Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Characterization and Functionalisation of Activated Carbon for the Enhancement of Enzyme Catalyst Activity

Background and objective:Activated carbon is commonly used as an immobilisation matrix due to its large surface area,making it a highly desirable matrix for use in immobilising enzymes as preparation for use on the industrial scale.The objective of this research is to determine the effectiveness of different acids for functionalisation on immobilisation capacity and also to characterize the functionalized activated carbon for the functional groups present.Materials and methods:Activated carbon was functionalised with three acids(hydrochloric acid,nitric acid and sulphuric acid)along with a control sample washed with distilled water.Immobilisation capacity was calculated with hydrochloric acid functionalized activated carbon(HCl-FAC)giving the highest immobilization capacity(6.022 U/g).Characterisation of the functionalised activated carbon was conducted using FT-IR(Fourier Transform Infra-Red)spectroscopy analysis of the samples with the aim of analyzing the various functional groups present to determine the sample with distinct characteristics thus telling the degree of adsorption of lipase onto the activated carbon powder.Results:HNO3-FAC(functionalized activated carbon)showed a very distinct pattern as a larger number of surface functional groups emerged.The immobilisation on a matrix ensures thermal stability and increased reusability of the enzyme.Therefore,in this research,lipase sourced from Candida antarctica was immobilised on acid functionalised activated carbon.The best acid for functionalisation was found to be hydrochloric acid.Conclusion:Due to the very distinct patterns shown by the FT-IR spectrum of the HNO3-FAC after a fair comparison with others,it allows for a larger number of surface functional groups which will definitely enhance the stability of the enzyme lipase.

Correlation of cognitive function with acetylcholinesterase activity and P300 event-related potential of patients with type 2 diabetes mellitus

BACKGROUND： At present, central cholinergic neuron system is regarded the most major structural basis of cognitive function. Changes in structure of cholinergic neuron system of brain and receptor expression after brain injury can cause cognitive impairment. OBJECTIVE＂ To comparatively observe the intelligence quotient （IQ）, latent period and wave amplitude of P300 event-related potential and the difference of activity of acetylcholinesterase （ACHE） in blood and cerebrospinal fluid between patients with type 2 diabetes mellitus and with non-diabetes mellitus, and analyze the correlation of IQ of cognitive impairment patients with diabetes mellitus with AChE activity, latent period and wave amplitude of P300 event-related potential in cerebrospinal fluid. DESIGN： Correlation analysis of contrast observation SETTING： Department of Endocrinology, Affiliated Hospital of Binzhou Medical College PARTICIPANTS： Totally 32 patients with type 2 diabetes mellitus who received the treatment in the Department of Endocrinology, Affiliated Hospital of Binzhou Medical College between April 2004 and April 2005 were recruited, serving as diabetes mellitus group. They, including 19 male and 13 female, aged 49 to 73 years, with disease course of 4 to 11 years, all met the diagnostic criteria of diabetes mellitus revised by World Health Organization in 1999. Another 30 patients with non-diabetes mellitus who homeochronously underwent lumbar anesthesia in the Department of Surgery and Department of Gynecology were recruited, serving as non-diabetes mellitus group. The 30 patients included 18 male and 12 female, and their age ranged from 46 to 71 years. Informed consents of detected items were obtained from the involved patients. METHODS： ① Evaluation,on IQ： The IQ of involved subjects was evaluated with Chinese Version of the Wechsler Adult Intelligence Scale revised by Gong Yao-xian （WAIS-RC）. WAIS-RC included 6 verbal subscales and 5 performance subscales. The test scores of the 11 subscales integrated into the scores of the whole scale, and the scores on the WAIS-RC included verbal IQ （VlQ）, performance IQ （PIQ） and full scale IQ （FIQ）. FIQ ≤79 scores indicated low IQ and FIQ≤69 indicated intelligence impairment. ② Detection of P300 wave： P300 wave was detected with evoked potential instrument （MYTOPRO, Italian）, and data of latent period and amplitude of P300 event-related potential were automatically shown by computer. ③ Detection of AChE activity in blood and cerebrospinal fluid： Activity of AChE of blood and cerebrospinal fluid was measured with biochemical methods by using CORNING-560 autoanalyzer.④Correlation analysis： Correlation of FIQ with AChE of cerebrospinal fluid and P300 wave of patients with type 2 diabetes mellitus was analyzed, t test was used in intergroup comparison and linear correlation analysis for relevant treatment. MAIN OUTCOME MEASURES： ① Comparison of IQ, latent period and wave amplitude of P300 wave as well as the activity of AChE between two groups. ② Analysis on the correlation of FIQ of patients with type 2 diabetes mellitus with AChE of cerebrospinal fluid and P300 wave. RESULTS： Thirty-two patients with diabetes mellitus and 30 non-diabetes mellitus participated in the result analysis. ①Comparison of IQ, latent period and wave amplitude of P300 wave as well as the activity of AChE between two groups： The scores of VIP, PIQ and FIQ of patients with type 2 diabetes mellitus were （97.4±10.4）. （92.6±8.4） and （95.2±9.7） scores, respectively; and those of patients with non-diabetes mellitus were （104.7±9.6）, （102.5±8.5）and（102.7±8.9） scores, respectively, and P 〈 0.05-0.01 was set in intergroup comparison. The latent period of P300 wave at points Fz , Cz and Pz of patients with type 2 diabetes mellitus was （370.8±41.8）.（371.5±39.1）and （375.1±43.1） ms, respectively, and that of patients with non-diabetes mellitus was （ 332.1 ±28.3 ）, （335.7 ±29.4）and （339.7 ±27.3） ms, respectively, and P 〈 0.01 was set in intergroup comparison; Wave amplitude of P300 of patients with type 2 diabetes mellitus was （8.6±4.1）,（8.6±4.0） and （7.7±4.0） μV, respectively and that of patients with non-diabetes mellitus was （11.9±4.1）,（11.5±4.4） and （10.9±5.0） μV, respectively , and P 〈 0.05-0.01 was set in intergroup comparison; The level of AChE in blood and cerebrospinal fluid of patients with type 2 diabetes mellitus was （235.61 ±50.34）and （17.89±4.46） μkat/L, respectively, which was significantly higher than that of patients with non-diabetes mellitus [（205.03±44.15）and （14.63±0.48） μkat /L, respectively], and P 〈 0.05-0.01 was set in the intergroup comparison. ② Correlation of FIQ value of patients with type 2 diabetes mellitus with AChE of cerebrospinal fluid and P300 wave： The value of FIQ was significantly negatively correlated with the AChE activity of cerebrospinal fluid （r=-0.588 1, P 〈 0.01 ）, significantly negatively correlated with the latent period at points Fz. C and Pz of P300 wave （r= -0.700 5, -0.689 4, -0.688 5, P 〈 0.01 ）, and significantly positively correlated with the amplitude at points Fz . Cz and Pz of P300 wave（r= 0.607 4,0.616 1,0.592 0,P 〈 0.01 ）. CONCLUSION： ① Cognitive impairment of patients with type 2 diabetes mellitus might be related to the increase of activity of AChE in cerebrospinal fluid. ②Combined application of examination of P300 wave and evaluation of IQ is more useful in deciding the state of cognitive function of patients with type 2 diabetes mellitus.

