A Pseudo DNA Cryptography Method

The DNA cryptography is a new and very promising direction in cryptography research. DNA can be used in cryptography for storing and transmitting the information, as well as for computation. Although in its primitive stage, DNA cryptography is shown to be very effective. Currently, several DNA computing algorithms are proposed for quite some cryptography, cryptanalysis and steganography problems, and they are very powerful in these areas. However, the use of the DNA as a means of cryptography has high tech lab requirements and computational limitations, as well as the labor intensive extrapolation means so far. These make the efficient use of DNA cryptography difficult in the security world now. Therefore, more theoretical analysis should be performed before its real applications. In this project, We do not intended to utilize real DNA to perform the cryptography process; rather, We will introduce a new cryptography method based on central dogma of molecular biology. Since this method simulates some critical processes in central dogma, it is a pseudo DNA cryptography method. The theoretical analysis and experiments show this method to be efficient in computation, storage and transmission; and it is very powerful against certain attacks. Thus, this method can be of many uses in cryptography, such as an enhancement insecurity and speed to the other cryptography methods. There are also extensions and variations to this method, which have enhanced security, effectiveness and applicability.Comment: A small work that quite some people asked abou

Dynamic residual deep learning with photoelectrically regulated neurons for immunological classification

Dynamic deep learning is considered to simulate the nonlinear memory process of the human brain during long-term potentiation and long-term depression. Here, we propose a photoelectrically modulated synaptic transistor based on MXenes that adjusts the nonlinearity and asymmetry by mixing controllable pulses. According to the advantage of residual deep learning, the rule of dynamic learning is thus elaborately developed to improve the accuracy of a highly homologous database (colorimetric enzyme-linked immunosorbent assay [c-ELISA]) from 80.9% to 87.2% and realize the fast convergence. Besides, mixed stimulation also remarkably shortens the iterative update time to 11.6 s as a result of the photoelectric effect accelerating the relaxation of ion migration. Finally, we extend the dynamic learning strategy to long short-term memory (LSTM) and standard datasets (Cifar10 and Cifar100), which well proves the strong robustness of dynamic learning. This work paves the way toward potential synaptic bionic retina for computer-aided detection in immunology

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Cryptocurrency network (graph) data analysis

Due to advancements in technology, there is a new form of asset invented in the world known as cryptocurrency and it’s used by many users globally. The emergence of cryptocurrency and its blockchain technology has seen great progress and more widespread acknowledgment of its value as a digital currency. One major player of cryptocurrency would be Bitcoin. Upon completion of FYP, I wish to acquire new skills and knowledge on cryptocurrency and programming skills, all of these will be covered in this report. Firstly, I would discuss on how blockchain technology works and what are the Pros and Cons of it. Next, I will discuss how to do data visualization on Neo4j and import this data visualization into a webpage Lastly, I will discuss the functions that were implemented on the web-based application that I created, understanding the meaning behind all the graphs and have a better understanding of Bitcoin transactions.Bachelor of Engineering (Computer Engineering

Determination of Free Amino Acids in Three Species of Duckweed (Lemnaceae)

In this study, a fast, simple, precise, and sensitive hydrophilic interaction liquid chromatography (HILIC) method was established for simultaneous determination of free amino acids in three different varieties of duckweed including Spirodela polyrhiza (L.) Schleid., Landoltia punctata (G. Mey.) Les & D. J. Crawford, and Lemna aequinoctialis Welwitsch by ultrahigh performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Method validation was processed in terms of linearity, precision, stability, repeatability, and accuracy as well as limits of detection and quantification. The developed method was applied for quantification of 59 batches of samples. Then chemometric analysis was used to evaluate different duckweeds by principle component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). The results demonstrated that there was no significant difference in FAAs’ profile among three varieties of duckweed

Application of the ITS2 Region for Barcoding Medicinal Plants of Selaginellaceae in Pteridophyta

<div><p>Background</p><p>Selaginellaceae is a family of nonseed plants with special evolutionary significance. Plants of the family Selaginellaceae are similarly shaped and easily confused, complicating identification via traditional methods. This study explored, for the first time, the use of the DNA barcode ITS2 to identify medicinal plants of the Selaginellaceae family.</p><p>Methodology/Principal Findings</p><p>In our study, 103 samples were collected from the main distribution areas in China; these samples represented 34 species and contained almost all of the medicinal plants of Selaginellaceae. The ITS2 region of the genome was amplified from these samples and sequenced using universal primers and reaction conditions. The success rates of the PCR amplification and sequencing were 100%. There was significant divergence between the interspecific and intraspecific genetic distances of the ITS2 regions, while the presence of a barcoding gap was obvious. Using the BLAST1 and nearest distance methods, our results proved that the ITS2 regions could successfully identify the species of all Selaginellaceae samples examined. In addition, the secondary structures of ITS2 in the helical regions displayed clear differences in stem loop number, size, position, and screw angle among the medicinal plants of Selaginellaceae. Furthermore, cluster analysis using the ITS2 barcode supported the relationship between the species of Selaginellaceae established by traditional morphological methods.</p><p>Conclusion</p><p>The ITS2 barcode can effectively identify medicinal plants of Selaginellaceae. The results provide a scientific basis for the precise identification of plants of the family Selaginellaceae and the reasonable development of these resources. This study may broaden the application of DNA barcoding in the medicinal plant field and benefit phylogenetic investigations.</p></div