Fluctuations in Protein Aggregation: Design of Preclinical Screening for Early Diagnosis of Neurodegenerative Disease

Autocatalytic fibril nucleation has recently been proposed to be a determining factor for the spread of neurodegenerative diseases, but the same process could also be exploited to amplify minute quantities of protein aggregates in a diagnostic context. Recent advances in microfluidic technology allow the analysis of protein aggregation in micron-scale samples, potentially enabling such diagnostic approaches, but the theoretical foundations for the analysis and interpretation of such data are, so far, lacking. Here, we study computationally the onset of protein aggregation in small volumes and show that the process is ruled by intrinsic fluctuations whose volume-dependent distribution we also estimate theoretically. Based on these results, we develop a strategy to quantify in silico the statistical errors associated with the detection of aggregate-containing samples. Our work explores a different perspective on the forecasting of protein aggregation in asymptomatic subjects

Some Parallel Algorithms for Integer Factorisation

Algorithms for finding the prime factors of large composite numbers are of practical importance because of the widespread use of public key cryptosystems whose security depends on the presumed difficulty of the factorisation problem. In recent years the limits of the best integer factorisation algorithms have been extended greatly, due in part to Moore's law and in part to algorithmic improvements. It is now routine to factor 100-decimal digit numbers, and feasible to factor numbers of 155 decimal digits (512 bits). We describe several integer factorisation algorithms, consider their suitability for implementation on parallel machines, and give examples of their current capabilities