A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

Activation Functions Effect on Fractal Coding Using Neural Networks

Activation functions play an essential role in converting the output of the artificial neural network into nonlinear results,since without this nonlinearity,the results of the network will be less accurate.Nonlinearity is the mission of all nonlinear functions,except for polynomials.The activation function must be dif-ferentiable for backpropagation learning.This study’s objective is to determine the best activation functions for the approximation of each fractal image.Different results have been attained using Matlab and Visual Basic programs,which indi-cate that the bounded function is more helpful than other functions.The non-lin-earity of the activation function is important when using neural networks for coding fractal images because the coefficients of the Iterated Function System are different according to the different types of fractals.The most commonly cho-sen activation function is the sigmoidal function,which produces a positive value.Other functions,such as tansh or arctan,whose values can be positive or negative depending on the network input,tend to train neural networks faster.The coding speed of the fractal image is different depending on the appropriate activation function chosen for each fractal shape.In this paper,we have provided the appro-priate activation functions for each type of system of iterated functions that help the network to identify the transactions of the system.

THE ANTITUMOR ACTIVITY OF LYMPHOTOXIN AND THE EFFECT OF INTERLEUKIN-2 ON ITS FUNCTION

The effect of lymphotoxin (LT)-containing supernatant produced by lectin-stimulated human lymphocytes on tumor cells and the relation between interleukin-2 (IL-2) and LT were studied in this article. Results showed that LT-containing superna-tants had cytotoxicities on many different kinds of tumor cells from human and mice, that actinomycin D increased the LT activities on target cells and that IL-2 had the ability to increase the cytotoxicity of human PBMC on tumor cells, after being treated with LT, the target cells were more easy to kill by PBMC as well.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Large-scale self-normalizing neural networks

