scholarly journals Protein identification with a nanopore and a binary alphabet

2017 ◽  
Author(s):  
G. Sampath
Keyword(s):  
Single Molecule ◽  
High Speed ◽  
Protein Identification ◽  
Binary Sequences ◽  
False Detection Rate ◽  
False Detection ◽  
Post Translational Modifications ◽  
Identification Rate ◽  
Binary Alphabet ◽  
Protein Molecules

AbstractProtein sequences are recoded with a binary alphabet obtained by dividing the 20 amino acids into two subsets based on volume. A protein is identified from subsequences by database search. Computations on the Helicobacter pylori proteome show that over 93% of binary subsequences of length 20 are correct at a confidence level exceeding 90%. Over 98% of the proteins can be identified, most have multiple identifiers so the false detection rate is low. Binary sequences of unbroken protein molecules can be obtained with a nanopore from current blockade levels proportional to residue volume; only two levels, rather than 20, need be measured to determine a residue’s subset. This procedure can be translated into practice with a sub-nanopore that can measure residue volumes with ~0.07 nm3 resolution as shown in a recent publication. The high detector bandwidth required by the high speed of a translocating molecule can be reduced more than tenfold with an averaging technique, the resulting decrease in the identification rate is only 10%. Averaging also mitigates the homopolymer problem due to identical successive blockade levels. The proposed method is a proteolysis-free single-molecule method that can identify arbitrary proteins in a proteome rather than specific ones. This approach to protein identification also works if residue mass is used instead of mass; again over 98% of the proteins are identified by binary subsequences of length 20. The possibility of using this in mass spectrometry studies of proteins, in particular those with post-translational modifications, is under investigation.

scholarly journals A minimalist approach to protein identification

2017 ◽  
Author(s):  
G. Sampath
Keyword(s):  
Single Molecule ◽  
Protein Identification ◽  
Small Samples ◽  
Identification Rate ◽  
E Coli ◽  
Linear Sequence ◽  
Available Technology ◽  
H Pylori ◽  
Parameter Values ◽  
Non Destructive

AbstractComputations on proteome sequence databases show that most proteins can be identified from a protein’s isoelectric point (IEP) and digitized linear sequence volume (equal to the total volume of its residues). This is illustrated with four proteomes: H. pylori (1553 proteins), E. coli (4306 proteins), S. cerevisiae (6721 proteins), and H. sapiens (20207 proteins); the identification rate exceeds 90% in all four cases for appropriate parameter values. IEP can be obtained with 1-d gel electrophoresis (GE), whose accuracy is better than 0.01. Linear protein sequence volumes of unbroken proteins can be obtained with a sub-nanometer diameter nanopore that can measure residue volume with a resolution of 0.07-0.1 nm3 (Kennedy et al., Nature Nanotech., 2016, 11, 968-976; Dong et al., ACS Nano, 2017, doi: 10.1021/acsnano.6b08452); the blockade current due to a translocating protein is roughly proportional to the volume it excludes in the pore. There is no need to identify any of the residues. More than 90% of all the proteins have estimated translocation times higher than 1 μs, which is within the time resolution of available detectors. This is a minimalist proteolysis-free GE-and nanopore-based single-molecule approach requires very small samples, is non-destructive (the sample can be recovered for reuse), and can be translated with currently available technology into a portable device for possible use in the field, an academic lab, or a pre-screening step preceding conventional mass spectrometry.

High-Speed Lens-Free Holographic Sensing of Protein Molecules Using Quantitative Agglutination Assays

ACS Sensors ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 1208-1217
Author(s):  
Zhen Xiong ◽  
Colin J. Potter ◽  
Euan McLeod
Keyword(s):  
High Speed ◽  
Protein Molecules

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

High-Speed Atomic Force Microscopy for Studying the Dynamic Behavior of Protein Molecules at Work

2006 ◽  
Vol 45 (3B) ◽  
pp. 1897-1903 ◽  
Author(s):  
Toshio Ando ◽  
Takayuki Uchihashi ◽  
Noriyuki Kodera ◽  
Atsushi Miyagi ◽  
Ryo Nakakita ◽  
...  
Keyword(s):  
Atomic Force Microscopy ◽  
Dynamic Behavior ◽  
High Speed ◽  
Force Microscopy ◽  
Atomic Force ◽  
Protein Molecules

scholarly journals Single-molecule protein identification by sub-nanopore sensors

2017 ◽  
Vol 13 (5) ◽  
pp. e1005356 ◽  
Author(s):  
Mikhail Kolmogorov ◽  
Eamonn Kennedy ◽  
Zhuxin Dong ◽  
Gregory Timp ◽  
Pavel A. Pevzner
Keyword(s):  
Single Molecule ◽  
Protein Identification ◽  
Nanopore Sensors

scholarly journals A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Yao Liu ◽  
Zhengfan Zhao ◽  
Yue Zhang ◽  
Li He
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Training Model ◽  
Learning Approach ◽  
Flow State ◽  
False Detection Rate ◽  
False Detection ◽  
Road Vehicles ◽  
Traffic Video ◽  
Flow States

Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.

scholarly journals Single-molecule imaging of DNA gyrase activity in livingEscherichia coli

2018 ◽  
Author(s):  
Mathew Stracy ◽  
Adam J.M. Wollman ◽  
Elzbieta Kaja ◽  
Jacek Gapinski ◽  
Ji-Eun Lee ◽  
...  
Keyword(s):  
Single Molecule ◽  
High Speed ◽  
Replication Fork ◽  
Dna Gyrase ◽  
Chromosomal Dna ◽  
Replication Fork Progression ◽  
Dna Strands ◽  
Steady State Levels ◽  
Negative Supercoiling ◽  
Supercoil Relaxation

ABSTRACTBacterial DNA gyrase introduces negative supercoils into chromosomal DNA and relaxes positive supercoils introduced by replication and transiently by transcription. Removal of these positive supercoils is essential for replication fork progression and for the overall unlinking of the two duplex DNA strands, as well as for ongoing transcription. To address how gyrase copes with these topological challenges, we used high-speed single-molecule fluorescence imaging in liveEscherichia colicells. We demonstrate that at least 300 gyrase molecules are stably bound to the chromosome at any time, with ∼12 enzymes enriched near each replication fork. Trapping of reaction intermediates with ciprofloxacin revealed complexes undergoing catalysis. Dwell times of ∼2 s were observed for the dispersed gyrase molecules, which we propose maintain steady-state levels of negative supercoiling of the chromosome. In contrast, the dwell time of replisome-proximal molecules was ∼8 s, consistent with these catalyzing processive positive supercoil relaxation in front of the progressing replisome.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

A Microsystem Platform Interfacing MALDI-TOF MS for High Speed Automated Protein Identification

2000 ◽  
pp. 455-458 ◽  
Author(s):  
Simon Ekström ◽  
Patrik Önnerfjord ◽  
Martin Bengtsson ◽  
Tasso Miliotis ◽  
David Ericsson ◽  
...  
Keyword(s):  
High Speed ◽  
Protein Identification ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms
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