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Objective: The aim of this study was to investigate the impact of illiteracy on physical health and mental health. Design: Matching methods (nearest neighbour matching, Mahalanobis metric matching, and propensity score matching). Setting: Elderly people at least 65 years old in 22 provinces of China. Methods: The analysis used data from Chinese Longitudinal Healthy Longevity Survey (CLHLS). The independent variable was a dummy variable, which was coded as 1 for illiterate or semiliterate, and 0 for literate. Dependent variables were indicators of physical and mental health derived from the survey results. Matching methodologies controlled for confounding variables including age, sex, living sites, access to tap water and financial support. Results: Illiteracy was found to have a significant impact on physical health, exercise habits, anxiety, loneliness and happiness. On average, illiteracy decreased physical health by 19.9%, decreased exercise habits by 7%, increased anxiety by 11.56%, increased loneliness by 17.6% and decreased happiness by 11.3%. Conclusion: Findings confirm the past literature in which illiteracy has been found to be adversely associated with physical and mental health. The analysis uniquely found that illiteracy had a higher cost on mental health as compared to physical health for elderly people in China. [ABSTRACT FROM AUTHOR]

Keywords: 2-Buten; Ethen; Metathese; Propen; SSITKA DE 2-Buten Ethen Metathese Propen SSITKA 1 1 1 09/27/23 20231002 NES 231002 B Heterogeneous Catalysis b . In their Communication (e202308872), Tatiana Otroshchenko, Evgenii V. Kondratenko et al. report performance descriptors for catalysts based on molybdenum, tungsten, or rhenium oxides for ethylene metathesis with 2-butenes to propene. [Extracted from the article]

Frontispiece: Performance Descriptors for Catalysts Based on Molybdenum, Tungsten, or Rhenium Oxides for Metathesis of Ethylene with 2-Butenes to Propene Keywords: 2-Butene; Ethylene; Metathesis; Propene; SSITKA EN 2-Butene Ethylene Metathesis Propene SSITKA 1 1 1 09/27/23 20231002 NES 231002 B Heterogeneous Catalysis b . In their Communication (e202308872), Tatiana Otroshchenko, Evgenii V. Kondratenko et al. report performance descriptors for catalysts based on molybdenum, tungsten, or rhenium oxides for ethylene metathesis with 2-butenes to propene. [Extracted from the article]

Multi-GPUs accelerated BPF algorithm is developed for improving computational efficiency. Three major acceleration techniques are introduced: (1) dividing reconstructed volume into subsets vertically which reduces the computational cost on the boundary term between parallel chords; (2) transposed method is used to avoid low efficiency of access in global memory which is caused by different chords selection; (3) optimized memory allocation scheme is adopted. Experimental data are used to evaluate the image quality and reconstruction time. It takes only 4.118 s to reconstruct a volume image of 512 × 512 × 512 with 360 projection data of 512 × 512 on dual NVIDIA Tesla C2070 cards. Added the time consuming on data read, transfer and storage part, the complete reconstruction process could be finished in less than 9 s. In particular, BPF-based ROI-reconstruction for cone beam Ortho-CT shows promising application prospect. [ABSTRACT FROM AUTHOR]

Micro-CT with a high spatial resolution in combination with computer-based-reconstruction techniques is considered a powerful tool for morphological study of insects. The quality of CT images crucially depends on the precise knowledge of the scan geometry of the micro-CT system. In this paper, we have proposed a method to calculate the deviation of rotating axis for compensating deficiency of existing methods. A practical application of this geometric calibration method of the micro-CT system for insect imaging is presented. We have performed the computer-simulation study and experimental study with our prototype micro-CT system. The results demonstrate that the proposed technique is accurate and robust. In addition, we have evaluated the imaging characteristics of the detector in terms of modulation-transfer function (MTF). Finally, insect imaging performance and image reconstruction from data acquired with different energies are presented. [ABSTRACT FROM PUBLISHER]