Self-normalizing neural networks(SNN)regulate the activation and gradient flows through activation functions with the self-normalization property.As SNNs do not rely on norms computed from minibatches,they are more friendly to data parallelism,kernel fusion,and emerging architectures such as ReRAM-based accelerators.However,existing SNNs have mainly demonstrated their effectiveness on toy datasets and fall short in accuracy when dealing with large-scale tasks like ImageNet.They lack the strong normalization,regularization,and expression power required for wider,deeper models and larger-scale tasks.To enhance the normalization strength,this paper introduces a comprehensive and practical definition of the self-normalization property in terms of the stability and attractiveness of the statistical fixed points.It is comprehensive as it jointly considers all the fixed points used by existing studies:the first and second moment of forward activation and the expected Frobenius norm of backward gradient.The practicality comes from the analytical equations provided by our paper to assess the stability and attractiveness of each fixed point,which are derived from theoretical analysis of the forward and backward signals.The proposed definition is applied to a meta activation function inspired by prior research,leading to a stronger self-normalizing activation function named‘‘bi-scaled exponential linear unit with backward standardized’’(bSELU-BSTD).We provide both theoretical and empirical evidence to show that it is superior to existing studies.To enhance the regularization and expression power,we further propose scaled-Mixup and channel-wise scale&shift.With these three techniques,our approach achieves 75.23%top-1 accuracy on the ImageNet with Conv MobileNet V1,surpassing the performance of existing self-normalizing activation functions.To the best of our knowledge,this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.

Progress on the melanoidins produced by the Maillard reaction of fermented food and traditional Chinese medicine

FMs(Food-borne melanoidins)are brown high molecular weight polymers formed by the Mailiard reaction between carbohydrates and nitrogen-containing compounds during the processing of food or Traditional Chinese Medicine(TCM),and are widely found in food-borne products such as TCM concoctions,bakery,brewing,soy sauce,ferment and other food-borne products.FMs not only have a variety of biological activities,such as antioxidant,antibacterial,immunomodulation,regulation of intestinal flora,etc.,and can change the color,aroma and taste of food.The diversity of its components has become a research hotspot at home and abroad in recent years,with a wide range of application prospects.Therefore,this paper summarizes the existing information on FMs at home and abroad,mainly describes their preparation process,physicochemical properties,structural characteristics and functional activity research progress.Typical FMs,such as coffee,biscuits,wine and soy sauce in daily food,and Polygonatum,Perilla oil,Black ginseng,and Red jujube in T,were highlighted.Summarising the current status of research between the chemistry and pharmacodynamics of relevant FMs and presenting challenges and future recommendations for melanoidin research.In future research on FMs,one should pay more attention to basic research,especially isolation and purification and generation mechanisms,to further demonstrate the biological activity of FMs in vivo and in clinical trials.Thus,the potential value of its existence is deeply exploited to meet the needs of technology,production and health.

Electrochemically reduced graphene oxide with enhanced electrocatalytic activity toward tetracycline detection

An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） detection compared to the ERGO-12v （GO applied to a negative potential of-1.2 V）, GO, chemically reduced GO （CRGO）-modified glassy carbon electrode （GC） and bare GC electrodes. The redox peaks of TTC on an ERGO-0.8v-modifled glass carbon electrode （GC/ERGO-0.8v） were within 0-0.5 V in a pH 3.0 buffer solution with the oxidation peak current correlating well with TTC concentration over a wide range from 0.1 to 160 mg/L Physical characterizations with Fourier transform infrared （FT-IR）, Raman, and X-ray photoelectron spectroscopies （XPS） demonstrated that the oxygen-containing functional groups on GO diminished after the electrochemical reduction at -0.8 V, yet still existed in large amounts, and the defect density changed as new sp2 domains were formed. These changes demonstrated that this adjustment in the number of oxygen-containing groups might be the main factor affecting the electrocatalytic behavior of ERGO. Additionally, the defect density and sp2 domains also exert a profound influence on this behavior. A possible mechanism for the TTC redox reaction at the GC/ERGO-0.8v electrode is also presented. This work suggests that the electrochemical reduction is an effective method to establish new catalytic activities of GO by setting appropriate parameters.

