Phosphomolybdic acid-responsive Pickering emulsions stabilized by ionic liquid functionalized Janus nanosheets

<p><b>A</b> Representative photomicrographs of Caspase-3 immunofluorescence staining (400×). <b>B</b> Quantification of Caspase-3 fluorescence intensity in different groups. <b>C</b> Representative Western blot band of Caspase-3 activation in the ischemic cortex at 24 h after reperfusion. <b>D</b> Effect of LBP (40 mg/kg) on the Caspase-3 activation in MCAO mice cortex at 24 h after reperfusion. Data are expressed as mean±SEM (n = 6). ^##P<0.01 vs. sham-operated group; **P<0.01 vs. vehicle group.</p

بررسی حیطه‌های موجود در فرم‌های ارزشیابی از دیدگاه دانشجویان در دانشگاه علوم پزشکی زنجان در سال تحصیلی 86- 87

زمینه و هدف: ارزشیابی استادان متداول‌ترین روش جهت سنجش کیفیت آموزش می‌باشد. دانشجویان بیش از دست‌اندرکاران در جریان روند آموزش قرار‌دارند بنابراین با نظرخواهی از آنان دیدگاه کاملی برای مسئولین در مورد نقاط قوت و ضعف استادان به‌دست می‌آید. هدف از این پژوهش بررسی حیطه‌های موجود در فرم‌های ارزشیابی از دیدگاه دانشجویان در دانشکده‌های پزشکی، پیراپزشکی و پرستاری و مامایی می‌باشد. روش بررسی: این تحقیق به صورت توصیفی انجام گرفت. 1683 برگ ارزشیابی دانشجویان از استادان هیأت علمی (73 نفر) مربوط به دانشکده‌های پزشکی، پیراپزشکی و پرستاری- مامایی بررسی شد. پرسش‌نامه‌ی دانشجویان پزشکی حاوی 15 سؤوال و دانشجویان پیراپزشکی و پرستاری مامایی 21 سؤوال بود که بر اساس مقیاس لیکرات از حیطه‌های مختلف مقرراتی، علمی و آموزشی، نظارتی و نگرشی تشکیل شده بود. نمرات سؤوالات از نمره‌ی 100 محاسبه شد، نمرات بالاتر بیانگر عملکرد مطلوب‌تراستادان می‌باشد. تجزیه و تحلیل داده‌ها به‌صورت آمار توصیفی با نرم‌افزار SPSS انجام شد. یافته‌ها: نتایج نشان داد مقایسه در سطوح کلی بین دانشکده‌ها، دانشکده‌ی پیراپزشکی با میانگین کل و انحراف معیار 61/3 ±50/85 نسبت به سایر دانشکده‌ها برتری دارد. دانشکده‌ی پیراپزشکی در حیطه‌ی مقرراتی با میانگین و انحراف معیار 89/3±01/91، دانشکده‌ی پزشکی در حیطه‌ی نگرشی با میانگین و انحراف معیار 45/5±48/90 و دانشکده‌ی پرستاری مامایی در حیطه‌ی مقرراتی با میانگین و انحراف معیار 25/4±34/88 بیشترین امتیاز را داشتند. نتیجه‌نهایی نشان می‌دهد، حیطه‌ی علمی و آموزشی نسبت به سایر حیطه‌ها در سطح پایین‌تر می‌باشد. نتایج حیطه‌ها (علمی و آموزشی، نظارتی و نگرشی) بین دانشکده‌ها معنی‌دار می‌باشد (0001/0=P). نتیجه‌گیری: به نظر می‌رسد با برنامه‌ریزی جهت برگزاری کارگاه‌های آموزشی، روش تدریس و تحقیق جهت ارتقای آموزش استادان، اعطا‌ی فرصت مطالعاتی و تشویق انجام کارهای تحقیقاتی و پژوهشی گام مؤثری جهت ارتقای سطح علمی و بالاخره عملکرد بالای استادان خواهد بود

Media 1: Volumetric imaging of turbulent reactive flows at kHz based on computed tomography

