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An in situ dielectric barrier discharge (DBD) employed as a preconcentration device was coupled with hydride generation atomic fluorescence spectrometry (HG-AFS) for the determination of antimony (Sb) that migrated from plastic to bottle water. In the presence of oxygen under a 7.5 kV discharge, Sb was completely trapped on the surface of dielectric barrier discharge quartz tube. After 50 s of Ar carrier sweeping, water vapor interference was effectively eliminated and Sb was rapidly released by a discharge containing hydrogen using 6.8 kV. In addition, the investigation of Sb migration from bottle materials to water was carried out considering temperature and bottle materials. Under the optimized conditions, the detection of limit (LOD) was 9 pg or 5 pg/mL. The linearity of the calibration relationship R2 exceeded 0.996 for concentrations from of 0.05 to 50 μg/L. In addition, the determined Sb levels of water certified reference materials (CRMs) were in good agreement with the certified values. The spiked recoveries for bottled water samples were from 95% to 104% with 3% to 8% relative standard deviation values. In comparison, the HG-in situ DBD-AFS method is capable to increase the analytical sensitivity using the peak height by 7-fold compared to standard HG-AFS. DBD offers gas phase enrichment at room temperature with instrumental miniaturization for elemental analysis with low energy consumption, small size and low cost. [ABSTRACT FROM AUTHOR]

Primary dysmenorrhea (PDM) is cyclic menstrual pain in the absence of pelvic anomalies, and it is thought to be a sex‐hormone related disorder. Existing study has focused on the effects of menstrual cramps on brain function and structure, ignoring the psychological changes associated with menstrual pain. Here we examined whether pain empathy in PDM differs from healthy controls (HC) using task‐based functional magnetic resonance imaging (fMRI). Fifty‐seven PDM women and 53 matched HC were recruited, and data were collected at the luteal and menstruation phases, respectively. During fMRI scans, participants viewed pictures displaying exposure to painful situations and pictures without any pain cues and assessed the level of pain experienced by the person in the picture. Regarding the main effect of the pain pictures, our results showed that compared to viewing neutral pictures, viewing pain pictures caused significantly higher activation in the anterior insula (AI), anterior cingulate cortex, and the left inferior parietal lobule; and only the right AI exhibited a significant interaction effect (group × picture). Post‐hoc analyses confirmed that, relative to neutral pictures, the right AI failed to be activated in PDM women viewing painsss pictures. Additionally, there was no significant interaction effect between the luteal and menstruation phases. It suggests that intermittent pain can lead to abnormal empathy in PDM women, which does not vary with the pain or pain‐free phase. Our study may deepen the understanding of the relationship between recurrent spontaneous pain and empathy in a clinical disorder characterized by cyclic episodes of pain. [ABSTRACT FROM AUTHOR]

Slotted reinforced concrete (RC) wall exhibited satisfactory seismic damage reduction performance and rapid rehabilitation. However, the optimal design of the slotted RC wall, i.e. the section division and strength and stiffness of shear links have not been derived. A numerical model is developed in ABAQUS, which was verified with the physical test results. Based on the calibrated model, the parametric analysis was carried out to investigate the influence of the section division and the strength of the shear links. The analytical results indicated a non-uniform section division, which is helpful in reducing the peak compressive strain. The height-width ratio of the side component is the critical issue for enhancing the deformability. A novel concept of strength ratio was proposed to the contribution from shear links in the load-carrying. Orientating maximizing the energy-dissipation ratio of the shear links, the optimal strength ratio of the shear links is derived. [ABSTRACT FROM AUTHOR]

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In this work, an in situ dielectric barrier discharge trap (DBDT) was first utilized to preconcentrate inorganic arsenic (iAs) species coupled with liquid chromatography hydride generation atomic fluorescence spectrometry (LC-HG-AFS), and a novel LC-HG-in situ DBDT-AFS instrument was thereby fabricated. Considering the relatively deficient sensitivity of LC-HG-AFS and retention of As(V) in a strong anion exchange column, As(V) was chosen as a model species for iAs preconcentration. During the extraction of arsenic species in a rice sample using 1% (v/v) HNO3 and 1% (v/v) H2O2, As(III) was oxidized to As(V) being regarded as iAs. After LC separation, according to the retention time, only As(V) was trapped by 11 kV discharging under 110 mL min−1 air mixed with Ar; following sweeping by Ar gas for 190 s, iAs was released with 13 kV at 180 mL min−1 H2-rich Ar to the AFS for measurement. Via preconcentration, the limit of detection (LOD) of iAs reached 0.05 μg L−1 with ∼10 times enhancement in analytical sensitivity; the linearity (r) was > 0.997 ranging from 0.5 μg L−1 to 50 μg L−1 with 1.9% RSD (5 μg L−1, n = 6). The measured iAs in certified reference materials (CRMs) were all within their certified values, and the spike recoveries were in the range of 93–102%. With sensitivity, robustness, low power, small size and easy operation, the combination of LC and DBDT fulfilled the analysis of trace iAs in rice samples. In fact, other arsenic species can also be preconcentrated and detected to enhance the sensitivity by this proposed LC-HG-in situ DBDT technique via precisely synchronizing DBD trap and release with chromatographic retention. To focus on the food safety, iAs was chosen as a model arsenic species to verify the availability of in situ DBDT. Considering the versatility of DBD, in situ DBDT demonstrates a promising application for elemental speciation analysis and LC atomic spectrometry. [ABSTRACT FROM AUTHOR]

Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache accompanying functionally and structurally maladaptive changes in brain. As the headache days/month is often measured by patient self‐report and tends to be overestimated than actually experienced, the possibility of using neuroimaging data to predict migraine attack frequency is of great interest. To identify neuroimaging features that could objectively evaluate patients' headache days, a total of 179 migraineurs were recruited from two data center with one dataset used as the training/test cohort and the other used as the validating cohort. The guidelines for controlled trials of prophylactic treatment of chronic migraine in adults were used to identify the frequency of attacks and migraineurs were divided into low (MOl) and high (MOh) subgroups. Whole‐brain functional connectivity was used to build multivariate logistic regression models with model iteration optimization to identify MOl and MOh. The best model accurately discriminated MOh from MOl with AUC of 0.91 (95%CI [0.86, 0.95]) in the training/test cohort and 0.79 in the validating cohort. The discriminative features were mainly located within the limbic lobe, frontal lobe, and temporal lobe. Permutation tests analysis demonstrated that the classification performance of these features was significantly better than chance. Furthermore, the indicator of functional connectivity had a higher odds ratio than behavioral variables with implementing a holistic regression analysis. The current findings suggested that the migraine attack frequency could be distinguished by using machine‐learning algorithms, and highlighted the role of brain functional connectivity in revealing underlying migraine‐related neurobiology. [ABSTRACT FROM AUTHOR]

In this study, the slurry sampling hydride generation (SLS-HG) system was first coupled with in situ dielectric barrier discharge atomic fluorescence spectrometry (DBD-AFS) for arsenic analysis in biological samples based on the gas phase enrichment (GPE) principle. After a simple slurry dilution with 5% HCl (v/v) for microbiological and hair samples, the as-prepared slurry was introduced into the SLS-HG unit for arsine generation under 8 g L−1 KBH4 in 1.5 g L−1 KOH. Here, the in situ DBD apparatus, comprising of the three-concentric quartz tubes, was employed to pre-concentrate arsenic to enhance the analytical sensitivity of the HG-AFS, as well as to eliminate interferences due to the dilution effect and matrix separation. This was followed by trapping with 11 kV at 110 mL min−1 of air carrier gas, and 190 s Ar gas sweeping, arsenic was released with 13 kV at 180 mL min−1 H2 to the AFS for measurement. Under optimal conditions, 8 pg or 14 pg detection limits (2 mL sampling) for the microbiological or hair samples were achieved without extra pre-concentration, respectively. The linearity R2 was 0.996 ranging from 0.05 μg L−1 to 300 μg L−1. The spiked recoveries for the real paramecium and micro diatom samples were 93–102%, and the measurements were in accordance with the certified reference material (CRM) values. The proposed SLS-HG-in situ DBD-AFS method is extremely suitable for ultratrace arsenic determination in biological samples to protect human health and environmental safety. [ABSTRACT FROM AUTHOR]

To investigate whether interindividual variability of white matter (WM) tract microstructure of the medial prefrontal cortex (mPFC)‐amygdala circuit could predict 8‐week placebo treatment outcomes in patients with migraine without aura (MO) using diffusion tensor imaging (DTI) with a tractography atlas‐based analysis algorithm and a linear support vector machine algorithm. This study received institutional review board approval, and all subjects gave informed consent. One hundred and twenty‐four MO had an 8‐week sham acupuncture treatment. Patients were subdivided into recovering (MOr, >50% improvement in migraine attack frequency after treatment) and persisting (MOp, <50% reduction in number of migraine days). Neuroimaging was collected via magnetic resonance imaging (MRI) in all subjects. Patients were imaged during the interictal phase of migraine (at least 72 hr after, and not within 24 hr of a migraine) before the treatment. WM microstructures were quantified along the selected fiber pathway and were used to evaluate the discrimination performance for classifying MOr and MOp. The combined features of diffusion measures from vertices along the pathways of the mPFC‐amygdala accurately discriminated MOr from MOp migraineurs with an accuracy of 84.0% (p < .005, permutation test). The most discriminative WM features that contributed to the classification were located in the external capsule and ACC/mPFC. Our findings suggested that the variability of placebo treatment outcomes in migraineurs could be predicted from priori diffusion measures along the fiber pathways of the mPFC‐amygdala, which may demonstrate a potential of WM neuroimaging features as imaging markers for identifying placebo responders in migraine patients. [ABSTRACT FROM AUTHOR]

