Origin of spatial organization of DNA-polymer in bacterial chromosomes

In-vivo DNA organization at large length scales ($\sim 100nm$) is highly debated and polymer models have proved useful to understand the principle of DNA-organization. Here, we show that $<2$% cross-links at specific points in a ring polymer can lead to a distinct spatial organization of the polymer. The specific pairs of cross-linked monomers were extracted from contact maps of bacterial DNA. We are able to predict the structure of 2 DNAs using Monte Carlo simulations of the bead-spring polymer with cross-links at these special positions. Simulations with cross-links at random positions along the chain show that the organization of the polymer is different in nature from the previous case.Comment: arXiv admin note: text overlap with arXiv:1701.0506

A study of machine learning and deep learning models for solving medical imaging problems

Application of machine learning and deep learning methods on medical imaging aims to create systems that can help in the diagnosis of disease and the automation of analyzing medical images in order to facilitate treatment planning. Deep learning methods do well in image recognition, but medical images present unique challenges. The lack of large amounts of data, the image size, and the high class-imbalance in most datasets, makes training a machine learning model to recognize a particular pattern that is typically present only in case images a formidable task. Experiments are conducted to classify breast cancer images as healthy or non-healthy, and to detect lesions in damaged brain MRI (Magnetic Resonance Imaging) scans. Random Forest, Logistic Regression and Support Vector Machine perform competitively in the classification experiments, but in general, deep neural networks beat all conventional methods. Gaussian Naïve Bayes (GNB) and the Lesion Identification with Neighborhood Data Analysis (LINDA) methods produce better lesion detection results than single path neural networks, but a multi-modal, multi-path deep neural network beats all other methods. The importance of pre-processing training data is also highlighted and demonstrated, especially for medical images, which require extensive preparation to improve classifier and detector performance. Only a more complex and deeper neural network combined with properly pre-processed data can produce the desired accuracy levels that can rival and maybe exceed those of human experts

Numerical Analysis of Three-dimensional Acoustic Cloaks and Carpets

We start by a review of the chronology of mathematical results on the Dirichlet-to-Neumann map which paved the way towards the physics of transformational acoustics. We then rederive the expression for the (anisotropic) density and bulk modulus appearing in the pressure wave equation written in the transformed coordinates. A spherical acoustic cloak consisting of an alternation of homogeneous isotropic concentric layers is further proposed based on the effective medium theory. This cloak is characterised by a low reflection and good efficiency over a large bandwidth for both near and far fields, which approximates the ideal cloak with a inhomogeneous and anisotropic distribution of material parameters. The latter suffers from singular material parameters on its inner surface. This singularity depends upon the sharpness of corners, if the cloak has an irregular boundary, e.g. a polyhedron cloak becomes more and more singular when the number of vertices increases if it is star shaped. We thus analyse the acoustic response of a non-singular spherical cloak designed by blowing up a small ball instead of a point, as proposed in [Kohn, Shen, Vogelius, Weinstein, Inverse Problems 24, 015016, 2008]. The multilayered approximation of this cloak requires less extreme densities (especially for the lowest bound). Finally, we investigate another type of non-singular cloaks, known as invisibility carpets [Li and Pendry, Phys. Rev. Lett. 101, 203901, 2008], which mimic the reflection by a flat ground.Comment: Latex, 21 pages, 7 Figures, last version submitted to Wave Motion. OCIS Codes: (000.3860) Mathematical methods in physics; (260.2110) Electromagnetic theory; (160.3918) Metamaterials; (160.1190) Anisotropic optical materials; (350.7420) Waves; (230.1040) Acousto-optical devices; (160.1050) Acousto-optical materials; (290.5839) Scattering,invisibility; (230.3205) Invisibility cloak

BDDC and FETI-DP under Minimalist Assumptions

The FETI-DP, BDDC and P-FETI-DP preconditioners are derived in a particulary simple abstract form. It is shown that their properties can be obtained from only on a very small set of algebraic assumptions. The presentation is purely algebraic and it does not use any particular definition of method components, such as substructures and coarse degrees of freedom. It is then shown that P-FETI-DP and BDDC are in fact the same. The FETI-DP and the BDDC preconditioned operators are of the same algebraic form, and the standard condition number bound carries over to arbitrary abstract operators of this form. The equality of eigenvalues of BDDC and FETI-DP also holds in the minimalist abstract setting. The abstract framework is explained on a standard substructuring example.Comment: 11 pages, 1 figure, also available at http://www-math.cudenver.edu/ccm/reports

Inclinations of Members of the Teaching Staff Towards Factors Leading to Job Satisfaction—A Comparative Study between Public and Private Universities

The study aimed at identifying job satisfaction and inclinations towards factors, such as salary, feeling of job security, extent of empowerment, nature of work relations among different parties and social status the instructor feels, all of which lead to job satisfaction among members of teaching staff in both public and private universities in Lebanon. Furthermore, the study aimed at prioritizing these factors as related to instructors at the Lebanese University and those at private universities. The study also tried to find whether instructors preferred teaching at public or private universities as related to the country from which they obtained their Ph. D’s. To achieve this goal, a five-point Likert-style questionnaire was constructed and distributed to 100 instructors in the public university (Lebanese University) and to another 100 instructors in various private universities. Thus, the society of the study comprises instructors in both public and private universities. Of these questionnaires, the researchers retrieved 184 which were valid for analysis. The study yielded some important findings, mainly that there is a significant difference between instructors in public and private universities regarding some factors leading to job satisfaction (salary, feeling of job security, work relations among colleagues and students, and social status that the instructor feels) in Lebanon. The study also showed a difference in prioritizing factors which lead to job satisfaction relative to workplace (public or private university) in Lebanon. Moreover, the study concluded that instructors at universities have different preferences to work at the Lebanese University (public) relative to the country from which they obtained their Ph. D’s.

Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer

Guidelines from the American National Comprehensive Cancer Network (NCCN)recommend neoadjuvant chemotherapy (NAC) to patients with locally advanced breast cancer (LABC) to downstage tumors before surgery. However, only a small fraction (15-17%) of LABC patients achieve complete pathologic response (pCR), i.e. no residual tumor in the breast, after treatment. Measuring tumor response during 53 neoadjuvant chemotherapy can potentially help physicians adapt treatment thus, potentially improving the pCR rate. Recently, imaging biomarkers that are used to measure the tumor’s functional and biological features have been studied as pre-treatment markers for pCR or as an indicator for intra-treatment tumor response. Also, imaging biomarkers have been the focus of intense research to characterize tumor heterogeneity as well as to advance our understanding of the principle mechanisms behind chemoresistance. Advances in investigational radiology are moving rapidly to high-resolution imaging, capturing metabolic data, performing tissue characterization and statistical modelling of imaging biomarkers, with an endpoint of personalized medicine in breast cancer treatment. In this commentary, we present studies within the framework of imaging biomarkers used to measure breast tumor response to chemotherapy. Current studies are showing that significant progress has been made in the accuracy of measuring tumor response either before or during chemotherapy, yet the challenges at the forefront of these works include translational gaps such as needing large-scale clinical trials for validation, and standardization of imaging methods. However, the ongoing research is showing that imaging biomarkers may play an important role in personalized treatments for LABC