Effects of clump spacing on nutrient distribution and root activity of Dendrocalamus strictus in the humid region of Kerala,peninsular India

The humid agroclimatic conditions of Kerala,India permit the cultivation of an array of bamboo species of which Dendrocalamus strictus Roxb.(Nees.) is an important one on account of its high growth rate and multiple uses. Stand density, a potential tool in controlling the productivity of woody ecosystems, its effect on growth and root distribution patterns may provide a better understanding of productivity optimization especially when bamboo-based intercropping options are considered.Growth attributes of 7-year-old bamboo(D. strictus) stands managed at variable spacing(4×4 m, 6×6 m, 8×8 m,10×10 m, 12×12 m) were studied. Functional root activity among bamboo clumps were also studied using a radio tracer soil injection method in which the radio isotopeP was applied to soil at varying depths and lateral distances from the clump. Results indicate that spacing exerts a profound influence on growth of bamboo. Widely spaced bamboo exhibited higher clump diameters and crown widths while clump heights were better under closer spacing. Clump height was 30% lower and DBH 52%higher at the widest spacing(12×12 m) compared to the closest spacing(4×4 m). With increasing soil depth and lateral distance, root activity decreased significantly. Root activity near the clump base was highest(809 counts per minute, cpm; 50 cm depth and 50 cm lateral distance) at 4×4 m. Tracer study further showed wider distribution of root activity with increase in clump spacing. It may be concluded that the intensive foraging zone of bamboo is within a 50-cm radius around the clump irrespective of spacing. N, P and K content in the upper 20 cm was 2197,21, and 203 kg/ha respectively for the closely spaced bamboo(4×4 m) which were significantly higher than corresponding nutrient content at wider spacings. About 50% of N, P and K were present within the 0–20 cm soil layer, which decreased drastically beyond the 20 cm depth.The results suggest that stand management practices through planting density regulation can modify the resource acquisition patterns of D. strictus which in turn can change growth and productivity considerably. Such information on root activities, spatial and temporal strategies of resource sharing will be helpful in deciding the effective nutrition zone for D. strictus. Further, the study throws light on the spatial distribution of non-competitive zones for productivity optimization yields, especially when intercropping practices are considered.

Microbial community structure and functional metabolic diversity are associated with organic carbon availability in an agricultural soil

Exploration of soil environmental characteristics governing soil microbial community structure and activity may improve our understanding of biogeochemical processes and soil quality. The impact of soil environmental characteristics especially organic carbon availability after 15-yr different organic and inorganic fertilizer inputs on soil bacterial community structure and functional metabolic diversity of soil microbial communities were evaluated in a 15-yr fertilizer experiment in Changping County, Beijing, China. The experiment was a wheat-maize rotation system which was established in 1991 including four different fertilizer treatments. These treatments included： a non-amended control（CK）, a commonly used application rate of inorganic fertilizer treatment（NPK）; a commonly used application rate of inorganic fertilizer with swine manure incorporated treatment（NPKM）, and a commonly used application rate of inorganic fertilizer with maize straw incorporated treatment（NPKS）. Denaturing gradient gel electrophoresis（DGGE） of the 16 S r RNA gene was used to determine the bacterial community structure and single carbon source utilization profiles were determined to characterize the microbial community functional metabolic diversity of different fertilizer treatments using Biolog Eco plates. The results indicated that long-term fertilized treatments significantly increased soil bacterial community structure compared to CK. The use of inorganic fertilizer with organic amendments incorporated for long term（NPKM, NPKS） significantly promoted soil bacterial structure than the application of inorganic fertilizer only（NPK）, and NPKM treatment was the most important driver for increases in the soil microbial community richness（S） and structural diversity（H）. Overall utilization of carbon sources by soil microbial communities（average well color development, AWCD） and microbial substrate utilization diversity and evenness indices（H＇ and E） indicated that long-term inorganic fertilizer with organic amendments incorporated（NPKM, NPKS） could significantly stimulate soil microbial metabolic activity and functional diversity relative to CK, while no differences of them were found between NPKS and NPK treatments. Principal component analysis（PCA） based on carbon source utilization profiles also showed significant separation of soil microbial community under long-term fertilization regimes and NPKM treatment was significantly separated from the other three treatments primarily according to the higher microbial utilization of carbohydrates, carboxylic acids, polymers, phenolic compounds, and amino acid, while higher utilization of amines/amides differed soil microbial community in NPKS treatment from those in the other three treatments. Redundancy analysis（RDA） indicated that soil organic carbon（SOC） availability, especially soil microbial biomass carbon（Cmic） and Cmic/SOC ratio are the key factors of soil environmental characteristics contributing to the increase of both soil microbial community structure and functional metabolic diversity in the long-term fertilization trial. Our results showed that long-term inorganic fertilizer and swine manure application could significantly improve soil bacterial community structure and soil microbial metabolic activity through the increases in SOC availability, which could provide insights into the sustainable management of China＇s soil resource.

