Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology

In recent years, the use of psychedelic drugs to study brain dynamics has flourished due to the unique opportunity they offer to investigate the neural mechanisms of conscious perception. Unfortunately, there are many difficulties to conduct experiments on pharmacologically-induced hallucinations, especially regarding ethical and legal issues. In addition, it is difficult to isolate the neural effects of psychedelic states from other physiological effects elicited by the drug ingestion. Here, we used the DeepDream algorithm to create visual stimuli that mimic the perception of hallucinatory states. Participants were first exposed to a regular video, followed by its modified version, while recording electroencephalography (EEG). Results showed that the frontal region’s activity was characterized by a higher entropy and lower complexity during the modified video, with respect to the regular one, at different time scales. Moreover, we found an increased undirected connectivity and a greater level of entropy in functional connectivity networks elicited by the modified video. These findings suggest that DeepDream and psychedelic drugs induced similar altered brain patterns and demonstrate the potential of adopting this method to study altered perceptual phenomenology in neuroimaging research.     

Compression Helps Deep Learning in Image Classification

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL. Specifically, for each original image, consider its 10 JPEG compressed versions with their quality factor (QF) values from {100,90,80,70,60,50,40,30,20,10}. Under the assumption that the ground truth label of the original image is known at the time of selecting an input, but unknown to the underlying DNN, we present a selector called Highest Rank Selector (HRS). It is shown that HRS is optimal in the sense of achieving the highest Top k accuracy on any set of images for any k among all possible selectors. When the underlying DNN is Inception V3 or ResNet-50 V2, HRS improves, on average, the Top 1 classification accuracy and Top 5 classification accuracy on the whole ImageNet validation dataset by 5.6% and 1.9%, respectively, while reducing the input size in bits dramatically—the compression ratio (CR) between the size of the original images and the size of the selected input images by HRS is 8 for the whole ImageNet validation dataset. When the ground truth label of the original image is unknown at the time of selection, we further propose a new convolutional neural network (CNN) topology which is based on the underlying DNN and takes the original image and its 10 JPEG compressed versions as 11 parallel inputs. It is demonstrated that the proposed new CNN topology, even when partially trained, can consistently improve the Top 1 accuracy of Inception V3 and ResNet-50 V2 by approximately 0.4% and the Top 5 accuracy of Inception V3 and ResNet-50 V2 by 0.32% and 0.2%, respectively. Other selectors without the knowledge of the ground truth label of the original image are also presented. They maintain the Top 1 accuracy, the Top 5 accuracy, or the Top 1 and Top 5 accuracy of the underlying DNN, while achieving CRs of 8.8, 3.3, and 3.1, respectively.     

Observation of persistent α‐helical content and discrete types of backbone disorder during a molten globule to ordered peptide transition via deep‐UV resonance Raman spectroscopy

The molten globule (MG) state can aid in the folding of a protein to a functional structure and is loosely defined as an increase in structural disorder with conservation of the ensemble secondary structure content. Simultaneous observation of persistent secondary structure content with increased disorder has remained experimentally problematic. As a consequence, modeling how the MG state remains stable and how it facilitates proper folding remains difficult due to a lack of amenable spectroscopic techniques to characterize this class of partially unfolded proteins. Previously, deep‐UV resonance Raman (dUVRR) spectroscopy has proven useful in the resolution of global and local structural fluctuations in the secondary structure of proteins. In this work, dUVRR was employed to study the MG to ordered transition of a model four‐helix bundle protein, HP7. Both the average ensemble secondary structure and types of local disorder were monitored, without perturbation of the solvent, pH, or temperature. The MG to ordered transition is induced by stepwise coordination of two heme molecules. Persistent dUVRR spectral features in the amide III region at 1295–1301 and 1335–1338 cm⁻¹ confirm previous observations that HP7 remains predominantly helical in the MG versus the fully ordered state. Additionally, these spectra represent the first demonstration of conserved helical content in a MG protein. With successive heme binding, significant losses are observed in the spectral intensity of the amide III₃ and S regions (1230–1260 and 1390 cm⁻¹, respectively), which are known to be sensitive to local disorder. These observations indicate that there is a decrease in the structural populations able to explore various extended conformations with successive heme binding events. DUVRR spectra indicate that the first heme coordination between two helical segments diminishes exploration of more elongated backbone structural conformations in the inter‐helical regions. A second heme coordination by the remaining two helices further restricts protein motion. Copyright © 2013 John Wiley & Sons, Ltd.     

