Number Of Neurons Research Articles

A core scientific problem in the treatment of central nervous system diseases: newborn neurons.

It has long been asserted that failure to recover from central nervous system diseases is due to the system's intricate structure and the regenerative incapacity of adult neurons. Yet over recent decades, numerous studies have established that endogenous neurogenesis occurs in the adult central nervous system, including humans'. This has challenged the long-held scientific consensus that the number of adult neurons remains constant, and that new central nervous system neurons cannot be created or renewed. Herein, we present a comprehensive overview of the alterations and regulatory mechanisms of endogenous neurogenesis following central nervous system injury, and describe novel treatment strategies that target endogenous neurogenesis and newborn neurons in the treatment of central nervous system injury. Central nervous system injury frequently results in alterations of endogenous neurogenesis, encompassing the activation, proliferation, ectopic migration, differentiation, and functional integration of endogenous neural stem cells. Because of the unfavorable local microenvironment, most activated neural stem cells differentiate into glial cells rather than neurons. Consequently, the injury-induced endogenous neurogenesis response is inadequate for repairing impaired neural function. Scientists have attempted to enhance endogenous neurogenesis using various strategies, including using neurotrophic factors, bioactive materials, and cell reprogramming techniques. Used alone or in combination, these therapeutic strategies can promote targeted migration of neural stem cells to an injured area, ensure their survival and differentiation into mature functional neurons, and facilitate their integration into the neural circuit. Thus can integration replenish lost neurons after central nervous system injury, by improving the local microenvironment. By regulating each phase of endogenous neurogenesis, endogenous neural stem cells can be harnessed to promote effective regeneration of newborn neurons. This offers a novel approach for treating central nervous system injury.

Morphological heterogeneity of neurons in the human central amygdaloid nucleus.

The central amygdaloid nucleus (CeA) has an ancient phylogenetic development and functions relevant for animal survival. Local cells receive intrinsic amygdaloidal information that codes emotional stimuli of fear, integrate them, and send cortical and subcortical output projections that prompt rapid visceral and social behavior responses. We aimed to describe the morphology of the neurons that compose the human CeA (N = 8 adult men). Cells within CeA coronal borders were identified using the thionine staining and were further analyzed using the "single-section" Golgi method followed by open-source software procedures for two-dimensional and three-dimensional image reconstructions. Our results evidenced varied neuronal cell body features, number and thickness of primary shafts, dendritic branching patterns, and density and shape of dendritic spines. Based on these criteria, we propose the existence of 12 morphologically different spiny neurons in the human CeA and discuss the variability in the dendritic architecture within cellular types, including likely interneurons. Some dendritic shafts were long and straight, displayed few collaterals, and had planar radiation within the coronal neuropil volume. Most of the sampled neurons showed a few to moderate density of small stubby/wide spines. Long spines(thin and mushroom) were observed occasionally. These novel data address the synaptic processing and plasticity in the human CeA. Our morphological description can be combined with further transcriptomic, immunohistochemical, and electrophysiological/connectional approaches.It serves also to investigate how neurons are altered in neurological and psychiatric disorders with hindered emotional perception, in anxiety, following atrophy in schizophrenia, and along different stages of Alzheimer's disease.

Power consumption during forward locomotion of C. elegans: an electrical circuit simulation

Biological neuronal networks are of great interest for emerging technological approaches such as neuromorphic engineering due to their capability to efficiently process information. To understand the principles governing this energy efficiency, it is useful to investigate model organisms with small and well-characterized neuronal networks. Caenorhabditis elegans (C. elegans) is such a model organism and perfectly suited for this purpose, because its neuronal network consists of only 302 neurons whose interconnections are known. In this work, we design an ideal electrical circuit modeling this neuronal network in combination with the muscles it controls. We simulate this circuit by a run-time efficient wave digital algorithm. This allows us to investigate the energy consumption of the network occurring during locomotion of C. elegans and hence deduce potential design principles from an energy efficiency point of view. Simulation results verify that a locomotion is indeed generated. We conclude from the corresponding energy consumption rates that a small number of neurons in contrast to a high number of interconnections is favorable for consuming only little energy. This underlines the importance of interneurons. Moreover, we find that gap junctions are a more energy-efficient connection type than synapses, and inhibitory synapses consume more energy than excitatory ones. However, the energetically cheapest connection types are not the most frequent ones in C. elegans’ neuronal network. Therefore, a potential design principle of the network could be a balance between low energy costs and a certain functionality.Graphical abstractEnergy consumption rates during forward locomotion of C. elegans. a Rates for the ion channels of all neurons, and b average rates for ion channels of a single, active neuron. c Comparison of average rates with respect to the number of active sensory, motor, and interneurons. d Rates for all gap junctions and synapses, and e rates for all synapses of a specific neurotransmitter type. f Average rates for a single synaptic or gap junctions connection vs the total number of connections present for the type of connection (i.e. ACh-synapse, GABA-synapse, Glu-synapse, gap junction).

