An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain–computer interfacing

The redundant data in multichannel electroencephalogram (EEG) signals significantly reduces the performance of brain–computer interface (BCI) systems. By removing redundant channels, a channel selection strategy increases the classification accuracy of BCI systems. In this work, a novel channel selection method (stdWC) based on the standard deviation of wavelet coefficients across channels is proposed to identify Motor Imagery (MI) based EEG signals. The wavelet coefficients are calculated by employing a Continuous Wavelet Transform (CWT) filter bank to decompose each trial from the EEG channel. The wavelet coefficient's standard deviation values are obtained across the channels, and these values are then sorted to determine the EEG channels with the highest standard deviation values. The channels with the largest wavelet coefficient divergence are chosen. MI trials are then spatially filtered with the Common Spatial Pattern (CSP), and CWT filter bank-based 2D images are generated from the spatially filtered trials. These images are then classified using a unique nine-layered convolutional neural network (CNN) model that combines two feature maps acquired with differing filter sizes. The proposed framework (stdWC-CSP-CNN) is evaluated using kappa score and classification accuracy on two publically accessible datasets (BCI Competition III dataset IVa and BCI Competition IV dataset 2a). The suggested framework achieved a mean test classification accuracy of 88.8% for dataset IVa from BCI Competition III and 75.03% for dataset 2a from BCI Competition IV, according to the results. The proposed channel selection method outperforms the other channel selection methods examined, according to the results. By rejecting redundant channels, the whole framework can improve the performance of MI-based BCIs.     

Boosting Glioblastoma Therapy with Targeted Pyroptosis Induction

Glioblastoma (GBM) is a highly aggressive cancer that currently lacks effective treatments. Pyroptosis has emerged as a promising therapeutic approach for cancer, but there is still a need for new pyroptosis boosters to target cancer cells. In this study, it is reported that Aloe-emodin (AE), a natural compound derived from plants, can inhibit GBM cells by inducing pyroptosis, making it a potential booster for pyroptosis-mediated GBM therapy. However, administering AE is challenging due to the blood-brain barrier (BBB) and its non-selectivity. To overcome this obstacle, AE@ZIF-8 NPs are developed, a biomineralized nanocarrier that releases AE in response to the tumor's acidic microenvironment (TAM). Further modification of the nanocarrier with transferrin (Tf) and polyethylene glycol-poly (lactic-co-glycolic acid) (PEG-PLGA) improves its penetration through the BBB and tumor targeting, respectively. The results show that AE-NPs (Tf-PEG-PLGA modified AE@ZIF-8 NPs) significantly increase the intracranial distribution and tumor tissue accumulation, enhancing GBM pyroptosis. Additionally, AE-NPs activate antitumor immunity and reduce AE-related toxicity. Overall, this study provides a new approach for GBM therapy and offers a nanocarrier that is capable of penetrating the BBB, targeting tumors, and attenuating toxicity.     

糯米酒发酵及对亚硝酸盐清除作用的研究

用分光光度法测定了糯米酒对亚硝酸盐的清除率。在模拟人体胃液条件下,研究了影响糯米酒清除亚硝酸盐的因素,并与抗坏血酸的清除率进行比较。试验表明:最佳清除条件为:离心转速为3 500 r/min、反应时间20 min、发酵时间48 h;5.0 mL糯米酒与10 mg的抗坏血酸对亚硝酸盐的清除率基本相同。     

干燥温度对改良挤压法制备的全谷物质构米品质的影响

研究干燥温度(40～80℃)对改良挤压法制备的全谷物质构米的营养特性、食用品质和热力学特性的影响。结果表明:干燥温度在40～80℃,全谷物质构米的粗脂肪和脂肪酸含量随温度升高而降低,对其他营养素无明显影响;当干燥温度低于50℃时,全谷物质构大米样品具有易消解、米饭较软、黏弹性大、口感较好的食味品质;同时,DSC的试验数据表明干燥温度低于50℃时,全谷物质构米具有起始糊化温度略低,糊化时间更短,糊化焓更小的热力学特性。     

