A New Algebraic Approach for String Reconstruction from Substring Compositions

We consider the problem of binary string reconstruction from the multiset of its substring compositions, i.e., referred to as the substring composition multiset, first introduced and studied by Acharya et al. We introduce a new algorithm for the problem of string reconstruction from its substring composition multiset which relies on the algebraic properties of the equivalent bivariate polynomial formulation of the problem. We then characterize specific algebraic conditions for the binary string to be reconstructed that guarantee the algorithm does not require any backtracking through the reconstruction, and, consequently, the time complexity is bounded polynomially. More specifically, in the case of no backtracking, our algorithm has a time complexity of $O(n^2)$ compared to the algorithm by Acharya et al., which has a time complexity of $O(n^2\log(n))$, where $n$ is the length of the binary string. Furthermore, it is shown that larger sets of binary strings are uniquely reconstructable by the new algorithm and without the need for backtracking leading to codebooks of reconstruction codes that are larger, by a linear factor in size, compared to the previously known construction by Pattabiraman et al., while having $O(n^2)$ reconstruction complexity

Application of X-ray Grating Interferometry to Polymer/Flame Retardant Blends in Additive Manufacturing

X-ray grating interferometry is a nondestructive tool for visualizing the internal structures of samples. Image contrast can be generated from the absorption of X-rays, the change in phase of the beam and small-angle X-ray scattering (dark-field). The attenuation and differential phase data obtained complement each other to give the internal composition of a material and large-scale structural information. The dark-field signal reveals sub-pixel structural detail usually invisible to the attenuation and phase probe, with the potential to highlight size distribution detail in a fashion faster than conventional small-angle scattering techniques. This work applies X-ray grating interferometry to the study of additively manufactured polymeric objects. Additively manufactured bunnies made from single material—acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA)—were studied by grating-based X-ray interferometric two-dimensional imaging and tomography. The dark-field images detected poor adhesion in the plane perpendicular to the build plate. Curvature analysis of the sample perimeter revealed a slightly higher propensity to errors in regions of higher curvature. Incorporation of flame-retardant molecules to near-surface regions of otherwise flammable objects through the fused deposition modeling additive manufacturing technique was also explored. The anticipated advantage was efficient use of the flame retardants while keeping them away from the surface for safety. To determine heat propagation effects, two-dimensional grating-based interferometry imaging at LSU CAMD was used to study heated samples. The focus was on the dark-field signals to highlight voids and gaps arising from layer delamination or gasification of chemical components. The resulting differential phase and dark-field x images were tainted by fringes attributed to inaccuracies in the grating-step position. Attempts to correct this will be presented. Interferometric tomography was also carried out on the heated samples using the W. M. Keck interferometric system at LSU. Grating-based interferometry was also used to probe scattering structure sizes of heated samples. Comparison of the data with the conventional small-angle x-ray scattering technique, SAXS, is being pursued. The results obtained so far from the above-mentioned experimental works are presented in this document

Emerging Approaches to DNA Data Storage: Challenges and Prospects

With the total amount of worldwide data skyrocketing, the global data storage demand is predicted to grow to 1.75 × 1014GB by 2025. Traditional storage methods have difficulties keeping pace given that current storage media have a maximum density of 103GB/mm3. As such, data production will far exceed the capacity of currently available storage methods. The costs of maintaining and transferring data, as well as the limited lifespans and significant data losses associated with current technologies also demand advanced solutions for information storage. Nature offers a powerful alternative through the storage of information that defines living organisms in unique orders of four bases (A, T, C, G) located in molecules called deoxyribonucleic acid (DNA). DNA molecules as information carriers have many advantages over traditional storage media. Their high storage density, potentially low maintenance cost, ease of synthesis, and chemical modification make them an ideal alternative for information storage. To this end, rapid progress has been made over the past decade by exploiting user-defined DNA materials to encode information. In this review, we discuss the most recent advances of DNA-based data storage with a major focus on the challenges that remain in this promising field, including the current intrinsic low speed in data writing and reading and the high cost per byte stored. Alternatively, data storage relying on DNA nanostructures (as opposed to DNA sequence) as well as on other combinations of nanomaterials and biomolecules are proposed with promising technological and economic advantages. In summarizing the advances that have been made and underlining the challenges that remain, we provide a roadmap for the ongoing research in this rapidly growing field, which will enable the development of technological solutions to the global demand for superior storage methodologies

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach with Enhanced Error Resilience and Biological Constraint Optimization

In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content

Chemical analysis of polymer blends via synchrotron X-ray tomography

Material properties of industrial polymer blends are of great importance. X-ray tomography has been used to obtain spatial chemical information about various polymer blends. The spatial images are acquired with synchrotron X-ray tomography because of its rapidity, good spatial resolution, large field-of-view, and elemental sensitivity. The spatial absorption data acquired from X-ray tomography experiments is converted to spatial chemical information via a linear least squares fit of multi-spectral X-ray absorption data. A fiberglass-reinforced polymer blend with a new-generation flame retardant is studied with multi-energy synchrotron X-ray tomography to assess the blend homogeneity. Relative to other composite materials, this sample is difficult to image due to low x-ray contrast between the fiberglass reinforcement and the polymer blend. To investigate chemical composition surrounding the glass fibers, new procedures were developed to find and mark the fiberglass, then assess the flame retardant distribution near the fiber. Another polymer blending experiment using three-dimensional chemical analysis techniques to look at a polymer additive problem called blooming was done. To investigate the chemical process of blooming, new procedures are developed to assess the flame retardant distribution as a function of annealing time in the sample. With the spatial chemical distribution we fit the concentrations to a diffusion equation to each time step in the annealing process. Finally the diffusion properties of a polymer blend composed of hexabromobenzene and o-terphenyl was studied. The diffusion properties were compared with computer simulations of the blend