Altered intrinsic functional connectivity of the primary visual cortex in youth patients with comitant exotropia: a resting state fMRI study

AIM： To evaluate the differences in the functional connectivity（FC） of the primary visual cortex（V1） between the youth comitant exotropia（CE） patients and health subjects using resting functional magnetic resonance imaging（f MRI） data.METHODS： Totally, 32 CEs（25 males and 7 females） and 32 healthy control subjects（HCs）（25 males and 7 females） were enrolled in the study and underwent the MRI scanning. Two-sample t-test was used to examine differences in FC maps between the CE patients and HCs. RESULTS： The CE patients showed significantly less FC between the left brodmann area（BA17） and left lingual gyrus/cerebellum posterior lobe, right middle occipital gyrus, left precentral gyrus/postcentral gyrus and right inferior parietal lobule/postcentral gyrus. Meanwhile, CE patients showed significantly less FC between right BA17 and right middle occipital gyrus（BA19, 37）.CONCLUSION： Our findings show that CE involves abnormal FC in primary visual cortex in many regions, which may underlie the pathologic mechanism of impaired fusion and stereoscopic vision in CEs.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Neural Networks on an FPGA and Hardware-Friendly Activation Functions

This paper describes our implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset—the Modified National Institute of Standards and Technology (MNIST) database. We also propose a novel hardware-friendly activation function called the dynamic Rectifid Linear Unit (ReLU)—D-ReLU function that achieves higher performance than traditional activation functions at no cost to accuracy. We built a 2-layer online training multilayer perceptron (MLP) neural network on an FPGA with varying data width. Reducing the data width from 8 to 4 bits only reduces prediction accuracy by 11%, but the FPGA area decreases by 41%. Compared to networks that use the sigmoid functions, our proposed D-ReLU function uses 24% - 41% less area with no loss to prediction accuracy. Further reducing the data width of the 3-layer networks from 8 to 4 bits, the prediction accuracies only decrease by 3% - 5%, with area being reduced by 9% - 28%. Moreover, FPGA solutions have 29 times faster execution time, even despite running at a 60× lower clock rate. Thus, FPGA implementations of neural networks offer a high-performance, low power alternative to traditional software methods, and our novel D-ReLU activation function offers additional improvements to performance and power saving.

Effect of Preparation Process of Functional Polymer Active Materials on Properties of Gadolinium Ion Selective Electrode

Recently we have studied the rare earth ion-selective electrodes with active materials of the func-tional polymers and found that the process chosen for the functional polymers had an effect on the propertiesof gadolinium ion selective electrode besides the effects of their structures.1.Effect of preparation process of the grafted polymers on the properties ofgadolinium ion selective electrodesThe electrode membranes which consist of functional polymers as active materials were prepared by re-action of gadolinium chloride with the radiation grafted clmer of acrlic acid and polystyrene of which

Functional magnetic resonance imaging evidence for activated functional brain areas following acupoint needling in the extremities

Totally three articles focusing on functional magnetic resonance imaging features of brain function in the activated brain regions of stroke patients undergoing acupuncture on the healthy limbs and healthy controls undergoing acupuncture on the lower extremities are published in three issues. We hope that our readers find these papers useful to their research.

Antitumor Activity and Theoretical Calculation of Evodiamine and Rutaecarpine

The antitumor activities of two alkaloids, evodiamine（EVO） and rutaecarpine（RUT）, against MCF-7, SMMC-7721 and SW-1353 cells growth in vitro were investigated by MTT assay. The results showed that the anti-tumor effects of two alkaloids were remarkably different. In order to discover the relationship of antitumor activity and structures of the compounds, the dihedral angle, Natural Electron Configuration, frontier molecular orbital profiles（HOMO, LUMO）and bandgaps of these two compounds have been studied based on density functional theory（DFT）by means of DFT-B3LYP/6-31G（d） in Gaussian 03. The calculation results of dihedral angle showed that EVO, due to the existence of methyl group attached to the N（14） atom, have non-planar and twisted structures, which decrease the stability of EVO and increase the activity of EVO. Furthermore, the bandgaps of RUT are lower than that of EVO, indicating RUT has higher stability than EVO, so the activity of EVO is higher than that of RUT. In addition, the negative charge of N14 atom in EVO is lower than that of in RUT, so the positive charge of N（14） atom in EVO is higher than that of in RUT, which suggests that the nucleophile is easier to aggress the N（14） atom in EVO than that in RUT, so the reason of the different antitumor activities of EVO and RUT may be attacked by nucleophile.