Bio inspired heuristic computing scheme for the human liver nonlinear model

In this research, a bio-inspired heuristic computing approach has been developed to solve the nonlinear behavior of the human liver, which is categorized into the liver and blood. The solutions of the human liver model are presented by using the stochastic computation procedure based on the artificial neural network (ANN) along with the optimization of genetic algorithm (GA) and interior-point (IP). A fitness function is designed through the differential form of the nonlinear human liver model and then optimized by using the hybrid competency of GAIP scheme. The correctness and exactness of the proposed approach are observed through the overlapping of the obtained (GAIP) and reference (Adams scheme) solutions, while the calculated absolute error values in good order enhance the worth of the proposed solver. The log-sigmoid transfer function together with ten numbers of neurons is executed to perform the solutions of the human liver nonlinear model. Furthermore, the statistical approaches have been applied in order to observe the reliability of the designed approach for solving the nonlinear human liver model.

A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG.

The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals. Then, we verified the consistency of optically-pumped magnetometer and electroencephalogram in detecting N400 using mutual information index. Multivariate pattern analysis revealed the difference in decoding performance of these two modalities, which can be further validated by dynamic/stable coding analysis on the temporal generalization matrix. The results from searchlight analysis provided a neural basis for this dissimilarity at the magnetoencephalography source level and the electroencephalogram sensor level. This study opens a new avenue for investigating the brain's coding patterns using wearable magnetoencephalography and reveals the differences in sensitivity between the two modalities in reflecting neuron representation patterns.

A stochastic model for neural progenitor dynamics in the mouse cerebral cortex

We have designed a stochastic model of embryonic neurogenesis in the mouse cerebral cortex, using the formalism of compound Poisson processes. The model accounts for the dynamics of different progenitor cell types and neurons. The expectation and variance of the cell number of each type are derived analytically and illustrated through numerical simulations. The effects of stochastic transition rates between cell types, and stochastic duration of the cell division cycle have been investigated sequentially. The model does not only predict the number of neurons, but also their spatial distribution into deeper and upper cortical layers. The model outputs are consistent with experimental data providing the number of neurons and intermediate progenitors according to embryonic age in control and mutant situations.

Prediction of Hydrodynamic Parameters of the State of the Bottomhole Zone of Wells Using Machine Learning Methods

The relevance of the development of a methodology for the operational assessment of the bottom-hole formation zone (the permeability of the bottom-hole formation zone and the skin factor) is primarily due to economic considerations, since existing approaches to its definition based on hydrodynamic studies lead to shortages and increased risks of failure to ensure the output of the well. In this regard, the use of modern methods of working with big data, such as deep learning of artificial neural networks, will ensure monitoring of the condition of the bottom-hole zone of the well formation without stopping them for hydrodynamic tests, which will reduce losses for oil production enterprises. It will allow for operational analysis for effective and timely application of intensification technologies, enhanced oil recovery. The authors analyzed the existing methods for determining the bottom-hole characteristics of the formation and machine learning approaches in the direction of solving this problem. The article presents a methodology for the operational assessment of the state of the bottom-hole formation zone: the permeability of the near bottomhole zone (NBHZ) and the skin factor using artificial neural network training approaches based on geological, operational data and the results of interpretation of hydrodynamic studies on the example of sandstones of oil fields in the Perm Region. A fully connected neural network was used to predict the NBHZ permeability. The article presents the results of testing various neural network architectures: the number of layers and neurons in layers with the choice of the best one. Some techniques were used to prevent over-training of models. The author’s methodology for assessing the skin factor of wells is proposed using a comprehensive analysis of the constructed statistical models and training models of artificial neural networks to solve the regression problem. In future studies, it is planned to use recurrent and convolutional neural networks to study the dynamic components of the formation of the bottom-hole formation zone and create an integrated approach to solve the problem.

Integrated Transcriptomic and Proteomic Study of the Mechanism of Action of the Novel Small-Molecule Positive Allosteric Modulator 1 in Targeting PAC1-R for the Treatment of D-Gal-Induced Aging Mice.

