Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia.

Childhood B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by recurrent genetic abnormalities that drive risk-directed treatment strategies. Using current techniques, accurate detection of such aberrations can be challenging, due to the rapidly expanding list of key genetic abnormalities. Whole genome sequencing (WGS) has the potential to improve genetic testing, but requires comprehensive validation. We performed WGS on 210 childhood B-ALL samples annotated with clinical and genetic data. We devised a molecular classification system to subtype these patients based on identification of key genetic changes in tumour-normal and tumour-only analyses. This approach detected 294 subtype-defining genetic abnormalities in 96% (202/210) patients. Novel genetic variants, including fusions involving genes in the MAP kinase pathway, were identified. WGS results were concordant with standard-of-care methods and whole transcriptome sequencing (WTS). We expanded the catalogue of genetic profiles that reliably classify PAX5alt and ETV6::RUNX1-like subtypes. Our novel bioinformatic pipeline improved detection of DUX4 rearrangements (DUX4-r): a good-risk B-ALL subtype with high survival rates. Overall, we have validated that WGS provides a standalone, reliable genetic test to detect all subtype-defining genetic abnormalities in B-ALL, accurately classifying patients for the risk-directed treatment stratification, while simultaneously performing as a research tool to identify novel disease biomarkers.

Learning to predict synchronization of coupled oscillators on randomly generated graphs.

Suppose we are given a system of coupled oscillators on an unknown graph along with the trajectory of the system during some period. Can we predict whether the system will eventually synchronize? Even with a known underlying graph structure, this is an important yet analytically intractable question in general. In this work, we take an alternative approach to the synchronization prediction problem by viewing it as a classification problem based on the fact that any given system will eventually synchronize or converge to a non-synchronizing limit cycle. By only using some basic statistics of the underlying graphs such as edge density and diameter, our method can achieve perfect accuracy when there is a significant difference in the topology of the underlying graphs between the synchronizing and the non-synchronizing examples. However, in the problem setting where these graph statistics cannot distinguish the two classes very well (e.g., when the graphs are generated from the same random graph model), we find that pairing a few iterations of the initial dynamics along with the graph statistics as the input to our classification algorithms can lead to significant improvement in accuracy; far exceeding what is known by the classical oscillator theory. More surprisingly, we find that in almost all such settings, dropping out the basic graph statistics and training our algorithms with only initial dynamics achieves nearly the same accuracy. We demonstrate our method on three models of continuous and discrete coupled oscillators-the Kuramoto model, Firefly Cellular Automata, and Greenberg-Hastings model. Finally, we also propose an "ensemble prediction" algorithm that successfully scales our method to large graphs by training on dynamics observed from multiple random subgraphs.

Lethal drugs in capital punishment in USA: History, present, and future perspectives.

Lethal injection is the preferred method for the execution of condemned prisoners in the United States. A recent decision of The European Union to prohibit the export of drugs used in capital punishment to the USA along with domestic firms ceasing to manufacture these drugs has resulted in a drug shortage and a search for alternative drugs and new drug combinations that have not been previously validated for inducing death. As a consequence, some of the executions did not proceed as expected and sparked public debate regarding whether recent executions by lethal injection serve the purpose of avoiding "cruel and unusual punishment" in executions. Moreover, a cottage industry comprised of compounding pharmacies as emerged as a source of drug combinations used in capital punishment. Although there is a growing trend toward the abolishment of capital punishment in United States, the controversy concerning the efficacy of drug and involvement of health care professionals in the execution procedure is far from over.