Synthesis and pharmacological profiling of analogues of benzyl quinolone carboxylic acid (BQCA) as allosteric modulators of the M1 muscarinic receptor

Established therapy in Alzheimer’s disease involves potentiation of the endogenous orthosteric ligand, acetylcholine, at the M1 muscarinic receptors found in higher concentrations in the cortex and hippocampus. Adverse effects, due to indiscriminate activation of other muscarinic receptor subtypes, are common. M1 muscarinic positive allosteric modulators/allosteric agonists such as BQCA offer an attractive solution, being exquisitely M1-selective over other muscarinic subtypes. A common difficulty with allosteric ligands is interpreting SAR, based on composite potency values derived in the presence of fixed concentration of agonist. In reality these values encompass multiple pharmacological parameters, each potentially and differentially sensitive to structural modification of the ligand. We report novel BQCA analogues which appear to augment ligand affinity for the receptor (pKB), intrinsic efficacy (τB), and both binding (α) and functional (β) cooperativity with acetylcholine. Ultimately, development of such enriched SAR surrounding allosteric modulators will provide insight into their mode of action

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Transgenesis in Animal Agriculture: Addressing Animal Health and Welfare Concerns

The US Food and Drug Administration’s final Guidance for Industry on the regulation of transgenesis in animal agriculture has paved the way for the commercialization of genetically engineered (GE) farm animals. The production-related diseases associated with extant breeding technologies are reviewed, as well as the predictable welfare consequences of continued emphasis on prolificacy at the potential expense of physical fitness. Areas in which biotechnology could be used to improve the welfare of animals while maintaining profitability are explored along with regulatory schema to improve agency integration in GE animal oversight

Process Mining for Healthcare: Characteristics and Challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future