Small-molecule positive allosteric modulator 1 (SPAM1), which targets pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R), has been found to have a neuroprotective effect, and the underlying mechanism was explored in this study. First, using a D-galactose (D-gal)-induced aging mouse model, we confirmed that SPAM1 improves the structure of the hippocampal dentate gyrus and restores the number of neurons. Compared with D-gal model mice, SPAM1-treated mice showed up-regulated expression of Sirtuin 6 (SIRT6) and Lamin B1 and down-regulated expression of YinYang 1 (YY1) and p16. A similar tendency was observed in senescent RGC-5 cells induced by long-term culture, indicating that SPAM1 exhibits significant in vitro and in vivo anti-senescence activity in neurons. Then, using whole-transcriptome sequencing and proteomic analysis, we further explored the mechanism behind SPAM1's neuroprotective effects and found that SPAM is involved in the longevity-regulating pathway. Finally, the up-regulation of neurofilament light and medium polypeptides indicated by the proteomics results was further confirmed by Western blotting. These results help to lay a pharmacological network foundation for the use of SPAM1 as a potent anti-aging therapeutic drug to combat neurodegeneration with anti-senescence, neuroprotective, and nerve regeneration activity.

Reactive changes of kisspeptin-producing hypothalamic neuroendocrine cells during hypogonadism and its replacement therapy by the kisspeptin analogue in rats

BACKGROUND: This study is devoted to the morphological substantiation of the model of male hypogonadism and to establishing the effectiveness of its replacement therapy at the level of the central link of the hypothalamic-pituitary-testicular axis using morphological methods. Information about reactive changes in neuroendocrine cells that synthesize the peptide kisspeptin, which regulates the production of gonadoliberin when modeling male and female hypogonadism, has not been described in the literature, which prevents the creation of a micro-morphological basis for the development of models of hypogonadism and the implementation of further preclinical studies of the effectiveness of its replacement therapy. The goal is to carry out a morphological analysis of kisspeptin-producing neuroendocrine cells of the hypothalamus in normal conditions, with experimental hypogonadism and after replacement therapy.
 AIM: To carry out a morphological analysis of kisspeptin-producing neuroendocrine cells of the hypothalamus in normal conditions, with experimental hypogonadism and after replacement therapy.
 MATERIALS AND METHODS: The objects of the study were 3 groups of adult male Wistar rats 6–8 months of age. In animals of the first and second groups, after anesthesia, total ischemia of both testicles was caused by ligating the left and right spermatic cord with the vascular bundle of the testicle for 60 minutes. Rats of the second group, a few minutes after restoration of testicular blood flow, were given replacement therapy by daily administration of a synthetic analogue of kisspeptin KS6 for 7 days. Control animals of the third group were subjected to sham surgery. After 10 days, all animals were sacrificed, their brains were removed and embedded in paraffin. Nissl-stained frontal histological sections of the most massive areas of the kisspeptin-producing nuclei of the hypothalamus-periventricular and arcuate-were examined using the Imagescope program (Electronic Analysis, Russia). The number of cell bodies of viable and dead neurons was counted (under the control of immunohistochemical identification of the caspase-3 antigen), and the area of the body, nucleus and cytoplasm of viable cells was calculated. Statistical processing of the data was carried out using the GraphPad PRISM (USA) program to determine the median, upper and lower quartiles. Differences were considered significant at p 0.01.
 RESULTS: Simulation of acute ischemia caused a significant increase in the number of dead neurons, a slight decrease in the number of viable neurons and a decrease in the area of their cytoplasm in both kisspeptin-producing nuclei. As a result of KS6 replacement therapy, most neuronal cell bodies retained their original phenotype, but the number of dead neurons was high in both experimental groups.
 CONCLUSIONS: Modeling of male hypogonadism using the method of bilateral acute testicular ischemia induces death and partially reversible degenerative changes in kisspeptin-producing neuroendocrine cells of the hypothalamus. Neuropeptide KS6 has a pronounced restorative effect on kisspeptin-producing neurons of the hypothalamus, which is due to its specific activating effect on endocrine cells of all parts of the hypothalamic-pituitary-testicular axis.

Artificial neural network for drying behavior prediction of paddy in developed chamber fluidized–bed dryer

After rice is harvested, it must be dried before its products can be stored. Therefore, this paper presented a novel and simple method for improving paddy drying process in a column fluidized bed dryer. Additionally, artificial neural network methods were applied to predict the drying behavior. The experiments were conducted under two different drying chamber characteristics (a conventional chamber and a chamber fitted with nozzle) and four different air flow velocities (3.03, 3.52, 4.12 and 4.85 m/s) at a drying air temperature of 60 °C. The results showed that the chamber fitted with the nozzle reduced the drying time by approximately 67, 52 and 38 % at the air velocity of 3.52, 4.12 and 4.85 m/s, respectively. The specified experimental conditions and the calculated moisture content of the paddy in this work were used as input and output data for the artificial neural network, respectively, to predict drying characteristics. The artificial neural networks were developed with various parameters, including activation function, sampling type, split ratio, number of neurons and epoch number. The optimal model provided the root mean squared error of 0.111 and the coefficient of determination of 0.999. The best prediction was observed in the model using rectifier activation function with a split ratio of 0.85, epoch number of 1800, and 50 and 90 neurons in the first and second hidden layers, respectively.

