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Objective: Pancreatic ductal adenocarcinoma (PDAC) still carries a dismal prognosis with an overall 5-year survival rate of 9%. Conventional combination chemotherapies are a clear advance in the treatment of PDAC; however, subtypes of the disease exist, which exhibit extensive resistance to such therapies. Genomic MYC amplifications represent a distinct subset of PDAC with an aggressive tumour biology. It is clear that hyperactivation of MYC generates dependencies that can be exploited therapeutically. The aim of the study was to find and to target MYC-associated dependencies.
Design: We analysed human PDAC gene expression datasets. Results were corroborated by the analysis of the small ubiquitin-like modifier (SUMO) pathway in a large PDAC cohort using immunohistochemistry. A SUMO inhibitor was used and characterised using human and murine two-dimensional, organoid and in vivo models of PDAC.
Results: We observed that MYC is connected to the SUMOylation machinery in PDAC. Components of the SUMO pathway characterise a PDAC subtype with a dismal prognosis and we provide evidence that hyperactivation of MYC is connected to an increased sensitivity to pharmacological SUMO inhibition.
Conclusion: SUMO inhibitor-based therapies should be further developed for an aggressive PDAC subtype.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries.
Premise: Both universal and family-specific targeted sequencing probe kits are becoming widely used for reconstruction of phylogenetic relationships in angiosperms. Within the pantropical Ochnaceae, we show that with careful data filtering, universal kits are equally as capable in resolving intergeneric relationships as custom probe kits. Furthermore, we show the strength in combining data from both kits to mitigate bias and provide a more robust result to resolve evolutionary relationships.
Methods: We sampled 23 Ochnaceae genera and used targeted sequencing with two probe kits, the universal Angiosperms353 kit and a family-specific kit. We used maximum likelihood inference with a concatenated matrix of loci and multispecies-coalescence approaches to infer relationships in the family. We explored phylogenetic informativeness and the impact of missing data on resolution and tree support.
Results: For the Angiosperms353 data set, the concatenation approach provided results more congruent with those of the Ochnaceae-specific data set. Filtering missing data was most impactful on the Angiosperms353 data set, with a relaxed threshold being the optimum scenario. The Ochnaceae-specific data set resolved consistent topologies using both inference methods, and no major improvements were obtained after data filtering. Merging of data obtained with the two kits resulted in a well-supported phylogenetic tree.
Conclusions: The Angiosperms353 data set improved upon data filtering, and missing data played an important role in phylogenetic reconstruction. The Angiosperms353 data set resolved the phylogenetic backbone of Ochnaceae as equally well as the family specific data set. All analyses indicated that both Sauvagesia L. and Campylospermum Tiegh. as currently circumscribed are polyphyletic and require revised delimitation.
Aim: It can be challenging to distinguish COVID-19 in children from other common infections. We set out to determine the rate at which children consulting a primary care paediatrician with an acute infection are infected with SARS-CoV-2 and to compare distinct findings. Method: In seven out-patient clinics, children aged 0–13 years with any new respiratory or gastrointestinal symptoms and presumed infection were invited to be tested for SARS-CoV-2. Factors that were correlated with testing positive were determined. Samples were collected from 25 January 2021 to 01 April 2021. Results: Seven hundred and eighty-three children participated in the study (median age 3 years and 0 months, range 1 month to 12 years and 11 months). Three hundred and fifty-eight were female (45.7%). SARS-CoV-2 RNA was detected in 19 (2.4%). The most common symptoms in children with as well as without detectable SARS-CoV-2 RNA were rhinitis, fever and cough. Known recent exposure to a case of COVID-19 was significantly correlated with testing positive, but symptoms or clinical findings were not. Conclusion: COVID-19 among the children with symptoms of an acute infection was uncommon, and the clinical presentation did not differ significantly between children with and without evidence of an infection with SARS-CoV-2.
Aim: It can be challenging to distinguish COVID-19 in children from other common infections. We set out to determine the rate at which children consulting a primary care paediatrician with an acute infection are infected with SARS-CoV-2 and to compare distinct findings. Method: In seven out-patient clinics, children aged 0–13 years with any new respiratory or gastrointestinal symptoms and presumed infection were invited to be tested for SARS-CoV-2. Factors that were correlated with testing positive were determined. Samples were collected from 25 January 2021 to 01 April 2021. Results: Seven hundred and eighty-three children participated in the study (median age 3 years and 0 months, range 1 month to 12 years and 11 months). Three hundred and fifty-eight were female (45.7%). SARS-CoV-2 RNA was detected in 19 (2.4%). The most common symptoms in children with as well as without detectable SARS-CoV-2 RNA were rhinitis, fever and cough. Known recent exposure to a case of COVID-19 was significantly correlated with testing positive, but symptoms or clinical findings were not. Conclusion: COVID-19 among the children with symptoms of an acute infection was uncommon, and the clinical presentation did not differ significantly between children with and without evidence of an infection with SARS-CoV-2.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.