Noninvasive cellular internalization of silver molecules by chitosan nanoneedles: a novel nanocarrier

We explore with molecular modeling, dynamics simulations, and a statistical model the ability of chitosan nanoneedles (CNNs) to be internalized into a model lipid bilayer as a function of their length, keeping in view of their applications in the field of biomedicine for advanced targeted drug delivery. In this study, we have computationally modeled and studied the structural geometry and the stability of CNNs formed by 4, 6, and 8 subunits. We reported the molecular surface analysis of the modeled CNNs along with molecular dynamic (MD) simulations studies toward revealing the noninvasive cellular internalization potential of these CNNs and a case study has been carried to study the ability of CNNs to translocate silver nanoparticles across membrane. The present results are strongly in support of further exploration of 8 subunits based CNNs for their application as target drug delivery vehicles. The hydrophilicity of the CNNs has been attributed as one of the key factors responsible for the internalization process. Moreover, our MD simulation studies marched the ability of CNNs to translocate silver nanoparticles through biological membrane in a similar manner that resembles cell-penetrating peptides.     

Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects.     

Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation

In order to assess the relative contributions to pattern-generation of the intrinsic properties of individual neurons and of their connectivity, we examined a ring circuit composed of four complex physiologically based oscillators. This circuit produced patterns that correspond to several quadrupedal gaits, including the walk, the bound, and the gallop. An analysis using the phase response curve (PRC) of an uncoupled oscillator accurately predicted all modes exhibited by this circuit and their phasic relationships – with the caveat that in certain parameter ranges, bistability in the individual oscillators added nongait patterns that were not amenable to PRC analysis, but further enriched the pattern-generating repertoire of the circuit. The key insights in the PRC analysis were that in a gait pattern, since all oscillators are entrained at the same frequency, the phase advance or delay caused by the action of each oscillator on its postsynaptic oscillator must be the same, and the sum of the normalized phase differences around the ring must equal to an integer. As suggested by several previous studies, our analysis showed that the capacity to exhibit a large number of patterns is inherent in the ring circuit configuration. In addition, our analysis revealed that the shape of the PRC for the individual oscillators determines which of the theoretically possible modes can be generated using these oscillators as circuit elements. PRCs that have a complex shape enable a circuit to produce a wider variety of patterns, and since complex neurons tend to have complex PRCs, enriching the repertoire of patterns exhibited by a circuit may be the function of some intrinsic neuronal complexity. Our analysis showed that gait transitions, or more generally, pattern transitions, in a ring circuit do not require rewiring the circuit or any changes in the strength of the connections. Instead, transitions can be achieved by using a control parameter, such as stimulus intensity, to sculpt the PRC so that it has the appropriate shape for the desired pattern(s). A transition can then be achieved simply by changing the value of the control parameter so that the first pattern either ceases to exist or loses stability, while a second pattern either comes into existence or gains stability. Our analysis illustrates the predictive value of PRCs in circuit analysis and can be extended to provide a design method for pattern-generating circuits. Received: 20 November 1996 / Accepted: 29 July 1997     

Analysis of a neural oscillator

Although the Matsuoka neural oscillator, which was originally proposed as a model of central pattern generators, has widely been used for various robots performing rhythmic movements, its characteristics are not clearly explained even now. This article shows two closed-form relations that express the frequency and amplitude of the generated oscillation as functions of the parameters of the model. Although they are derived based on a rough linear approximation, they accord with the result obtained by a simulation considerably. The obtained relations also give us some nontrivial predictions about the properties of the oscillator.     

Passive acoustic monitoring of animal populations with transfer learning

Progress in deep learning, more specifically in using convolutional neural networks (CNNs) for the creation of classification models, has been tremendous in recent years. Within bioacoustics research, there has been a large number of recent studies that use CNNs. Designing CNN architectures from scratch is non-trivial and requires knowledge of machine learning. Furthermore, hyper-parameter tuning associated with CNNs is extremely time consuming and requires expensive hardware. In this paper we assess whether it is possible to build good bioacoustic classifiers by adapting and re-using existing CNNs pre-trained on the ImageNet dataset – instead of designing them from scratch, a strategy known as transfer learning that has proved highly successful in other domains. This study is a first attempt to conduct a large-scale investigation on how transfer learning can be used for passive acoustic monitoring (PAM), to simplify the implementation of CNNs and the design decisions when creating them, and to remove time consuming hyper-parameter tuning phases. We compare 12 modern CNN architectures across 4 passive acoustic datasets that target calls of the Hainan gibbon Nomascus hainanus, the critically endangered black-and-white ruffed lemur Varecia variegata, the vulnerable Thyolo alethe Chamaetylas choloensis, and the Pin-tailed whydah Vidua macroura. We focus our work on data scarcity issues by training PAM binary classification models very small datasets, with as few as 25 verified examples. Our findings reveal that transfer learning can result in up to 82% F1 score while keeping CNN implementation details to a minimum, thus rendering this approach accessible, easier to design, and speeding up further vocalisation annotations to create PAM robust models.     