Prediction of penetration rate and optimization of weight on a bit using artificial neural networks

Relevance. Achieving the greatest rate of penetration is the aim of every drilling engineer because it is one of the most significant factors influencing drilling costs. However, a variety of drilling conditions could have an impact on rate of penetration, complicating its forecast. Aim. To suggest a novel strategy to accurately predict rate of penetration and optimize drilling parameters. Objects. Real-time drilling data of a few wells in the Ca Tam oil field, Vietnam, with more than 900 datasets including significant parameters like rotary speed, weight on bit, standpipe pressure, flow rate, weight of mud, torque. Methods. Various methods using Artificial Neural Network was proposed to estimate rate of penetration. Results. The number of neurons in a hidden layer was varied then the results of different Artificial Neural Network models were compared in order to obtain the optimal model. The final Artificial Neural Network model shows high exactness when contrasted with actual rate of penetration, in this manner, it tends to be suggested as a successful and reasonable approach to predict the rate of penetration of different wells in the Ca Tam oil field. Based on the proposed Artificial Neural Network model, the optimal weight on bit was determined for the drilling interval from 1800 to 2300 m of oil wells in the research region.

Prediction of laser-welded deformation using artificial neural networks

When predicting welding deformation of the laser-manufactured vehicles and aerospaces, analytical solutions or empirical formulas are not usually accessible in complex problems. Based on the inherent deformation method, a machine learning (ML) approach for predicting welding deformation of welded structures is proposed based on an artificial neural network (ANN). This method is a promising substitute for analytical, empirical, and finite element (FE) solutions due to its accuracy, easy-to-use, efficiency, and universality. First, the outputs of the ANN are determined via dimensionless analysis and comparison of numerical results, which are dimensionally independent. Then, based on the inherent deformation method, the training and validation sets of the ANN are generated through an elastic finite element analysis. At last, the structure of the ANN is determined by analyzing the ANN prediction accuracy with different hidden layers, numbers of neurons, and activation functions. The results show that the ML solutions are in good agreement with the FE results, verifying the effectiveness and generalization ability of the proposed method.

Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques.

Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.

How many neurons do we need? A refined analysis for shallow networks trained with gradient descent

We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive fast rates of convergence that are known to be minimax optimal in the framework of non-parametric regression in reproducing kernel Hilbert spaces. On our way, we precisely keep track of the number of hidden neurons required for generalization and improve over existing results. We further show that the weights during training remain in a vicinity around initialization, the radius being dependent on structural assumptions such as degree of smoothness of the regression function and eigenvalue decay of the integral operator associated to the NTK.

Treadmill exercise pretreatment ameliorated structural synaptic plasticity impairments of medial prefrontal cortex in vascular dementia rat and improved recognition memory

