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"Chen, Wei"
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Effects of phosphorus deficiency on the absorption of mineral nutrients, photosynthetic system performance and antioxidant metabolism in Citrus grandis
Phosphorus (P) is an essential macronutrient for plant growth, development and production. However, little is known about the effects of P deficiency on nutrient absorption, photosynthetic apparatus performance and antioxidant metabolism in citrus. Seedlings of ‘sour pummelo’ ( Citrus grandis ) were irrigated with a nutrient solution containing 0.2 mM (Control) or 0 mM (P deficiency) KH 2 PO 4 until saturated every other day for 16 weeks. P deficiency significantly decreased the dry weight (DW) of leaves and stems, and increased the root/shoot ratio in C . grandis but did not affect the DW of roots. The decreased DW of leaves and stems might be induced by the decreased chlorophyll (Chl) contents and CO 2 assimilation in P deficient seedlings. P deficiency heterogeneously affected the nutrient contents of leaves, stems and roots. The analysis of Chl a fluorescence transients showed that P deficiency impaired electron transport from the donor side of photosystem II (PSII) to the end acceptor side of PSI, which showed a greater impact on the performance of the donor side of PSII than that of the acceptor side of PSII and photosystem I (PSI). P deficiency increased the contents of ascorbate (ASC), H 2 O 2 and malondialdehyde (MDA) as well as the activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR) and glutathione reductase (GR) in leaves. In contrast, P deficiency increased the ASC content, reduced the glutathione (GSH) content and the activities of SOD, CAT, APX and monodehydroascorbate reductase (MDHAR), but did not increase H 2 O 2 production, anthocyanins and MDA content in roots. Taking these results together, we conclude that P deficiency affects nutrient absorption and lowers photosynthetic performance, leading to ROS production, which might be a crucial cause of the inhibited growth of C . grandis .
Journal Article
A practical model‐based segmentation approach for improved activation detection in single‐subject functional magnetic resonance imaging studies
Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low‐signal contexts and single‐subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single‐subject and low‐signal fMRI by developing a computationally feasible and methodologically sound model‐based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two‐ and three‐dimensional simulation experiments as well as on multiple real‐world datasets. Finally, the value of our suggested approach in low‐signal and single‐subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.
Journal Article
Business adaptation to climate change
\"This book addresses management and public policy students and scholars seeking a better understanding of the elements of successful business climate change adaptation strategies. It is also aimed at business and non-profit organization leaders and policy makers interested in developing and promoting such effective strategies\"-- Provided by publisher.
Book
Overcoming the energy gap law in near-infrared OLEDs by exciton–vibration decoupling
The development of high-performance near-infrared organic light-emitting diodes is hindered by strong non-radiative processes as governed by the energy gap law. Here, we show that exciton delocalization, which serves to decouple the exciton band from highly vibrational ladders in the S0 ground state, can bring substantial enhancements in the photoluminescence quantum yield of emitters, bypassing the energy gap law. Experimental proof is provided by the design and synthesis of a series of new Pt(ii) complexes with a delocalization length of 5–9 molecules that emit at 866–960 nm with a photoluminescence quantum yield of 5–12% in solid films. The corresponding near-infrared organic light-emitting diodes emit light with a 930 nm peak wavelength and a high external quantum efficiency up to 2.14% and a radiance of 41.6 W sr−1 m−2. Both theoretical and experimental results confirm the exciton–vibration decoupling strategy, which should be broadly applicable to other well-aligned molecular solids.Pt(ii) complexes allow the fabrication of efficient near-infrared organic light-emitting diodes that operate beyond the 900 nm region.
Journal Article
Atherogenic index of plasma as predictors for metabolic syndrome, hypertension and diabetes mellitus in Taiwan citizens: a 9-year longitudinal study
Deeply involved with dyslipidemia, cardiovascular disease has becoming the leading cause of mortality since the early twentieth century in the modern world. Whose correlation with metabolic syndrome (MetS), hypertension and type 2 diabetes mellitus (T2DM) has been well established. We conducted a 9-year longitudinal study to identify the association between easily measured lipid parameters, future MetS, hypertension and T2DM by gender and age distribution. Divided into three groups by age (young age: < 40, middle age: ≥ 40 and < 65 and old age: ≥ 65), 7670 participants, receiving standard medical inspection at Tri-Service General Hospital (TSGH) in Taiwan, had been enrolled in this study. Atherogenic index of plasma (AIP) was a logarithmically transformed ratio of triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C). Through multivariate regression analyses, the hazard ratio (HR) of AIP for MetS, hypertension and T2DM were illustrated. AIP revealed significant association with all the aforementioned diseases through the entire three models for both genders. Additionally, AIP revealed significant correlation which remained still after fully adjustment in MetS, hypertension, and T2DM groups for subjects aged 40–64-year-old. Nevertheless, for participants aged above 65-year-old, AIP only demonstrated significant association in MetS group. Our results explore the promising value of AIP to determine the high-risk subjects, especially meddle-aged ones, having MetS, hypertension, and T2DM in the present and the future.
Journal Article
قصة نجاح \علي بابا\ : حياة \جاك ما\ أغني رجل في الصين
طفل صغير لعائلة فقيرة تهوى التمثيل، طالب فاشل يتعلم الإنجليزية، مدرس متوسط المستوى، سائق عربة بضائع، عاطل عن العمل، يفشل في الالتحاق بأي وظيفة، مترجم لغة إنجليزية محترف، صاحب شركة ترجمة، صاحب موقع إلكتروني لجمع البيانات، مؤسس أكبر موقع متخصص في التجارة بين الشركات، صاحب مؤسسة \"علي بابا\" أكبر رجل أعمال في الصين، أغنى أغنياء الصين، كل هؤلاء في الحقيقة هم شخص واحد، أو بمعنى أصح هي قصة حياة أسطورة الصين حالياً، الرجل الذي غير خريطة التجارة العالمية، إنه \"ما يون\" حسب الاسم الصيني أو جاك ما حسب ما هو معروف به في العالم، والذي يحكي كتاب \"قصة نجاح علي بابا\"، قصة صعوده والتي هي أيضا قصة صعود الصين. يقترب المؤلف من عالم \"جاك ما\" الحقيقي ويحكي قصته منذ نعومة أظفاره، حتى صار إمبراطور التجارة في العالم، إنها قصة صعود شخص وصعود دولة بحجم الصين، قصة ترفض أن يكون هناك مستحيل، وتثبت أن الأفكار هي رأس المال الأكبر في العالم، قصة الفضائي كما يجب أن يسميه الصينيون، الذي جعل حياتهم أفضل، قصة رجل حفر اسمه في تاريخ البشرية، والذي يفتح لنا بتجربته آفاقا أرحب نرى بها العالم ونرى بها أنفسنا بشكل مختلف.
BOOK
The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior
This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists’ attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists’ purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists’ purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists’ willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers’ needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields.
Journal Article