A cycle of enzymatic reactions that behaves like a neuronal circuit.

A cycle of four enzymatic reactions with repression or allosteric inhibition of the enzymes has been proposed by analogy to a neural oscillator. The system is analysed in a situation remote from full symmetry. Asymmetry has been introduced by treating one of the reagents as a reservoir substance with constant concentration and having essentially different rate constants for forward and backward reactions. It is demonstrated that, for certain values of parameters, the system can work as a strength of stimulus to frequency transducer. For other values of the parameters, it acquires the features of an excitable system.     

Detection of Focal and Non-focal Epileptic Seizure Using Continuous Wavelet Transform-Based Scalogram Images and Pre-trained Deep Neural Networks

Epilepsy is a neurological disease from which a large number of younger and older people suffer all over the world. The status of the patients is primarily examined by using Electroencephalogram (EEG) signals. The most important part for successful surgery is to locate the epileptic seizure in the brain. For this reason, it is very useful to detect the seizure area automatically before surgery. In this research, a novel method based on continuous wavelet transform (CWT) and two-dimensional (2D) convolutional neural networks (CNNs) has been proposed to predict focal and non-focal epileptic seizure. The AlexNet, InceptionV3, Inception-ResNetV2, ResNet50 and VGG16 pre-trained models have been used to automatically classify 2D-scalogram images into focal and non-focal epileptic seizure. The performances of 5 pre-trained models were compared and the detection results of 2D-scalograms were examined. The best classification accuracy of 92.27% is yielded by the InceptionV3 model among the other used four pre-trained models. As a result, it may be said that the pre-trained models and 2D-scalogram images of focal and non-focal EEG signals will be useful to neurologists for rapid and robust prediction epileptic seizure before surgery.     

High performing ensemble of convolutional neural networks for insect pest image detection

Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic identification of invasive insects would significantly speed up the recognition of pests and expedite their removal. In this paper, we generated ensembles of CNNs based on different topologies (EfficientNetB0, ResNet50, GoogleNet, ShuffleNet, MobileNetv2, and DenseNet201) optimized with different Adam variants for pest identification. Two new Adam algorithms for deep network optimization based on DGrad are proposed that introduce a scaling factor in the learning rate. Six CNN architectures that vary in their optimization function were trained on the Deng (SMALL), large IP102, and Xie2 (D0) pest data sets. Ensembles were compared and evaluated using several performance indicators. The best performing ensemble, which combined the CNNs using the different Adam variants, including the new ones proposed here, competed with human expert classifications on the Deng data set and achieved state of the art on all three insect data sets: 95.52% on Deng, 74.11% on IP102, and 99.81% on Xie2. Additional tests were performed on data sets for medical imagery classification that further validated the robustness and power of the proposed Adam optimization variants. All MATLAB source code is available at https://github.com/LorisNanni/.     

Heuristics for the Hodgkin–Huxley system

Hodgkin and Huxley (HH) discovered that voltages control ionic currents in nerve membranes. This led them to describe electrical activity in a neuronal membrane patch in terms of an electronic circuit whose characteristics were determined using empirical data. Due to the complexity of this model, a variety of heuristics, including relaxation oscillator circuits and integrate-and-fire models, have been used to investigate activity in neurons, and these simpler models have been successful in suggesting experiments and explaining observations. Connections between most of the simpler models had not been made clear until recently. Shown here are connections between these heuristics and the full HH model. In particular, we study a new model (Type III circuit): It includes the van der Pol-based models; it can be approximated by a simple integrate-and-fire model; and it creates voltages and currents that correspond, respectively, to the h and V components of the HH system.     

Toxic gas dispersion prediction for point source emission using deep learning method

Abstract

Accurate and rapid toxic gas concentration prediction model plays an important role in emergency aid of sudden gas leak. However, it is difficult for existing dispersion model to achieve accuracy and efficiency requirements at the same time. Although some researchers have considered developing new forecasting models with traditional machine learning, such as back propagation (BP) neural network, support vector machine (SVM), the prediction results obtained from such models need to be improved still in terms of accuracy. Then new prediction models based on deep learning are proposed in this paper. Deep learning has obvious advantages over traditional machine learning in prediction and classification. Deep belief networks (DBNs) as well as convolution neural networks (CNNs) are used to build new dispersion models here. Both models are compared with Gaussian plume model, computation fluid dynamics (CFD) model and models based on traditional machine learning in terms of accuracy, prediction time, and computation time. The experimental results turn out that CNNs model performs better considering all evaluation indexes.     