SERS‐based detection of barcoded gold nanoparticle assemblies from within animal tissue

We have explored the potential of deep Raman spectroscopy, specifically surface‐enhanced spatially offset Raman spectroscopy (SESORS), for non‐invasive detection from within animal tissue, by employing SERS‐barcoded nanoparticle (NP) assemblies as the diagnostic agent. This concept has been experimentally verified in a clinically‐relevant backscattered Raman system with an excitation line of 785 nm under ex vivo conditions. We have shown that our SORS system, with a fixed offset of 2–3 mm, offered sensitive probing of injected 2‐quinolinethiol‐barcoded NP assemblies through animal tissue containing both protein and lipid. In comparison with that of non‐aggregated SERS‐barcoded gold NPs, we have demonstrated that the tailored SERS‐barcoded aggregated NP assemblies have significantly higher detection sensitivity. We report that these NP assemblies can be readily detected at depths of 7–8 mm from within animal proteinaceous tissue with high signal‐to‐noise ratio. In addition, they could also be detected from beneath 1–2 mm of animal tissue with high lipid content, which generally poses a challenge because of high absorption of lipids in the near‐infrared region. We have also shown that the signal intensity and signal‐to‐noise ratio at a particular depth is a function of the SERS tag concentration used and that our SORS system has a 2‐quinolinethiol detection limit of 10⁻⁶ M. Higher detection depths may possibly be obtained with optimization of the NP assemblies, along with improvements in the instrumentation. Such NP assemblies offer prospects for in vivo, non‐invasive detection of tumours along with scope for incorporation of drugs and their targeted and controlled release at tumour sites. These diagnostic agents combined with drug delivery systems could serve as a ‘theranostic agent’, an integration of diagnostics and therapeutics into a single platform. Copyright © 2013 John Wiley & Sons, Ltd.     

超高数值孔径Schwarzschild投影光刻物镜的光学设计

针对45nm及以下节点光刻相关技术的研究需求,确定了实验型投影光刻物镜的结构型式及设计指标。依据像差理论在非同心小遮拦的Schwarzschild反射系统中添加折射补偿镜组来进一步减小系统的中心遮拦,扩大像方数值孔径。设计了一套小中心遮拦,数值孔径为1.20的Schwarzschild折反式投影光刻物镜。设计结果表明,该投影光刻物镜工作带宽为100pm,像方视场为50μm,线中心遮拦比为13%,光学分辨力为80nm时(6240lp/mm)的系统调制传递函数大于0.45,全视场最大畸变为6.5nm,满足了45nm深紫外(DUV)浸没光刻实验平台对投影光刻物镜的需求。     

EMIES/p-TsOH型低共熔溶剂的合成及其氧化脱硫性能的研究

通过1-乙基-3-甲基咪唑硫酸乙酯离子液体（EMIES）和对甲苯磺酸（p-TsOH）的混合物制备EMIES/p-TsOH型低共熔溶剂。其结构特征通过红外光谱、氢谱和热重技术进行了分析。并以EMIES/p-TsOH作为催化剂与萃取剂,H₂O₂作为氧化剂研究了其对模拟油中的硫化物的脱除性能。考察了反应温度、n（H₂O₂）/n（S）比、低共熔溶剂加入量及硫化物类型对脱硫效果的影响。在最佳的条件下,模拟油中二苯并噻吩（DBT）、4,6-二甲基二苯并噻吩（4,6-DMDBT）和苯并噻吩（BT）的脱除率分别为96.2%、92.2%和88.8%。经过五次循环使用后,DBT的脱除率仍达到93.6%。对该脱硫体系进行了动力学分析,其表观活化能为66.4kJ/mol。     

preconcentration and determination of amoxicillin and ceftriaxone in hospital sewage using vortex-assisted liquid-phase microextraction based on the solidification of the deep eutectic solvent followed by HPLC–UV