Together, the Yinggehai and Qiongdongnan basins have received a large amount of terrigenous sediments, but the provenance evolution of Cenozoic sediments in the two basins remains disputable. Combined with previous studies in the Yinggehai and Qiongdongnan basins, the elemental geochemistry of Oligocene to Pliocene sediment samples in the junction area of the two basins were analyzed to explore the tectonic implications, parent rock characteristics, and provenance evolution of the two basins during the Cenozoic. The results reveal that all the sediment samples were derived from continental island arc to passive continental margin settings. The light REE enrichment and stable content of heavy REE with large negative Eu anomalies indicate that they were probably derived from Hainan Island. The reconstructed provenance evolution model showed that the Red River Source (RRS) provided sedimentary materials for the Central Depression of Yinggehai Basin from the Oligocene to the Pliocene, and Hainan Island Source (HIS) was also one of the sources for sediments deposited in the Central Depression of Yinggehai Basin during the Miocene. However, most of the sediments preserved in the Yingdong Slope and Qiongdongnan Basin were derived from the HIS from the Oligocene to the Pliocene, and sediments deposited in the Yingdong Slope were also derived from the RRS during the Miocene. Furthermore, the junction area of the two basins had a mixed source of the RRS and HIS during the Cenozoic. [ABSTRACT FROM AUTHOR]

• We propose a co-planar optical power splitter based on cascaded hybrid-core vertical directional couplers, which serves to split arbitrary ratio power of arbitrary guide modes. • We fabricate the optical power splitter using micro-fabrication facilities. • Our fabricated chip demonstrates power splitting for the E 11 , E 21 and E 12 modes between two few-mode waveguides. • The experimental device shows that optical power splitting ranges can be larger than 11.0 dB, 12.1 dB and 11.3 dB for the E 11 , E 21 and E 12 modes, respectively, over the C + L band and beyond. • The proposed optical power splitter could be used in the mode division-multiplexing systems where power splitting for arbitrary guide modes in few-mode waveguide is required in the ultra-broadband wavelength range for mode manipulating, such as mode power distribution, mode dependent loss equalization and all-optical MIMO signal processes. We propose a co-planar optical power splitter based on cascaded hybrid-core vertical directional couplers, which serves to split arbitrary ratio power of arbitrary guide modes. We fabricate the optical power splitter using micro-fabrication facilities. Our fabricated chip demonstrates power splitting for the E 11 , E 21 and E 12 modes between two few-mode waveguides. The experimental device shows that optical power splitting ranges can be larger than 11.0 dB, 12.1 dB and 11.3 dB for the E 11 , E 21 and E 12 modes, respectively, over the C + L band and beyond. The proposed optical power splitter could be used in the mode division-multiplexing systems where power splitting for arbitrary guide modes in few-mode waveguide is required in the ultra-broadband wavelength range for mode manipulating, such as mode power distribution, mode dependent loss equalization and all-optical MIMO signal processes. [ABSTRACT FROM AUTHOR]

• Anatomical prior information is introduced to estimate high-resolution CT images. • A united framework is applied instead of designing multiple models for individual human body sites. • An improved performance is achieved by Wasserstein GAN with an attribute loss function. Currently, many deep learning (DL)-based low-dose CT image postprocessing technologies fail to consider the anatomical differences in training data among different human body sites, such as the cranium, lung and pelvis. In addition, we can observe evident anatomical similarities at the same site among individuals. However, these anatomical differences and similarities are ignored in the current DL-based methods during the network training process. In this paper, we propose a deep network trained by introducing anatomical site labels, termed attributes for training data. Then, the network can adaptively learn to obtain the optimal weight for each anatomical site. By doing so, the proposed network can take full advantage of anatomical prior information to estimate high-resolution CT images. Furthermore, we employ a Wasserstein generative adversarial network (WGAN) augmented with attributes to preserve more structural details. Compared with the traditional networks that do not consider the anatomical prior and whose weights are consequently the same for each anatomical site, the proposed network achieves better performance by adaptively adjusting to the anatomical prior information. [ABSTRACT FROM AUTHOR]

