GOFFA: Gene Ontology For Functional Analysis – A FDA Gene Ontology Tool for Analysis of Genomic and Proteomic Data

BACKGROUND: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated. RESULTS: Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions. CONCLUSION: With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses. AVAILABILITY: GOFFA is available through ArrayTrack software

Development of active packaging films utilized natural colorants derived from plants and their diverse applications in protein-rich food products

With the increasing demand for environmentally friendly, safe, preservative and intelligent food packaging, there is a growing trend towards using plant-derived natural colorants that posses green, non-toxic, antioxidant, antibacterial, and pH-sensitive properties. As a result, the development of active intelligent packaging films containing plant-derived natural colorants has become a research priority in the realm of food packaging. As a novel packaging approach, it can serve as an active and intelligent packaging system to prolong shelf life and monitor food quality. On the basis of introducing several widely used natural colorants derived from plants, this review examines the preparation, structural characterization, physical properties, and functional aspects of these plant-derived pigments. The preparation procedures of various film forming substrates and natural pigment based films are also comprehensively discussed. Furthermore, the utilization of natural pigment-based films as active and intelligent packaging materials in food is discussed in depth, providing valuable insights into the future development of this cutting-edge research area

Experimental Study on Mechanical Behaviour of Glass Fiber Reinforced Polymer Bars under Compression

The requirements for using GFRP bars are growing as several researchers have shown the functionality of bars in concrete columns. The demand to characterize the mechanical properties of GFRP bars is therefore rising, although there is no standardized test method for evaluating the compressive behavior of these bars. This experimental study presents the determination of the mechanical properties of GFRP composite bars in compression, namely the stress-strain curves, compressive strength, ultimate crushing strain, and modulus of elasticity. The compressive properties of these bars were calculated following ASTM D695-10 (Compression Test) with some modifications. A total of 27 specimens were tested for the proposed test procedure. The diameter of the GFRP tendon used in the test was 10, 12, and 14 mm, and the length to bar diameter ratio Le/db (4, 8, and 16) was investigated for the compressive strength of the bars. These two parameters were used to establish the relationship between the length to diameter ratio and strength. Besides, two steel caps with a length of 50 mm each were installed to both ends of each specimen to avoid premature failure. It was observed that the test method enables to successfully evaluate the compressive characteristics of the GFRP bars. Experimental discussions were performed based on the test results from stress-strain curves, bar graphs, and scatter curves. The results indicate the increase in length to diameter ratio decrease the buckling stress and the compressive to tensile strength ratio for Le/db ratio of 16 specimens in buckling failure mode. The failure mode transformed from crushing to buckling and a combination of crushing and buckling between the two different failures modes with an improvement in the Le/db ratio. It shows that there was no yield section on the test specimens during the entire test loading process. The compressive GFRP bars present typical brittle failure. Keywords: Compressive Test, GFRP Bars, Diameter, Le/Db Ratio, Stress-Strain Curve, Buckling DOI: 10.7176/CER/13-5-04 Publication date:August 31st 202

Experimental Research on Material Behaviour of Glass Fiber Reinforced Polymer Bars in Tension

Glass Fiber Reinforced Polymer (GFRP) rebars have been widely used to solve the corrosion problem of steel bars in concrete structures. It has been produced as a lightweight and corrosion-resistant than steel reinforcement in many structural applications. They are regarded as a promising substitute for steel bars in concrete infrastructures. It is necessary to test GFRP bars to fully understand their material properties to ensure the safe and efficient use of the material. In this study, five specimens of each type of GFRP bars with a diameter of 6, 8, 10, 12, and 14 mm were tested under tension. Therefore, a total of 25 samples were examined from the same manufacturer. According to ASTM’s recommendations (D7205/D7205M-06) for tensile tests of GFRP bars, the diameter and thickness of the steel pipes for both ends were considered in the preparation of the test specimens to keep the GFRP bars consistent and aligned throughout the experiment. The experimental test results included the stress-strain curves, tensile strength, ultimate strain, and modulus of elasticity. The study showed an accurate result that indicated the tensile strength of the GFRP bars can be expressed by a linear distribution. For a bar diameter of 10mm, the length to diameter ratio Le/db=8 showed a maximum tensile to compressive strength ratio. In the failure results of the test, there were two-mode failures of GFRP bars: fracture failure and pull-out failure of GFRP bars. Most of the specimens had GFRP bar fracture failures, only two specimens (GBT1-10-2 and GBT1-10-3) were damaged due to the pull-off of the GFRP bars which was not a typical failure mode. Keywords: GFRP Bars, Tensile Test, Stress-Strain Curve, Fracture Failure DOI: 10.7176/CER/13-5-05 Publication date:August 31st 202

A generic method to synthesise graphitic carbon coated nanoparticles in large scale and their derivative polymer nanocomposites

A versatile Rotary Chemical Vapour Deposition (RCVD) technique for the in-situ synthesis of large scale carbon-coated non-magnetic metal oxide nanoparticles (NPs) is presented, and a controllable coating thickness varying between 1–5 nm has been achieved. The technique has significantly up-scaled the traditional chemical vapour deposition (CVD) production for NPs from mg level to 10 s of grams per batch, with the potential for continuous manufacturing. The resulting smooth and uniform C-coatings sheathing the inner core metal oxide NPs are made of well-crystallised graphitic layers, as confirmed by electron microscopy imaging, electron dispersive spectrum elemental line scan, X-ray powder diffractions and Raman spectroscopy. Using nylon 12 as an example matrix, we further demonstrate that the inclusion of C-coated composite NPs into the matrix improves the thermal conductivity, from 0.205 W∙m−1∙K−1 for neat nylon 12 to 0.305 W∙m−1∙K−1 for a 4 wt% C-coated ZnO composite, in addition to a 27% improvement in tensile strength at 2 wt% addition

Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies

The Application Research of Inverse Finite Element Method for Frame Deformation Estimation

A frame deformation estimation algorithm is investigated for the purpose of real-time control and health monitoring of flexible lightweight aerospace structures. The inverse finite element method (iFEM) for beam deformation estimation was recently proposed by Gherlone and his collaborators. The methodology uses a least squares principle involving section strains of Timoshenko theory for stretching, torsion, bending, and transverse shearing. The proposed methodology is based on stain-displacement relations only, without invoking force equilibrium. Thus, the displacement fields can be reconstructed without the knowledge of structural mode shapes, material properties, and applied loading. In this paper, the number of the locations where the section strains are evaluated in the iFEM is discussed firstly, and the algorithm is subsequently investigated through a simple supplied beam and an experimental aluminum wing-like frame model in the loading case of end-node force. The estimation results from the iFEM are compared with reference displacements from optical measurement and computational analysis, and the accuracy of the algorithm estimation is quantified by the root-mean-square error and percentage difference error

A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes

Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the method using the microarray data of both diffuse B cell lymphoma and colon cancer to demonstrate its performance for identifying key features from the profile data of high-dimension gene expression. The method was compared with a probabilistic model based on genetic algorithm and another hybrid method based on both genetics algorithm and support vector machine. The results showed that the proposed method provides new computational strategy for hunting candidate disease genes from the profile data of disease gene expression. The selected candidate disease genes may help to improve the diagnosis and treatment for diseases