Asymmetric Synthesis of Fluorine-containing Compounds Using Organocatalysts

Asymmetric synthesis of fluorine-containing compounds using organocatalysts has been extensively investigated and several important strategies have been developed in the last decade. This review focuses on the recent advances in the introduction of the fluorine atom into organic molecules by: i) electrophilic fluorination reactions; ii) the use of easily available fluorine-containing building blocks, both of interest in our research laboratory

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability

EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. Hence, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller paradigms, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general online decoding model for various EEG tasks.Comment: 20 pages, 7 Figure

RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

This paper presents a new data augmentation algorithm for natural understanding tasks, called RPN:Random Position Noise algorithm.Due to the relative paucity of current text augmentation methods. Few of the extant methods apply to natural language understanding tasks for all sentence-level tasks.RPN applies the traditional augmentation on the original text to the word vector level. The RPN algorithm makes a substitution in one or several dimensions of some word vectors. As a result, the RPN can introduce a certain degree of perturbation to the sample and can adjust the range of perturbation on different tasks. The augmented samples are then used to give the model training.This makes the model more robust. In subsequent experiments, we found that adding RPN to the training or fine-tuning model resulted in a stable boost on all 8 natural language processing tasks, including TweetEval, CoLA, and SST-2 datasets, and more significant improvements than other data augmentation algorithms.The RPN algorithm applies to all sentence-level tasks for language understanding and is used in any deep learning model with a word embedding layer.Comment: 10 pages, 4 figure

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page

Successful Features of Crowdfunding Campaigns: An Analysis of Requests for Coronavirus Food Relief

Crowdfunding is an emerging industry in the past decades, which proliferates and has attracted an enormous population from the public to be involved in various funding projects in multiple fields such as business entrepreneurship, healthcare, and fintech. Meanwhile, charitable crowdfunding platforms such as GoFundMe, Indiegogo, and Kickstarter have allowed internet users to provide help and donation to the fundraisers directly. As the year 2020 is surrounded by the COVID-19 global pandemic spreading out the world, the topic of coronavirus relief has surged. Thus, it is worthy of evaluating the crowdfunding campaign\u27s effectiveness during the coronavirus context by making a connection between fundraising activities and coronavirus relief. This paper aims to investigate the effects of various factors affecting a donation-based crowdfunding campaign for coronavirus relief of food donation in the United States and determine the significant factors affecting the campaign\u27s success rate. To achieve this research purpose, secondary data were extensively collected from the crowdfunding platform GoFundMe for regression analysis. The sample data was derived from crowdfunding campaigns launched from March 1st, 2020, to May 31st, 2020. During this period, the United States was severely affected by the COVID-19 pandemic with an exponentially surged number of confirmed cases. This paper derives the independent variables that have been examined from previous studies and further applies in the coronavirus context to identify whether these factors are significant influencers to the success of crowdfunding campaigns for coronavirus relief of food donation. The factors being examined include target funding amount, the existence of spelling mistakes, the presence of pictures, video, social network sites, project updates, comments between fundraisers and backers, and links to external websites. That the significant factors contributing to a successful funding project are similar, as identified in previous reward-based and equity crowdfunding studies. On the other hand, several independent variables\u27 effectiveness varied between the normal scenario and the coronavirus context, as such variables demonstrate a much compelling role to attract donors for the coronavirus relief activations. The analysis is valuable and worthy of different viewpoints. First, understanding the donor\u27s motivation and the success features of funding projects is valuable for fundraisers to have a strategic mindset for decision-making criteria when initiating funding projects to attract more donors and the amount of money. Second, because of the lack of literature focusing on examining the success features for donation-based crowdfunding campaigns, this study fills the gap and further focus on the crowdfunding activations in the context of coronavirus food relief in the US. Therefore, this study provides significant insight to understand the dynamics of the donation-based crowdfunding campaign and provides a recommendation to develop coronavirus relief more efficiently

