Serendipity has played a role in many groundbreaking scientific discoveries. Key to their identification and exploitation is the ability to recognize the unexpected and invest time trying to understand it. Like any other field of scientific research, total synthesis requires determination and perseverance. When the first-generation route towards a target compound fails, new approaches are developed based on insights gained in the initial studies. Careful analysis of data obtained in a 'failed’ approach, e.g. when a reaction did not yield the desired or any expected outcome, can lead to spectacularly improved routes and discoveries that have impact beyond the synthesis of the selected target compound. Serendipity has further led to the identification of intriguing properties that materials or single molecules have, as exemplified by the discovery of electrically conductive polymers. During our total synthesis endeavors towards a complex natural product, we identified a small molecule with interesting olfactory properties, which we decided to investigate further.Drug discovery is in constant need of new molecules to develop drugs addressing unmet medical needs. To assess the chemical space available for drug design, our group investigates the generated databases (GDBs) listing all possible organic molecules up to a defined size, the largest of which is GDB-17 featuring 166.4 billion molecules up to 17 non-hydrogen atoms. While known drugs and bioactive compounds are mostly aromatic and planar, the GDBs contain a plethora of non-aromatic 3D-shaped molecules, which are very useful for drug discovery since they generally have more desirable absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Here we review GDB enumeration methods and the selection and synthesis of GDB molecules as modulators of ion channels. We summarize the constitution of GDB subsets focusing on fragments (FDB17), medicinal chemistry (GDBMedChem) and ChEMBL-like molecules (GDBChEMBL), and the ring system database GDB4c as a rich source of novel 3D-shaped chiral molecules containing quaternary centers, such as the recently reported trinorbornane.The selective partial oxidation of methane to methanol remains a great challenge in the field of catalysis. Cu-exchanged zeolites are promising materials, directly and selectively converting methane to methanol with high yield under cyclic conditions. However, the economic viability of these aluminosilicate materials for potential industrial applications remains a challenge. Exploring copper supported on non-microporous oxide supports and rationalising the structure/reactivity relationships extends the scope of material investigation and opens new possibilities. Recently, copper on alumina was demonstrated to be active and selective for the partial oxidation of methane. This work aims to explore the formation of well-defined Cu(II) oxo species on silica via surface organometallic chemistry and examines their reactivity for the selective transformation of methane to methanol. Isolated Cu(II) sites were generated via grafting of a tailored molecular precursor. Activation under oxidative conditions and subsequent removal of organic moieties from the grafted copper centres led to the formation of small copper (II) oxide clusters, which are active in the partial oxidation of methane under mild conditions, albeit significantly less efficient than the corresponding isolated Cu(II) sites on alumina.Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density functional theory, the electron density is a particularly compelling target for non-linear regression. Nevertheless, the scalability and the transferability of the existing machine-learning models of ρ(r) are limited by its complex rotational symmetries. Recently, in collaboration with Ceriotti and coworkers, we combined an efficient electron density decomposition scheme with a local regression framework based on symmetry-adapted Gaussian process regression able to accurately describe the covariance of the electron density spherical tensor components. The learning exercise is performed on local environments, allowing high transferability and linear-scaling of the prediction with respect to the number of atoms. Here, we review the main characteristics of the model and show its predictive power in a series of applications. The scalability and transferability of the trained model are demonstrated through the prediction of the electron density of Ubiquitin.Spectator oxo ligands are ubiquitous in catalysis, in particular in olefin epoxidation and olefin metathesis. Here we use computationally derived 17O NMR parameters to probe the electronic structure of spectator oxo ligands in these two reactions. We show that 17O NMR parameters allow to distinguish between doubly-bonded and triply-bonded oxo ligands, giving detailed insights into the frontier molecular orbitals involved in the metaloxo bonds along the reaction pathway. On the one hand, our study shows that in olefin epoxidation catalysed by methyltrioxorhenium (MTO), the oxo ligand significantly changes its bonding mode upon formation of the oxygen-transferring Re-oxo-bisperoxo-species, changing its nature from a doubly bonded to a triply bonded oxo ligand. On the other hand, only minor changes in the binding mode are found along the olefin metathesis reaction pathway with Mo- and W-based oxo-alkylidene species, in which the oxo ligand behaves as a triply bonded ligand throughout the reaction. This finding contrasts earlier studies that proposed that the change of binding mode of the oxo ligand was key to metallacyclobutane formation.Microfluidic autosamplers for electrospray ionization