Zeolite-tailored bimetallic Pt-Sn catalysts have been shown to be effective for propane dehydrogenation (PDH). Here, we have successfully prepared and compared two Sn-Beta-supported bimetallic PtSn catalysts (Pt/Sn-Beta (DG) and Pt/Sn-Beta (DA)), in which the Sn-Beta zeolite was prepared by a dry-gel (DG) synthesis method involving in situ Sn incorporation or a post-synthesis method involving dealumination and Sn incorporation (DA), respectively. The as-prepared Pt/Sn-Beta (DG), possessing a less fraction of extra-framework Sn species comparing to Pt/Sn-Beta (DA), shows a superior performance in terms of activity (TOF, 1.8 s-1), selectivity (99.0%) and stability (7360 min) at 550 °C using 40 kPa propane and 60 kPa nitrogen gas, reflecting one of the highest PDH performances for bimetallic Pt-Sn catalysts under similar conditions. By a combination of the state-of-the-art electron microscopy-based techniques and advanced spectroscopy techniques, we found that the proper Pt-Sn nano-alloying in Pt/Sn-Beta (DG), rather than Sn coverage on Pt species in Pt/Sn-Beta (DA) owing to the migration of excess extraframework Sn species, is essential for providing an enhanced PDH performance, as the active Pt species could be well isolated, accessed and stablized in Pt/Sn-Beta (DG) by alloying. We demonstrate a process that constructs highly active Pt–Sn alloy nanoparticles on Sn-Beta zeolite with a high activity (TOF of 1.8 h−1), selectivity (99%) and stability (7360 min one-pass durability) for propane dehydrogenation. [Display omitted] • Sn-Beta zeolite from the dry-gel synthesis method was served as catalyst support. • Pt–Sn nanoalloy was formed via the interaction of Pt and Sn in Sn-Beta zeolite. • Pt/Sn-Beta shows a high activity and selectivity for propane dehydrogenation. • The excellent stability of Pt/Sn-Beta is benefited from the of Pt–Sn nanoalloy. [ABSTRACT FROM AUTHOR]

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works. • A framework is proposed to focus on the improvement of image quality under different noise intensities. • To gain better performance, we introduce several strategies for overview network framework. • The quantitative and visual results reflect the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technology with wide field-of-view (FOV) and high resolution. However, previous studies thought that the conventional FPM algorithm approximated the LED as a coherent point light source, so FPM imaging is regarded as a coherent imaging process, and the coherent transfer function(CTF)was constrained in the Fourier domain, which seemed to be problematic. At the same time, the conventional FPM data requires the acquisition of multiple data, which takes a long time and limits its real-time application. To investigate these two problems, this paper proposes the discrete cosine (DCT) coefficient Predictive searching method for FPM with Bessel function weighted apodized coherent transfer function (B-AC) constraint. The experimental results demonstrate that without adding any algorithmic complexity and computational burden, the proposed method can reduce the number of acquired images by about 60%, while retaining the accuracy of the reconstructed results. • A pre-search method of DCT coefficients under B-AC constraint is proposed. • The pre-search method of DCT coefficients can significantly improve the efficiency. • B-AC constraint is more suitable for FPM actual imaging process. • The proposed method hardly increases the computational burden. [ABSTRACT FROM AUTHOR]

