Road to sexual maturity: Behavioral event schedule from eclosion to first mating in each sex of Drosophila melanogaster.

Animals achieve their first mating through the process of sexual maturation. This study examined the precise and detailed timing of a series of behavioral events, including wing expansion, first feeding, first excretion, and courtship, during sexual maturation from eclosion to first mating in D. melanogaster. We found that the time of first mating is genetically invariant and is not affected by light/dark cycle or food intake after eclosion. We also found sexual dimorphism in locomotor activity after eclosion, with females increasing locomotor activity earlier than males. In addition, we found a time rapidly changing from extremely low to high sexual activity in males post eclosion (named "drastic male courtship arousal" or DMCA). These behavioral traits leading up to the first mating could serve as clear indicators of sexual maturation and establish precisely timed developmental landmarks to explore further the mechanisms underlying the integration of behavioral and physiological sexual maturation.

Noncanonical function of the Sex lethal gene controls the protogyny phenotype in Drosophila melanogaster.

Drosophila melanogaster females eclose on average 4 h faster than males owing to sexual differences in the pupal period, referred to as the protogyny phenotype. Here, to elucidate the mechanism underlying the protogyny phenotype, we used our newly developed Drosophila Individual Activity Monitoring and Detecting System (DIAMonDS) that detects the precise timing of both pupariation and eclosion in individual flies. Although sex transformation induced by tra-2, tra alteration, or msl-2 knockdown-mediated disruption of dosage compensation showed no effect on the protogyny phenotype, stage-specific whole-body knockdown and mutation of the Drosophila master sex switch gene, Sxl, was found to disrupt the protogyny phenotype. Thus, Sxl establishes the protogyny phenotype through a noncanonical pathway in D. melanogaster.

Reduction of the ERP Measurement Time by a Weighted Averaging Using Deep Learning.

In clinical examination, event-related potentials (ERPs) are estimated by averaging across multiple responses, which suppresses background EEG. However, acquiring the number of responses needed for this process is time consuming. We therefore propose a method for shortening the measurement time using weighted-average processing based on the output of deep learning. Using P300 as a representative component, here we focused on the shape of the ERP and evaluated whether our method emphasizes the P300 peak amplitude more than conventional averaging, while still maintaining the waveform shape and the P300 peak latency. Thus, using either CNN or EEGNet, the correlation coefficient reflecting the waveform shape, the peak P300 amplitude, and the peak latency were evaluated and compared with the same factors obtained from conventional waveform averaging. Additionally, the degree of background EEG suppression provided by our method was evaluated using the root mean square of the pre-stimulation waveform, and the number of fewer responses required for averaging (i.e., the reduction in measurement time) was calculated.The results showed that compared with P300 values obtained through conventional averaging, our method allowed for the same shape and response latency, but with a higher amplitude, while requiring a smaller number of responses. Our method showed that by using EEGNet, measurement time could be reduced by 13.7%. This corresponds to approximately a 40-second reduction for every 5 minutes of measurement time.

The Drosophila Individual Activity Monitoring and Detection System (DIAMonDS).

Here, we have developed DIAMonDS (Drosophila Individual Activity Monitoring and Detection System) comprising time-lapse imaging by a charge-coupled device (CCD) flatbed scanner and Sapphire, a novel algorithm and web application. DIAMonDS automatically and sequentially identified the transition time points of multiple life cycle events such as pupariation, eclosion, and death in individual flies at high temporal resolution and on a large scale. DIAMonDS performed simultaneous multiple scans to measure individual deaths (≤1152 flies per scanner) and pupariation and eclosion timings (≤288 flies per scanner) under various chemical exposures, environmental conditions, and genetic backgrounds. DIAMonDS correctly identified 74-85% of the pupariation and eclosion events and ~ 92% of the death events within ± 10 scanning frames. This system is a powerful tool for studying the influences of genetic and environmental factors on fruit flies and efficient, high-throughput genetic and chemical screening in drug discovery.

Paternal restraint stress affects offspring metabolism via ATF-2 dependent mechanisms in Drosophila melanogaster germ cells.

Paternal environmental factors can epigenetically influence gene expressions in offspring. We demonstrate that restraint stress, an experimental model for strong psychological stress, to fathers affects the epigenome, transcriptome, and metabolome of offspring in a MEKK1-dATF2 pathway-dependent manner in Drosophila melanogaster. Genes involved in amino acid metabolism are upregulated by paternal restraint stress, while genes involved in glycolysis and the tricarboxylic acid (TCA) cycle are downregulated. The effects of paternal restraint stress are also confirmed by metabolome analysis. dATF-2 is highly expressed in testicular germ cells, and restraint stress also induces p38 activation in the testes. Restraint stress induces Unpaired 3 (Upd3), a Drosophila homolog of Interleukin 6 (IL-6). Moreover, paternal overexpression of upd3 in somatic cells disrupts heterochromatin in offspring but not in offspring from dATF-2 mutant fathers. These results indicate that paternal restraint stress affects metabolism in offspring via inheritance of dATF-2-dependent epigenetic changes.

Publisher Correction: Paternal restraint stress affects offspring metabolism via ATF-2 dependent mechanisms in Drosophila melanogaster germ cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Non-invasive quality evaluation of confluent cells by image-based orientation heterogeneity analysis.