A novel approach to the van der pol oscillator: Natural frequency and entrainment

Abstract

The physics of the van der Pol oscillator as realized by the Meissner circuit is discussed by analogy to the beat phenomenon and by a consequent analysis of current balance. The current balance method leads to a new, very accurate equation for the dependence of the oscillator frequency on the feedback parameter. Several aspects of entrainment (existence, limited frequency range, dependence on parameters, phase shift) can be explained, too. Numerical results are presented which have been obtained by solving the homogeneous and inhomogeneous van der Pol equation with a Runge‐Kutta method.     

Two-terminal electronic circuit neuron model with excitable membrane V-I-t characteristics

Two-terminal electronic circuit neuron model is described. The model has time-variant voltagecurrent characteristics of an excitable membrane. Corresponding equivalent circuit is shown by the use of time-invariant elements as a voltage-controlled oscillator. The design principle of the model is outlined. Two application examples are demonstrated: (1) Simulator of the excitable membrane for the investigation of a voltage-clamp instrument, which works in the low current and low voltage region. (2) Circuit model for an electric organ of a weakly electric fish, Apteronotus.     

A structural and a functional aspect of stable information processing by the brain

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space.     

Modeling the dual pacemaker system of the tau mutant hamster

Circadian pacemakers in many animals are compound. In rodents, a two-oscillator model of the pacemaker composed of an evening (E) and a morning (M) oscillator has been proposed based on the phenomenon of "splitting" and bimodal activity peaks. The authors describe computer simulations of the pacemaker in tau mutant hamsters viewed as a system of mutually coupled E and M oscillators. These mutant animals exhibit normal type 1 PRCs when released into DD but make a transition to a type 0 PRC when held for many weeks in DD. The two-oscillator model describes particularly well some recent behavioral experiments on these hamsters. The authors sought to determine the relationships between oscillator amplitude, period, PRC, and activity duration through computer simulations. Two complementary approaches proved useful for analyzing weakly coupled oscillator systems. The authors adopted a "distinct oscillators" view when considering the component E and M oscillators and a "system" view when considering the system as a whole. For strongly coupled systems, only the system view is appropriate. The simulations lead the authors to two primary conjectures: (1) the total amplitude of the pacemaker system in tau mutant hamsters is less than in the wild-type animals, and (2) the coupling between the unit E and M oscillators is weakened during continuous exposure of hamsters to DD. As coupling strength decreases, activity duration (alpha) increases due to a greater phase difference between E and M. At the same time, the total amplitude of the system decreases, causing an increase in observable PRC amplitudes. Reduced coupling also increases the relative autonomy of the unit oscillators. The relatively autonomous phase shifts of E and M oscillators can account for both immediate compression and expansion of activity bands in tau mutant and wild-type hamsters subjected to light pulses.     

Isolation and identification of 2-hydroxyplectaniaxanthin from Rhodotorula aurantiaca

A new trihydroxyl carotenoid has been isolated from the yeast Rhodotorula aurantiaca (Saito) Lodder C.B.S. 317 and identified as 2-hydroxyplectaniaxanthin (3′,4′-didehydro,1′,2′-dihydro-β, ψ-caroten-2,1′,2′-triol). Its m.p., partition coefficient, _Rf, extinction coefficient, ms and NMR spectra are reported. Since the hydroxyl group at C-2 of the β-ionone ring is unusual, a possible mechanism for the biosynthesis of this carotenoid has been proposed.     

Persistence of Morning Anticipation Behavior and High Amplitude Morning Startle Response Following Functional Loss of Small Ventral Lateral Neurons in Drosophila

Light-activated large ventral lateral clock neurons (large LNv) modulate behavioral arousal and sleep in Drosophila while their counterparts, the small LNv (s-LNv) are important for circadian behavior. Recently, it has been proposed that the pattern of day-night locomotor behavioral activity is mediated by two anatomically distinct oscillators composed of a morning oscillator in the small LNv and an evening oscillator in the lateral dorsal neurons and an undefined number of dorsal pacemaker neurons. This contrasts with a circuit described by network models which are not as anatomically constrained. By selectively ablating the small LNv while sparing the large LNv, we tested the relative importance of the small and large LNv for regulating morning behavior of animals living in standard light/dark cycles. Behavioral anticipation of the onset of morning and the high amplitude morning startle response which coincides with light onset are preserved in small LNv functionally-ablated animals. However, the amplitude of the morning behavioral peak is severely attenuated in these animals during the transition from regular light/dark cycles to constant darkness, providing further support that small LNv are necessary for circadian behavior. The large LNv, in combination with the network of other circadian neurons, in the absence of functional small LNv are sufficient for the morning anticipation and the high amplitude light-activated morning startle response.     

