Local DNA damage by proton microbeam irradiation induces poly(ADP-ribose) synthesis in mammalian cells

Cellular recovery from ionizing radiation (IR)-induced damage involves poly(ADP-ribose) polymerase (PARP-1 and PARP-2) activity, resulting in the induction of a signalling network responsible for the maintenance of genomic integrity. In the present work, a charged particle microbeam delivering 3.2 MeV protons from a Van de Graaff accelerator has been used to locally irradiate mammalian cells. We show the immediate response of PARPs to local irradiation, concomitant with the recruitment of ATM and Rad51 at sites of DNA damage, both proteins being involved in DNA strand break repair. We found a co-localization but no connection between two DNA damage-dependent post-translational modifications, namely poly(ADP-ribosyl)ation of nuclear proteins and phosphorylation of histone H2AX. Both of them, however, should be considered and used as bona fide immediate sensitive markers of IR damage in living cells. This technique thus provides a powerful approach aimed at understanding the interactions between the signals originating from sites of DNA damage and the subsequent activation of DNA strand break repair mechanisms.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

The MNC as an externally embedded organization: An investigation of embeddedness overlap in local subsidiary networks

MNCs have been conceptualized as differentiated networks that, in turn, are embedded in external networks. Previous research has predominantly focused on the embeddedness of established subsidiaries into their local environment, omitting to shed light on the phenomenon of headquarters linkages to the local context which creates embeddedness overlap. We develop a model of why MNCs develop overlapping linkages to local subsidiary networks even if the subsidiaries have grown out of the initial start-up phase. Using detailed information on 168 European subsidiaries, we find that MNCs build and maintain more overlapping network ties when subsidiaries are high performers, hold important resources, operate in turbulent environments, and are closely connected to multinational actors as opposed to purely domestic firms.