Advances in AFM Imaging Applications for Characterizing the Biophysical Properties of Amyloid Fibrils

Although the formation mechanism of amyloid fibrils in bodies is still debated, it has recently been reported how amyloid fibrils can be formed in vitro. Accordingly, we have gained a better understanding of the self-assembly mechanism and intrinsic properties of amyloid fibrils. Because the structure of amyloid fibrils consists of nanoscaled insoluble strands (a few nanometers in diameter and micrometers long), a special tool is needed to study amyloid fibrils at length. Atomic force microscopy (AFM) is supposed to be a versatile toolkit to probe such a tiny biomolecule. The physical/chemical properties of amyloid fibrils have been explored by AFM. In particular, AFM enables the visualization of amyloid fibrillation with different incubation times as well as the concentrations of the formed amyloid fibrils as affected by fibril diameters and lengths. Very recently, the minute structural changes and/or electrical properties of amyloid fibrils have been made by using advanced AFM techniques including dynamic liquid AFM, PeakForce QNM (quantitative nanomechanical mapping), and Kelvin probe force microscopy (KPFM). Herein, we summarize the biophysical properties of amyloid fibrils that are newly discovered with the help of those advanced AFM techniques and suggest our perspectives and future directions for the study of amyloid fibrils

Application of Red Cell Membrane in Nanobiotechnology

Red cells are full of unique biological properties such as immune evasion and molecular-specific permeability. These properties originate from various membrane proteins on the surface of the cell membrane. For this reason, red cell membrane is coated on nanomaterials or sensors to bestow the functionalities of the membrane proteins. In this chapter, various types of membrane proteins of red cell and its functions are described. Also, the following two experimental procedures are summarized: (I) the extraction of red cell membrane containing membrane proteins and (II) coating of the extracted cell membrane onto the nanoparticles and solid surface of sensors. Finally, the applications of red cell membrane in drug delivery system and biosensor are discussed

Folding machineries displayed on a cation-exchanger for the concerted refolding of cysteine- or proline-rich proteins

<p>Abstract</p> <p>Background</p> <p><it>Escherichia coli </it>has been most widely used for the production of valuable recombinant proteins. However, over-production of heterologous proteins in <it>E. coli </it>frequently leads to their misfolding and aggregation yielding inclusion bodies. Previous attempts to refold the inclusion bodies into bioactive forms usually result in poor recovery and account for the major cost in industrial production of desired proteins from recombinant <it>E. coli</it>. Here, we describe the successful use of the immobilized folding machineries for <it>in vitro </it>refolding with the examples of high yield refolding of a ribonuclease A (RNase A) and cyclohexanone monooxygenase (CHMO).</p> <p>Results</p> <p>We have generated refolding-facilitating media immobilized with three folding machineries, mini-chaperone (a monomeric apical domain consisting of residues 191–345 of GroEL) and two foldases (DsbA and human peptidyl-prolyl <it>cis-trans </it>isomerase) by mimicking oxidative refolding chromatography. For efficient and simple purification and immobilization simultaneously, folding machineries were fused with the positively-charged consecutive 10-arginine tag at their C-terminal. The immobilized folding machineries were fully functional when assayed in a batch mode. When the refolding-facilitating matrices were applied to the refolding of denatured and reduced RNase A and CHMO, both of which contain many cysteine and proline residues, RNase A and CHMO were recovered in 73% and 53% yield of soluble protein with full enzyme activity, respectively.</p> <p>Conclusion</p> <p>The refolding-facilitating media presented here could be a cost-efficient platform and should be applicable to refold a wide range of <it>E. coli </it>inclusion bodies in high yield with biological function.</p

CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals

Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is robust in extracting features from data, research on decoding electroencephalograms by applying deep learning has progressed in the BCI domain. However, the application of deep learning in the BCI domain has issues with a lack of data and overconfidence. To solve these issues, we proposed a novel data augmentation method, CropCat. CropCat consists of two versions, CropCat-spatial and CropCat-temporal. We designed our method by concatenating the cropped data after cropping the data, which have different labels in spatial and temporal axes. In addition, we adjusted the label based on the ratio of cropped length. As a result, the generated data from our proposed method assisted in revising the ambiguous decision boundary into apparent caused by a lack of data. Due to the effectiveness of the proposed method, the performance of the four EEG signal decoding models is improved in two motor imagery public datasets compared to when the proposed method is not applied. Hence, we demonstrate that generated data by CropCat smooths the feature distribution of EEG signals when training the model.Comment: 4 pages, 1 tabl

Phase Frequency Detector and Charge Pump for Low Jitter PLL Applications

In this paper a new technique is presented to improve the jitter performance of conventional phase frequency detectors by completely removing the unnecessary one-shot pulse. This technique uses a variable pulse-height circuit to control the unnecessary one-shot pulse height. In addition, a novel charge-pump circuit with perfect current-matching characteristics is used to improve the output jitter performance of conventional charge pumps. This circuit is composed of a pair of symmetrical pump circuits to obtain a good current matching. As a result, the proposed charge-pump circuit has perfect current-matching characteristics, wide output range, no glitch output current, and no jump output voltage. In order to verify such operation, circuit simulation is performed using 0.18 μm CMOS process parameters

Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment

The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different workload levels, specifically normal state, low workload, and high workload. To the best of our knowledge, this study is the first attempt to classify workload levels of pilots. Our approach involves the hybrid deep neural network that consists of five convolutional blocks and one long short-term memory block to extract the significant features from electroencephalography signals. Ten pilots participated in the experiment, which was conducted within the simulated flight environment. In contrast to four conventional models, our proposed model achieved a superior grand--average accuracy of 0.8613, surpassing other conventional models by at least 0.0597 in classifying workload levels across all participants. Our model not only successfully classified workload levels but also provided valuable feedback to the participants. Hence, we anticipate that our study will make the significant contributions to the advancement of autonomous flight and driving leveraging artificial intelligence technology in the future.Comment: 5 pages, 3 figures, 1 table, 1 algorith