Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.

Peptide nucleic acids: Mechanism of tight binding and application in artificial nuclease

Peptide nucleic acids (PNA) are mimics of nucleic acids with a peptidic backbone. Duplexes and triplexes formed between PNA and DNA or RNA possess remarkable thermal stability, they are resistant to nuclease cleavage and can better discriminate mismatches. Understanding the mechanism for the tight binding between PNA and oligonucleotides is important for the design and development of better PNA-based drugs.^ We have performed molecular dynamics (MD) simulations of 8-mer PNA/DNA duplex and two analogous duplexes with chiral modification of PNA strand (D- or L-Alanine modification). MD simulations were performed with explicit water and Na$\sp{+}$ counter ions. The 1.5-ns simulations were carried out with AMBER using periodic boundary and particle mesh Ewald summation. The point charges for PNA monomers were derived from fitting electrostatic potentials, obtained from ab initio calculation, to atomic centers using RESP. Derived charges reveal significantly altered charge distribution on the PNA bases and predict the Watson-Crick H-bonds involving PNA to be stronger. Results from NMR studies investigating H-bond interactions between DNA-DNA and DNA-PNA base pairs in non-polar environment are consistent with this prediction. MD simulations demonstrated that the PNA strand is more flexible than the DNA strand in the same duplex. That this flexibility might be important for the duplex stability is tested by introducing modification into the PNA backbones. Results from MD simulation revealed dramatically altered structures for the modified PNA-DNA duplexes. Consistent with previous NMR results, we also found no intrachain hydrogen bonds between O7$\sp\prime$ and N1$\sp\prime$ of the neighboring residues in our MD study. Our study reveals that in addition to the lack of charge repulsion, stronger Watson-Crick hydrogen bonds together with flexible backbone are important factors for the enhanced stability of the PNA-DNA duplex.^ In a related study, we have developed an application of Gly-Gly-His-(Gly)$\sb3$-PNA conjugate as an artificial nuclease. We were able to demonstrate cleavage of single stranded DNA at a single site upon Ni(II) binding to Gly-Gly-His tripeptide and activation of nuclease with monoperoxyphthalic acid. ^

Regulation of the Telecommunications in Switzerland: A Network Approach to Assess the Regulatory Agencies' Independence

The liberalization process of the Swiss telecommunications sector follows a logic of ‘autonomous adaptation’ to the regulations of the European Union (EU). Switzerland, which is not a Member State of the EU, voluntarily adapts to the European policy without being for- mally required to do so (Sciarini et al., 2004). This process went hand in hand with the partial privatization of the legal statute and assets of the former monopolist and with the re-regulation of the liberalized telecommunications sector.

GoCARB in the Context of an Artificial Pancreas

Background: In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework. Method: The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics’ skills in carbohydrate estimation. Results: For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB. Conclusion: The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.