Features of Polymeric Structures By Surface—Selective Laser Sintering of Polymer Particles Using Water as Sensitizer

The development of scaffolds with strictly specific properties is a key aspect of functional tissue regeneration, and it still remains one of the greatest challenges for tissue engineering. This study is aimed to determine the possibility of producing three-dimensional polylactide (PLA) scaffolds using the method of surface-selectiv  laser sintering (SSLS) for bone tissue regeneration. In this work, the authors also improved PLA scaffold adhesion properties, which are crucial for successful cellular growth and expansion. Thus, SSLS method proved to be effective in designing threedimensional porous scaffolds with differentiated mechanical properties. Keywords: regenerative medicine, scaffolds, polylactide, surface – selective laser . sintering, tissue engeneering

Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

<p>Abstract</p> <p>Background</p> <p>The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by <it>Miklós, I. and Meyer, I.M. </it>describes a memory sparse version of the Baum-Welch algorithm with modifications to the original probabilistic table topologies to make memory use independent of sequence length (and linearly dependent on state number). The original description of the technique has some errors that we amend. We then compare the corrected implementation on a variety of data sets with conventional and checkpointing implementations.</p> <p>Results</p> <p>We provide a correct recurrence relation for the emission parameter estimate and extend it to parameter estimates of the Normal distribution. To accelerate estimation of the prior state probabilities, and decrease memory use, we reverse the originally proposed forward sweep. We describe different scaling strategies necessary in all real implementations of the algorithm to prevent underflow. In this paper we also describe our approach to a linear memory implementation of the Viterbi decoding algorithm (with linearity in the sequence length, while memory use is approximately independent of state number). We demonstrate the use of the linear memory implementation on an extended Duration Hidden Markov Model (DHMM) and on an HMM with a spike detection topology. Comparing the various implementations of the Baum-Welch procedure we find that the checkpointing algorithm produces the best overall tradeoff between memory use and speed. In cases where sequence length is very large (for Baum-Welch), or state number is very large (for Viterbi), the linear memory methods outlined may offer some utility.</p> <p>Conclusion</p> <p>Our performance-optimized Java implementations of Baum-Welch algorithm are available at <url>http://logos.cs.uno.edu/~achurban</url>. The described method and implementations will aid sequence alignment, gene structure prediction, HMM profile training, nanopore ionic flow blockades analysis and many other domains that require efficient HMM training with EM.</p

Local delivery of pirfenidone by pla implants modifies foreign body reaction and prevents fibrosis

Peri-implant fibrosis (PIF) increases the postsurgical risks after implantation and limits the efficacy of the implantable drug delivery systems (IDDS). Pirfenidone (PF) is an oral anti-fibrotic drug with a short (1100 Μm in control groups) approaching the intact derma thickness value (302 ± 15 Μm). In PLA@PF group, the implant biodegradation developed faster, while arginase-1 expression was suppressed in comparison with other groups. This study proves the feasibility of the local control of fibrotic response on implants via modulation of foreign body reaction with slowly biodegradable PF-loaded IDDS

A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector

<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions – while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.</p> <p>Results</p> <p>A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extraction is then used to establish the first pattern recognition-informed (PRI) sampling control of a Nanopore Detector Device (on a "live" data-stream).</p> <p>Conclusion</p> <p>The improved accuracy of the new HMMwD implementation, at the same order of computational cost as the standard HMM, is an important augmentation for applications in gene structure identification and channel current analysis, especially PRI sampling control, for example, where speed is essential. The PRI experiment was designed to inherit the high accuracy of the well characterized and distinctive blockades of the DNA hairpin molecules used as controls (or blockade "test-probes"). For this test set, the accuracy inherited is 99.9%.</p

Identification of novel conserved peptide uORF homology groups in Arabidopsis and rice reveals ancient eukaryotic origin of select groups and preferential association with transcription factor-encoding genes

Abstract Background Upstream open reading frames (uORFs) can mediate translational control over the largest, or major ORF (mORF) in response to starvation, polyamine concentrations, and sucrose concentrations. One plant uORF with conserved peptide sequences has been shown to exert this control in an amino acid sequence-dependent manner but generally it is not clear what kinds of genes are regulated, or how extensively this mechanism is invoked in a given genome. Results By comparing full-length cDNA sequences from Arabidopsis and rice we identified 26 distinct homology groups of conserved peptide uORFs, only three of which have been reported previously. Pairwise Ka/Ks analysis showed that purifying selection had acted on nearly all conserved peptide uORFs and their associated mORFs. Functions of predicted mORF proteins could be inferred for 16 homology groups and many of these proteins appear to have a regulatory function, including 6 transcription factors, 5 signal transduction factors, 3 developmental signal molecules, a homolog of translation initiation factor eIF5, and a RING finger protein. Transcription factors are clearly overrepresented in this data set when compared to the frequency calculated for the entire genome (p = 1.2 × 10-7). Duplicate gene pairs arising from a whole genome duplication (ohnologs) with a conserved uORF are much more likely to have been retained in Arabidopsis (Arabidopsis thaliana) than are ohnologs of other genes (39% vs 14% of ancestral genes, p = 5 × 10-3). Two uORF groups were found in animals, indicating an ancient origin of these putative regulatory elements. Conclusion Conservation of uORF amino acid sequence, association with homologous mORFs over long evolutionary time periods, preferential retention after whole genome duplications, and preferential association with mORFs coding for transcription factors suggest that the conserved peptide uORFs identified in this study are strong candidates for translational controllers of regulatory genes.</p