Synthetic eco-evolutionary dynamics in simple molecular environment.

The understanding of eco-evolutionary dynamics, and in particular the mechanism of coexistence of species, is still fragmentary and in need of test bench model systems. To this aim we developed a variant of SELEX in vitro selection to study the evolution of a population of ∼1015 single-strand DNA oligonucleotide 'individuals'. We begin with a seed of random sequences which we select via affinity capture from ∼1012 DNA oligomers of fixed sequence ('resources') over which they compete. At each cycle ('generation'), the ecosystem is replenished via PCR amplification of survivors. Massive parallel sequencing indicates that across generations the variety of sequences ('species') drastically decreases, while some of them become populous and dominate the ecosystem. The simplicity of our approach, in which survival is granted by hybridization, enables a quantitative investigation of fitness through a statistical analysis of binding energies. We find that the strength of individual resource binding dominates the selection in the first generations, while inter- and intra-individual interactions become important in later stages, in parallel with the emergence of prototypical forms of mutualism and parasitism.

Combined Pseudo-Random Sequence Generator for Cybersecurity.

Random and pseudo-random number and bit sequence generators with a uniform distribution law are the most widespread and in demand in the market of pseudo-random generators. Depending on the specific field of application, the requirements for their implementation and the quality of the generator's output sequence change. In this article, we have optimized the structures of the classical additive Fibonacci generator and the modified additive Fibonacci generator when they work together. The ranges of initial settings of structural elements (seed) of these generators have been determined, which guarantee acceptable statistical characteristics of the output pseudo-random sequence, significantly expanding the scope of their possible application, including cybersecurity. When studying the statistical characteristics of the modified additive Fibonacci generator, it was found that they significantly depend on the signal from the output of the logic circuit entering the structure. It is proved that acceptable statistical characteristics of the modified additive Fibonacci generator, and the combined generator realized on its basis, are provided at odd values of the module of the recurrent equation describing the work of such generator. The output signal of the combined generator has acceptable characteristics for a wide range of values of the initial settings for the modified additive Fibonacci generator and the classic additive Fibonacci generator. Regarding the use of information security, it is worth noting the fact that for modern encryption and security programs, generators of random numbers and bit sequences and approaches to their construction are crucial and critical.

[Design and application of neural electrical stimulation system with time-varying parameters].

Currently, commercial devices for electrical neural stimulations can only provide fixed stimulation paradigms with preset constant parameters, while the development of new stimulation paradigms with time-varying parameters has emerged as one of the important research directions for expanding clinical applications. To facilitate the performance of electrical stimulation paradigms with time-varying parameters in animal experiments, the present study developed a well-integrated stimulation system to output various pulse sequences by designing a LabVIEW software to control a general data acquisition card and an electrical stimulus isolator. The system was able to generate pulse sequences with inter-pulse-intervals (IPI) randomly varying in real time with specific distributions such as uniform distribution, normal distribution, gamma distribution and Poisson distribution. It was also able to generate pulse sequences with arbitrary time-varying IPIs. In addition, the pulse parameters, including pulse amplitude, pulse width, interphase delay of biphasic pulse and duration of pulse sequence, were adjustable. The results of performance tests of the stimulation system showed that the errors of the parameters of pulse sequences output by the system were all less than 1%. By utilizing the stimulation system, pulse sequences with IPI randomly varying in the range of 5~10 ms were generated and applied in rat hippocampal regions for animal experiments. The experimental results showed that, even with a same mean pulse frequency of ~130 Hz, for neuronal populations, the excitatory effect of stimulations with randomly varying IPIs was significantly greater than the effect of stimulations with fixed IPIs. In conclusion, the stimulation system designed here may provide a useful tool for the researches and the development of new paradigms of neural electrical stimulations.

Exploring the efficacy of tumor electric field therapy against glioblastoma: An in vivo and in vitro study.