In the present study, a new method for extraction and preconcentration of amoxicillin and ceftriaxone was used in hospitalised sewage samples, called vortex-assisted liquid-phase microextraction based on the solidification of deep eutectic solvent. Samples were analysed by high-performance liquid chromatography–ultraviolet detection after preparation and extraction. In this method, the new deep eutectic solvent is used as the extraction solvent, which is obtained from the combination of 1-decyl-3-methylimidazolium chloride and n-butanoic acid. The important advantages of this novel extraction solvent include material stability, low density and good freezing point near room temperature. Under the optimum conditions, enrichment factors are in the range of 164–172. Repeatability and reproducibility of the method based on seven replicate measurements of 50.0 µg L^?1 of the target analytes in analysed samples were in the range of 2.1–3.5% and 3.8–5.2%, respectively. The limit of detections and linearity are in the range of 0.005–0.10 and 3–600 µg L^?1, respectively. The method was successfully applied for the determination of amoxicillin and ceftriaxone in the real sewage samples. The relative recoveries of sewage samples spiked with amoxicillin and ceftriaxone are 91–107%.     

焊缝超声相控阵检测数据深度学习降噪方法

超声相控阵检测技术在焊缝检测中具有广泛的应用.超声相控阵检测技术检测信号中常混入噪声导致检测成像时难以分辨真实的缺陷特征.这些噪声主要为无关的反射信号和局部相关的结构噪声,传统的超声图像降噪方法难以有效滤除这些噪声,且存在计算效率低、参数优化复杂等问题.该文提出了一种基于深度学习的焊缝超声相控阵检测技术检测S扫图像的降...     

Novel and Rapid Deep Eutectic Solvent (DES) Homogeneous Liquid–Liquid Microextraction (HLLME) with Flame Atomic Absorption Spectrometry (FAAS) Detection for the Determination of Copper in Vegetables

Novel and fast deep eutectic solvent (DES)-based homogeneous liquid–liquid microextraction (HLLME) was applied for the extraction of copper from vegetable samples followed by flame atomic absorption spectrometry (FAAS). 1,5-diphenyl carbazone (DPC) was used as the chelating agent, and a DES was used as the extraction media. The utilized DES was based on benzyl triphenyl phosphonium bromide and ethylene glycol in a 1:8?mole ratio. The phase separation phenomenon was occurred by changing of sample temperature. Several factors influencing the extraction efficiency were investigated and optimized. Under the optimized conditions, an enhancement factor of 64 was obtained. The limit of detection, based on three signal-to-noise ratio, and limit of quantification were found to be 0.13?µg L^?1 and 5.0?µg L^?1, respectively. The calibration curve was linear within the range of 5.0–250?µg L^?1 with r² > 0.9957. Intra- and inter-day relative standard deviations (%) of 2.1% and 2.6% were obtained at the concentration of 25?µg L^?1. The accuracy of the proposed method was evaluated by analyzing a tomato leaves certified reference material and the results were to be in agreement with the certified value. Finally, the feasibility of the method was successfully confirmed by determination of copper in spinach, lettuce, broccoli, potato, carrot and parsley samples.     

基于真实情境的深度学习——以研究木耳中铁含量为例

教师创设研究木耳中铁含量的课题,引导学生充分利用已经学习过的铁单质、铁的氧化、铁的检验等知识及溶解、过滤等实验技能,通过在真实背景下自觉地进行研究并解决问题,在实践中学习新知识和新方法,将课本上的浅表性知识批判性吸收,有效提升学生对知识的理解与应用能力。