A life-cycle assessment of poly-hydroxybutyrate extraction from microbial biomass using dimethylcarbonate

Poly-hydroxyalkanoates are an example of biodegradable and biocompatible polymers, produced from renewable raw materials. With respect to other bioplastics the market share of poly-hydroxyalkanoates is still limited because of their commercial costs. To develop more cost-effective processes, a multilevel approach is usually undertaken combining innovative, cheaper and more effective microbial cultivation with safe and cheap extraction and purification methodologies. This study assesses the potential life cycle environmental impacts related to a novel protocol poly-hydroxyalkanoates extraction based on dimethyl carbonate in comparison to the use of halogenated hydrocarbons (in particular 1,2 dicholoroethane). Four scenarios are analysed for the dimethyl carbonate protocol considering: extraction from microbial slurry or from dried biomass, and recovery by solvent evaporation or polymer precipitation. The life cycle assessment demonstrates that the environmental performances of dimethyl carbonate-based protocols are far better than those of the most comparative process using the halogenated hydrocarbons. The scenario that foresees the extraction of dried biomass and recovers solvent by evaporation appears to be the most promising in terms of environmental sustainability performance

Colorimetric analysis of painting materials using polymer-supported polydiacetylene films

Polydiacetylenes, exhibiting different headgroups, distinguish among different painting materials

Shipborne measurements of Antarctic submicron organic aerosols: an NMR perspective linking multiple sources and bioregions

Abstract. The concentrations of submicron aerosol particles in maritime regions around Antarctica are influenced by the extent of sea ice. This effect is two ways: on one side, sea ice regulates the production of particles by sea spray (primary aerosols); on the other side, it hosts complex communities of organisms emitting precursors for secondary particles. Past studies documenting the chemical composition of fine aerosols in Antarctica indicate various potential primary and secondary sources active in coastal areas, in offshore marine regions, and in the sea ice itself. In particular, beside the well-known sources of organic and sulfur material originating from the oxidation of dimethylsulfide (DMS) produced by microalgae, recent findings obtained during the 2015 PEGASO cruise suggest that nitrogen-containing organic compounds are also produced by the microbiota colonizing the marginal ice zone. To complement the aerosol source apportionment performed using online mass spectrometric techniques, here we discuss the outcomes of offline spectroscopic analysis performed by nuclear magnetic resonance (NMR) spectroscopy. In this study we (i) present the composition of ambient aerosols over open-ocean waters across bioregions, and compare it to the composition of (ii) seawater samples and (iii) bubble-bursting aerosols produced in a sea-spray chamber onboard the ship. Our results show that the process of aerosolization in the tank enriches primary marine particles with lipids and sugars while depleting them of free amino acids, providing an explanation for why amino acids occurred only at trace concentrations in the marine aerosol samples analyzed. The analysis of water-soluble organic carbon (WSOC) in ambient submicron aerosol samples shows distinct NMR fingerprints for three bioregions: (1) the open Southern Ocean pelagic environments, in which aerosols are enriched with primary marine particles containing lipids and sugars; (2) sympagic areas in the Weddell Sea, where secondary organic compounds, including methanesulfonic acid and semivolatile amines abound in the aerosol composition; and (3) terrestrial coastal areas, traced by sugars such as sucrose, emitted by land vegetation. Finally, a new biogenic chemical marker, creatinine, was identified in the samples from the Weddell Sea, providing another confirmation of the importance of nitrogen-containing metabolites in Antarctic polar aerosols

Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease

Machine learning in Alzheimer’s disease genetics

: Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores.

Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease