Embedded Cloud System for Ann-Cod Analysis Using UV Spectroscopy

One of the many parameters indicating water quality is chemical oxygen demand (COD), which is an indirect measurement of the amount of organic compound material in water. There have been many studies, in both academia and the industry, to analyze the COD content of water using spectral analysis. The proposal of this thesis was to study, analyze, and identify methods to determine the presence of COD using UV spectroscopy data and an artificial neural network (ANN) in a cloud-connected embedded system. The system was implemented using an ARM11 board and a portable spectrometer. Light in the UV range was used to analyze the water sample. As an analysis strategy, the spectral data were converted into a real number value in the range of 0 to 1. Twenty equidistance samples were taken out of the converted data to be fed into the ANN, and the ANN was trained with known samples to identify any presence of COD. Experiments used laboratory-calibrated water samples with known COD and some real-life water samples. All the experiments showed that the implemented system could successfully indicate the presence or absence of COD in the given water sample. The system also successfully demonstrated the application of a cloud-connected embedded system to an area in environmental engineering. This indicated that the system was a bridge between computer and environmental engineering

Research in advanced concepts in biotechnology, human analogs, and bionics Final report

Advanced concepts in biotechnology, human analogs, and bionic

A quantitative FRET approach to characterize protein-protein interactions in living cells

The ability of proteins to specifically interact with each other is a key feature in the regulation of biological processes. Knowledge about interaction partners and characterization of protein-protein interactions contribute to the understanding of proper protein function and cell physiology. In particular, Förster resonance energy transfer (FRET) is a suitable method to analyze interactions between proteins in living cells. During the progress of this thesis, a quantitative FRET approach was established that aims to evaluate binding curves for interaction partners. Moreover, the quantitative FRET approach was applied to study biological questions, including the investigation of putative interaction partners of the endolysosomal ion channel two-pore channel 2 (TPC2), the Kv7.2 potassium channel and the photoreceptor-specific transmembrane protein peripherin 2. The FRET approach described in manuscript I computes calibrated FRET efficiencies from fluorescent measurements using three-filter cubes and correlates the FRET efficiencies to the concentration of donor and acceptor molecules to determine binding curves, which bear information about maximal FRET efficiencies and relative binding constants for individual FRET pairings. Calibration factors that represent the optical properties of the imaging setup and the fluorophores are crucial for quantitative measurements. A detailed description how to assess these factors is provided. The quantitative FRET approach is very robust as both donor-centric (E-FRET) and acceptor-centric (SE-FRET) efficiencies are obtained simultaneously from multiple cells. The method was further applied to investigate protein-protein interactions of membrane proteins. First of all, in manuscript II, an epilepsy-causing mutation in the Kv7.2 potassium channel was shown to be implicated in a reduced calmodulin binding affinity to the channel, which affects channel regulation. A second study identified SNARE proteins, such as syntaxin 7 and syntaxin 6, as novel interaction partners of the intracellular ion channel TPC2 (manuscript III), revealing TPC2 as a putative member of the late endosome-lysosome fusion machinery. In manuscript IV, the impact of polymorphic variants of TPC2 on channel dimerization and mTOR binding was investigated. Furthermore, in a study covered by manuscripts V and VI, rhodopsin as well as S- and M-opsins were identified as novel interaction partners of the retinal protein peripherin 2 in rods and cones, respectively. The binding domain underlying the interaction between peripherin 2 and rhodopsin, could be assigned to the fourth transmembrane domain of peripherin 2. Moreover, it could be demonstrated that disease-associated mutations in peripherin 2 attenuated this particular binding, suggesting differential pathophysiological consequences of disrupted interactions in rods and cones. In manuscript VIII, peripherin 2 and its homolog Rom-1 were shown to have opposing effects on rod outer segment targeting of disease-linked peripherin 2 mutants by evaluating their binding affinities. Peripherin 2 is a scaffold protein exclusively expressed in outer segments of rods and cones. As photoreceptors are polarized cells, FRET measurements were not only performed on transfected HEK293 cells but also on acutely isolated outer segments of virally transduced murine photoreceptors (manuscript VII). The results gained in this thesis demonstrate that protein interactions play a crucial role in the regulation of proper protein function. Loss of binding partners or a reduced binding affinity to particular proteins may result in pathophysiological conditions. A deeper knowledge about molecular interactions will contribute to the understanding of cellular mechanisms, etiology of diseases and may further evaluate putative targets of pharmacological interest

