EXPLORING EXTRACELLULAR DOPAMINE CONCENTRATION AND ITS REGULATION ON DOPAMINE RELEASE BY VOLTAMMETRY

Extracellular dopamine (DA) is critical in regulating DA release as well as interacting with other neurotransmission systems. Microdialysis and voltammetry are the major techniques for extracellular DA measurement in vivo. These two techniques provide distinct results due to their different detection volumes. Carbon fiber microelectrode is 10,000 times smaller than that of a microdialysis probe. With such a small size, carbon fiber microelectrode provides a high spatial resolution and causes unobservable damage to the brain which paints a completely different picture of DA release in the brain as compared to the knowledge obtained by microdialysis. In Chapter I, with the high temporal and spatial resolution provided by carbon fiber microelectrode in conjunction with fast scan cyclic voltammetry (FSCV), we are able to detect DA terminal populations with different autoinhibition levels in rat striatum. We revealed a coupling between resting DA and local autoinhibition level. The recording sites with high resting DA concentration (micromolar) exhibit a high autoinhibition on evoked DA release induced by medial forebrain bundle (MFB) stimulation, and vice versa. These different types of DA release will never be observed by microdialysis due to its large dimension. On the contrary, microdialysis result is an average of all the DA release sites (high and low) that the microdialysis probe goes through. This averaging method could contribute to the low measurement of the DA concentration by microdialysis. In Chapter II, we examined the resting DA by a carbon fiber microelectrode at ~200 micron away from a microdialysis probe. We found TTX-insensitive DA was decreased by microdialysis probe implantation. This reduction contributes to the low DA measurement by microdialysis. In Chapter III, we monitored evoked DA induced by MFB stimulation in the tissue near microdialysis probe. We found DA terminals near a microdialysis probe are hyper-sensitive to D2 receptor antagonist and DA transporter inhibitor. This suggests that the DA terminals in the tissue near microdialysis probe are under an altered neurochemical state with a loss of DA homeostasis

Orientation-Shared Convolution Representation for CT Metal Artifact Learning

During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at \url{https://github.com/hongwang01/OSCNet

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Although many face anti-spoofing (FAS) methods perform well in intra-dataset scenarios, their generalization remains a challenge. To address this issue, some methods adopt domain adversarial training (DAT) to extract domain-invariant features. However, the competition between the encoder and the domain discriminator can cause the network to be difficult to train and converge. In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment. Moreover, since models trained on limited data and types of attacks cannot generalize well to unknown attacks, we propose a dual perceptual and generative knowledge distillation framework for face anti-spoofing that utilizes pre-trained face-related models containing rich face priors. Specifically, we adopt two different face-related models as teachers to transfer knowledge to the target student model. The pre-trained teacher models are not from the task of face anti-spoofing but from perceptual and generative tasks, respectively, which implicitly augment the data. By combining both DAA and dual-teacher knowledge distillation, we develop a dual teacher knowledge distillation with domain alignment framework (DTDA) for face anti-spoofing. The advantage of our proposed method has been verified through extensive ablation studies and comparison with state-of-the-art methods on public datasets across multiple protocols

Medical treatment and long-term outcome of chronic atrial fibrillation in the aged with chest distress: a retrospective analysis versus sinus rhythm

Although “chest distress” is the most frequent complication in the aged with chronic atrial frbrillation (AF) in clinical practice, there are few data on the association between chronic AF and coronary artery disease (CAD) in the aged in terms of medical treatment and long-term outcome. We assessed coronary artery lesions in such patients and evaluated the efficacy of medical treatment in long-term follow-ups. Of 315 elderly patients (mean age: 77.39 ± 6.33 years) who had undergone coronary angiography for chest distress, 297 exhibited sinus rhythm (SR), whereas 18 patients exhibited chronic AF. Patients with AF were followed for 4.22 ± 2.21 years. Average diastolic blood pressure (DBP) of AF patients was observed to be markedly less than that of patients with SR (57.33 ± 6.87 mmHg vs 71.08 ± 10.54 mmHg, t-test: P < 0.01). Compared with SR patients, severe stenosis of the coronary artery in AF patients was reduced (73.06% vs 44.44%, Chi-square test: P < 0.01). AF patients with chest distress had high CHADS2 score (3.72 ± 1.27), but only 33.3% patients received oral anticoagulants, and such patients had a significantly lower rate of revascularization (21.43% vs 55.63%, Chi-square test: P < 0.01), and higher rate of all-cause death (22.22% vs 4.38%, Chi-square test: P < 0.01) and thromboembolism (16.67% vs 1.68%, Chi-square test: P < 0.01) in the long-term follow-ups compared with SR patients. Chest distress in the aged with AF was related to insufficient coronary blood supply that was primarily due to a reduced DBP rather than to occult CAD. Adequate and safe medical therapy was difficult to achieve in these patients. Such patients typically have a poor prognosis, and optimal therapeutic strategies to treat them are urgently needed

Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural Pakistan

The electricity crisis in Pakistan has been triggering grid power outages (load shedding) for many decades, which has not only affected the commercial and industrial sectors but also the domestic sector, specifically the livelihood of rural areas of the country. However, the extant literature advocates that renewable energy technologies (RETs), such as solar photovoltaic (PV) can be the remedy. Given the abundant availability of solar energy in Pakistan that can be converted into electrical energy using a solar PV system, this study examines the determinants of solar PV adoption in rural areas of Pakistan. Our preliminary investigations—using government/official publications—indicate that despite the huge potential of solar energy in Pakistan, the usage of solar PV systems at the household level in rural areas is still untapped, which makes this research agenda more appealing and provocative. In doing so, this study first conducts surveys, face-to-face comprehensive interviews, and questionnaires in four different districts of Pakistan and then implements a stepwise two-stage novel approach on a sample of 1,140 selected rural households. The first stage focuses on the determinants of solar PV system adoption, whereas the second stage focuses on the determinants of the type of solar PV system adopted. Using logistic regression, this study finds that age, education, children in school, income level, access to credit, gender (female), and price of a solar PV system are the factors significantly affecting the solar PV system adoption. In the second stage, we use a multivariate probit model and find that among these significant factors, the former five are significantly positive for the uptake of solar home-system, whereas the latter two are significant for both solar shed-lighting and solar panel-kit systems. In addition to these factors, landholding and access-to-road are significant for solar home systems, whereas household size, distance-to-market, and access-to-grid-electricity are significant for both solar shed-lighting and solar panel-kit systems. Since burning fossil fuels and solid biomass fuels for domestic energy needs are common in rural areas globally and cause carbon emissions and several severe health issues, the findings of this study are useful in many ways. In specific, we contribute to the literature examining the determinants of RETs in rural communities in developing countries

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na2CO3 and NaHCO3 in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R2Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area