9,273 research outputs found

    MolFM: A Multimodal Molecular Foundation Model

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    Molecular knowledge resides within three different modalities of information sources: molecular structures, biomedical documents, and knowledge bases. Effective incorporation of molecular knowledge from these modalities holds paramount significance in facilitating biomedical research. However, existing multimodal molecular foundation models exhibit limitations in capturing intricate connections between molecular structures and texts, and more importantly, none of them attempt to leverage a wealth of molecular expertise derived from knowledge graphs. In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs. We propose cross-modal attention between atoms of molecular structures, neighbors of molecule entities and semantically related texts to facilitate cross-modal comprehension. We provide theoretical analysis that our cross-modal pre-training captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule, as well as molecules sharing similar structures or functions. MolFM achieves state-of-the-art performance on various downstream tasks. On cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04% absolute gains under the zero-shot and fine-tuning settings, respectively. Furthermore, qualitative analysis showcases MolFM's implicit ability to provide grounding from molecular substructures and knowledge graphs. Code and models are available on https://github.com/BioFM/OpenBioMed.Comment: 31 pages, 15 figures, and 15 table

    Irish Ocean Climate and Ecosystem Status Report

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    Summary report for Irish Ocean Climate & Ecosystem Status Report also published here. This Irish Ocean Climate & Ecosystem Status Summary for Policymakers brings together the latest evidence of ocean change in Irish waters. The report is intended to summarise the current trends in atmospheric patterns, ocean warming, sea level rise, ocean acidification, plankton and fish distributions and abundance, and seabird population trends. The report represents a collaboration between marine researchers within the Marine Institute and others based in Ireland’s higher education institutes and public bodies. It includes authors from Met Éireann, Maynooth University, the University of Galway, the Atlantic Technological University, National Parks and Wildlife, Birdwatch Ireland, Trinity College Dublin, University College Dublin, Inland Fisheries Ireland, The National Water Forum, the Environmental Protection Agency, and the Dundalk Institute of Technology.This report is intended to summarise the current trends in Ireland’s ocean climate. Use has been made of archived marine data held by a range of organisations to elucidate some of the key trends observed in phenomena such as atmospheric changes, ocean warming, sea level rise, acidification, plankton and fish distributions and abundance, and seabirds. The report aims to summarise the key findings and recommendations in each of these areas as a guide to climate adaptation policy and for the public. It builds on the previous Ocean Climate & Ecosystem Status Report published in 2010. The report examines the recently published literature in each of the topic areas and combines this in many cases with analysis of new data sets including long-term time series to identify trends in essential ocean variables in Irish waters. In some cases, model projections of the likely future state of the atmosphere and ocean are presented under different climate emission scenarios.Marine Institut

    Simulating substrate binding sites in the S. aureus Type II NADH Dehydrogenase

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    "Type II NADH Oxidoreductase (NDH-2) from Staphylococcus aureus was established as a therapeutic target against the virulency of this bacterium and an alternative to treat Complex I-derived diseases. To accurately model interactions of NDH-2 with its substrates such as menaquinones and NADH, Coarse-Grain (CG) simulations were employed. "N/

    Genetic insights into immune mechanisms of Alzheimer’s and Parkinson’s disease

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    Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer’s disease (AD) and Parkinson’s disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders

    Modification of Physicochemical Properties of Active Pharmaceutical Ingredient by Pharmaceutical Co-Crystals

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    The oral drug delivery is widely used and accepted routes of administration, but it fails to provide the therapeutic effectiveness of drugs due to low solubility, poor compression and oral bioavailability. Crystal engineering is the branch where the modification of API is of great importance. Co-crystallization of API using a co-former is a hopeful and emerging approach to improve the performance of pharmaceuticals, such as micromeritic properties, solubility, dissolution profile, pharmacokinetics and stability. Pharmaceutical co-crystals are multicomponent systems in which one component is an active pharmaceutical ingredient and the others are pharmaceutically acceptable ingredients that are of GRAS category. In multidrug co-crystals one drug acts as API and other drug acts as coformer. This chapter illustrates the guidance for more efficient design and manufacture of pharmaceutical co-crystals with the desired physicochemical properties and applications

