Artificial Neural Networks in Biological and Environmental Analysis (Analytical Chemistry)

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Simultaneous determination of flufenamic and meclofenamic acids in human urine samples by second-order multivariate calibration of micellar-enhanced excitation-emission fluorescence data. Evaluation of partial least-squares with second-order advantage for the multi-way spectroscopic analysis of complex biological samples in the presence of analyte-background interactions.

Different strategies for the direct determination of amoxicillin in human urine by second-order multivariate analysis of kinetic-spectrophotometric data. Analysis of amoxicillin in human urine by photo-activated generation of fluorescence excitation-emission matrices and artificial neural networks combined with residual bilinearization. A Experimental study of non-linear second-order analytical data with focus on the second-order advantage. Multi-way partial least-squares coupled to residual trilinearization: a genuine multi-dimensional tool for the study of third-order data.

Simultaneous analysis of procaine and its metabolite p-amino benzoic acid in equine serum. Photoinduced fluorimetric determination of folic acid and 5-methyltetrahydrofolic acid in serum using the kinetic evolution of the emission spectra accomplished with multivariate second-order calibration methods.

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Analysis of benzoic acid in beverage samples. Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping.

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A multivariate curve resolution - alternating least-squares approach. Screening of oil samples on the basis of excitation-emission room-temperature phosphorescence data and multi-way chemometric techniques. Introducing the second-order advantage in a classification study. Determination of folic acid and its two main metabolites in serum by on line photochemically induced excitation-emission-kinetic four-way data. Non-linear four-way kinetic-excitation-emission fluorescence data processed by a variant of parallel factor analysis and by a neural network model achieving the second-order advantage: malonaldehyde determination in olive oil samples.

The results substantiated the basic concept of functional cellomics that enables functional annotation of neural networks in a high-throughput, hypothesis-free, single-cell-resolution, and simple manner. Stochastic labeling of individual neurons with an effector gene that induces cell autonomous activity provides a high-throughput and hypothesis-free means of functional cellomics at a single-cell resolution. Brainbow technologies 8 refer to systems that can stochastically determine whether a certain gene is expressed in a certain cell through the application of the Cre-lox recombination system.

In Brainbow technologies, multiple lox variants e. If Cre recombinase is allowed to act on this sequence, excision occurs exclusively either between loxP sequences or between lox sequences. Consequently, it becomes possible to determine which of these two genes is expressed in a Cre-dependent manner. Stochastic labeling of neurons based on Brainbow technologies. When an excision occurs between lox sequences, QF2 w is produced, and the production of mCherry continues from pF25B3.

A brief heat shock was applied to the transgenic C. For negative control experiments, the transgenic C. The 2D images shown are the maximum-intensity projection reconstructed from the z-stacks of the images acquired with the confocal microscope. The cells producing both fluorescent proteins are presented in white and indicated by an arrow. The white-dyed cells differed from one individual to another, indicating the success of stochastic labeling.

We established three transgenic lines harboring all plasmids shown in Fig.

Artificial Neural Networks In Biological And Environmental Analysis (Analytical Chemistry):

In each individual, we counted 12—26 fluorescent cells. In this experiments, we counted fluorescent cells of the mid-body and tail sections, because cytoplasmic production of mCherry made it difficult to precisely count the number of fluorescent neurons around the head ganglia. To implement functional cellomics, we designed four plasmids Fig.

The plasmid pCre expresses Cre recombinase in response to a heat shock. Since the constructs producing a transcription factor or an effector are modularized, it is easy to use not only opsin but also various other effectors. We also constructed pF25B3. When all of these plasmids are introduced into C. After a heat shock is applied to induce Cre recombinase, QF2 w is produced if an excision occurs between lox sequences, and the production of mCherry continues from pF25B3. In neurons producing QF2 w , ChR2—GFP is produced as an effector, enabling the on-demand activation of these neurons by light illumination.

Since GFP is fused to ChR2, it is easy to identify which neurons are producing opsin following a behavioral experiment. We introduced the above-mentioned four plasmids into C. After this C. After isolating at least nine individuals, we observed their mid-body sections where the neuron density was low at a magnification of 40x. In Fig. Though we observed that production of ChR2—GFP was randomized using any lines, the labelling ratio slightly differed probably because of differences in structures of extrachromosomal Ex arrays Fig. Using the same dataset in Fig.

Though we found no significant difference using t -test, the mid-body region showed higher variation. In addition, we carried out three types of negative control experiments using the transgenic C. In each negative control experiment, we established three lines, and observed ten animals from each line. As a result, we found no green fluorescence in neurons Fig.

In functional cellomics, stochastic labeling of an effector gene makes it possible to explore the relationships between neural networks and behaviors in a hypothesis-free and comprehensive manner. To demonstrate the feasibility of functional cellomics, we selected the egg-laying behavior of C. It is known that a relatively simple neural network is responsible for controlling the egg-laying behavior of this nematode.

If the HSNs can be identified by functional cellomics in a high-throughput manner, it shows that this strategy actually works. We constructed a C. Individuals of this transgenic C. When a similar experiment was conducted without all- trans retinal ATR , a cofactor of ChR2, no egg-laying behavior was observed. These results indicate that the egg-laying behavior observed in this experiment is ChR2-dependent, and that individual nematodes exhibiting the target phenotype can be readily obtained through the stochastic labeling of an effector.

Acquisition of individuals exhibiting egg-laying behavior in a light-dependent manner. During the filming, blue light was turned on and off at 5-second intervals. The arrow indicates where the eggs are. In these experiments, at least six individuals from each line were used. After isolating egg-laying and non-egg-laying individuals and observing the vicinity of the vulva, we confirmed that GhR2—GFP was produced in the HSNs in the all of egg-laying individuals Fig.