A quantitative FRET approach to characterize protein-protein interactions in living cells

The ability of proteins to specifically interact with each other is a key feature in the regulation of biological processes. Knowledge about interaction partners and characterization of protein-protein interactions contribute to the understanding of proper protein function and cell physiology. In particular, Förster resonance energy transfer (FRET) is a suitable method to analyze interactions between proteins in living cells. During the progress of this thesis, a quantitative FRET approach was established that aims to evaluate binding curves for interaction partners. Moreover, the quantitative FRET approach was applied to study biological questions, including the investigation of putative interaction partners of the endolysosomal ion channel two-pore channel 2 (TPC2), the Kv7.2 potassium channel and the photoreceptor-specific transmembrane protein peripherin 2. The FRET approach described in manuscript I computes calibrated FRET efficiencies from fluorescent measurements using three-filter cubes and correlates the FRET efficiencies to the concentration of donor and acceptor molecules to determine binding curves, which bear information about maximal FRET efficiencies and relative binding constants for individual FRET pairings. Calibration factors that represent the optical properties of the imaging setup and the fluorophores are crucial for quantitative measurements. A detailed description how to assess these factors is provided. The quantitative FRET approach is very robust as both donor-centric (E-FRET) and acceptor-centric (SE-FRET) efficiencies are obtained simultaneously from multiple cells. The method was further applied to investigate protein-protein interactions of membrane proteins. First of all, in manuscript II, an epilepsy-causing mutation in the Kv7.2 potassium channel was shown to be implicated in a reduced calmodulin binding affinity to the channel, which affects channel regulation. A second study identified SNARE proteins, such as syntaxin 7 and syntaxin 6, as novel interaction partners of the intracellular ion channel TPC2 (manuscript III), revealing TPC2 as a putative member of the late endosome-lysosome fusion machinery. In manuscript IV, the impact of polymorphic variants of TPC2 on channel dimerization and mTOR binding was investigated. Furthermore, in a study covered by manuscripts V and VI, rhodopsin as well as S- and M-opsins were identified as novel interaction partners of the retinal protein peripherin 2 in rods and cones, respectively. The binding domain underlying the interaction between peripherin 2 and rhodopsin, could be assigned to the fourth transmembrane domain of peripherin 2. Moreover, it could be demonstrated that disease-associated mutations in peripherin 2 attenuated this particular binding, suggesting differential pathophysiological consequences of disrupted interactions in rods and cones. In manuscript VIII, peripherin 2 and its homolog Rom-1 were shown to have opposing effects on rod outer segment targeting of disease-linked peripherin 2 mutants by evaluating their binding affinities. Peripherin 2 is a scaffold protein exclusively expressed in outer segments of rods and cones. As photoreceptors are polarized cells, FRET measurements were not only performed on transfected HEK293 cells but also on acutely isolated outer segments of virally transduced murine photoreceptors (manuscript VII). The results gained in this thesis demonstrate that protein interactions play a crucial role in the regulation of proper protein function. Loss of binding partners or a reduced binding affinity to particular proteins may result in pathophysiological conditions. A deeper knowledge about molecular interactions will contribute to the understanding of cellular mechanisms, etiology of diseases and may further evaluate putative targets of pharmacological interest

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

Neural Networks in the Analysis of Water-Soluble Sulfonylurea Herbicides Using an LC/MS