Weighted Semiparameter Model and Its Application

A weighted semiparameter estimate model is proposed. The parameter components and nonparameter components are weighted. The weights are determined by the characters of different data. Simulation data and real GPS data are both processed by the new model and least square estimate, ridge estimate, and semiparameter estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The mean square error is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum mean square error. The weighted semiparameter estimate model has effectiveness and high precision

Emissive Platinum(II) Cages with Reverse Fluorescence Resonance Energy Transfer for Multiple Sensing

It is quite challenging to realize fluorescence resonance energy transfer (FRET) between two chromophores with specific positions and directions. Herein, through the self-assembly of two carefully selected fluorescent ligands via metal-coordination interactions, we prepared two tetragonal prismatic platinum(II) cages with a reverse FRET process between their faces and pillars. Bearing different responses to external stimuli, these two emissive ligands are able to tune the FRET process, thus making the cages sensitive to solvents, pressure, and temperature. First, these cages could distinguish structurally similar alcohols such as n-butanol, t-butanol, and i-butanol. Furthermore, they showed decreased emission with bathochromic shifts under high pressure. Finally, they exhibited a remarkable ratiometric response to temperature over a wide range (223–353 K) with high sensitivity. For example, by plotting the ratio of the maximum emission (I600/I480) of metallacage 4b against the temperature, the slope reaches 0.072, which is among the highest values for ratiometric fluorescent thermometers reported so far. This work not only offers a strategy to manipulate the FRET efficiency in emissive supramolecular coordination complexes but also paves the way for the future design and preparation of smart emissive materials with external stimuli responsiveness

Inhibition of P2X7 receptors improves outcomes after traumatic brain injury in rats

Traumatic brain injury (TBI) is the leading cause of death and disability for people under the age of 45 years worldwide. Neuropathology after TBI is the result of both the immediate impact injury and secondary injury mechanisms. Secondary injury is the result of cascade events, including glutamate excitotoxicity, calcium overloading, free radical generation, and neuroinflammation, ultimately leading to brain cell death. In this study, the P2X7 receptor (P2X7R) was detected predominately in microglia of the cerebral cortex and was up-regulated on microglial cells after TBI. The microglia transformed into amoeba-like and discharged many microvesicle (MV)-like particles in the injured and adjacent regions. A P2X7R antagonist (A804598) and an immune inhibitor (FTY720) reduced significantly the number of MV-like particles in the injured/adjacent regions and in cerebrospinal fluid, reduced the number of neurons undergoing apoptotic cell death, and increased the survival of neurons in the cerebral cortex injured and adjacent regions. Blockade of the P2X7R and FTY720 reduced interleukin-1βexpression, P38 phosphorylation, and glial activation in the cerebral cortex and improved neurobehavioral outcomes after TBI. These data indicate that MV-like particles discharged by microglia after TBI may be involved in the development of local inflammation and secondary nerve cell injury

Neural Consequences of Chronic Short Sleep: Reversible or Lasting?

Approximately one-third of adolescents and adults in developed countries regularly experience insufficient sleep across the school and/or work week interspersed with weekend catch up sleep. This common practice of weekend recovery sleep reduces subjective sleepiness, yet recent studies demonstrate that one weekend of recovery sleep may not be sufficient in all persons to fully reverse all neurobehavioral impairments observed with chronic sleep loss, particularly vigilance. Moreover, recent studies in animal models demonstrate persistent injury to and loss of specific neuron types in response to chronic short sleep (CSS) with lasting effects on sleep/wake patterns. Here, we provide a comprehensive review of the effects of chronic sleep disruption on neurobehavioral performance and injury to neurons, astrocytes, microglia, and oligodendrocytes and discuss what is known and what is not yet established for reversibility of neural injury. Recent neurobehavioral findings in humans are integrated with animal model research examining long-term consequences of sleep loss on neurobehavioral performance, brain development, neurogenesis, neurodegeneration, and connectivity. While it is now clear that recovery of vigilance following short sleep requires longer than one weekend, less is known of the impact of CSS on cognitive function, mood, and brain health long term. From work performed in animal models, CSS in the young adult and short-term sleep loss in critical developmental windows can have lasting detrimental effects on neurobehavioral performance