7. We generated presumptive gain-of-function (gf) mutations in twk-40, twk-2, twk-7, and unc-58 and found that they caused paralysis. Further analyses detected variable genetic interactions between twk-40 and other twk genes, an interdependence between twk-40 and twk-2, and opposite behavioral effects between NALCN and twk-2, twk-7, or unc-58. Finally, we found that the hydrophobicity/hydrophilicity property of TWK-40 residue 159 could affect the channel activity. Together, our study identified twk-40 as a novel modulator of the motor behavior, uncovered potential behavioral effects of five other K2P genes and suggests that NALCN and some K2Ps can oppositely affect C. elegans behavior.

Modeling for COVID-19 college reopening decisions: Cornell, a case study.

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

The Role of Multiple Birth and Birth Complications in the Association Between Assisted Reproductive Technology Conception and Autism Diagnosis.

In recent decades, the use of assisted reproductive technology (ART) has increased rapidly. To assess the relationship between ART and autism diagnosis, we linked California birth records from 2000 through 2016 with contemporaneous records from the National ART Surveillance System (NASS) and autism caseload records from California's Department of Developmental Services from 2000 through November 2019. All 95,149 birth records that were successfully linked to a NASS record, indicating an ART birth, were matched 1:1 using propensity scores to non-ART births. We calculated the hazard risk ratio (HRR) for autism diagnosis and the proportions of the relationship between ART conception and autism diagnosis mediated by multiple birth pregnancy and related birth complications. The HRR for autism diagnosis following ART compared with non-ART conception is 1.26 (95% CI, 1.17-1.35). Multiple birth, preterm birth, and Cesarean delivery jointly mediate 77.9% of the relationship between ART conception and autism diagnosis. Thus, increased use of single embryo transfer in the United States to reduce multiple births and related birth complications may be a strategy to address the risk of autism diagnosis among ART-conceived children.

Preclinical Study on a Novel Fluoroderivative of Dabigatran Etexilate in Animal Models.

ABSTRACT: Here, the fluorinated derivative, R1, was synthesized from the fluorinated dabigatran derivative (R0). The in vivo pharmacokinetic characteristics of orally administered R1, R0 injection, and dabigatran etexilate in rats were compared. Safety evaluation results showed no significant changes in the QRS wave or PR and QT intervals in rat lead II electrocardiograms. The possible toxicity of R1 was studied using the limit test method, and no obvious toxicity occurred in mice after the acute oral administration of R1. R1 inhibited thrombin-induced platelet aggregation in a dose-dependent manner, had an inhibitory effect on platelet aggregation induced by arachidonic acid and adenosine diphosphate, could significantly prolong prothrombin time and activated partial thromboplastin time, and increased fibrinogen levels. R1 is the optimal candidate compound from among more than 100 candidate compounds designed and synthesized by our research group. It was first selected through preliminary in vitro anticoagulant activity screening and further through in vivo mouse activity testing. A systematic pharmacodynamic study showed that R1 was superior to the raw material drug dabigatran ester; particularly, the absolute bioavailability of R1 increased by 206%, and this can overcome the low bioavailability defect associated with the marketed drug dabigatran ester. Another safety assessment of R1 indicated that there were no risks of acute poisoning in rats and cardiac toxicity in mice or rats. Therefore, R1 can be considered a new candidate anticoagulant compound with great potential and significance for further clinical research.

Photocatalytic Hydrogen and Oxygen Evolution Performed by a Long-Afterglow Material.

A micron-sized long-afterglow material, Sr2MgSi2O7:Eu,Ce, was utilized to conduct the hydrogen evolution reaction and oxygen evolution reaction, two half-reactions of water splitting, in the presence of sacrificial agents under both light and dark conditions for the first time. The as-synthesized Sr2MgSi2O7:Eu,Ce exhibited higher photocatalytic activity compared to that of the referenced Sr2MgSi2O7:Eu and Sr2MgSi2O7:Ce samples. Herein, in addition to benefiting from the long photogenerated carrier lifetime of long-afterglow materials, the higher photocatalytic activity was attributed to the conjugated electronic structure between Eu and Ce ions. This structure facilitates charge and energy transfer between them, leading to an enhanced photocatalytic efficiency. This research provides a new strategy for designing efficient long-afterglow material photocatalysts through the construction of conjugated electronic structures.

