Expression profiling of cancer-related long non-coding RNAs revealed upregulation and biomarker potential of HAR1B and JPX in colorectal cancer

Molecular Biology Reports - Aberrant expressions of long non-coding RNAs promote cancer development including colorectal cancer. Expression profiling of cancer-related lncRNAs may introduce new...     

Tenascin-C as a noninvasive biomarker of coronary artery disease

Molecular Biology Reports - Coronary artery disease (CAD), is the leading cause of mortality and morbidity worldwide. Tenascin-C (TNC) with high expression levels in inflammatory and cardiovascular...     

First volumetric records of airborne Cladosporium and Alternaria spores in the atmosphere of Al Khor (northern Qatar): a preliminary survey

Aerobiologia - Daily monitoring of airborne fungal spores was carried out for the first time in Al Khor city, Qatar, using a Hirst type 7-day recording volumetric spore trap, from May 2017 to May...     

Elevated serum levels of kynurenine pathway metabolites in patients with Behçet disease

Amino Acids - Behçet disease (BD) is an inflammatory, multisystemic vasculitis of unknown etiopathogenesis. However, innate and adaptive immune system involvement and immune-mediated networks...     

Advances in application of machine learning to life cycle assessment: a literature review

Purpose

Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making.

Methods

In this study, previous research on ML for LCA were considered, and a literature review was undertaken.

Results

The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases.

Conclusions

The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

     

Contamination of breast milk with lead,mercury, arsenic,and cadmium in Iran: a systematic review and meta-analysis

BioMetals - Breast milk is a complete food for the development of the newborn, but it can also be an important route for environmental pollutants transmission to the infants. This study was aimed...     

Copper (II) complexes with N,S donor pyrazole-based ligands as anticancer agents

BioMetals - A group of bidentate nitrogen and sulfur donor pyrazole derivative ligands abbreviated as Na[RNCS(Pz)], Na[RNCS(PzMe2)], Na[RNCS(PzMe3)], Na[RNCS(PzPhMe)], Na[RNCS(PzPh2)], where...