A universal Hamiltonian for the motion and the merging of Dirac cones in a two-dimensional crystal

We propose a simple Hamiltonian to describe the motion and the merging of Dirac points in the electronic spectrum of two-dimensional electrons. This merging is a topological transition which separates a semi-metallic phase with two Dirac cones from an insulating phase with a gap. We calculate the density of states and the specific heat. The spectrum in a magnetic field B is related to the resolution of a Schrodinger equation in a double well potential. They obey the general scaling law e_n \propto B^{2/3} f_n(Delta /B^{2/3}. They evolve continuously from a sqrt{n B} to a linear (n+1/2)B dependence, with a [(n+1/2)B]^{2/3} dependence at the transition. The spectrum in the vicinity of the topological transition is very well described by a semiclassical quantization rule. This model describes continuously the coupling between valleys associated with the two Dirac points, when approaching the transition. It is applied to the tight-binding model of graphene and its generalization when one hopping parameter is varied. It remarkably reproduces the low field part of the Rammal-Hofstadter spectrum for the honeycomb lattice.Comment: 18 pages, 15 figure

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

KMT2A rearranged acute lymphoblastic leukaemia: unravelling the genomic complexity and heterogeneity of this high-risk disease

KMT2A rearranged (KMT2Ar) acute lymphoblastic leukaemia (ALL) is a high-risk genomic subtype, with long-term survival rates of less than 60% across all age groups. These cases present a complex clinical challenge, with a high incidence in infants, high-risk clinical features and propensity for aggressive relapse. KMT2A rearrangements are highly pathogenic leukaemic drivers, reflected by the high incidence of KMT2Ar ALL in infants, who carry few leukaemia-associated cooperative mutations. However, transgenic murine models of KMT2Ar ALL typically exhibit long latency and mature or mixed phenotype, and fail to recapitulate the aggressive disease observed clinically. Next-generation sequencing has revealed that KMT2Ar ALL also occurs in adolescents and adults, and potentially cooperative genomic lesions such as PI3K-RAS pathway variants are present in KMT2Ar patients of all ages. This review addresses the aetiology of KMT2Ar ALL, with a focus on the cell of origin and mutational landscape, and how genomic profiling of KMT2Ar ALL patients in the era of next-generation sequencing demonstrates that KMT2Ar ALL is a complex heterogenous disease. Ultimately, understanding the underlying biology of KMT2Ar ALL will be important in improving long-term outcomes for these high-risk patients.Michelle O.Forgione, Barbara J.McClure, Laura N.Eadie, David T.Yeung, Deborah L.Whit

Neural networks for predicting Kaposi's sarcoma

This paper demonstrates a medical application of Bayesian neural networks, whose parameters and hyper-parameters are sampled from the posterior distribution by means of Monte Carlo Markov chain. The main objective is the determination of the relevance of various input variables. The paper focuses on typical difficulties one has to face when dealing with sparse data sets

MLLT10 rearranged acute leukemia: incidence, prognosis and possible therapeutic strategies

Rearrangements of the MLLT10 gene occur in acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), most commonly T-lineage ALL (T-ALL), in patients of all ages. MLLT10 rearranged (MLLT10r) acute leukemia presents a complex diagnostic and therapeutic challenge due to frequent presentation of immature or mixed phenotype, and a lack of consensus regarding optimal therapy. Cases of MLLT10r AML or T-ALL bearing immature phenotype are at high risk of poor outcome, but the underlying molecular mechanisms and sensitivity to targeted therapies remain poorly characterized. This review addresses the incidence and prognostic significance of MLLT10r in acute leukemia, and how the aberrant gene expression profile of this disease can inform potential targeted therapeutic strategies. Understanding the underlying genomics of MLLT10r acute leukemia, both clinically and molecularly, will improve prognostic stratification and accelerate the development of targeted therapeutic strategies, to improve patient outcomes.Michelle O. Forgione, Barbara J. McClure, David T. Yeung, Laura N. Eadie, Deborah L. Whit