mass spectrometry (ESI-MS) are of major importance when using ESI-MS as a high-throughput and low sample consumption analytical method. In this article, microfluidic ESI-MS autosampler designs are overviewed and a group-owned prototype is discussed. The socalled gap sampler is a pin-based sampler for miniaturized flow injection (FI) analysis. To date, it has been used in various applications. Following proof of concept applications with FI of small molecules, pin modifications were implemented for unspecific and specific extraction for the analysis of complex samples. Most recently, further optimization allowed the study of non-covalent protein-ligand interactions for bioaffinity screenings, which constitutes a major milestone in the development of this novel high-throughput autosampler.Although oxaliplatin serves as one of the first-line drugs prescribed for treating26 colorectal cancer (CRC), the therapeutic effect is disappointing due to drug resistance.27 So far, the molecular mechanisms mediating oxaliplatin resistance remain unclear. In28 this study, we found the chemo-resistance in oxaliplatin-resistant HCT116 cells29 (HCT116/OXA) was mediated by the up-regulation of ERCC1 expression. Besides, the30 acquisition of resistance induced epithelial-mesenchymal transition (EMT), as well as31 the Slug over-expression. On the contrary, Slug silencing reversed EMT phenotype,32 decreased ERCC1 expression, and ameliorate the drug resistance. Further mechanistical33 studies revealed the enhanced Slug expression was resulted from the activation of34 AKT/GSK3β signaling. Moreover, in CRC patients, co-expression of Slug and ERCC135 was observed, and increased Slug expression was significantly correlated with36 clinicopathological factors and prognosis. Taken together, the simultaneous inhibition of37 AKT/GSK3β/Slug axis may be of significance for surmounting metastasis and38 chemo-resistance, thereby improving the therapeutic outcome of oxaliplatin.Clinical exome sequencing is frequently used to identify gene-disrupting variants in individuals with neurodevelopmental disorders. While splice-disrupting variants are known to contribute to these disorders, clinical interpretation of cryptic splice variants outside of the canonical splice site has been challenging. Here, we discuss papers that improve such detection.The authorship list on the original article [1] was incorrect and should instead show as Aklilu Abera Ayele, Kemal Ahmed Seid and Oumer Sada Muhammed. The authors apologise for this error.BACKGROUND Neuronal hyperexcitability and hypersynchrony have been described as key features of neurophysiological dysfunctions in the Alzheimer’s disease (AD) continuum. Conversely, physical activity (PA) has been associated with improved brain health and reduced AD risk. However, there is controversy regarding whether AD genetic risk (in terms of APOE ε4 carriage) modulates these relationships. The utilization of multiple outcome measures within one sample may strengthen our understanding of this complex phenomenon. METHOD The relationship between PA and functional connectivity (FC) was examined in a sample of 107 healthy older adults using magnetoencephalography. Additionally, we explored whether ε4 carriage modulates this association. The correlation between FC and brain structural integrity, cognition, and mood was also investigated. RESULTS A relationship between higher PA and decreased FC (hyposynchrony) in the left temporal lobe was observed among all individuals (across the whole sample, in ε4 carriers, and in ε4 non-carriers), but its effects manifest differently according to genetic risk. In ε4 carriers, we report an association between this region-specific FC profile and preserved brain structure (greater gray matter volumes and higher integrity of white matter tracts). In this group, decreased FC also correlated with reduced anxiety levels. In ε4 non-carriers, this profile is associated with improved cognition (working and episodic memory). CONCLUSIONS PA could mitigate the increase in FC (hypersynchronization) that characterizes preclinical AD, being beneficial for all individuals, especially ε4 carriers.BACKGROUND The importance of teaching the skills and practice of evidence-based medicine (EBM) for medical professionals has steadily grown in recent years. Alongside this growth is a need to evaluate the effectiveness of EBM curriculum as assessed by competency in the five 'A’s’ asking, acquiring, appraising, applying and assessing (impact and performance). EBM educators in medical education will benefit from a compendium of existing assessment tools for assessing EBM competencies in their settings. The purpose of this review is to provide a systematic review and taxonomy of validated tools that evaluate EBM teaching in medical education. METHODS We searched MEDLINE, EMBASE, Cochrane library, Educational Resources Information Centre (ERIC), Best Evidence Medical Education (BEME) databases and references of retrieved articles published between January 2005 and March 2019. We have presented the identified tools along with their psychometric properties including validity, reliability and relevance to the five dent benefit. Of the twelve tools identified, six were high quality. We have also provided a taxonomy of tools using the CREATE framework, for EBM teachers in medical education. CONCLUSIONS Six tools of reasonable validity are available for evaluating most steps of EBM and some domains of EBM learning. Further development and validation of tools that evaluate all the steps in EBM and all educational outcome domains are needed. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42018116203.