The present study demonstrated a noninvasive preocular sensor system for the concurrent monitoring of diabetes and one of its prevalent complications, dry eye syndrome (DES), using tear fluids. Two distinct sensors, i.e., the glucose and DES sensors, were prepared and encased together in a single housing unit to produce the sensor system, and the tip was designed to be in contact with the eye surface noninvasively to collect and deliver tear fluid to the sensors. The glucose sensor was modified from a commercially available electrochemical sensor to allow for the measurement of glucose concentrations, even in a small amount of collected tear fluid. The DES sensor was equipped with a microchannel spaced with two parallel electrodes to determine the amount of collected tear fluid. In vivo experimental results revealed that with the collected tear fluid of about 0.6-1.0 μl, the sensor system estimated the blood glucose concentrations with acceptable accuracy compared with that of the glucometer in clinical use. The DES condition in animals was diagnosed with high sensitivity (91.7%) and specificity (83.3%).To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. read more In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.The COVID-19 pandemic, caused by type 2 Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), puts all of us to the test. Epidemiologic observations could critically aid the development of protective measures to combat this devastating viral outbreak. Recent observations, linked nation based universal Bacillus Calmette-Guerin (BCG) vaccination to potential protection against morbidity and mortality from SARS-CoV-2, and received much attention in public media. We wished to validate the findings by examining the country based association between COVID-19 mortality per million population, or daily rates of COVID-19 case fatality (i.e. Death Per Case/Days of the endemic [dpc/d]) and the presence of universal BCG vaccination before 1980, or the year of the establishment of universal BCG vaccination. These associations were examined in multiple regression modeling based on publicly available databases on both April 3rd and May 15th of 2020. COVID-19 deaths per million negatively associated with universal BCG vaccination in a country before 1980 based on May 15th data, but this was not true for COVID-19 dpc/d on either of days of inquiry. We also demonstrate possible arbitrary selection bias in such analyses. Consequently, caution should be exercised amidst the publication surge on COVID-19, due to political/economical-, arbitrary selection-, and fear/anxiety related biases, which may obscure scientific rigor. We argue that global COVID-19 epidemiologic data is unreliable and therefore should be critically scrutinized before using it as a nidus for subsequent hypothesis driven scientific discovery.Learning new content and vocabulary in a foreign language can be particularly difficult. Yet, there are educational programs that require people to study in a language they are not native speakers of. For this reason, it is important to understand how these learning processes work and possibly differ from native language learning, as well as to develop strategies to ease this process. The current study takes advantage of emotionality-operationally defined as positive valence and high arousal-to improve memory. In two experiments, the present paper addresses whether participants have more difficulty learning the names of objects they have never seen before in their foreign language and whether embedding them in a positive semantic context can help make learning easier. With this in mind, we had participants (with a minimum of a B2 level of English) in two experiments (43 participants in Experiment 1 and 54 in Experiment 2) read descriptions of made-up objects-either positive or neutral and either in their native or a foreign language. The effects of language varied with the difficulty of the task and measure used. In both cases, learning the words in a positive context improved learning. Importantly, the effect of emotionality was not modulated by language, suggesting that the effects of emotionality are independent of language and could potentially be a useful tool for improving foreign language vocabulary learning.

Repeated practice to acquire expertise could result in the structural and functional changes in relevant brain circuits as a result of long-term potentiation, neurogenesis, glial genesis, and remodeling.

The goal of this study is to use surface-based morphology (SBM) to study cortical thickness differences in Chinese chess experts and novices, and to use regions of cortical thickness differences as seeds to guide a resting state connectivity analysis of the same population.

A raw public dataset from Huaxi MR Research Center consisting of 29 Chinese chess experts and 29 novices was used in this study, with both T1-weighted and resting state functional MRI. Surface based morphometry was performed on the T1 images with the Freesurfur pipeline, with a vertex significance threshold of p<0.05 and a cluster false discovery rate of α < 0.05. Regions with significant differences were used in a seed-based comparison of resting state functional connectivity carried out with Statistical Parameter Mapping (SPM) and the Connectivity Toolbox (CONN).