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Zirconium Metal-Organic Frameworks That contain a Biselenophene Linker: Functionality, Portrayal, and Luminescent Components.
They suggested that IP3 diffusion plays an important role in mediating this synaptic modulation.Color perception is a major guiding factor in the evolutionary process of human civilization, but most of the neurological background of the same are yet unknown. This work attempts to address this area with an EEG based neuro-cognitive study on response of brain to different color stimuli. With respect to a Grey baseline seven colors of the VIBGYOR were shown to 16 participants with normal color vision and corresponding EEG signals from different lobes (Frontal, Occipital & Parietal) were recorded. In an attempt to quantify the brain response while watching these colors, the corresponding EEG signals were analysed using two of the latest state of the art non-linear techniques (MFDFA and MFDXA) of dealing complex time series. MFDFA revealed that for all the participants the spectral width, and hence the complexity of the EEG signals, reaches a maximum while viewing color Blue, followed by colors Red and Green in all the brain lobes. MFDXA, on the other hand, suggests a lower degree of inter and intra lobe correlation while watching the VIBGYOR colors compared to baseline Grey, hinting towards a post processing of visual information. We hope that along with the novelty of methodologies, the unique outcomes of this study may leave a long term impact in the domain of color perception research.It is well known that different names of color can lead to distinct attractions to people. To study the neural mechanism underlying this phenomenon, an implicit association test task was designed for color names, in which participants were required to select the possible meanings of a Greek phrase from two color names (in Chinese). The behavioral results showed that the participants were more likely to select novel names for long Greek phrases and dates names for short Greek phrases. The EEG results showed that the mean amplitude of N1 was greater for selections of novel color names than selections of dates names for Greek phrases. Meanwhile, the mean amplitude of N3 for novel color names was more negative than that of dates color names. Significant interaction effect of N3 was also found for the four kinds of selections between Greek phrases and Chinese color names. Moreover, a frontal-positive and occipital-negative distribution for scalp topography of N1 was found, while the scalp topography of N3 was opposite as frontal-negative and occipital-positive distribution, suggesting the importance of visual cortex for perception of the color names and prefrontal cortex for integration and decision of selection. In summary, the results here indicated that colors with novel names could easily attract people's attention than colors with dates names, which might shed light on the usage of color names in real life.Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age 25 ± 5 years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value  less then  0.05), while disrupted pattern flow, in an opposite way, was found in ADHD children. In the beta band, information flow in pathways between anterior regions was significantly higher in healthy individuals than in the ADHD group. These differences are more indicated in connectivity that leads from frontal and central regions to the right frontal regions of the brain (F8 electrode). Furthermore, connections from central and lateral parietal areas to Pz electrode areas are statistically significant and higher in healthy children in this band. In the delta band, internal connections in the anterior region show a significant difference between the two groups, as this amount is higher in the ADHD group. Our analysis may provide new insights into information flow in brain regions of ADHD children in comparison with healthy children.Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects the social abilities of patients. Studies have shown that a small number of abnormal functional connections (FCs) exist in the cerebral hemisphere of ASD patients. The identification of these abnormal FCs provides a biological ground for the diagnosis of ASD. In this paper, we propose a combined deep feature selection (DFS) and graph convolutional network method to classify ASD. Firstly, in the DFS process, a sparse one-to-one layer is added between the input and the first hidden layer of a multilayer perceptron, thus each functional connection (FC) feature can be weighted and a subset of FC features can be selected accordingly. Then based on the selected FCs and the phenotypic information of subjects, a graph convolutional network is constructed to classify ASD and typically developed controls. Finally, we test our proposed method on the ABIDE database and compare it with some other methods in the literature. Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features. With DFS, the performance of GCN classifier can be improved dramatically. The proposed method achieves state-of-the-art performance with an accuracy of 79.5% and an area under the receiver operating characteristic curve (AUC) of 0.85 on the preprocessed ABIDE dataset; it is superior to the other methods. Further studies on the top-ranked thirty FCs obtained by DFS show that these FCs are widespread over the cerebral hemisphere, and the ASD group appears a significantly higher number of weak connections compared to the typically developed group.To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.There is currently a growing interest in a deeper understanding of consumer behaviour. In this context, the union of different disciplines such as neuroscience and marketing has given birth to new fields of knowledge, e.g. neuromarketing. This study is mainly aimed at carrying out a systematic revision of the literature on neuromarketing from a holistic point of view, analysing its definition and processes, as well as more specific aspects such as its ethics and applications. Based on the results of our review, following a combined methodology with a base dictionary and text mining, our study presents both the current lines of research and the future lines of work.
The aim of this study was to evaluate the efficacy and safety of gemcitabine plus nab-paclitaxel (GnP) as second-line chemotherapy following first-line FOLFIRINOX treatment failure in advanced pancreatic cancer.

This was a multicenter, single-arm, open-label, phase 2 trial done at three tertiary centers in South Korea from May 2018 to December 2019. Eligible patients were aged 20 years or older, had histologically confirmed advanced pancreatic ductal adenocarcinoma, and disease progression after receiving first-line FOLFIRINOX. Patients received a second-line GnP regimen as intravenous nab-paclitaxel at a dose of 125 mg/m
and gemcitabine at a dose of 1000 mg/m
, on days 1, 8, and 15 every 4 weeks until disease progression or unacceptable toxicity. The primary outcome was survival rate at 6 months and the secondary outcomes were median progression-free survival (PFS), overall survival (OS), disease control rate (DCR), and adverse events. This study is registered with Clinicaltrials.gov. (NCT03401827).

Forty patients were enrolled in the study.
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