Background: Lymph node retrieval deficiency can lead to understagement and postoperative cancer recurrence, it is crucial to establish the standard number of retrieved lymph nodes (rLNs) and negative lymph nodes (nLNs) for patients undergoing gastrectomy., Methods: Patients who has gastric adenocarcinoma and underwent either radical subtotal gastrectomy (RSG) or radical total gastrectomy (RTG) between 2000 and 2022 were retrospectively included. We utilized restricted cubic spline (RCS) analysis to determine the ideal threshold for rLNs and nLNs. Survival analysis was conducted using Kaplan-Meier (KM) curves, log-rank tests and forest plots. Propensity score matching (PSM) was utilized to balance parameters between two groups. The median follow-up time for this study was 3,095 days., Results: Our study found that there are significant tumor characteristic differences between RSG and RTG. For patients with N0-N3a stage undergoing RSG, retrieving≥24 lymph nodes intraoperatively were associated with better prognosis both before and after PSM (OS: P<0.001, P=0.019); whereas for N3b stage, at least 32 rLNs were required (OS: P=0.006, P=0.023). Similarly, for patients with N0-N3a stage undergoing RTG, retrieving≥27 lymph nodes intraoperatively were associated with better prognosis both before and after PSM (OS: P<0.001, P=0.047); whereas for N3b stage, at least 34 rLNs were required (OS: P<0.001, P=0.003). Additionally, for patients undergoing RSG, having ≥21 nLNs (OS: P<0.001, P=0.013), and for those undergoing RTG, having ≥22 nLNs (OS: P<0.001, P<0.001), were also associated with better prognosis both before and after PSM., Conclusions: For patients receiving RSG, rLNs should reach 24 when lymph nodes are limited, and 32 when lymph node metastasis is more extensive, with a minimum number of nLNs ideally reaching 21. Similarly, for patients receiving RTG, rLNs should reach 27 when lymph nodes are limited, 34 when lymph node metastasis is more extensive, and a minimum number of nLNs ideally reaching 22., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: This study examined the relationship between perceived discrimination and psychological distress among international students during the COVID-19 pandemic., Methods: A total of 188 international students from two large U.S. universities participated in the study. Perceived discrimination, psychological distress, and demographic information were assessed using self-reported questionnaires., Results: COVID-related variables and perceived discrimination were significantly associated with international students' psychological distress. Their COVID-related anxiety mediated the relationship between perceived discrimination and psychological distress., Conclusion: Initiatives to mitigate the perceived discrimination experienced by international students may improve their mental health.

Two-dimensional van der Waals (2D vdW) heterostructure photodetectors have garnered significant attention for their potential applications in next-generation optoelectronic systems. However, current 2D vdW photodetectors inevitably encounter compromises between responsivity, detectivity, and response time due to the absence of multilevel regulation for free and photoexcited carriers, thereby restricting their widespread applications. To address this challenge, we propose an efficient 2D WS 2 /CuInP 2 S 6 vdW heterostructure photodetector by combining band engineering and ferroelectric modulation. In this device, the asymmetric conduction and valence band offsets effectively block the majority carriers (free electrons), while photoexcited holes are efficiently tunneled and rapidly collected by the bottom electrode. Additionally, the ferroelectric CuInP 2 S 6 layer generates polarization states that reconfigure the built-in electric field, reducing dark current and facilitating the separation of photocarriers. Moreover, photoelectrons are trapped during long-distance lateral transport, resulting in a high photoconductivity gain. Consequently, the device achieves an impressive responsivity of 88 A W -1 , an outstanding specific detectivity of 3.4 × 10 13 Jones, and a fast response time of 37.6/371.3 μs. Moreover, the capability of high-resolution imaging under various wavelengths and fast optical communication has been successfully demonstrated using this device, highlighting its promising application prospects in future optoelectronic systems.

Objectives: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques., Methods: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18 F-fluorodeoxyglucose ( 18 F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses., Results: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data., Conclusion: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts., (© 2024. The Author(s).)

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not being sufficiently exploited. In this paper, we propose a learning framework that deeply integrates DL and an alternating direction of multipliers method (ADMM)-based iterative optimization model. The innovative feature of this method is that we break the inherent forms of the fidelity operators and use neural networks to process them. The regularization term is deeply generalized. The proposed method is evaluated on simulated data and real data. Both the qualitative and quantitative results show that our proposed neural network method can outperform partial operator expansion-based neural network methods, neural network denoising methods and traditional methods.