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review.

BACKGROUND AND OBJECTIVES: Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients. METHODS: Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed. RESULTS: Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models. CONCLUSION: A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

"All in one" nanoprobe Au-TTF-1 for target FL/CT bioimaging, machine learning technology and imaging-guided photothermal therapy against lung adenocarcinoma.

The accurate preoperative diagnosis and tracking of lung adenocarcinoma is hindered by non-targeting and diffusion of dyes used for marking tumors. Hence, there is an urgent need to develop a practical nanoprobe for tracing lung adenocarcinoma precisely even treating them noninvasively. Herein, Gold nanoclusters (AuNCs) conjugate with thyroid transcription factor-1 (TTF-1) antibody, then multifunctional nanoprobe Au-TTF-1 is designed and synthesized, which underscores the paramount importance of advancing the machine learning diagnosis and bioimaging-guided treatment of lung adenocarcinoma. Bright fluorescence (FL) and strong CT signal of Au-TTF-1 set the stage for tracking. Furthermore, the high specificity of TTF-1 antibody facilitates selective targeting of lung adenocarcinoma cells as compared to common lung epithelial cells, so machine learning software Lung adenocarcinoma auxiliary detection system was designed, which combined with Au-TTF-1 to assist the intelligent recognition of lung adenocarcinoma jointly. Besides, Au-TTF-1 not only contributes to intuitive and targeted visualization, but also guides the following noninvasive photothermal treatment. The boundaries of tumor are light up by Au-TTF-1 for navigation, it penetrates into tumor and implements noninvasive photothermal treatment, resulting in ablating tumors in vivo locally. Above all, Au-TTF-1 serves as a key platform for target bio-imaging navigation, machine learning diagnosis and synergistic PTT as a single nanoprobe, which demonstrates attractive performance on lung adenocarcinoma.

The effect of intermittent theta burst stimulation for cognitive dysfunction: a meta-analysis.

BACKGROUND: Growing evidence suggests that cognitive dysfunction significantly impacts patients' quality of life. Intermittent theta burst stimulation (iTBS) has emerged as a potential intervention for cognitive dysfunction. However, consensus on the iTBS protocol for cognitive impairment is lacking. METHODS: We conducted searches in the Cochrane Central Register of Controlled Trials, EMBASE, PubMed, Chinese National Knowledge Infrastructure, Wanfang Database and the Chongqing VIP Chinese Science and Technology Periodical Database from their inception to January 2024. Random-effects meta-analyzes were used to calculate standardized mean differences and 95% confidence intervals. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Twelve studies involving 506 participants were included in the meta-analysis. The analysis showed a trend toward improvement of total cognitive function, activities of daily living and P300 latency compared to sham stimulation in patients with cognitive dysfunction. Subgroup analysis demonstrated that these effects were restricted to patients with post-stroke cognitive impairment but not Alzheimer's disease or Parkinson's disease. Furthermore, subthreshold stimulation also exhibited a significant improvement. CONCLUSIONS: The results suggest that iTBS may improve cognitive function in patients with cognitive dysfunction, although the quality of evidence remains low. Further studies with better methodological quality should explore the effects of iTBS on cognitive function.

Systematic Review and Meta-analysis of Tongxinluo Capsule Therapy for Acute Myocardial Infarction.

