Pediatric acute myeloid leukemia with t(7;21)(p22;q22).

The t(7;21)(p22;q22) resulting in RUNX1-USP42 fusion, is a rare but recurrent cytogenetic abnormality associated with acute myeloid leukemia (AML) and myelodysplastic syndromes. The prognostic significance of this translocation has not been well established due to the limited number of patients. Herein, we report three pediatric AML patients with t(7;21)(p22;q22). All three patients presented with pancytopenia or leukopenia at diagnosis, accompanied by abnormal immunophenotypic expression of CD7 and CD56 on leukemic blasts. One patient had t(7;21)(p22;q22) as the sole abnormality, whereas the other two patients had additional numerical and structural aberrations including loss of 5q material. Fluorescence in situ hybridization analysis on interphase cells or sequential examination of metaphases showed the RUNX1 rearrangement and confirmed translocation 7;21. Genomic SNP microarray analysis, performed on DNA extracted from the bone marrow from the patient with isolated t(7;21)(p22;q22), showed a 32.2 Mb copy neutral loss of heterozygosity (cnLOH) within the short arm of chromosome 11. After 2-4 cycles of chemotherapy, all three patients underwent allogeneic hematopoietic stem cell transplantation (HSCT). One patient died due to complications related to viral reactivation and graft-versus-host disease. The other two patients achieved complete remission after HSCT. Our data displayed the accompanying cytogenetic abnormalities including del(5q) and cnLOH of 11p, the frequent pathological features shared with other reported cases, and clinical outcome in pediatric AML patients with t(7;21)(p22;q22). The heterogeneity in AML harboring similar cytogenetic alterations may be attributed to additional uncovered genetic lesions.

Sub-picosecond photon-efficient 3D imaging using single-photon sensors.

Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisition times, high timing accuracy, and high detection sensitivity. Single-photon avalanche diodes (SPADs) have emerged as one of the most promising detector technologies to achieve all of these requirements. However, these detectors are plagued by measurement distortions known as pileup, which fundamentally limit their precision. In this work, we develop a probabilistic image formation model that accurately models pileup. We devise inverse methods to efficiently and robustly estimate scene depth and reflectance from recorded photon counts using the proposed model along with statistical priors. With this algorithm, we not only demonstrate improvements to timing accuracy by more than an order of magnitude compared to the state-of-the-art, but our approach is also the first to facilitate sub-picosecond-accurate, photon-efficient 3D imaging in practical scenarios where widely-varying photon counts are observed.

SnapVX: A Network-Based Convex Optimization Solver.

SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. It combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use Python interface with "out-of-the-box" functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, parallelize, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at http://snap.stanford.edu/snapvx.

CVXPY: A Python-Embedded Modeling Language for Convex Optimization.

CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.