In this research, a hidden node pruning algorithm was developed for an artificial neural network (ANN) that automatically determined a more efficient size of the hidden layer, caused the ANN to resize itself, and then continued to train using a standard back-propagation algorithm. The hidden-node pruning algorithm was based on determining the number of significant eigenvalues present in the matrix of values produced by the hidden layer, starting with an excessive number of hidden nodes. Eight sulfonylurea herbicides were used as the target analytes in this study. The ability of an ANN to simplify the sample preparation needed for analysis using a liquid chromatograph/particle beam/mass spectrometer (LC/PB/MS) was evaluated. The results derived from this research demonstrated that ANNs allow the clean-up procedure to be simplified, while still obtaining reliable identification of the sulfonylurea herbicides in complex matrices such as soil. Specifically, this was accomplished by using retention times from the LC and MS when the herbicides were injected individually in pure forms combined with MS data obtained from extracted samples. This information was used by a trained neural network to identify sulfonylurea herbicides as both individual components and components in a mixture. Two different neural networks were created. One was trained with a single mass spectrum from each herbicide, resulting in an 8-training-sample network, and one was trained with five mass spectra of each herbicide, resulting in a 40-training-sample network. Both ANNs had 47 input nodes and eight output nodes. Starting with an excess of 20 hidden nodes, the networks resized themselves to contain 6 hidden nodes for the 8-training-sample network and 7 hidden nodes for the 40-training-sample network. An optimum sum-squared error (SSE) goal was determined to be 0.3 for the 8-training-sample network by using a statistical t-test . to establish the smallest SSE where the standard error of prediction was not significantly greater than the standard error of calibration. Results demonstrated that the 8-training-sample ANN performed just as well as the 40-training-sample ANN. When compared to the Hewlett-Packard probability-based matching (HP- PBM) library searching system, both neural networks out-performed the HP-PBM system in the identification of unknown mass spectra

Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems

This book aims to convey the most recent progress in hardware-driven neuromorphic systems based on semiconductor memory technologies. Machine learning systems and various types of artificial neural networks to realize the learning process have mainly focused on software technologies. Tremendous advances have been made, particularly in the area of data inference and recognition, in which humans have great superiority compared to conventional computers. In order to more effectively mimic our way of thinking in a further hardware sense, more synapse-like components in terms of integration density, completeness in realizing biological synaptic behaviors, and most importantly, energy-efficient operation capability, should be prepared. For higher resemblance with the biological nervous system, future developments ought to take power consumption into account and foster revolutions at the device level, which can be realized by memory technologies. This book consists of seven articles in which most recent research findings on neuromorphic systems are reported in the highlights of various memory devices and architectures. Synaptic devices and their behaviors, many-core neuromorphic platforms in close relation with memory, novel materials enabling the low-power synaptic operations based on memory devices are studied, along with evaluations and applications. Some of them can be practically realized due to high Si processing and structure compatibility with contemporary semiconductor memory technologies in production, which provides perspectives of neuromorphic chips for mass production

HIGH-ACTIVITY MUTANTS OF HUMAN BUTYRYLCHOLINESTERASE FOR COCAINE ABUSE TREATMENT

Cocaine is a widely abused drug without an FDA-approved medication. It has been recognized as an ideal anti-cocaine medication to accelerate cocaine metabolism producing biologically inactive metabolites via a route similar to the primary cocaine-metabolizing pathway, i.e. butyrylcholinesterase (BChE)-catalyzed hydrolysis. However, the native BChE has a low catalytic activity against cocaine. We recently designed and discovered a set of BChE mutants with a high catalytic activity specifically for cocaine. An ideal, therapeutically valuable mutant of human BChE should have not only a significantly improved catalytic activity against cocaine, but also certain selectivity for cocaine over neurotransmitter acetylcholine (ACh) such that one would not expect systemic administration of the BChE mutant to interrupt cholinergic transmission. Through integrated computational-experimental studies, several BChE mutants were identified to have not only a considerably improved catalytic efficiency against cocaine, but also the desirable selectivity for cocaine over ACh. Representative BChE mutants have been confirmed to be potent in actual protection of mice from acute toxicity (convulsion and lethality) of a lethal dose of cocaine (180 mg/kg, LD100). Pretreatment with the BChE mutant (i.e. 1 min prior to cocaine administration) dose-dependently protected mice against cocaine-induced convulsions and lethality. The in vivo data reveal the primary factor, i.e. the relative catalytic efficiency, determining the efficacy in practical protection of mice from the acute cocaine toxicity and future direction for further improving the efficacy of the enzyme in the cocaine overdose treatment. For further characterization in animal models, we successfully developed high-efficiency stable cell lines efficiently expressing the BChE mutants by using a lentivirus-based repeated-transduction method. The large-scale protein production enabled us to further characterize the in vivo profiles of the BChE mutant concerning the biological half-life and potency in accelerating cocaine clearance. In particular, it has been demonstrated that the BChE mutant can rapidly metabolize cocaine and completely eliminate cocaine-induced hyperactivity in rodents, implying that the BChE mutant may be developed as a promising therapeutic agent for cocaine abuse treatment

