The pros and cons of apoptosis assays for use in the study of cells, tissues, and organs.

Programmed cell death or apoptosis occurs in many tissues during normal development and in the normal homeostasis of adult tissues. Apoptosis also plays a significant role in abnormal development and disease. Increased interest in apoptosis and cell death in general has resulted in the development of new techniques and the revival of old ones. Each assay has its advantages and disadvantages that can render it appropriate and useful for one application, but inappropriate or difficult to use in another. Understanding the strengths and limitations of the assays would allow investigators to select the best methods for their needs.     

Design of new imidazole derivatives with anti-HCMV activity: QSAR modeling,synthesis and biological testing

Journal of Computer-Aided Molecular Design - The problem of designing new antiviral drugs against Human Cytomegalovirus (HCMV) was addressed using the Online Chemical Modeling Environment (OCHEM)....     

Biophysical characterization of DNA binding from single molecule force measurements

Single molecule force spectroscopy is a powerful method that uses the mechanical properties of DNA to explore DNA interactions. Here we describe how DNA stretching experiments quantitatively characterize the DNA binding of small molecules and proteins. Small molecules exhibit diverse DNA binding modes, including binding into the major and minor grooves and intercalation between base pairs of double-stranded DNA (dsDNA). Histones bind and package dsDNA, while other nuclear proteins such as high mobility group proteins bind to the backbone and bend dsDNA. Single-stranded DNA (ssDNA) binding proteins slide along dsDNA to locate and stabilize ssDNA during replication. Other proteins exhibit binding to both dsDNA and ssDNA. Nucleic acid chaperone proteins can switch rapidly between dsDNA and ssDNA binding modes, while DNA polymerases bind both forms of DNA with high affinity at distinct binding sites at the replication fork. Single molecule force measurements quantitatively characterize these DNA binding mechanisms, elucidating small molecule interactions and protein function.     

Normals in a Minkowski Plane

In this paper we will establish bounds on the average number of normals through a point in a convex body in a Minkowski plane for certain classes of convex bodies. Also, a related Euler relation is discussed.     

Zeros of Jacobi polynomials Pn( α, β)$ P_{n}^{(\alpha ,\beta)} $, − 2 < α, β < − 1

The sequence of Jacobi polynomials \(\{P_{n}^{(\alpha ,\beta )}\}_{n = 0}^{\infty }\) is orthogonal on (??1,1) with respect to the weight function (1 ? x)^α(1 + x)^β provided α > ??1,β > ??1. When the parameters α and β lie in the narrow range ??2 < α, β < ??1, the sequence of Jacobi polynomials \(\{P_{n}^{(\alpha ,\beta )}\}_{n = 0}^{\infty }\) is quasi-orthogonal of order 2 with respect to the weight function (1 ? x)^α+?1(1 + x)^β+?1 and each polynomial of degree n,n ≥?2, in such a Jacobi sequence has n real zeros. We show that any sequence of Jacobi polynomials \(\{P_{n}^{(\alpha ,\beta )}\}_{n = 0}^{\infty }\) with ??2 < α, β < ??1, cannot be orthogonal with respect to any positive measure by proving that the zeros of Pn??1(α,β) do not interlace with the zeros of Pn(α,β) for any \(n \in \mathbb {N},\)n ≥?2, and any α,β lying in the range ??2 < α, β < ??1. We also investigate interlacing properties satisfied by the zeros of equal degree Jacobi polynomials Pn(α,β),Pn(α,β+?1) and Pn(α+?1,β+?1) where ??2 < α, β < ??1. Upper and lower bounds for the extreme zeros of quasi-orthogonal order 2 Jacobi polynomials Pn(α,β) with ??2 < α, β < ??1 are derived.     

Padé approximations to the logarithm II: Identities, recurrences, and symbolic computation

Combinatorial identities that were needed in [25] are proved, mostly with C. Schneider’s computer algebra package Sigma. The form of the Padé approximation of the logarithm of arbitrary order is stated as a conjecture. 2000 Mathematics Subject Classification Primary—41A21, 05A19, 33F10 Supported by NRF-grant 2047226. Supported by NRF-grant 2053748. Supported by the Austrian Academy of Sciences, by the John Knopfmacher Research Centre for Applicable Analysis and Number Theory, and by the SFB-grant F1305 and the grant P16613-N12 of the Austrian FWF. Supported by NRF-grant 2053756.     

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.