Characterizing Strength and Fracture of Wood Micropillars Under Uniaxial Compression

The structure of the actual wood cell wall is very complex and it consists of several layers. Some research has been done to measure the mechanical properties of wood cell wall. For example, the hardness and modulus of wood cell wall was estimated using a nanoindentation test. But the mechanical contribution of wood cell wall is not fully understood and documented in the literature. Understanding the micro mechanical properties of the wood cell wall are paramount because of the potential for applications in cellulose nano-composites research and development. The focus of this research was to investigate the essential of the strength and fracture of wood cell walls by uniaxial micro-compression test. Keranji and loblolly pine were chosen to perform the micro-compression tests. After initial sample preparation by microtoming, a novel method for sample preparation was adopted. The cylindrical shaped micro pillars were milled using a Focused Ion Beam (FIB) while each pillar was milled in a single wood cell wall. The beam voltage of this FIB system was 30 KV. After measuring the dimension of the micropillar through analyzing the SEM images by ImageJ software, the uniaxial compression test on the micro pillar was conducted using a Nano II Indenter system with a 10 micrometers diameter flat tip. The loading rate of 20 nm/s was used to obtain the load-displacement curves. As a result, the yield stress of keranji cell wall was 136.5 MPa and the compression strength was 160 MPa. The yield stress of loblolly pine cell wall was 111.3 MPa and the compression strength was 125 iv MPa. The fracture behavior of wood micropillar confirmed that wood cell wall also is a brittle type of material. KEY WORDS: wood, cell wall, loblolly pine, keranji, focused ion beam (FIB), scanning electron microscopy (SEM), micropillar, uniaxial micro-compression test, fracture behavior

Positive solutions of higher order fractional integral boundary value problem with a parameter

In this paper, we study a higher-order fractional differential equation with integral boundary conditions and a parameter. Under different conditions of nonlinearity, existence and nonexistence results for positive solutions are derived in terms of different intervals of parameter. Our approach relies on the Guo–Krasnoselskii fixed point theorem on cones

Discerning Novel Splice Junctions Derived from RNA-Seq Alignment: A Deep Learning Approach

Background: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation. Results: In this work, we present a deep learning based splice junction sequence classifier, named DeepSplice, which employs convolutional neural networks to classify candidate splice junctions. We show (I) DeepSplice outperforms state-of-the-art methods for splice site classification when applied to the popular benchmark dataset HS3D, (II) DeepSplice shows high accuracy for splice junction classification with GENCODE annotation, and (III) the application of DeepSplice to classify putative splice junctions generated by Rail-RNA alignment of 21,504 human RNA-seq data significantly reduces 43 million candidates into around 3 million highly confident novel splice junctions. Conclusions: A model inferred from the sequences of annotated exon junctions that can then classify splice junctions derived from primary RNA-seq data has been implemented. The performance of the model was evaluated and compared through comprehensive benchmarking and testing, indicating a reliable performance and gross usability for classifying novel splice junctions derived from RNA-seq alignment