Neuroimaging studies have revealed brain functional abnormalities in frontal-mesolimbic regions in obesity. However, the effects of obesity on brain network topology remains largely unknown. In the current study, we employed resting-state functional magnetic resonance imaging and graph theory methods to investigate obesity-related changes in brain network topology in 26 obese patients and 28 normal weight subjects. Results revealed that the whole-brain networks of the two groups exhibited typical features of small-world topology. Obese patients showed significantly increased shortest path length (Lp) and decreased global efficiency (Eglob). Moreover, decreased nodal-degree/efficiency was found in frontal (medial orbitofrontal cortex-mOFC, rostral anterior cingulate cortex-rACC), striatal (caudate/nucleus accumbens) and limbic regions (insula, amygdala, hippocampus/parahippocampal gyrus) and thalamus in obese patients. Network-based statistics showed that a sub-network, composed of 31 nodes and 30 edges, was significantly disrupted in obese patients; 29 out of 30 connections were associated with the right rACC. In the obese group, Lp and Eglob were negatively correlated with body mass index (BMI, P < 0.005), and BMI was negatively correlated with nodal-degree/efficiency of the mOFC (P < 0.001). Findings suggest disruption of the small-world organization and a global reduction of integration of functional brain networks involving the right rACC in obesity and implicating the mOFC in mediating severity. [ABSTRACT FROM AUTHOR]

The self-propulsion performance of an autonomous underwater vehicle (AUV) is predicted based on computational fluid dynamics (CFD) and response surface methodology (RSM). The innovations are that a data-driven predictive framework is proposed and a model of coupled rotation speed, self-propulsion velocity and power is developed. In the present study, the discretized propeller method and multiple reference frames (MRF) model are adopted. The design of experiment (DOE) and numerical simulations are executed with inlet velocity and propeller rotation speed as inputs and drag, thrust and torque as outputs. Using the simulation results as sample data, the prediction accuracy of multiple models is compared. The polynomial model is applied to fit the response points, describing the relationship between inputs and outputs. The goodness of fit and predictive ability is assessed using the adjusted coefficient of determination (R adj 2), root mean square error (RMSE) and relative error (RE). Finally, the predicted results are further validated by experiments, and all relative errors are below 6%. A linear function of self-propulsion velocity versus rotation speed and a specific cubic function of propeller input power versus self-propulsion velocity is obtained. The prediction method and achievements are beneficial for the navigation, control and safety of AUVs. • A novel data-driven predictive framework is proposed based on CFD and RSM. • The self-propulsion characteristics of an AUV is investigated using the proposed method. • The properties and predictive ability of various response surface models are compared. • A linear function of self-propulsion velocity versus rotation speed is obtained. [ABSTRACT FROM AUTHOR]

The hydrodynamic characteristics and depth control of the towed sensors array system (TSAS) are investigated based on lumped mass method and computational fluid dynamics (CFD). Firstly, the flexible towed cable is discretized into multiple rigid links with the lumped mass point, and the terminal point is the towed underwater vehicle (TUV). Coupling analysis of each sensors integrated module to a finite length of towed cable is performed, taking the module as the lumped mass point. The variation of normal and tangential forces with the change of angle are discussed for different towed speeds. Then, the dynamics model of the TUV is established and solved. The equilibrium equation for each lumped mass point is established using the recursive method from the bottom up. The angle between each link and the horizontal plane is calculated. Finally, the coordinate of each node is derived, and the position of the towed cable is fitted. The study shows that the hydrodynamic characteristics of the module and towed cable significantly impact the depth control and profile measurement. Sea trials validate the reliability of the method proposed in this paper, which is a guide to the design and manipulation of the TSAS. • The hydrodynamic characteristics and depth control of the towed sensors array system are studied. • The lumped mass method and computational fluid dynamics are employed. • The dynamics and mathematical model are established by the recursive method. • The calculated position and plotted profile are validated by sea trials. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid framework for fault detection in underwater thrusters based on the combination of a physical model and a Generative Adversarial Network (GAN). The proposed framework allows incorporating physical information within the architecture and training process of GAN, and relying only on a small dataset without fault samples to complete the training. Firstly, a Variational Autoencoder (VAE) is used to extract the informative distribution from the voltage signal, and this distribution is introduced into the generator of the GAN. Then, the thruster torque estimated by an Extended State Observer (ESO) is used as the real sequence of values for the discriminator of the GAN. Furthermore, a physical loss is aided for the training of the generator to improve the convergence of the GAN. The proposed hybrid framework is validated on a real dataset with multiple faults. The experimental results show that the proposed framework allows accurately detecting the different degrees of propeller damage faults and biofouling faults in underwater thrusters. • Integration the voltage signal and estimated torque into the structure and loss function of the GAN to realize the physical guidance of the training process. • The voltage-related informative distribution and the estimated torque are used as input of the generator and the discriminator, respectively, to realize the Physics-Guided design of architecture. • The errors of the estimated torque and the generated torque are input into the loss function with a threshold to realize Physics-Guided loss function design. • The effectiveness of the proposed framework is verified on real fault datasets. The role of each part is determined through ablation experiments. [ABSTRACT FROM AUTHOR]