Head and neck oncology – 2010, part I

SummaryThis article reviewed the current state of the art in head and neck oncology. These include very important and stimulating new areas of interest including the marked acceptance of chemoradiation in favor of surgery in patients with cancer of the head and neck. The concept of HPV as a cause of cancer of the oropharynx is relatively new and very important in the epidemiology of these tumors. New modalities such as PET CT scanning and robotic surgery are discussed and appear to be very important in management of cancer of the head and neck. Endoscopic endonasal skull base surgery is another new high technology contribution to the field of head and neck surgery as is the use of endoscopic assisted thyroid surgery. These and other new concepts are discussed in this manuscript

Estimation of Optical Aberrations in 3D Microscopic Bioimages

The quality of microscopy images often suffers from optical aberrations. These aberrations and their associated point spread functions have to be quantitatively estimated to restore aberrated images. The recent state-of-the-art method PhaseNet, based on a convolutional neural network, can quantify aberrations accurately but is limited to images of point light sources, e.g. fluorescent beads. In this research, we describe an extension of PhaseNet enabling its use on 3D images of biological samples. To this end, our method incorporates object-specific information into the simulated images used for training the network. Further, we add a Python-based restoration of images via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with the predicted PSF can not only remove the simulated aberrations but also improve the quality of the real raw microscopic images with unknown residual PSF. We provide code for fast and convenient prediction and correction of aberrations.Comment: 7 pages, 9 figures, presented at ICFSP on 9 Sept 2022 in Paris, France, to be published in ICFSP conference proceedings in IEEE Xplore digital librar

Community orientations toward a protective agency

Thesis (M.S.)--Boston Universit

Alignment Metric Accuracy

We propose a metric for the space of multiple sequence alignments that can be used to compare two alignments to each other. In the case where one of the alignments is a reference alignment, the resulting accuracy measure improves upon previous approaches, and provides a balanced assessment of the fidelity of both matches and gaps. Furthermore, in the case where a reference alignment is not available, we provide empirical evidence that the distance from an alignment produced by one program to predicted alignments from other programs can be used as a control for multiple alignment experiments. In particular, we show that low accuracy alignments can be effectively identified and discarded. We also show that in the case of pairwise sequence alignment, it is possible to find an alignment that maximizes the expected value of our accuracy measure. Unlike previous approaches based on expected accuracy alignment that tend to maximize sensitivity at the expense of specificity, our method is able to identify unalignable sequence, thereby increasing overall accuracy. In addition, the algorithm allows for control of the sensitivity/specificity tradeoff via the adjustment of a single parameter. These results are confirmed with simulation studies that show that unalignable regions can be distinguished from homologous, conserved sequences. Finally, we propose an extension of the pairwise alignment method to multiple alignment. Our method, which we call AMAP, outperforms existing protein sequence multiple alignment programs on benchmark datasets. A webserver and software downloads are available at http://bio.math.berkeley.edu/amap/

Efficient Algorithms for Moral Lineage Tracing

Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task. Jug et al. have proposed a mathematical abstraction of this task, the moral lineage tracing problem (MLTP), whose feasible solutions define both a segmentation of every image and a lineage forest of cells. Their branch-and-cut algorithm, however, is prone to many cuts and slow convergence for large instances. To address this problem, we make three contributions: (i) we devise the first efficient primal feasible local search algorithms for the MLTP, (ii) we improve the branch-and-cut algorithm by separating tighter cutting planes and by incorporating our primal algorithms, (iii) we show in experiments that our algorithms find accurate solutions on the problem instances of Jug et al. and scale to larger instances, leveraging moral lineage tracing to practical significance.Comment: Accepted at ICCV 201

Two algorithms for LCS Consecutive Suffix Alignment

AbstractThe problem of aligning two sequences A and B to determine their similarity is one of the fundamental problems in pattern matching. A challenging, basic variation of the sequence similarity problem is the incremental string comparison problem, denoted Consecutive Suffix Alignment, which is, given two strings A and B, to compute the alignment solution of each suffix of A versus B.Here, we present two solutions to the Consecutive Suffix Alignment Problem under the LCS (Longest Common Subsequence) metric, where the LCS metric measures the subsequence of maximal length common to A and B. The first solution is an O(nL) time and space algorithm for constant alphabets, where the size of the compared strings is O(n) and L⩽n denotes the size of the LCS of A and B.The second solution is an O(nL+nlog|Σ|) time and O(n) space algorithm for general alphabets, where Σ denotes the alphabet of the compared strings