In recent years, cell and tissue therapy in regenerative medicine have advanced rapidly towards commercialization. However, conventional invasive cell quality assessment is incompatible with direct evaluation of the cells produced for such therapies, especially in the case of regenerative medicine products. Our group has demonstrated the potential of quantitative assessment of cell quality, using information obtained from cell images, for non-invasive real-time evaluation of regenerative medicine products. However, image of cells in the confluent state are often difficult to evaluate, because accurate recognition of cells is technically difficult and the morphological features of confluent cells are non-characteristic. To overcome these challenges, we developed a new image-processing algorithm, heterogeneity of orientation (H-Orient) processing, to describe the heterogeneous density of cells in the confluent state. In this algorithm, we introduced a Hessian calculation that converts pixel intensity data to orientation data and a statistical profiling calculation that evaluates the heterogeneity of orientations within an image, generating novel parameters that yield a quantitative profile of an image. Using such parameters, we tested the algorithm's performance in discriminating different qualities of cellular images with three types of clinically important cell quality check (QC) models: remaining lifespan check (QC1), manipulation error check (QC2), and differentiation potential check (QC3). Our results show that our orientation analysis algorithm could predict with high accuracy the outcomes of all types of cellular quality checks (>84% average accuracy with cross-validation).

Image-based focused counting of dividing cells for non-invasive monitoring of regenerative medicine products.

Despite the growing numbers of successful applications in regenerative medicine, biotechnologies for evaluating the quality of cells remain limited. To evaluate the cultured cells non-invasively, image-based cellular assessment method holds great promise. However, although there are various image-processing algorithms, very few studies have focused to prove the effectiveness of phase contrast images with risk assessment example that reflects actual difficulties in regenerative medicine products. In this study, we developed a simple image-processing method to recognize the number of dividing cells in time-course phase-contrast microscopic images, and applied this method to assess the irregular proliferation behavior in normal cells. Practically, as a model, rapid proliferating human fibrosarcoma cells were mixed in normal human fibroblasts in the same culture dish, and their sarcoma existence was evaluated. As a result, the existence of sarcoma population in normal cell sample could be feasibly detected within earliest period of cell culture by their irregular rise of accumulated counts of dividing cells. Our image-processing technique also illustrates the technical effectiveness of combining intra-frame and inter-frame image processing to accurately count only the dividing cells. Our concept of focused counting of dividing cells shows a successful example of image-based analysis to quickly and non-invasively monitor the regular state of regenerative medicine products.

Hippocampal polysynaptic computation.

Neural circuitry is a self-organizing arithmetic device that converts input to output and thereby remodels its computational algorithm to produce more desired output; however, experimental evidence regarding the mechanism by which information is modified and stored while propagating across polysynaptic networks is sparse. We used functional multineuron calcium imaging to monitor the spike outputs from thousands of CA1 neurons in response to the stimulation of two independent sites of the dentate gyrus in rat hippocampal networks ex vivo. Only pyramidal cells were analyzed based on post hoc immunostaining. Some CA1 pyramidal cells were observed to fire action potentials only when both sites were simultaneously stimulated (AND-like neurons), whereas other neurons fired in response to either site of stimulation but not to concurrent stimulation (XOR-like neurons). Both types of neurons were interlaced in the same network and altered their logical operation depending on the timing of paired stimulation. Repetitive paired stimulation for brief periods induced a persistent reorganization of AND and XOR operators, suggesting a flexibility in parallel distributed processing. We simulated these network functions in silico and found that synaptic modification of the CA3 recurrent excitation is pivotal to the shaping of logic plasticity. This work provides new insights into how microscopic synaptic properties are associated with the mesoscopic dynamics of complex microcircuits.

Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI) fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

Bidirectional plasticity in fast-spiking GABA circuits by visual experience.

Experience-dependent plasticity in the brain requires balanced excitation-inhibition. How individual circuit elements contribute to plasticity outcome in complex neocortical networks remains unknown. Here we report an intracellular analysis of ocular dominance plasticity-the loss of acuity and cortical responsiveness for an eye deprived of vision in early life. Unlike the typical progressive loss of pyramidal-cell bias, direct recording from fast-spiking cells in vivo reveals a counterintuitive initial shift towards the occluded eye followed by a late preference for the open eye, consistent with a spike-timing-dependent plasticity rule for these inhibitory neurons. Intracellular pharmacology confirms a dynamic switch of GABA (gamma-aminobutyric acid) impact to pyramidal cells following deprivation in juvenile mice only. Together these results suggest that the bidirectional recruitment of an initially binocular GABA circuit may contribute to experience-dependent plasticity in the developing visual cortex.

[Formation of cortical cell assemblies--synaptic plasticity and beyond].

Information processing by the brain relies on the functions of neuronal networks. Therefore, understanding the structure and computational mechanisms of the brain circuitry is crucial for clarifying how cognitive functions emerge and how they can be best modeled for engineering applications. The manner in which neocortical and hippocampal circuits represent and process information has not been understood in detail. However, if sloppy processors like neurons can process enormous amounts of information efficiently, a large assembly of neurons is likely to operate in a parallel manner. In this report, we discuss the structure of cortical circuits that undergo self-organization through spike-timing-dependent plasticity, under the influence of two-state membrane potential fluctuations. Furthermore, we propose a stochastic rule for the generation of synapses, i.e., neuronal wiring, in a large population of cortical neurons.

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model.

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP-DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons.

Recent studies have revealed that in vivo cortical neurons show spontaneous transitions between two subthreshold levels of the membrane potentials, 'up' and 'down' states. The neural mechanism of generating those spontaneous states transitions, however, remains unclear. Recent electrophysiological studies have suggested that those state transitions may occur through activation of a hyperpolarization-activated cation current (H-current), possibly by inhibitory synaptic inputs. Here, we demonstrate that two-state membrane potential fluctuations similar to those exhibited by in vivo neurons can be generated through a spike-timing-dependent self-organizing process in a network of inhibitory neurons and excitatory neurons expressing the H-current.