Context: An acute myocardial infarction (AMI) is a serious, life-threatening disease. Practitioners of traditional Chinese medicine (TCM) commonly use the Tongxinluo (TXL) capsule, a Chinese patent medicine, to treat AMIs. The benefits of TXL capsules for AMIs remain unknown. Objective: The systematic review and meta-analysis intended to investigate the effects of TXL capsules for AMI patients. Design: The research team conducted a comprehensive literature search of the PubMed, Embase, Cochrane Library, and Web of Science databases from inception to February 2023. The team used the search terms acute myocardial infarction, myocardial infarction, TXL Capsule Therapy, and TXL Capsule. The team also performed a meta-analysis and evaluated the features of the included studies using the Cochrane Collaboration tool for assessing the risk of bias. Setting: The study took place at the Second Affiliated Hospital at Heilongjiang University of Chinese Medicine in Harbin City, Heilongjiang Province, China. Outcome Measures: The research team: (1) evaluated the studies' quality using the Cochrane Collaboration tool for assessing the risk of bias; (2) analyzed the curative effect of the TXL capsules for AMI; (3) explored the effects of the TXL capsules on left ventricular end-diastolic dimension (LVEDD), left ventricular end systolic diameter (LVESD), and left ventricular ejection fraction (LVEF); and (4) explored the effects of the TXL capsules on creatine kinase isoenzyme (CK-MB) peak time, CK-MB peak value, and cardiac index. Results: The literature search found ten studies. Compared with routine treatment alone, a combination of routine treatment and TXL capsules significantly improved the curative effects (odds ratio = 3.48; 95% CI: 2.34, 5.17; P < .00001) Compared with the control groups, the TXL capsule groups' LVESD and LVEF were significantly lower, with MD=-0.23; 95% CI: -0.37, -0.10; and P = .0007 and MD=-0.43; 95% CI: -0.61, -0.25; and P < .00001, respectively, and its LVEDD was significantly higher, with MD=5.27; 95% CI: 4.33, 6.21; and P < .00001. For myocardial enzymes, the TXL capsule groups' creatine kinase isoenzyme (CK-MB) peak values and cardiac indexes were significantly lower than those of the control groups, with MD=-53.11; 95% CI: -55.26, -50.97; and P < .00001 and MD=-1.87; 95% CI: -2.03, -1.70; and P < .00001, respectively. Conclusions: The meta-analysis showed that the TXL capsule can bring greater therapeutic benefits for AMI patients in combination with routine treatment. The current study was a meta-analysis, and the field needs more well-designed studies.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions.

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications.

Research and Verification of a Novel Interferometry Method by Joint Processing of Downlink Pseudo-Noise Ranging and DOR Signals for Deep Space Exploration.

The remarkably long distances covered by deep space probes result in extremely weak downlink signals, which poses great challenges for ground measurement systems. In the current climate, improving the comprehensive utilization of downlink signal power to increase the detection distance or enhance the measurement accuracy is of great significance in deep space exploration. Facing this problem, we analyze the delta Differential One-way Range (ΔDOR) error budget of the X-band of the China Deep Space Network (CDSN). Then, we propose a novel interferometry method that detunes one group of DOR beacons and reuses the clock components of regenerative pseudo-code ranging signals for interferometry delay estimation. The primary advantage of this method is its ability to enhance the power utilization efficiency of downlink signals, thereby facilitating more efficient tracking and measurement without necessitating additional design requirements for deep space transponders. Finally, we analyze and verify the correctness and effectiveness of our proposed method using measured data from CDSN. Our results indicate that the proposed method can save approximately 13% of the downlink signal power and increase the detection distance by about 6.25% using typical modulation parameters. Furthermore, if the relative power of other signal components remains unchanged, the power of the DOR tone can be directly increased by more than 100%, improving the deep space exploration ability more significantly.

In Situ Monitoring of the Spatial Distribution of Oxygen Vacancies and Enhanced Photocatalytic Performance at the Single-Particle Level.

