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We present a method for achieving temporally and spatially precise photoactivation of neurons without the need for genetic expression of photosensitive proteins. Our method depends upon conduction of thermal energy via absorption by a dye or carbon particles and does not require the presence of voltage-gated channels to create transmembrane currents. We demonstrate photothermal initiation of action potentials in Hirudo verbana neurons and of transmembrane currents in Xenopus oocytes. Thermal energy is delivered by focused 50 ms, 650 nm laser pulses with total pulse energies between 250 and 3500 muJ. We document an optical delivery system for targeting specific neurons that can be expanded for multiple target sites. Our method achieves photoactivation reliably (70 - 90% of attempts) and can issue multiple pulses (6-9) with minimal changes to cellular properties as measured by intracellular recording. Direct photoactivation presents a significant step towards all-optical analysis of neural circuits in animals such as Hirudo verbana where genetic expression of photosensitive compounds is not feasible.
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Individual locations of many neuronal cell bodies (>10^4) are needed to enable statistically significant measurements of spatial organization within the brain such as nearest-neighbor and microcolumnarity measurements. In this paper, we introduce an Automated Neuron Recognition Algorithm (ANRA) which obtains the (x,y) location of individual neurons within digitized images of Nissl-stained, 30 micron thick, frozen sections of the cerebral cortex of the Rhesus monkey. Identification of neurons within such Nissl-stained sections is inherently difficult due to the variability in neuron staining, the overlap of neurons, the presence of partial or damaged neurons at tissue surfaces, and the presence of non-neuron objects, such as glial cells, blood vessels, and random artifacts. To overcome these challenges and identify neurons, ANRA applies a combination of image segmentation and machine learning. The steps involve active contour segmentation to find outlines of potential neuron cell bodies followed by artificial neural network training using the segmentation properties (size, optical density, gyration, etc.) to distinguish between neuron and non-neuron segmentations. ANRA positively identifies 86[5]% neurons with 15[8]% error (mean[st.dev.]) on a wide range of Nissl-stained images, whereas semi-automatic methods obtain 80[7]%/17[12]%. A further advantage of ANRA is that it affords an unlimited increase in speed from semi-automatic methods, and is computationally efficient, with the ability to recognize ~100 neurons per minute using a standard personal computer. ANRA is amenable to analysis of huge photo-montages of Nissl-stained tissue, thereby opening the door to fast, efficient and quantitative analysis of vast stores of archival material that exist in laboratories and research collections around the world.
The batch fermentation process, inoculated by pulsed electric field (PEF) treated wine yeasts (S. cerevisiae Actiflore F33), was studied. PEF treatment was applied to the aqueous yeast suspensions (0.12 % wt.) at the electric field strengths of E=100 and 6000 V/cm using the same pulse protocol (number of pulses of n=1000, pulse duration of ti=100 mks, and pulse repetition time of dt=100 ms). Electro-stimulation was confirmed by the observed growth of electrical conductivity of suspensions. The fermentation was running at 30{deg}C for 150 hours in an incubator with synchronic agitation. The obtained results clearly evidence the positive impact of PEF treatment on the batch fermentation process. Electro-stimulation resulted in improvement of such process characteristics as mass losses, consumption of soluble matter content ({deg}Brix) and synthesis of proteins. It also resulted in a noticeable acceleration of consumption of sugars at the initial stage of fermentation in the lag phase. At the end of the lag phase (t=40 hours), consumption of fructose in samples with electrically activated inocula exceeded fructose consumption in samples with control inocula by 2.33 times when it was activated at E=100 V/cm and by 3.98 times after treatment at E=6000 V/cm. At the end of the log phase (120 hours of fermentation), 30% mass reduction was reached in samples with PEF-treated inocula (E=6000 V/cm), whereas the same mass reduction of the control sample required approximately, 20 hours of extra fermentation. The possible mechanisms of electro-stimulation are also discussed in details.
Large-scale research endeavors can be hindered by logistical constraints limiting the amount of available data. For example, global ecological questions require a global dataset, and traditional sampling protocols are often too inefficient for a small research team to collect an adequate amount of data. Citizen science offers an alternative by crowdsourcing data collection. Despite growing popularity, the community has been slow to embrace it largely due to concerns about quality of data collected by citizen scientists. Using the citizen science project Floating Forests (http://floatingforests.org), we show that consensus classifications made by citizen scientists produce data that is of comparable quality to expert generated classifications. Floating Forests is a web-based project in which citizen scientists view satellite photographs of coastlines and trace the borders of kelp patches. Since launch in 2014, over 7,000 citizen scientists have classified over 750,000 images of kelp forests largely in California and Tasmania. Images are classified by 15 users. We generated consensus classifications by overlaying all citizen classifications and assessed accuracy by comparing to expert classifications. Matthews correlation coefficient (MCC) was calculated for each threshold (1-15), and the threshold with the highest MCC was considered optimal. We showed that optimal user threshold was 4.2 with an MCC of 0.400 (0.023 SE) for Landsats 5 and 7, and a MCC of 0.639 (0.246 SE) for Landsat 8. These results suggest that citizen science data derived from consensus classifications are of comparable accuracy to expert classifications. Citizen science projects should implement methods such as consensus classification in conjunction with a quantitative comparison to expert generated classifications to avoid concerns about data quality.
We investigate the role of the noise in the mating behavior between individuals of Nezara viridula (L.), by analyzing the temporal and spectral features of the non-pulsed type female calling song emitted by single individuals. We have measured the threshold level for the signal detection, by performing experiments with the calling signal at different intensities and analyzing the insect response by directionality tests performed on a group of male individuals. By using a sub-threshold signal and an acoustic Gaussian noise source, we have investigated the insect response for different levels of noise, finding behavioral activation for suitable noise intensities. In particular, the percentage of insects which react to the sub-threshold signal, shows a non-monotonic behavior, characterized by the presence of a maximum, for increasing levels of the noise intensity. This constructive interplay between external noise and calling signal is the signature of the non-dynamical stochastic resonance phenomenon. Finally, we describe the behavioral activation statistics by a soft threshold model which shows stochastic resonance. We find that the maximum of the ensemble average of the input-output cross-correlation occurs at a value of the noise intensity very close to that for which the behavioral response has a maximum.
Mapping of the forces on biomolecules in cell membranes has spurred the development of effective labels, e.g. organic fluorophores and nanoparticles, to track trajectories of single biomolecules. Standard methods use particular statistics, namely the mean square displacement, to analyze the underlying dynamics. Here, we introduce general inference methods to fully exploit information in the experimental trajectories, providing sharp estimates of the forces and the diffusion coefficients in membrane microdomains. Rapid and reliable convergence of the inference scheme is demonstrated on trajectories generated numerically. The method is then applied to infer forces and potentials acting on the receptor of the $epsilon$-toxin labeled by lanthanide-ion nanoparticles. Our scheme is applicable to any labeled biomolecule and results show show its general relevance for membrane compartmentation.
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