    Novel approaches for the control of fungal pathogens

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    Fungal pathogens are a continual threat with potential impacts on human health, agriculture, food and goods security. Despite this, currently used treatments are limited to a handful of drug or fungicide classes. The limited availability of treatment options is further challenged by growing fungal resistance, tightening legislation over drug/fungicide use and evolving public opinion. In this thesis, certain novel approaches were explored for their potential in the control of fungal pathogens of humans or crops. One approach utilised the concept of combinatorial treatments, applied specifically to synergistic interactions among natural product (NP) compounds. NPs have been questioned for their translational applications due to promiscuous activity; this study proposed the potential of synergy for potentiating antifungal activity and improving target specificity. In a high-throughput screening approach, selected NPs were screened pairwise against a wider NP chemical library. Screening of 800 NP combinations revealed 34 pairs that were potentially synergistic in their inhibitory effects on yeast growth. Moreover, scaled-up validation tests for three combinations of particular interest showed that synergy was present against several important pathogens. One synergistic combination was explored mechanistically and found to promote synergistic mitochondrial membrane depolarization and ROS formation. This work indicated the potential for synergistic NP combinations in fungal pathogen control. An additional study focussed on relationships between NP interactions and their underlying mechanisms of synergy, focusing on a particular triangle of NP interactions (involving two synergies but also no interaction). Results indicated that the NP sclareol, found to synergise with a number of other NPs, could also induce synergy between the previously non-synergistic pair of compounds. Results supported that this action of sclareol involved uncoupling of oxidative phosphorylation, which may be an activity that enables synergies against fungal pathogens more widely. An additional approach explored the potential of collateral sensitivity (CS) as a potential drug-repurposing strategy against azole-resistant Candida albicans. CS is where resistance to one drug is linked to sensitivity to another, so offering means to target drug resistant strains. Two azole-resistant clinical isolates of C. albicans showed hypersensitivity to several non-antifungal drugs, particularly aminoglycosides. The mutants were slow growers, but slow growth was not sufficient to explain the hypersensitivity, neither were the isolates’ alleles of erg11, the gene encoding the lanosterol demethylase targeted by azoles. Moreover, the hypersensitivity was not reproduced in other azole-resistant isolates. Mechanistic studies pointed to a possible role for cell wall glycosylation or integrity defects in the original two isolates. Further work expanded the search for CS compounds against azole-resistant C. albicans through a screen of a 1,280-compound library. The results did not identify any hit compounds, but reproducibility and dosage concerns meant that hit compounds could have been missed. A final approach set out to assess mechanistic bases for reported fungal anti-attachment properties of certain polymer materials. One strategy was an accelerated evolution experiment, designed to select C. albicans variants hyper-attaching to polymer. However, attachment propensity did not change, indicating resilience of the anti-attachment material properties. Another strategy examined cell wall properties that may affect anti-attachment, in C. albicans and the plant pathogen Zymoseptoria tritici. Results with selective fluorescent probes highlighted certain cell wall components that were enriched in polymer-attaching or glass-attaching cells. This offers a path for understanding cell properties important for (anti-) attachment to the polymer materials, valuable for informing design of improved polymers. Taken together the three approaches explored in this thesis offer exciting potential for bolstering efforts to control fungal pathogens, providing bases for further mechanistic and possible translational developmen

    The application of machine learning in nanoparticle treated water: A review

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    Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment

    Identification and analysis of the secretome of plant pathogenic fungi reveals lifestyle adaptation

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    The secretory proteome plays an important role in the pathogenesis of phytopathogenic fungi. However, the relationship between the large-scale secretome of phytopathogenic fungi and their lifestyle is not fully understood. In the present study, the secretomes of 150 plant pathogenic fungi were predicted and the characteristics associated with different lifestyles were investigated. In total, 94,974 secreted proteins (SPs) were predicted from these fungi. The number of the SPs ranged from 64 to 1,662. Among these fungi, hemibiotrophic fungi had the highest number (average of 970) and proportion (7.1%) of SPs. Functional annotation showed that hemibiotrophic and necrotroph fungi, differ from biotrophic and symbiotic fungi, contained much more carbohydrate enzymes, especially polysaccharide lyases and carbohydrate esterases. Furthermore, the core and lifestyle-specific SPs orthogroups were identified. Twenty-seven core orthogroups contained 16% of the total SPs and their motif function annotation was represented by serine carboxypeptidase, carboxylesterase and asparaginase. In contrast, 97 lifestyle-specific orthogroups contained only 1% of the total SPs, with diverse functions such as PAN_AP in hemibiotroph-specific and flavin monooxygenases in necrotroph-specific. Moreover, obligate biotrophic fungi had the largest number of effectors (average of 150), followed by hemibiotrophic fungi (average of 120). Among these effectors, 4,155 had known functional annotation and pectin lyase had the highest proportion in the functionally annotated effectors. In addition, 32 sets of RNA-Seq data on pathogen-host interactions were collected and the expression levels of SPs were higher than that of non-SPs, and the expression level of effector genes was higher in biotrophic and hemibiotrophic fungi than in necrotrophic fungi, while secretase genes were highly expressed in necrotrophic fungi. Finally, the secretory activity of five predicted SPs from Setosphearia turcica was experimentally verified. In conclusion, our results provide a foundation for the study of pathogen-host interaction and help us to understand the fungal lifestyle adaptation

    A narrative review of the effect of wildfire exposure on pregnancy & birth outcomes

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    Wildfires pose a significant and growing threat to human health. Current trends in climate change predict that wildfire occurrence and severity will increase in the near future, and therefore the adverse health effects associated with wildfire and its air quality effects are becoming increasingly relevant. Even with current efforts to stem future rises in temperature, wildfire activity will continue to increase due to lags in the climate system itself. Thus, in addition to the known increase in mortality, respiratory, and cardiovascular risks, there is a growing need to investigate other health outcomes associated with wildfire smoke exposure, especially their effect on pregnancy and birth outcomes. In order to provide a broad overview of the state of wildfire research on the topic of pregnancy and birth outcomes, this narrative review will summarize the existing literature on pregnancy and birth outcomes associated with wildfire smoke exposure, with consideration for the ambient air pollution literature that informs wildfire research. As research in this specific topic is still developing, a pattern of limitations to study designs is beginning to emerge, which will guide future research needs. Finally, practical considerations for implementing research findings into land management and public health policies that reduce wildfire exposure in order to mitigate the health risks associated with it will be explained
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