Oxygen vacancies (OVs) on specific sites/facets can strengthen the interaction between reactants and oxide surfaces, facilitating interfacial charge transfer. However, precise monitoring of the spatial distribution of OVs remains a grand challenge. We report here that a single-particle spectroscopy technique addresses this challenge by establishing a positive correlation relationship between defects and bound exciton luminescence across different facets. Taking monoclinic BiVO4 as an example, on the basis of theoretical guidance, by in situ tracking the PL lifetimes and PL spectra of different facets on single particles before and after hydrogen treatment, we provide evidence that the PL emission originates from the OV state and determine that OVs is more inclined to be generated at the {010} facets. This anisotropic defect engineering significantly prolongs the lifetime of carriers and accelerates the activation of molecular oxygen. These findings not only verify preference rules of OVs in metal oxides but also provide a time-space-resolved monitoring method.

Metabolomic Profiling Reveals the Anti-Herbivore Mechanisms of Rice (Oryza sativa).

The use of secondary metabolites of rice to control pests has become a research hotspot, but little is known about the mechanism of rice self-resistance. In this study, metabolomics analysis was performed on two groups of rice (T1, with insect pests; T2, without pests), indicating that fatty acids, alkaloids, and phenolic acids were significantly up-regulated in T1. The up-regulated metabolites (p-value < 0.1) were enriched in linoleic acid metabolism, terpene, piperidine, and pyridine alkaloid biosynthesis, α-linolenic acid metabolism, and tryptophan metabolism. Six significantly up-regulated differential metabolites in T1 were screened out: N-trans-feruloyl-3-methoxytyramine (1), N-trans-feruloyltyramine (2), N-trans-p-coumaroyltyramine (3), N-cis-feruloyltyramine (4), N-phenylacetyl-L-glutamine (5), and benzamide (6). The insect growth inhibitory activities of these six different metabolites were determined, and the results show that compound 1 had the highest activity, which significantly inhibited the growth of Chilo suppressalis by 59.63%. Compounds 2-4 also showed a good inhibitory effect on the growth of Chilo suppressalis, while the other compounds had no significant effect. RNA-seq analyses showed that larval exposure to compound 1 up-regulated the genes that were significantly enriched in ribosome biogenesis in eukaryotes, the cell cycle, ribosomes, and other pathways. The down-regulated genes were significantly enriched in metabolic pathways, oxidative phosphorylation, the citrate cycle (TCA cycle), and other pathways. Eighteen up-regulated genes and fifteen down-regulated genes from the above significantly enriched pathways were screened out and verified by real-time quantitative PCR. The activities of detoxification enzymes (glutathione S-transferase (GST); UDP-glucuronosyltransferase (UGT); and carboxylesterase (CarE)) under larval exposure to compound 1 were measured, which indicated that the activity of GST was significantly inhibited by compound 1, while the activities of the UGT and CarE enzymes did not significantly change. As determined by UPLC-MS, the contents of compound 1 in the T1 and T2 groups were 8.55 ng/g and 0.53 ng/g, respectively, which indicated that pest insects significantly induced the synthesis of compound 1. Compound 1 may enhance rice insect resistance by inhibiting the detoxification enzyme activity and metabolism of Chilo suppressalis, as well as promoting cell proliferation to affect its normal growth and development process. The chemical-ecological mechanism of the insect resistance of rice is preliminarily clarified in this paper.

High Overexpression of SiAAP9 Leads to Growth Inhibition and Protein Ectopic Localization in Transgenic Arabidopsis.

Amino acid permeases (AAPs) transporters are crucial for the long-distance transport of amino acids in plants, from source to sink. While Arabidopsis and rice have been extensively studied, research on foxtail millet is limited. This study identified two transcripts of SiAAP9, both of which were induced by NO3- and showed similar expression patterns. The overexpression of SiAAP9L and SiAAP9S in Arabidopsis inhibited plant growth and seed size, although SiAAP9 was found to transport more amino acids into seeds. Furthermore, SiAAP9-OX transgenic Arabidopsis showed increased tolerance to high concentrations of glutamate (Glu) and histidine (His). The high overexpression level of SiAAP9 suggested its protein was not only located on the plasma membrane but potentially on other organelles, as well. Interestingly, sequence deletion reduced SiAAP9's sensitivity to Brefeldin A (BFA), and SiAAP9 had ectopic localization on the endoplasmic reticulum (ER). Protoplast amino acid uptake experiments indicated that SiAAP9 enhanced Glu transport into foxtail millet cells. Overall, the two transcripts of SiAAP9 have similar functions, but SiAAP9L shows a higher colocalization with BFA compartments compared to SiAAP9S. Our research identifies a potential candidate gene for enhancing the nutritional quality of foxtail millet through breeding.