The metathesis of ethylene with 2-butenes to propene is an established large-scale process. However, the fundamentals behind in situ transformation of supported WO x , MoO x , or ReO x species into catalytically active metal-carbenes and the intrinsic activity of the latter as well as the role of metathesis-inactive cocatalysts are still unsolved. This is detrimental for catalyst development and process optimization. In this study, we provide the required essentials derived from steady-state isotopic transient kinetic analysis. For the first time, the steady-state concentration, the lifetime, and the intrinsic reactivity of metal carbenes were determined. The obtained results can be directly used for the design and the preparation of metathesis-active catalysts and cocatalysts, thereby opening up possibilities for optimizing propene productivity., (© 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Background: Total-body dynamic positron emission tomography (dPET) imaging using 18 F-fluorodeoxyglucose ( 18 F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 h seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate K i estimations of tumor lesions., Methods: Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18 F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 h PI. Patlak analysis-based K i parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubic Hermite interpolation was applied. The resultant K i images acquired by 30 min dynamic PET data and the standard 1 h K i images were compared in terms of visual imaging effect, region signal-to-noise ratio, and K i estimation accuracy to evaluate the performance of the proposed K i imaging method with a shortened scan duration., Results: With the help of Patlak data processing, acceptable K i parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with K i images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman plot and Pearson correlation coefficient analysis showed that that 30 min K i images obtained from the processed Patlak data had higher accuracy for tumor lesions., Conclusion: Satisfactory K i parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher K i imaging quality and higher accuracy regarding tumor lesion K i values. Clinically, it is possible to shorten the dynamic scan duration of 18 F-FDG PET to 30 min to acquire an accurate tumor K i and further effective tumor detection with uEXPLORER scanners., (© 2022 American Association of Physicists in Medicine.)

Past meta-analyses in mental health interventions failed to use stringent inclusion criteria and diverse moderators, therefore, there is a need to employ more rigorous methods to provide evidence-based and updated results on this topic. This study presents an updated meta-analysis of interventions targeting anxiety or depression using more stringent inclusion criteria (e.g., baseline equivalence, no significant differential attrition) and additional moderators (e.g., sample size and program duration) than previous reviews. This meta-analysis includes 29 studies of 32 programs and 22,420 students (52% female, 79% White). Among these studies, 22 include anxiety outcomes and 24 include depression outcomes. Overall, school-based mental health interventions in grades K-12 are effective at reducing depression and anxiety (ES = 0.24, p = 0.002). Moderator analysis shows that improved outcomes for studies with anxiety outcomes, cognitive behavioral therapy, interventions delivered by clinicians, and secondary school populations. Selection modeling reveals significant publication and outcome selection bias. This meta-analysis suggests school-based mental health programs should strive to adopt cognitive behavioral therapy and deliver through clinicians at the secondary school level where possible., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

International students continue to experience myriad of challenges, some of which further transpired disproportionately during the COVID-19 pandemic era. To this effect, this study investigated psychological capital (PsyCap), psychological distress, and well-being among 188 international students attending U.S universities. Results using Hayes PROCESS indicated that well-being mediated the relationship between PsyCap and psychological distress and in particular moderated the relationship between PsyCap and depression. When higher education institutions are considering steps to mitigate psychological distress experienced by international students during the COVID-19 pandemic and beyond, based on the findings of our study, we suggest investing efforts and resources into two aspects: (a) promotion of positive mental health and well-being and (b) identification and development of positive psychological capital. We further discuss these results and implications for mental health promotion of international students in light of its limitations and recommendations for future research., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Capillary electrophoresis (CE) is a powerful separation technique with advantages over HPLC in terms of separation efficiency, speed, and cost. However, CE suffers in poor reproducibility in quantitative chemical analysis, which is one of major drawbacks preventing its widespread use in routine analytical laboratories. Here we report a novel strategy to enhance the quantitative capability of flow-gated CE. The platform integrated dual flow branches to respectively supply a sample and its standard additions that were then alternately injected into a single capillary for rapid separations (typically 20-90 s). A micro-fabricated switch was used to enable the alternate injections. It was assumed that the analytical system maintained constant conditions during neighboring injections that served as external self-standards for quantitation. This strategy was expected to reduce uncertainties caused by the fluctuation in capillary conditions and the drift of detection systems. Experimental results demonstrated that the dual-branch flow-gated CE coupled with alternate injections significantly improved the reproducibility with respect to peak height ratios under deliberate variations in injection volumes, separation voltages, optical focusing, and laser power; and thus the interday precision was ensured. To demonstrate its applicability, cyanide and amino acids in human urine were quantified rapidly with the one-point standard addition method after fluorogenic derivatization with naphthalene-2,3-dicarboxaldehyde (NDA), and the measurement accuracy was validated by determining the recovery of standard cyanide added to a urinary matrix. This strategy would be valuable to enable the quantitative capability of flow-gated CE in the measurements of a broad range of analytes, especially those lacking suited internal standards., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Flow-gated capillary electrophoresis (CE) coupled with microdialysis has become an important tool for in vivo bioanalytical measurements because it is capable of performing rapid and efficient separations of complex biological mixtures thus enabling high temporal resolution in chemical monitoring. However, the limit of detection (LOD) is often limited to a micro- or nano-molar range while many important target analytes have picomolar or sub-nanomolar levels in brain and other tissues. To enhance the capability of flow-gated CE for catecholamine detection, a novel and simple on-line sample preconcentration method was developed exclusively for fluorescent derivatives of catecholamines that were fluorogenically derivatized with naphthalene-2,3-dicarboxaldehyde (NDA) in the presence of cyanide. The effective preconcentration coupled with the sensitive laser-induced fluorescence (LIF) detection lowered the LOD down to 20pM for norepinephrine (NE) and 50pM for dopamine (DA) at 3-fold of S/N ratio, and the signal enhancement was estimated to be over 100-fold relative to normal injection when standard analytes were dissolved in artificial cerebrospinal fluid (aCSF). The basic focusing principle is novel since the sample plug contains borate while the background electrolyte (BGE) is void of borate. This strategy took advantage of the complexation between diols and borate, through which one negative charge was added to the complex entity. The sample derivatization mixture was electrokinetically injected into a capillary via the flow-gated injection, and then NE and DA derivatives were selectively focused to a narrow zone by the reversible complexation. Separation of NE and DA derivatives was executed by incoming surfactants of cholate and deoxycholate mixed in the front BGE plug. This on-line preconcentration method was finally applied to the detection of DA in rat cerebrospinal fluid (CSF) via microdialysis and on-line derivatization. It is anticipated that the method would be valuable for in vivo monitoring of DA and NE in various brain regions of live animals on flow-gated CE or microchip platforms., (Published by Elsevier B.V.)

Cyanides are poisonous chemicals that widely exist in nature and industrial processes as well as accidental fires. Rapid and accurate determination of cyanide exposure would facilitate forensic investigation, medical diagnosis, and chronic cyanide monitoring. Here, a rapid and direct method was developed for the determination of cyanide ions in urinary samples. This technique was based on an integrated capillary electrophoresis system coupled with laser-induced fluorescence (LIF) detection. Cyanide ions were derivatized with naphthalene-2,3-dicarboxaldehyde (NDA) and a primary amine (glycine) for LIF detection. Three separate reagents, NDA, glycine, and cyanide sample, were mixed online, which secured uniform conditions between samples for cyanide derivatization and reduced the risk of precipitation formation of mixtures. Conditions were optimized; the derivatization was completed in 2-4min, and the separation was observed in 25s. The limit of detection (LOD) was 4.0nM at 3-fold signal-to-noise ratio for standard cyanide in buffer. The cyanide levels in urine samples from smokers and non-smokers were determined by using the method of standard addition, which demonstrated significant difference of cyanide levels in urinary samples from the two groups of people. The developed method was rapid and accurate, and is anticipated to be applicable to cyanide detection in waste water with appropriate modification., (Published by Elsevier B.V.)