GABA signaling in the thalamus

Inhibition of neuronal activity in networks of the mammalian central nervous system is essential for all fundamental brain functions, ranging from perception, to consciousness, to action. Both exacerbation and diminution of inhibition dramatically affect our behavioral capacities, indicating that, in the healthy brain, strength and dynamics of inhibition must be precisely balanced. Inhibitory functions are primarily accomplished by neurons releasing the neurotransmitter GABA. According to their wide variety of functions, GABAergic neurons show a tremendous diversity in morphological, biochemical and functional characteristics. The combination of these diverse properties allows the brain to generate interneurons acting as, for examples, filters, co-incidence detectors or contrast enhancers. GABAergic signaling in thalamus plays an essential role in controlling sensory information flow from the periphery to the cortical processing centers, and in generating sleep-related neuronal rhythms. Surprisingly, however, the diversity of GABAergic neurons is remarkably limited in thalamic networks. Both functions mentioned have been tightly associated with two homogeneous groups of GABAergic neurons arising within thalamic nuclei or within the nucleus reticularis, a shell of inhibitory nuclei surrounding the dorsal thalamus. The results arising from the present thesis challenge the view that the diversity of GABAergic signaling in thalamus is comparatively limited and proposes that, to fully understand GABAergic signaling in thalamus, at least two additional aspects have to be considered. First, it shows that GABAergic signaling arising from the nucleus reticularis can have a profound effect on the synthesis of second messenger compounds that are important in the control of neuronal rhythmicities and in the statedependent control of gene expression. Second, it demonstrates the functional relevance of a previously undescribed extrathalamic and extrareticular inhibitory pathway that arises within the anterior pretectal nuclei, indicating that the architecture of GABAergic signaling in thalamus has to be complemented by a conceptually novel, powerful afferent pathway. The first part investigates the modulation of cAMP synthesis by GABA in thalamocortical neurons through the activation of the Gi-coupled GABAB receptors. GABAB receptors can provide two different cAMP signals in the neurons. First, GABAB receptor activation depresses the level of cAMP inside thalamocortical neurons. However, a large and long cAMP signal is observed when GABAB receptors are activated concomitantly with b-adrenergic receptors, which are Gscoupled receptors. In the presence of GABAB receptor agonists, the moderate cAMP increase produced by b-adrenergic receptor activation is transformed into a large synthesis of cAMP. Remarkably, the activation of the GABAB receptors at the synapses between reticular neurons and thalamocortical neurons also potentiates the effects of b-adrenergic receptors. Thus, GABAB receptors modulate cAMP signals at synapses that are important for the regulation of the state of arousal. The second part provides the first electrophysiological description of synaptic connections between the anterior pretectum group and the thalamic higher-order nuclei. Electric stimulation in the anterior pretectum group evoked inhibitory postsynaptic responses (IPS) in the thalamocortical neurons of the higher-order nuclei. We showed that the IPS responses were mediated via the GABAA receptors activated through monosynaptic connections between the APT and the higher-order nuclei. Functionally, the anterior pretectum modulated the discharge properties of the thalamocortical neurons, suggesting an important role of this nucleus in the dialogue between the thalamus and the cortex