Enzyme-Enabled Droplet Biobattery for Powering Synthetic Tissues.

Enzyme-enabled biobatteries are promising green options to power the next-generation of bioelectronics and implantable medical devices. However, existing power sources based on enzymatic biofuel chemistry exhibit limited scale-down feasibility due to the solid and bulky battery structures. Therefore, miniature and soft alternatives are needed for integration with implants and tissues. Here, a biobattery built from nanolitre droplets, fuelled by the enzyme-enabled oxidation of reduced nicotinamide adenine dinucleotide, generates electrical outputs and powers ion fluxes in droplet networks. Optimization of the droplet biobattery components ensures a stable output current of ~13,000 pA for over 24 h, representing a more than 600-fold increase in output over previous approaches, including light-driven processes. The enzyme-enabled droplet biobattery opens new avenues in bioelectronics and bioiontronics, exemplified by tasks such as the ability to drive electrochemical signal transmission in integrated synthetic tissues.

Time-domain fit for improved contrast in quantitative coherent anti-Stokes Raman spectroscopy.

Among the multiple coherent anti-Stokes Raman scattering (CARS) techniques that provide important quantitative molecular microscopic contrast, Fourier-transform CARS (FT-CARS) stands out with the immunity to nonresonant background and high-speed detection capacity. However, by using FFT for the exponentially decaying signal, FT-CARS faces the dilemma of choosing the delay range of the signal for high SNR or high resolution, the lack of either of which is detrimental to the quantitative contrast of imaging. Here, time-domain fit (TDF) is proposed to fully utilize the time-domain information of FT-CARS, providing optimized SNR and vibrational feature distinguishment. The capacity of noise restriction and feature distinguishment of the traditional FFT and the proposed TDF is analysed with theoretical examination and simulation. Exploiting the matrix pencil extraction of vibrational parameters, TDF is performed for quantitative analysis for simulated FT-CARS signal, and shows more accurate and consistent performance than the FFT method. FT-CARS coupled with TDF intensity evaluation holds the promise to provide micro-spectroscopic contrast with higher SNR and free of spectral overlapping, contributing to a more powerful diagnostic tool.

Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm.

Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspectral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, ≈4 frames/s, for a pixel resolution of 95 × 95 pixels and a spectral bandwidth no less than 200 cm-1. To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum.

Extended Light Absorption and Improved Charge Separation by Protonation of the Organic Ligand in a Bismuth-Based Metal-Organic Framework.

In this work, a new method to extend the light absorption and improve the photocatalytic activity of metal-organic frameworks (MOFs) with nitrogen-containing ligand is reported, namely, the protonation of nitrogen. Specifically, a protonated Bi-based MOF synthesized by a hydrothermal method (Bi-MMTAA-H, MMTAA=2-mercapto-4-methyl-5-thiazoleacetic acid) displays a wider visible light absorption than Bi-MMTAA-R with the same single-crystal structure, but synthesized by a reflux method. The redshifted light absorption was confirmed to be caused by the protonation of nitrogen in the thiazolyl ring in MMTAA. Moreover, this protonation also facilitates the charge separation and transfer and improves the photocatalytic activity of selective oxidation of α-terpinene to p-cymene. Our results provide a new idea for nitrogen-containing Bi-based MOFs to extend the light absorption and improve the photocatalytic performance.