AIMS: Tumor electric fields therapy (TTFields) is emerging as a novel anti-cancer physiotherapy. Despite recent breakthroughs of TTFields in glioma treatment, the average survival time for glioblastoma patients with TTFields is <2 years, even when used in conjugation with traditional anti-cancer therapies. To optimize TTFields-afforded efficacy against glioblastoma, we investigated the cancer cell-killing effects of various TTFields paradigms using in vitro and in vivo models of glioblastoma. METHODS: For in vitro studies, the U251 glioma cell line or primary cell cultures prepared from 20 glioblastoma patients were treated with the tumor electric field treatment (TEFT) system. Cell number, volume, and proliferation were measured after TEFT at different frequencies (100, 150, 180, 200, or 220 kHz), durations (24, 48, or 72 h), field strengths (1.0, 1.5, or 2.2V/cm), and output modes (fixed or random sequence output). A transwell system was used to evaluate the influence of TEFT on the invasiveness of primary glioblastoma cells. For in vivo studies, the therapeutic effect and safety profiles of random sequence electric field therapy in glioblastoma-transplanted rats were assessed by calculating tumor size and survival time and evaluating peripheral immunobiological and blood parameters, respectively. RESULTS: In the in vitro settings, TEFT was robustly effective in suppressing cell proliferation of both the U251 glioma cell line and primary glioblastoma cell cultures. The anti-proliferation effects of TEFT were frequency- and "dose" (field strength and duration)-dependent, and contingent on the field sequence output mode, with the random sequence mode (TEFT-R) being more effective than the fixed sequence mode (TEFT-F). Genetic tests were performed in 11 of 20 primary glioblastoma cultures, and 6 different genetic traits were identified them. However, TEFT exhibited comparable anti-proliferation effects in all primary cultures regardless of their genetic traits. TEFT also inhibited the invasiveness of primary glioblastoma cells in transwell experiments. In the in vivo rat model of glioblastoma brain transplantation, treatment with TEFT-F or TEFT-R at frequency of 200 kHz and field strength of 2.2V/cm for 14 days significantly reduced tumor volume by 42.63% (TEFT-F vs. control, p = 0.0002) and 63.60% (TEFT-R vs. control, p < 0.0001), and prolonged animal survival time by 30.15% (TEFT-F vs. control, p = 0.0415) and 69.85% (TEFT-R vs. control, p = 0.0064), respectively. The tumor-bearing rats appeared to be well tolerable to TEFT therapies, showing only moderate increases in blood levels of creatine and red blood cells. Adverse skin reactions were common for TEFT-treated rats; however, skin reactions were curable by local treatment. CONCLUSION: Tumor electric field treatment at optimal frequency, strength, and output mode markedly inhibits the cell viability, proliferation, and invasiveness of primary glioblastoma cells in vitro independent of different genetic traits of the cells. Moreover, a random sequence electric field output confers considerable anti-cancer effects against glioblastoma in vivo. Thus, TTFields are a promising physiotherapy for glioblastoma and warrants further investigation.

Joint Lossless Image Compression and Encryption Scheme Based on CALIC and Hyperchaotic System.

For efficiency and security of image transmission and storage, the joint image compression and encryption method that performs compression and encryption in a single step is a promising solution due to better security. Moreover, on some important occasions, it is necessary to save images in high quality by lossless compression. Thus, a joint lossless image compression and encryption scheme based on a context-based adaptive lossless image codec (CALIC) and hyperchaotic system is proposed to achieve lossless image encryption and compression simultaneously. Making use of the characteristics of CALIC, four encryption locations are designed to realize joint image compression and encryption: encryption for the predicted values of pixels based on gradient-adjusted prediction (GAP), encryption for the final prediction error, encryption for two lines of pixel values needed by prediction mode and encryption for the entropy coding file. Moreover, a new four-dimensional hyperchaotic system and plaintext-related encryption based on table lookup are all used to enhance the security. The security tests show information entropy, correlation and key sensitivity of the proposed methods reach 7.997, 0.01 and 0.4998, respectively. This indicates that the proposed methods have good security. Meanwhile, compared to original CALIC without security, the proposed methods increase the security and reduce the compression ratio by only 6.3%. The test results indicate that the proposed methods have high security and good lossless compression performance.

A New Algorithm for Digital Image Encryption Based on Chaos Theory.

In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme's effectiveness and show that this technique is a suitable choice for actual image encryption.

Random-PE: an efficient integration of random sequences into mammalian genome by prime editing.

Prime editing (PE) enables efficiently targeted introduction of multiple types of small-sized genetic change without requiring double-strand breaks or donor templates. Here we designed a simple strategy to introduce random DNA sequences into targeted genomic loci by prime editing, which we named random prime editing (Random-PE). In our strategy, the prime editing guide RNA (pegRNA) was engineered to harbor random sequences between the primer binding sequence (PBS) and homologous arm (HA) of the reverse transcriptase templates. With these pegRNAs, we achieved efficient targeted insertion or substitution of random sequences with different lengths, ranging from 5 to 10, in mammalian cells. Importantly, the diversity of inserted sequences is well preserved. By fine-tuning the design of random sequences, we were able to make simultaneously insertions or substitutions of random sequences in multiple sites, allowing in situ evolution of multiple positions in a given protein. Therefore, these results provide a framework for targeted integration of random sequences into genomes, which can be redirected for manifold applications, such as in situ protospacer adjacent motif (PAM) library construction, enhancer screening, and DNA barcoding.

