A Theory of Merge-and-Shrink for Stochastic Shortest Path Problems

The merge-and-shrink framework is a powerful tool to construct state space abstractions based on factored representations. One of its core applications in classical planning is the construction of admissible abstraction heuristics. In this paper, we develop a compositional theory of merge-and-shrink in the context of probabilistic planning, focusing on stochastic shortest path problems (SSPs). As the basis for this development, we contribute a novel factored state space model for SSPs. We show how general transformations, including abstractions, can be formulated on this model to derive admissible and/or perfect heuristics. To formalize the merge-and-shrink framework for SSPs, we transfer the fundamental merge-and-shrink transformations from the classical setting: shrinking, merging, and label reduction. We analyze the formal properties of these transformations in detail and show how the conditions under which shrinking and label reduction lead to perfect heuristics can be extended to the SSP setting

Proteomics unravels the regulatory mechanisms in human tears following acute renouncement of contact lens use : a comparison between hard and soft lenses

Contact lenses (CLs) provide a superior alternative to spectacles. Although beneficial, the global burden of ocular dysfunctions attributed to regular use of CLs remains a topic of much challenge in ophthalmic research owing to debilitating clinical repercussions on the ocular surface, which are often manifested as breach in tear film integrity. This study elucidated the intricate tear proteome changes attributed to the use of different CLs (hard and soft) and unravelled, for the first time, the restorative mechanisms of several protein clusters following acute renouncement of CL use employing the label-free mass spectrometry-based quantitative proteomics approach. The expression patterns of certain proteins clusters were specific to the use of a particular lens type and a large majority of these actively regulates cell death and survival and, modulates cellular movement on the ocular surface. Noteworthy, CL use also evoked a significant upregulation of glycolytic enzymes associated with hypoxia and corresponding cognate metabolic pathways, particularly glucose metabolism and FXR/RXR pathways. Importantly, the assessment of CL renouncement unravelled the restorative properties of several clusters of proteins involved mainly in organismal injury and abnormalities and, cellular function and maintenance. These proteins play key roles in restoring tear homeostasis and wound-healing mechanisms post-CL use-elicited injury

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A Theory of Merge-and-Shrink for Stochastic Shortest Path Problems

The merge-and-shrink framework is a powerful tool to construct state space abstractions based on factored representations. One of its core applications in classical planning is the construction of admissible abstraction heuristics. In this paper, we develop a compositional theory of merge-and-shrink in the context of probabilistic planning, focusing on stochastic shortest path problems (SSPs). As the basis for this development, we contribute a novel factored state space model for SSPs. We show how general transformations, including abstractions, can be formulated on this model to derive admissible and/or perfect heuristics. To formalize the merge- and-shrink framework for SSPs, we transfer the fundamental merge-and-shrink transformations from the classical setting: shrinking, merging, and label reduction. We analyze the formal properties of these transformations in detail and show how the conditions under which shrinking and label reduction lead to perfect heuristics can be extended to the SSP setting.</p

Trisubstituted Imidazoles with a Rigidized Hinge Binding Motif Act As Single Digit nM Inhibitors of Clinically Relevant EGFR L858R/T790M and L858R/T790M/C797S Mutants: An Example of Target Hopping

The high genomic instability of non-small cell lung cancer tumors leads to the rapid development of resistance against promising EGFR tyrosine kinase inhibitors (TKIs). A recently detected triple mutation compromises the activity of the gold standard third-generation EGFR inhibitors. We have prepared a set of trisubstituted imidazoles with a rigidized 7-azaindole hinge binding motif as a new structural class of EGFR inhibitors by a target hopping approach from p38α MAPK inhibitor templates. On the basis of an iterative approach of docking, compound preparation, biological testing, and SAR interpretation, robust and flexible synthetic routes were established. As a result, we report two reversible inhibitors <b>11d</b> and <b>11e</b> of the clinically challenging triple mutant L858R/T790M/C797S with IC₅₀ values in the low nanomolar range. Furthermore, we developed a kinome selective irreversible inhibitor <b>45a</b> with an IC₅₀ value of 1 nM against the EGFR L858R/T790M double mutant. Target binding kinetics and metabolic stability data are included. These potent mutant EGFR inhibitors may serve as a basis for the development of structurally novel EGFR probes, tools, or candidates

Comparison of transcriptome profiles between medulloblastoma primary and recurrent tumors uncovers novel variance effects in relapses

Nowadays medulloblastoma (MB) tumors can be treated with risk-stratified approaches with up to 80% success rate. However, disease relapses occur in approximately 30% of patients and successful salvage treatment strategies at relapse remain scarce. Acquired copy number changes or TP53 mutations are known to occur frequently in relapses, while methylation profiles usually remain highly similar to those of the matching primary tumors, indicating that in general molecular subgrouping does not change during the course of the disease. In the current study, we have used RNA sequencing data to analyze the transcriptome profiles of 43 primary-relapse MB pairs in order to identify specific molecular features of relapses within various tumor groups. Gene variance analysis between primary and relapse samples demonstrated the impact of age in SHH-MB: the changes in gene expression relapse profiles were more pronounced in the younger patients (< 10 years old), which were also associated with increased DNA aberrations and somatic mutations at relapse probably driving this effect. For Group 3/4 MB transcriptome data analysis uncovered clear sets of genes either active or decreased at relapse that are significantly associated with survival, thus could be potential predictive markers. In addition, deconvolution analysis of bulk transcriptome data identified progression-associated differences in cell type enrichment. The proportion of undifferentiated progenitors increased in SHH-MB relapses with a concomitant decrease of differentiated neuron-like cells, while in Group 3/4 MB relapses cell cycle activity increases and differentiated neuron-like cells proportion decreases as well. Thus, our findings uncovered significant transcriptome changes in the molecular signatures of relapsed MB and could be potentially useful for further clinical purposes

Comparison of transcriptome profiles between medulloblastoma primary and recurrent tumors uncovers novel variance effects in relapses

Abstract Nowadays medulloblastoma (MB) tumors can be treated with risk-stratified approaches with up to 80% success rate. However, disease relapses occur in approximately 30% of patients and successful salvage treatment strategies at relapse remain scarce. Acquired copy number changes or TP53 mutations are known to occur frequently in relapses, while methylation profiles usually remain highly similar to those of the matching primary tumors, indicating that in general molecular subgrouping does not change during the course of the disease. In the current study, we have used RNA sequencing data to analyze the transcriptome profiles of 43 primary-relapse MB pairs in order to identify specific molecular features of relapses within various tumor groups. Gene variance analysis between primary and relapse samples demonstrated the impact of age in SHH-MB: the changes in gene expression relapse profiles were more pronounced in the younger patients (< 10 years old), which were also associated with increased DNA aberrations and somatic mutations at relapse probably driving this effect. For Group 3/4 MB transcriptome data analysis uncovered clear sets of genes either active or decreased at relapse that are significantly associated with survival, thus could be potential predictive markers. In addition, deconvolution analysis of bulk transcriptome data identified progression-associated differences in cell type enrichment. The proportion of undifferentiated progenitors increased in SHH-MB relapses with a concomitant decrease of differentiated neuron-like cells, while in Group 3/4 MB relapses cell cycle activity increases and differentiated neuron-like cells proportion decreases as well. Thus, our findings uncovered significant transcriptome changes in the molecular signatures of relapsed MB and could be potentially useful for further clinical purposes