The human proteome has millions of protein variants due to alternative RNA splicing and post-translational modifications, and variants that are related to diseases are frequently present in minute concentrations. For DNA and RNA, low concentrations can be amplified using the polymerase chain reaction, but there is no such reaction for proteins. Therefore, the development of single molecule protein sequencing is a critical step in the search for protein biomarkers. Here we show that single amino acids can be identified by trapping the molecules between two electrodes that are coated with a layer of recognition molecules and measuring the electron tunneling current across the junction. A given molecule can bind in more than one way in the junction, and we therefore use a machine-learning algorithm to distinguish between the sets of electronic ‘fingerprints’ associated with each binding motif. With this recognition tunneling technique, we are able to identify D, L enantiomers, a methylated amino acid, isobaric isomers, and short peptides. The results suggest that direct electronic sequencing of single proteins could be possible by sequentially measuring the products of processive exopeptidase digestion, or by using a molecular motor to pull proteins through a tunnel junction integrated with a nanopore.

Carbohydrates are one of the four main building blocks of life, and are categorized as monosaccharides (sugars), oligosaccharides and polysaccharides. Each sugar can exist in two alternative anomers (in which a hydroxy group at C-1 takes different orientations) and each pair of sugars can form different epimers (isomers around the stereocentres connecting the sugars). This leads to a vast combinatorial complexity, intractable to mass spectrometry and requiring large amounts of sample for NMR characterization. Combining measurements of collision cross section with mass spectrometry (IM–MS) helps, but many isomers are still difficult to separate. Here, we show that recognition tunnelling (RT) can classify many anomers and epimers via the current fluctuations they produce when captured in a tunnel junction functionalized with recognition molecules. Most importantly, RT is a nanoscale technique utilizing sub-picomole quantities of analyte. If integrated into a nanopore, RT would provide a unique approach to sequencing linear polysaccharides.

Previous measurements of the electronic conductance of DNA nucleotides or amino acids have used tunnel junctions in which the gap is mechanically adjusted, such as scanning tunneling microscopes or mechanically controllable break junctions. Fixed-junction devices have, at best, detected the passage of whole DNA molecules without yielding chemical information. Here, we report on a layered tunnel junction in which the tunnel gap is defined by a dielectric layer, deposited by atomic layer deposition. Reactive ion etching is used to drill a hole through the layers so that the tunnel junction can be exposed to molecules in solution. When the metal electrodes are functionalized with recognition molecules that capture DNA nucleotides via hydrogen bonds, the identities of the individual nucleotides are revealed by characteristic features of the fluctuating tunnel current associated with single-molecule binding events.

A reader molecule, which recognizes all the naturally occurring nucleobases in an electron tunnel junction, is required for sequencing DNA by a recognition tunneling (RT) technique, referred to as a universal reader. In the present study, we have designed a series of heterocyclic carboxamides based on hydrogen bonding and a large-sized pyrene ring based on a π-π stacking interaction as universal reader candidates. Each of these compounds was synthesized to bear a thiolated linker for attachment to metal electrodes and examined for their interactions with naturally occurring DNA nucleosides and nucleotides by H NMR, ESI-MS, computational calculations, and surface plasmon resonance. RT measurements were carried out in a scanning tunnel microscope. All of these molecules generated electrical signals with DNA nucleotides in tunneling junctions under physiological conditions (phosphate buffered aqueous solution, pH 7.4). Using a support vector machine as a tool for data analysis, we found that these candidates distinguished among naturally occurring DNA nucleotides with the accuracy of pyrene (by π-π stacking interactions)> azole carboxamides (by hydrogen-bonding interactions). In addition, the pyrene reader operated efficiently in a larger tunnel junction. However, the azole carboxamide could read abasic (AP) monophosphate, a product from spontaneous base hydrolysis or an intermediate of base excision repair. Thus, we envision that sequencing DNA using both π-π stacking and hydrogen-bonding-based universal readers in parallel should generate more comprehensive genome sequences than sequencing based on either reader molecule alone.

Real time anomaly detection is the need of the hour for any security applications. In this paper, we have proposed a real-time anomaly detection algorithm by utilizing cues from the motion vectors in H.264/AVC compressed domain. The discussed work is principally motivated by the observation that motion vectors (MVs) exhibit different characteristics during anomaly. We have observed that H.264 motion vector magnitude contains relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. Additionally, we have suggested a hierarchical approach through Motion Pyramid for High Resolution videos to further increase the detection rate. The proposed algorithm has performed extremely well on UMN and Peds Anomaly Detection Video datasets, with a detection speed of >150 and 65−75 frames per sec in respective datasets resulting in more than 200× speedup along with comparable accuracy to pixel domain state-of-the-art algorithms.

A group of persons can be analyzed at various semantic levels such as individual actions, their interactions, and the activity of the entire group. In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of individuals, their interactions, as well as the group activity. While previous structural recurrent neural networks assumed that the number of nodes and edges is constant, we use a grid pooling layer to address the fact that the number of individuals in a group can vary. We evaluate two variants of the structural recurrent neural network on the Volleyball Dataset.

In this paper, we have proposed a simple yet robust novel approach for segmentation of high density crowd flows based on super-pixels in H.264 compressed videos. The collective representation of the motion vectors of the compressed video sequence is transformed to color map and super-pixel segmentation is performed at various scales for clustering the coherent motion vectors. The number of dynamically meaningful flow segments is determined by measuring the confidence score of the accumulated multi-scale super-pixel boundaries. The final crowd flow segmentation is obtained from the edges that are consistent across all the super-pixel resolutions. Hence, our major contribution involves obtaining the flow segmentation by clustering the motion vectors and determination of number of flow segments using only motion super-pixels without any prior assumption of the number of flow segments. The proposed approach was benchmarked on standard crowd flow dataset. Experiments demonstrated better accuracy and speedup for the proposed approach compared to the state-of-the-art methods.

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