Artificial small RNA-mediated growth inhibition in Escherichia coli and Salmonella enterica serovar Typhimurium.

We developed a synthetic RNA approach to identify growth inhibition sequences by cloning random 24-nucleotide (nt) sequences into an arabinose-inducible expression vector. This vector expressed a small RNA (sRNA) of ∼140 nt containing a 24 nt random sequence insert. After transforming Escherichia coli with the vector, 10 out of 954 transformants showed strong growth defect phenotypes and two clones caused cell lysis. We then examined growth inhibition phenotypes in the Salmonella Typhimurium LT2 strain using the twelve sRNAs that exerted an inhibitory effect on E. coli growth. Three of these clones showed strong growth inhibition phenotypes in S. Typhimurium LT2. The most effective sRNA contained the same insert (N1) in both bacteria. The 24 nt random sequence insert of N1 was abundant in guanine residues (ten out of 24 nt), and other random sequences causing growth defects were also highly enriched for guanine (G) nucleotides. We, therefore, generated clones that express sRNAs containing a stretch of 16 to 24 continuous guanine sequences (poly-G16, -G18, -G20, -G22, and -G24). All of these clones induced growth inhibition in both liquid and agar plate media and the poly-G20 clone showed the strongest effect in E. coli. These results demonstrate that our sRNA expression system can be used to identify nucleotide sequences that are potential candidates for oligonucleotide antimicrobial drugs.

Rapid and easy method for in vitro determination of transcription factor binding core motifs.

We introduce a rapid method for easily elucidating transcription factor (TF) cis-elements by adopting a highly efficient in vitro protein synthesis method and identifying protein-DNA interactions using PCR. We determined two cis-elements for plant TFs using this method, and the results confirmed our method as an easy and time-saving alternative for elucidating TF cis-elements using common laboratory procedures.

In silico simulations of occurrence of transcription factor binding sites in bacterial genomes.

BACKGROUND: Interactions between transcription factors and their specific binding sites are a key component of regulation of gene expression. Until recently, it was generally assumed that most bacterial transcription factor binding sites are located at or near promoters. However, several recent works utilizing high-throughput technology to detect transcription factor binding sites in bacterial genomes found a large number of binding sites in unexpected locations, particularly inside genes, as opposed to known or expected promoter regions. While some of these intragenic binding sites likely have regulatory functions, an alternative scenario is that many of these binding sites arise by chance in the absence of selective constraints. The latter possibility was supported by in silico simulations for σ54 binding sites in Salmonella. RESULTS: In this work, we extend these simulations to more than forty transcription factors from E. coli and other bacteria. The results suggest that binding sites for all analyzed transcription factors are likely to arise throughout the genome by random genetic drift and many transcription factor binding sites found in genomes may not have specific regulatory functions. In addition, when comparing observed and expected patterns of occurrence of binding sites in genomes, we observed distinct differences among different transcription factors. CONCLUSIONS: We speculate that transcription factor binding sites randomly occurring throughout the genome could be beneficial in promoting emergence of new regulatory interactions and thus facilitating evolution of gene regulatory networks.

Short Review on Quantum Key Distribution Protocols.

Cryptographic protocols and mechanisms are widely investigated under the notion of quantum computing. Quantum cryptography offers particular advantages over classical ones, whereas in some cases established protocols have to be revisited in order to maintain their functionality. The purpose of this paper is to provide the basic definitions and review the most important theoretical advancements concerning the BB84 and E91 protocols. It also aims to offer a summary on some key developments on the field of quantum key distribution, closely related with the two aforementioned protocols. The main goal of this study is to provide the necessary background information along with a thorough review on the theoretical aspects of QKD, concentrating on specific protocols. The BB84 and E91 protocols have been chosen because most other protocols are similar to these, a fact that makes them important for the general understanding of how the QKD mechanism functions.

Estimation of the influence of sequencing errors and distribution of random-sequence tags on quantitative sequencing.

To simultaneously sequence and quantify target DNA, quantitative sequencing (qSeq) employs stochastic labeling of target DNA molecules with random-sequence tags (RSTs). This recently developed approach allows parallel quantification of hundreds of microorganisms in natural habitats in a single sequencing run. Yet, no study has addressed to what extent sequencing errors affect quantification and how many sequence reads are needed for quantification. Here, we addressed those issues by using numerical simulations and experimental data from second-generation sequencing of various RSTs. We found that heterogeneous distribution of observed RSTs affected the number of sequence reads required to quantitate target genes, whereas the effect of sequencing errors is smaller than of the RSTs distribution. Because of the heterogeneous RSTs distribution, 15-fold more sequence reads than the number of observed RSTs should be obtained to retrieve almost all RSTs needed for quantification; in that case, quantification error is estimated to be within 5%.

