Distribution of α7 Nicotinic Acetylcholine Receptor Subunit mRNA in the Developing Mouse.

Homomeric α7 nicotinic acetylcholine receptors (nAChRs) are abundantly expressed in the central and peripheral nervous system (CNS and PNS, respectively), and spinal cord. In addition, expression and functional responses have been reported in non-neuronal tissue. In the nervous system, α7 nAChR subunit expression appears early during embryonic development and is often transiently upregulated, but little is known about their prenatal expression outside of the nervous system. For understanding potential short-term and long-term effects of gestational nicotine exposure, it is important to know the temporal and spatial expression of α7 nAChRs throughout the body. To that end, we studied the expression of α7 nAChR subunit mRNA using highly sensitive isotopic in situ hybridization in embryonic and neonatal whole-body mouse sections starting at gestational day 13. The results revealed expression of α7 mRNA as early as embryonic day 13 in the PNS, including dorsal root ganglia, parasympathetic and sympathetic ganglia, with the strongest expression in the superior cervical ganglion, and low to moderate levels were detected in brain and spinal cord, respectively, which rapidly increased in intensity with embryonic age. In addition, robust α7 mRNA expression was detected in the adrenal medulla, and low to moderate expression in selected peripheral tissues during embryonic development, potentially related to cells derived from the neural crest. Little or no mRNA expression was detected in thymus or spleen, sites of immune cell maturation. The results suggest that prenatal nicotine exposure could potentially affect the nervous system with limited effects in non-neural tissues

Robust Inference of Kinase Activity Using Functional Networks

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io

Novel n-3 Docosapentaneoic Acid-Derived Pro-resolving Mediators Are Vasculoprotective and Mediate the Actions of Statins in Controlling Inflammation

“This is a post-peer-review, pre-copyedit version of a chapter published in Advances in Experimental Medicine and Biology book series (AEMB, volume 1161). The final publication is available athttps://doi.org/10.1007/978-3-030-21735-8_7

Spin- and Isospin-Dependent Momentum Distributions in Fermi Liquids at Non-zero Temperatures

We explore the structure of momentum distributions of Fermi liquids such as completely polarized 3He, unpolarized liquid 3He, and nuclear matter at nonzero temperatures. The study employs correlated density matrix theory and adapts the algorithm to deal with spin- and isospin-dependent correlations. The analysis is based on the factor decomposition of the one-body reduced density matrix. The decomposition permits to distinguish between statistical correlations and dynamic (direct) correlations. Together with the concept of renormalized fermions the formal results open the pathway to investigate the thermal boundaries of normal Fermi phases within correlated density matrix theory. We also discuss possible transitions from normal phases to anomalous fermion phases triggered by statistical correlations or by periodic phase-phase structures

An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations

Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions

Neuroprotection by Insulin-like Growth Factor-1 in Rats with Ischemic Stroke is Associated with Microglial Changes and a Reduction in Neuroinflammation

We and others have shown that insulin-like growth factor-1 (IGF-1) is neuroprotective when administered systemically shortly following stroke. In the current study, we addressed the hypothesis that microglia mediate neuroprotection by IGF-1 following ischemic stroke. Furthermore, we investigated whether IGF-1 modulates pro- and anti-inflammatory mediators in ischemic brain with a special reference to microglia. Ischemic stroke was induced in normal conscious Wistar rats by infusing the vasoconstrictor, endothelin-1 (Et-1), next to middle cerebral artery (MCA). IGF-1 (300 μg) was injected subcutaneously (SC) at 30 and 120 min following stroke. Microglial inhibitor, minocycline, was injected intraperitoneally (IP) at 1 h before stroke (25 mg/kg) and 11 h after stroke (45 mg/kg). Post-stroke IGF-1 treatment reduced the infarct size and increased the sensorimotor function which coincided with an increase in the number of ameboid microglia in the ischemic cortex. Minocycline treatment abrogated the increase in ameboid microglia by IGF-1, while the effect of IGF-1 in the reduction of infarct size was only partially affected. IGF-1 suppressed mRNA expression of inducible nitric oxide synthase (iNOS) and interleukin (IL)-1β in the ischemic hemisphere, while in purified microglia, only iNOS expression levels were reduced. Our findings show that microglia are a target for IGF-1 and that neuroprotection by IGF-1 coincides with down-regulation of inflammatory mediators which could be instrumental to the beneficial effects