水解酸化-SBR法处理米粉厂废水工程实践

介绍了水解酸化—SBR法处理米粉厂生产废水的工程实例．经过两年多的连续运行，处理效果稳定．多次监测结果表明：出水CODcr达到77．1mg／L，BOD5达到24．3mg／L，SS达到20．6mg／L，其去除率达到90％以上，完全达到国家规定的《污水综合排放标准》(GB8978—96)的新改扩一级标准．     

一种小波混合优化算法用于脑MR图像配准

优化算法是脑磁共振(MR)图像配准方法中的重要组成部分。针对目前已有的优化算法,提出了采用小波混合优化算法应用于脑MR图像配准。该优化算法采用小波分解技术实现脑MR图像的高低分辨率分解,在低分辨率图像上采用PSO(粒子群算法)进行优化获得初步配准参数,然后再采用Powell算法进一步精细优化,获得最终配准参数。实验比较结果表明,该优化算法相对于目前一些主流优化算法来说,具有较低的时间代价,较高的优化精度,以及较好的优化稳定性。     

Prediction of Long-Term Treatment Response to Selective Serotonin Reuptake Inhibitors (SSRIs) Using Scalp and Source Loudness Dependence of Auditory Evoked Potentials (LDAEP) Analysis in Patients with Major Depressive Disorder

Background: Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD). Methods: Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of >50% between baseline and follow-up. Results: The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017; p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041). Conclusions: The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.     

绍兴黄酒中一种ACE活性抑制肽的分离和鉴定

首次利用超滤、大孔吸附树脂柱层析和反相色谱法将绍兴黄酒中的肽类组分进行分离制备,并进行了血管紧张素转换酶(ACE)的活性抑制试验,通过高效液相色谱/电喷雾电离质谱联用分析和氨基酸组成分析鉴定出一种ACE活性抑制肽的氨基酸序列为Gln-Ser-Gly-Pro。     

糙米发芽过程中淀粉功能特性的变化

研究发芽对糙米（品种：武运粳46）淀粉组分及含量、流变、凝胶、热力学和加工特性的影响。提取发芽0、24、48、72、96、120 h的糙米中的淀粉,用流变仪测定淀粉的动态流变学特性,质构仪测定淀粉凝胶的质构特性,并分析发芽对淀粉差示扫描量热（differential scanning calorimetry,DSC）图谱和加工性质的变化。结果表明：发芽后糙米中淀粉和直链淀粉含量均呈下降趋势;随发芽时间的延长,糙米中的淀粉在升温过程中弹性模量（G’）和黏性模量（G”）的峰值增加,淀粉糊化变性时的温度下降,糙米淀粉的弹性和黏性得到改善;发芽对淀粉凝胶质构特性有不同程度的影响,其中凝胶强度、凝胶硬度、弹性、胶黏性、咀嚼性均在72 h达到最大值;DSC扫描图谱表明发芽使糊化焓增加,提高了其稳定性和结晶度,对淀粉的相转变温度（To、Tp、Tc）无显著影响（P＞0.05）;发芽改善了淀粉的乳化性和乳化稳定性;发芽后淀粉的持水性没有显著（P＞0.05）变化,持油性整体呈上升趋势。发芽在一定程度上改变了糙米淀粉的功能性质。     

Combined effect of injection timing,EGR and injection pressure in reducing the NOx emission of a biodiesel blend

In this study, an attempt was made to reduce the higher oxides of nitrogen (NO_x) emission of a crude rice bran oil methyl ester (CRBME) blend through modification of combustion process by retarding fuel injection timing and exhaust gas recirculation at an increased fuel injection pressure. At modified condition, delay period and peak pressure of CRBME blend were lower than those at normal condition. The occurrence of maximum heat release rate retarded with a higher magnitude when compared with normal condition. Experimental results show that as a result of combustion modification, NO_x and carbon monoxide emissions were reduced significantly with marginal increase in smoke density. Brake thermal efficiency and unburnt hydrocarbon emissions of the engine were increased significantly as a result of this modification process. This investigation shows that the NO_x emission of a biodiesel blend can be reduced with less sacrifice on smoke density and increase in the brake thermal efficiency by modifying the combustion process.