Computational and Imaging Methods for Studying Neuronal Populations during Behavior

One of the central questions in neuroscience is how the nervous system generates and regulates behavior. To understand the neural code for any behavior, an ideal experiment would entail (i) quantitatively defining that behavior, (ii) recording neuronal activity in relevant brain regions to identify the underlying neuronal circuits and eventually (iii) manipulating them to test their function. Novel methods in neuroscience have greatly advanced our abilities to conduct such experiments but are still insufficient. In this thesis, I developed methods for these three goals. In Chapter 2, I describe an automatic behavior identification and classification method for the cnidarian Hydra vulgaris using machine learning. In Chapter 3, I describe a fast volumetric two-photon microscope with dual-color laser excitation that can image in 3D the activity of populations of neurons from visual cortex of awake mice. In Chapter 4, I present a machine learning method that identifies cortical ensembles and pattern completion neurons in mouse visual cortex, using two-photon calcium imaging data. These methods advance current technologies, providing opportunities for new discoveries

Privacy Mining from IoT-based Smart Homes

Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elders' privacy might be disclosed from smart homes due to non-fully protected network communication or other reasons. To demonstrate how serious this issue is, we introduce in this paper a Privacy Mining Approach (PMA) to mine privacy from smart homes by conducting a series of deductions and analyses on sensor datasets generated by smart homes. The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.Comment: This paper, which has 11 pages and 7 figures, has been accepted BWCCA 2018 on 13th August 201

Performance of Protein Disorder Prediction Programs on Amino Acid Substitutions

Many proteins contain intrinsically disordered regions, which may be crucial for function, but on the other hand be related to the pathogenicity of variants. Prediction programs have been developed to detect disordered regions from sequences and used to predict the consequences of variants, although their performance for this task has not been assessed. We tested the performance of protein disorder prediction programs in detecting changes to disorder caused by amino acid substitutions. We assessed the performance of 29 protein disorder predictors and versions with 101 amino acid substitutions, whose effects have been experimentally validated. Disorder predictors detected the true positives at most with 6% success rate and true negatives with 34% rate for variants. The corresponding rates for the wild-type forms are 7% and 90%, respectively. The analysis revealed that disorder programs cannot reliably predict the effects of substitutions; consequently, the tested methods, and possibly similar programs, cannot be recommended for variant analysis without other information indicating to the relevance of disorder. These results inspired us to develop a new method, PON-Diso (http://structure.bmc.lu.se/PON-Diso), for disorder-related amino acid substitutions. With 50% success rate for independent test set and 70.5% rate in cross-validation, it outperforms the evaluated methods

Evolution of Neuropeptide Precursors in Polyneoptera (Insecta)

Neuropeptides are one of the major players in regulation of growth, reproduction and development as well as in information transfer in all multicellular animal organisms (Metazoa). Structurally, these short protein-like substances are among the most diverse signaling molecules, although they are under a stabilizing evolutionary pressure due to co-evolution with their respective receptor. Many of the peptidergic systems have a very ancient origin that can be traced back to the early evolution of Metazoa. In arthropods, especially insects, neuropeptide sequences and functions have been intensely studied in the past. In recent years, the increasing availability of genome and transcriptome data facilitated the investigation of complete neuropeptide precursor sequences and aided in the discovery of new neuropeptide genes. In my thesis, mainly transcriptome data were used to study the evolution of neuropeptide precursor sequences of polyneopteran insects, which resulted in three publications. In my dissertation, I could generate valuable insight into the evolution of neuropeptide precursor sequences. Specifically, I was able to demonstrate that neuropeptide precursor sequence evolution does not follow a uniform rate. It is unique in every precursor. Albeit a rather high degree of sequence conservation in Polyneoptera, differences in evolutionary rate can be seen between orthologs of different lineages. Even on a single neuropeptide precursor, the degree of sequence conservation differs between the functional units. Furthermore, I was able to establish neuropeptide precursor sequences as a basis for phylogenetic analyses

Neuropeptides as Ligands for GPCRs

Neuropeptides constitute an important part of the nervous system, since the simple nerve nets (i.e. of Hydra). The assigned functions of these peptides vary enormously. For instance, besides inhibiting or stimulating the release of some hormones, they can be responsible for tentacle contraction of the Hydra, dropping the tail of the lizard, postnatal care of the beetles and also aggressiveness of humans. They perform these tasks via activating their cognate GPCRs, which are hypothesized to be coevolved with their ligand neuropeptides. In this chapter, we will introduce the concept of neuropeptide, its intracellular maturation process, characteristics of some typical neuropeptide families and the common properties of their cognate GPCRs. At last, we will try to give information about the widely used methods for studying GPCR-neuropeptide interactions