In the representative individual shown in Fig. A previous study demonstrated that killing one HSN by laser ablation did not markedly affect the egg-laying behavior of the nematode, whereas killing both HSNs resulted in strong inhibition of the egg-laying behavior Our result that the egg-laying behavior was induced sufficiently by activating only one HSN is consistent with that of this previous study. In this paper, cytoplasmic production of mCherry in all neurons and the high probability of ChR2—GFP labelling made it difficult to precisely annotate neurons other than HSNs which might have a certain role on egg-laying behavior.

Identification of HSNs. The 2D images shown are the maximum-intensity projection reconstructed from the z-stacks of the images acquired with a confocal microscope. The cells that are producing both fluorescent proteins are presented in white. Light did not induce egg-laying behavior in this individual, and no production of ChR2—GFP was observed in neurons around the vulva.

Fluorescence profiles of dotted lines were quantified by ImageJ. Although various methodologies have been established to explore the properties of neural networks, no single methodology satisfies all of the criteria necessary for realizing the conceptual framework of functional cellomics. One typical example of existing methods, which is very easy yet effective, is to induce the production of effectors using cell-type-specific promoters. However, this approach is basically hypothesis-driven because one needs to select specific promoters a priori, meaning that it is not suitable for establishing entirely new hypotheses.


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Moreover, it is also difficult to analyze neural networks at single-cell resolution because C. Another example is laser ablation, which enables hypothesis-free and single-cell-resolution analysis Although this is a powerful technique applicable to any species, its low throughput makes it difficult to conduct experiments involving various patterns of intervention. Additionally, laser ablation lacks expandability in that it cannot activate or suppress neurons.

A recent study has suggested the possibility that contradictory results are generated depending on the mode of intervention 20 , indicating the need to compare results obtained from various modes of intervention activation, suppression, killing, etc. Another method enabling analysis at single-cell resolution involves application of a heat shock to only specific cells to induce effectors 21 , This method is capable of manipulating neurons in many ways; however, similar to laser ablation, it is also restricted by its low throughput capacity.

The patterned illumination technique using digital micromirror devices, whose development has been advancing in recent years, allows for a high degree of freedom in experimental design and has a relatively high throughput However, it is still difficult to perform accurate analysis at single-cell resolution with this method because multiple adjacent neurons may be illuminated simultaneously, unless sufficiently sparse expression patterns of effectors are provided Functional cellomics described in this study is the first approach to combine all properties necessary for achieving individual-level cellomics, that is, high-throughput, hypothesis-free, single-cell resolution, and simplicity.

In fact, by applying functional cellomics to the egg-laying behavior of C. Compared with the existing methodologies, this system has advantages in terms of throughput, resolution, and expandability. First, it can be easily implemented in any laboratory without requiring any specialized equipment. Second, having no limitations in feasible labeling patterns, it is completely hypothesis-free, facilitating easy labeling at single-cell resolution, even for bilaterally symmetrical neuron pairs with almost identical gene expression patterns.

Third, with one transgenic C. Fourth, it can intervene in neural networks in various fashions. Besides opsin, which was employed in the present study, any effectors can be used as long as they cause either loss of function or gain of function in neurons. This enables a variety of interventions, such as cell killing 24 , 25 , suppression 26 , activation 27 , and gene expression control 28 , Our approach will be more useful if it could determine which neurons are required for a target behavior.

Fifth, multitudes of experimental designs are available using C. In addition, simultaneously employing QF2 w and Gal4, one can label multiple effectors in a stochastic fashion. Although promising, there are three points of concern regarding functional cellomics.

One is robustness of the heat-shock promoter used in this study. Though previous studies showed the promoter causes robust induction of proteins in the nervous system 32 , 33 , 34 , it has not been proved that it works equally in all types of neuronal cells. The second point is the high probability of effector labeling, which makes it difficult to calculate the labeling rate accurately. To perform a well-designed experiment, it is necessary to achieve strict control of the probability of effector labeling, as is the case with forward genetics in which the mutation rate is predetermined.

The high copy number increases the chance of excising the lox sequence by Cre, resulting in a large proportion of neurons labeled by opsin. When too many neurons are labeled by effectors, the effectors cause high level of neuronal activation and it becomes difficult to determine which neurons are responsible for the target behavior. Concomitant use of single-copy integration and lox variants may be able to control the probability of labeling only a desired number of all neurons of a C. In our laboratory, we are proceeding with the construction of a more sophisticated system that will help improve this issue.

The third point is how to ensure that the obtained results are reproducible. If functional cellomics implies that a certain labeling pattern may affect the target behavior, it is still necessary to verify this by other methods. To reproduce the labeling pattern, methodologies that can evoke gene expression in arbitrary cells, such as use of a pulsed infrared laser 21 and multi-step optogenetics 22 , may be applied to verify the results relatively easily.

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Besides, the intersectional Cre-lox strategy 35 and multiple-feature Boolean logic 36 may also be applicable to reproducing the labeling pattern. In conclusion, we have demonstrated for the first time the possibility of identifying neurons responsible for a target behavior by randomizing the labeling patterns of effector genes based on Brainbow technologies. Though several improvements and additional data are necessary to prove the utility of our approach on various behaviors, the results substantiated the basic concept of functional cellomics, which enables functional annotation of neural networks of C.

Since its connectome information is already mapped and available, C. Dispatched from the UK in 3 business days When will my order arrive? Steve J. Teresa Cecchi. Piotr Konieczka. Richard J. Jacek Namiesnik.

Artificial neural networks in biological and environmental analysis

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