No abstract

The interspike interval spike trains of spontaneously active cortical neurons can display nonrandom internal structure. The degree of nonrandom structure can be quantified and was found to decrease during focal epileptic seizures. Greater statistical discrimination between the two physiological conditions (normal vs seizure) was obtained with measurements of context-free grammar complexity than by measures of the distribution of the interspike intervals such as the mean interval, its standard deviation, skewness, or kurtosis. An examination of fixed epoch data sets showed that two factors contribute to the complexity: the firing rate and the internal structure of the spike train. However, calculations with randomly shuffled surrogates of the original data sets showed that the complexity is not completely determined by the firing rate. The sequence-sensitive structure of the spike train is a significant contributor. By combining complexity measurements with statistically related surrogate data sets, it is possible to classify neurons according to the dynamical structure of their spike trains. This classification could not have been made on the basis of conventional distribution-determined measures. Computations with more sophisticated kinds of surrogate data show that the structure observed using complexity measures cannot be attributed to linearly correlated noise or to linearly correlated noise transformed by a static monotonic nonlinearity. The patterns in spike trains appear to reflect genuine nonlinear structure. The limitations of these results are also discussed. The results presented in this article do not, of themselves, establish the presence of a fine-structure encoding of neural information.

Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic collapse (feature deterioration) and reduce performance. DeepSVDD combats collapse by removing biases from architectures, but this limits the adaptation performance gain. In this work, we propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. In addition, we conduct a thorough investigation of Imagenet-pretrained features for one-class anomaly detection. Our method, PANDA, outperforms the state-of-the-art in the one-class and outlier exposure settings (CIFAR10: 96.2% vs. 90.1% and 98.9% vs. 95.6%).

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in Western populations. Therapies such as mRNA and siRNA encapsulated in lipid nanoparticles (LNPs) represent a clinically advanced platform and are utilized for a wide variety of applications. Unfortunately, transfection of RNA into CLL cells remains a formidable challenge and a bottleneck for developing targeted therapies for this disease. Therefore, we aimed to elucidate the barriers to efficient transfection of RNA-encapsulated LNPs into primary CLL cells to advance therapies in the future. To this end, we transfected primary CLL patient samples with mRNA and siRNA payloads encapsulated in an FDA-approved LNP formulation and characterized the transfection. Additionally, we tested the potential of repurposing caffeic acid, curcumin and resveratrol to enhance the transfection of nucleic acids into CLL cells. The results demonstrate that the rapid uptake of LNPs is required for successful transfection. Furthermore, we demonstrate that resveratrol enhances the delivery of both mRNA and siRNA encapsulated in LNPs into primary CLL patient samples, overcoming inter-patient heterogeneity. This study points out the important challenges to consider for efficient RNA therapeutics for CLL patients and advocates the use of resveratrol in combination with RNA lipid nanoparticles to enhance delivery into CLL cells.

No abstract