Looking through glass: Knowledge discovery from materials science literature using natural language processing.

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA) to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots is presented using the caption cluster plot (CCP), providing direct access to images buried in the papers. Finally, we combine the LDA and CCP with chemical elements to present an elemental map, a topical and image-wise distribution of elements occurring in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery.

Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment.

The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero.

Elucidating the constitutive relationship of calcium-silicate-hydrate gel using high throughput reactive molecular simulations and machine learning.

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium-silicate-hydrate (C-S-H) gel-the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C-S-H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C-S-H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C-S-H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C-S-H nanostructures to design efficient cementitious binders with targeted properties.

Diversified lineages and drug-resistance profiles of clinical isolates of Mycobacterium tuberculosis complex in Malaysia.

Background: Tuberculosis (TB) is still a major health problem in Malaysia with thousands of cases reported yearly. This is further burdened with the emergence of multidrug-resistant TB (MDR-TB). Whole-genome sequencing (WGS) provides high-resolution molecular epidemiological data for the accurate determination of Mycobacterium tuberculosis complex (MTBC) lineages and prediction of the drug-resistance patterns. This study aimed to investigate the diversity of MTBC in Malaysia in terms of lineage and drug-resistance patterns of the clinical MTBC isolates using WGS approach. Methods: The genomes of 24 MTBC isolated from sputum and pus samples were sequenced. The phenotypic drug susceptibility testing (DST) of the isolates was determined for ten anti-TB drugs. Bioinformatic analysis comprising genome assembly and annotation and single-nucleotide polymorphism (SNP) analysis in genes associated with resistance to the ten anti-TB drugs were done on each sequenced genome. Results: The draft assemblies covered an average of 97% of the expected genome size. Eleven isolates were aligned to the Indo-Oceanic lineage, eight were East-Asian lineage, three were East African-Indian lineage, and one was of Euro-American and Bovis lineages, respectively. Twelve of the 24 MTBC isolates were phenotypically MDR M. tuberculosis: one is polyresistance and another one is monoresistance. Twenty-six SNPs across nine genes associated with resistance toward ten anti-TB drugs were detected where some of the mutations were found in isolates that were previously reported as pan-susceptible using DST. A haplotype consisting of 65 variants was also found among the MTBC isolates with drug-resistance traits. Conclusions: This study is the first effort done in Malaysia to utilize 24 genomes of the local clinical MTBC isolates. The high-resolution molecular epidemiological data obtained provide valuable insights into the mechanistic and epidemiological qualities of TB within the vicinity of Southeast Asia.

Improving oil classification quality from oil spill fingerprint beyond six sigma approach.

This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC-MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat>Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.

Alocasia denudata Engler treatment enhance open wound healing activities in Wistar rat's skin.

ETHNOPHARMACOLOGICAL RELEVANCE: A. denudata is traditionally used to treat various skin disorders, including wounds. It is widely used by the traditional healers as an effective wound treatment. AIM OF STUDY: This study was done to determine A. denudata treatment effects on open wound healing activities in Wistar rat's skin. MATERIALS AND METHODS: 120 Wistar rats (250-300 g) were divided into four main groups, 1.5% and 3% A. denudata stem juice treated group, 10% Solcoseryl® gel treated group as positive control and phosphate buffer saline (PBS) treated group as negative control. Six full thicknesses wounds (6mm) were induced bilaterally on the dorsal of the rat's skin. Rats were sacrificed on day 1, 3, 6, 10 and 14. The percentage of wound contraction, biochemical estimations, macroscopic observation and histological examinations were done to evaluate the wound healing activities. RESULTS: Results showed wounds treated with A. denudata stem juice possess a significant higher rate of wound contraction (p<0.001), total protein concentration (p<0.05), hexosamine concentration (p<0.001) and uronic acid concentration (p<0.001). Moreover, cathepsin B (p<0.05) and hydroxyproline (p<0.05) level showed lower concentration in wounds treated with A. denudata stem juice. Histological observation of wounds treated with A. denudata stem juice displayed organized epithelial layer with dense and compact collagen fibers. CONCLUSION: Both doses of A. denudata stem juice were found to enhance wound healing process. However, wounds treated with 3% A. denudata stem juice were reported to be more effective as a wound healing agent thus support its traditional usage.