Natural protein sequences are more intrinsically disordered than random sequences.

Most natural protein sequences have resulted from millions or even billions of years of evolution. How they differ from random sequences is not fully understood. Previous computational and experimental studies of random proteins generated from noncoding regions yielded inclusive results due to species-dependent codon biases and GC contents. Here, we approach this problem by investigating 10,000 sequences randomized at the amino acid level. Using well-established predictors for protein intrinsic disorder, we found that natural sequences have more long disordered regions than random sequences, even when random and natural sequences have the same overall composition of amino acid residues. We also showed that random sequences are as structured as natural sequences according to contents and length distributions of predicted secondary structure, although the structures from random sequences may be in a molten globular-like state, according to molecular dynamics simulations. The bias of natural sequences toward more intrinsic disorder suggests that natural sequences are created and evolved to avoid protein aggregation and increase functional diversity.

Natural vs. random protein sequences: Discovering combinatorics properties on amino acid words.

Casual mutations and natural selection have driven the evolution of protein amino acid sequences that we observe at present in nature. The question about which is the dominant force of proteins evolution is still lacking of an unambiguous answer. Casual mutations tend to randomize protein sequences while, in order to have the correct functionality, one expects that selection mechanisms impose rigid constraints on amino acid sequences. Moreover, one also has to consider that the space of all possible amino acid sequences is so astonishingly large that it could be reasonable to have a well tuned amino acid sequence indistinguishable from a random one. In order to study the possibility to discriminate between random and natural amino acid sequences, we introduce different measures of association between pairs of amino acids in a sequence, and apply them to a dataset of 1047 natural protein sequences and 10,470 random sequences, carefully generated in order to preserve the relative length and amino acid distribution of the natural proteins. We analyze the multidimensional measures with machine learning techniques and show that, to a reasonable extent, natural protein sequences can be differentiated from random ones.

Diverse sequences are functional at the C-terminus of the E. coli periplasmic chaperone SurA.

SurA is a major periplasmic molecular chaperone in Escherichia coli and has been shown to assist the biogenesis of several outer membrane proteins. The C-terminal fragment of SurA folds into a short ß-strand, which forms a small three-stranded anti-parallel ß-sheet module with the N-terminal ß-hairpin. We found that the length of the C-terminal fragment, rather than its exact amino acid composition, had a big impact on SurA function. To investigate the determinant factor of the C-terminal sequence, we created a library of SurA constructs randomized in the last 10 residues. We screened the library and randomly analyzed 19 constructs that displayed SurA activity. The C-termini of these constructs shared little sequence similarity, except that ß-strand-forming residues were preferentially enriched. Three SurA constructs were expressed and purified for structural characterization. Circular dichroism and fluorescence spectroscopy analyses revealed that their structures were similar to the structure of the wild-type SurA. Our results suggest that for scaffolding purpose proteins may tolerate various sequences provided certain general requirements such as hydrophobicity and secondary structure propensity are satisfied. Furthermore, the sequence tolerance of SurA at the C-terminus indicates that this area is not likely to be involved in substrate binding.

Chemical synthetic biology: a mini-review.

Chemical synthetic biology (CSB) is a branch of synthetic biology (SB) oriented toward the synthesis of chemical structures alternative to those present in nature. Whereas SB combines biology and engineering with the aim of synthesizing biological structures or life forms that do not exist in nature - often based on genome manipulation, CSB uses and assembles biological parts, synthetic or not, to create new and alternative structures. A short epistemological note will introduce the theoretical concepts related to these fields, whereas the text will be largely devoted to introduce and comment two main projects of CSB, carried out in our laboratory in the recent years. The "Never Born Biopolymers" project deals with the construction and the screening of RNA and peptide sequences that are not present in nature, whereas the "Minimal Cell" project focuses on the construction of semi-synthetic compartments (usually liposomes) containing the minimal and sufficient number of components to perform the basic function of a biological cell. These two topics are extremely important for both the general understanding of biology in terms of function, organization, and development, and for applied biotechnology.

Comparison of Real Frequencies of Strings vs. the Expected Ones Reveals the Information Capacity of Macromoleculae.

The information capacity of nucleotide sequences is defined through the calculation of specific entropy of their frequency dictionary. The specificentropy of the frequency dictionary is calculated against the reconstructeddictionary; this latter bears the most probable continuations of the shorterstrings. This developed measure allows to distinguish the sequences both from the randons ones, and from those with high level of (rather simple) order. Some implications of the developed methodology in the fields of genetics,bioinformatics, and molecular biology are discussed.