Originally published in Optics Express on 24 February 2014 (oe-22-4-4768

Data_Sheet_1_A general dual-pathway network for EEG denoising.docx

IntroductionScalp electroencephalogram (EEG) analysis and interpretation are crucial for tracking and analyzing brain activity. The collected scalp EEG signals, however, are weak and frequently tainted with various sorts of artifacts. The models based on deep learning provide comparable performance with that of traditional techniques. However, current deep learning networks applied to scalp EEG noise reduction are large in scale and suffer from overfitting.MethodsHere, we propose a dual-pathway autoencoder modeling framework named DPAE for scalp EEG signal denoising and demonstrate the superiority of the model on multi-layer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), respectively. We validate the denoising performance on benchmark scalp EEG artifact datasets.ResultsThe experimental results show that our model architecture not only significantly reduces the computational effort but also outperforms existing deep learning denoising algorithms in root relative mean square error (RRMSE)metrics, both in the time and frequency domains.DiscussionThe DPAE architecture does not require a priori knowledge of the noise distribution nor is it limited by the network layer structure, which is a general network model oriented toward blind source separation.</p

Facile Fabrication of a Superhydrophobic Cu Surface via a Selective Etching of High-Energy Facets

The Cu surface with a dual-scale roughness has been prepared via a facile solution-phase etching route by the H₂O₂/HCl etchants. The selective etching of the high-energy {110} facets occurs at an ultralow rate of the redox etching reaction. The resultant surface is composed of many polyhedral microprotrusions and nanomastoids on the microprotrusions, exhibiting the binary micro/nanostructures. After hydrophobization, the resultant surface exhibits a water contact angle of 170° and a sliding angle of ∼2.8° for a 5 μL droplet. The combination of the dual-scale roughness and the low surface energy of the adsorbed stearic acid accounts for the superhydrophobicity. Such a superhydrophobic Cu surface has an excellent nonsticking behavior and anticorrosion against electrolyte solution. It also keeps its superhydrophobic ability after a long-time ultrasonication or abrasion test. Our work may shed light on the selective etching of other metal surfaces to create designed dual-scale roughness for superhydrophobicity

Proportion of attractions and brand ratings from transaction dataset.

Proportion of attractions and brand ratings from transaction dataset.</p

DataSheet_1_Genome-wide analysis and expression of the aquaporin gene family in Avena sativa L..zip

BackgroundOat (Avena sativa L.) belongs to the early maturity grass subfamily of the Gramineae subfamily oats (Avena) and has excellent characteristics, such as tolerance to barrenness, salt, cold, and drought. Aquaporin (AQP) proteins belong to the major intrinsic protein (MIP) superfamily, are widely involved in plant growth and development, and play an important role in abiotic stress responses. To date, previous studies have not identified or analyzed the AsAQP gene family system, and functional studies of oat AQP genes in response to drought, cold, and salt stress have not been performed.MethodsIn this study, AQP genes (AsAQP) were identified from the oat genome, and various bioinformatics data on the AQP gene family, gene structure, gene replication, promoters and regulatory networks were analyzed. Quantitative real-time PCR technology was used to verify the expression patterns of the AQP gene family in different oat tissues under different abiotic stresses.ResultsIn this study, a total of 45 AQP genes (AsAQP) were identified from the oat reference genome. According to a phylogenetic analysis, 45 AsAQP were divided into 4 subfamilies (PIP, SIP, NIP, and TIP). Among the 45 AsAQP, 23 proteins had interactions, and among these, 5AG0000633.1 had the largest number of interacting proteins. The 20 AsAQP genes were expressed in all tissues, and their expression varied greatly among different tissues and organs. All 20 AsAQP genes responded to salt, drought and cold stress. The NIP subfamily 6Ag0000836.1 gene was significantly upregulated under different abiotic stresses and could be further verified as a key candidate gene.ConclusionThe findings of this study provide a comprehensive list of members and their sequence characteristics of the AsAQP protein family, laying a solid theoretical foundation for further functional analysis of AsAQP in oats. This research also offers valuable reference for the creation of stress-tolerant oat varieties through genetic engineering techniques.</p

Data_Sheet_1_Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram.PDF

IntroductionRecently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition.MethodsWe proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP).ResultsFor the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network).DiscussionIn summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.</p

Attractions potential cooperation network structure based on structural holes.

(a) Effective size; (b) constraint; (c) scatter plot of effective size and constraint.</p