Silver amplification of mercury sulfide and selenide: a histochemical method for light and electron microscopic localization of mercury in tissue

A method for light and electron microscopic demonstration of mercury sulfides and mercury selenides in mammalian tissue is presented. Silver ions adhering to the surface of submicroscopic traces of mercury sulfides or selenides in the tissue are reduced to metallic silver by hydroquinone. Physical development thereupon renders deposits of mercury sulfides or mercury selenide visible as spheres of solid silver. Examples of localization of mercury in the central nervous system and various organs from animals exposed to mercury chloride or methyl mercury chloride with or without additional sodium selenide treatment are presented. Selenium treatment results in a considerable increase in the amount of mercury that can be made visible by silver amplification. After mercury chloride treatment, most of the mercury is localized in lysosomes and is only rarely seen in secretory granules. After simultaneous selenium treatment, mercury is also found in nuclei of proximal tubule cells in the kidney and in macrophages. The "sulfide-osmium" method for ultrastructural localization of mercury suggested by Silberberg, Lawrence, and Leider (Arch Environ Health 19:7, 1969) and the light microscopic method using a photographic emulsion suggested by Umeda, Saito, and Saito (Jpn J Exp Med 39:17, 1969) have been experimentally analyzed and commented on.     

The purification and properties of deoxyguanosine triphosphate triphosphohydrolase from Escherichia coli

Deoxyguanosine triphosphate (dGTP) triphosphohydrolase (EC 3.1.5.1) has been purified approximately 16,000-fold to apparent homogeneity from extracts of Escherichia coli. The enzyme has a native molecular weight of 230,000 and a sedimentation coefficient of 9.3 S. Its subunit molecular weight derived from electrophoresis in denaturing polyacrylamide gels is 58,900, and it has a unique N-terminal sequence for the first 25 amino acids, which indicate that the native enzyme is composed of 4 homologous subunits. It is insensitive to sulfhydryl reagents and EDTA and can be heated to 60 degrees C for 60 min without loss of activity. The enzyme requires Mg2+ for activity, is highly specific for dGTP among the canonical deoxynucleoside triphosphates, and has a unique activity among nucleoside triphosphatases in that the products of the reaction are deoxyguanosine and inorganic tripolyphosphate. Preliminary evidence suggest that this enzyme is responsible for the optA mutant phenotype first described by Saito and Richardson (Saito, H., and Richardson, C.C. (1981) J. Virol. 37, 343-351).     

Conjugation of both on-axis and off-axis light in Nipkow disk confocal microscope to increase availability of incoherent light source

Laser-scanning confocal microscopy has been employed for exploring structures at subcellular, cellular and tissue level in three dimensions. To acquire the confocal image, a coherent light source, such as laser, is generally required in conventional single-point scanning microscopy. The illuminating beam must be focused onto a small spot with diffraction-limited size, and this determines the spatial resolution of the microscopy system. In contrast, multipoint scanning confocal microscopy using a Nipkow disk enables the use of an incoherent light source. We previously demonstrated successful application of a 100 W mercury arc lamp as a light source for the Yokogawa confocal scanner unit in which a microlens array was coupled with a Nipkow disk to focus the collimated incident light onto a pinhole (Saito et al., Cell Struct. Funct., 33: 133-141, 2008). However, transmission efficiency of incident light through the pinhole array was low because off-axis light, the major component of the incident light, was blocked by the non-aperture area of the disk. To improve transmission efficiency, we propose an optical system in which off-axis light is able to be transmitted through pinholes surrounding the pinhole located on the optical axis of the collimator lens. This optical system facilitates the use of not only the on-axis but also the off-axis light such that the available incident light is considerably improved. As a result, we apply the proposed system to high-speed confocal and multicolor imaging both with a satisfactory signal-to-noise ratio.     

The molecular clockwork of a protein-based circadian oscillator

The circadian clock of the cyanobacterium Synechococcuselongatus PCC 7942 is governed by a core oscillator consisting of the proteins KaiA, KaiB, and KaiC. Remarkably, circadian oscillations in the phosphorylation state of KaiC can be reconstituted in a test tube by mixing the three Kai proteins and adenosine triphosphate. The in vitro oscillator provides a well-defined system in which experiments can be combined with mathematical analysis to understand the mechanism of a highly robust biological oscillator. In this Review, we summarize the biochemistry of the Kai proteins and examine models that have been proposed to explain how oscillations emerge from the properties of the oscillator’s constituents.