This study aimed to investigate structural synaptic plasticity in the medial prefrontal cortex of rats under treadmill exercise pretreatment or naive conditions in a vascular dementia model, followed by recognition memory performance in a novel object recognition task. In this study, 24 Sprague–Dawley rats were obtained and randomly assigned into 4 groups as follows: control group (Con group, n = 6), vascular dementia (VD group, n = 6), exercise and vascular dementia group (Exe + VD group, n = 6), and exercise group (Exe group, n = 6). Initially, 4 weeks of treadmill exercise intervention was administered to the rats in the Exe + VD and Exe groups. Then, to establish the vascular dementia model, the rats both in the VD and Exe + VD groups were subjected to bilateral common carotids arteries surgery. One week later, open-field task and novel recognition memory task were adopted to evaluate anxiety-like behavior and recognition memory in each group. Then, immunofluorescence and Golgi staining were used to evaluate neuronal number and spine density in the rat medial prefrontal cortex. Transmission electron microscopy was used to observe the synaptic ultrastructure. Finally, microdialysis coupled with high-performance liquid chromatography was used to assess the levels of 5-HT and dopamine in the medial prefrontal cortex. The behavior results showed that 4 weeks of treadmill exercise pretreatment significantly alleviated recognition memory impairment and anxiety-like behavior in VD rats (P < 0.01), while the rats in VD group exhibited impaired recognition memory and anxiety-like behavior when compared with the Con group (P < 0.001). Additionally, NeuN immunostaining results revealed a significant decrease of NeuN-marked neuron in the VD group compared to Con group (P < 0.01), but a significantly increase in this molecular marker was found in the Exe + VD group compared to the Con group (P < 0.01). Golgi staining results showed that the medial prefrontal cortex neurons in the VD group displayed fewer dendritic spines than those in the Con group (P < 0.01), and there were more spines on the dendrites of medial prefrontal cortex cells in Exe + VD rats than in VD rats (P < 0.01). Transmission electron microscopy further revealed that there was a significant reduction of synapses intensity in the medial prefrontal cortex of rats in the VD group when compared with the Con group(P < 0.01), but physical exercise was found to significantly increased synapses intensity in the VD model (P < 0.01). Lastly, the levels of dopamine and 5-HT in the medial prefrontal cortex of rats in the VD group was significantly lower compared to the Con group (P < 0.01), and treadmill exercise was shown to significantly increased the levels of dopamine and 5-HT in the VD rats (P < 0.05). Treadmill exercise pretreatment ameliorated structural synaptic plasticity impairments of medial prefrontal cortex in VD rat and improved recognition memory.

Effect of electroacupuncture on behavior and hippocampal inflammatory factors in rats with chronic fatigue syndrome.

To observe the effect of electroacupuncture (EA) on the changes of behavior and hippocampal inflammatory factors in rats with chronic fatigue syndrome (CFS), so as to explore its possible mechanisms in the treatment of CFS. Twenty-seven SD rats were randomly divided into control, model and electroacupuncture (EA) groups (n=9 rats in each group). The CFS model was established by multi-factor compound stress stimulation method. Rats of the EA group received EA (10 Hz) at "Shenting" (GV24) penetrating "Baihui" (GV20), "Dazhui" (GV14) for 15 min, twice a day for 14 days. The general conditions, Morris water maze test, open field test, the exhausted running platform were conducted for determining the rats' locomotor and learning-memory activities. H.E. staining was used to observe the morphological structure of neurons in hippocampal CA1 region. The contents of interleukin (IL)-10, IL-17 and transforming growth factor (TGF) β1 in hippocampus and serum of rats were detected by ELISA, and the positive expressions of IL-10, IL-17 and TGF-β1 in hippocampal CA1 region were detected by immunofluorescence staining. Compared with the control group, the score of general condition was increased (P<0.05), the escape latency was prolonged (P<0.05), the number of crossing the original platform was decreased (P<0.05), the numbers of crossing the grid and entering the central area were increased (P<0.05), and the exhaustive treadmill time was shortened (P<0.05) in the model group. The contents of IL-10 in the hippocampus and serum were decreased (P<0.05), while IL-17 and TGF-β1 contents were increased (P<0.05). The immunofluorescence intensity of IL-10 in the hippocampus was decreased (P<0.05), while the intensity of IL-17 and TGF-β1 were increased (P<0.05). After treatment, compared with the model group, the score of general condition was decreased (P<0.05), the escape latency was shortened (P<0.05), the number of crossing the original platform was increased (P<0.05), the numbers of crossing the grid and entering the central area were decreased (P<0.05), and the exhaustive treadmill time was prolonged (P<0.05) in the EA group. The contents of IL-10 in the hippocampus and serum were increased (P<0.05), while IL-17 and TGF-β1 levels were decreased (P<0.05). The immunofluorescence intensity of IL-10 in the hippocampus was increased (P<0.05), while the intensity of IL-17 and TGF-β1 were decreased (P<0.05). H.E. staining showed that in the model group, the number of neurons in the hippocampus decreased, with disordered arrangement and loose structure, and a small numbers of neuronal nuclei were missing. The degree of tissue damage of the EA group was milder than that of the model group. EA can alleviate fatigue and spatial learning and memory impairment in CFS rats, which may be related to the regulation of peripheral and central inflammation.

Adsorption/photocatalytic degradation of Cefixime by the green Bi2WO6/g-C3N4/ZIF-67 dual S-scheme heterojunction: Artificial neural network, genetic algorithm, density functional theory, and toxicity assessments

This study investigated the photocatalytic adsorption, degradation, and mineralization of Cefixime (CFX) through hydrothermally synthesized Bi2WO6(36 %)/g-C3N4(54 %)/ZIF-67(10 %) dual S-scheme heterojunction (BCZ). Full characterization analysis for the fresh and reused photocatalyst was explored to study the formation of heterojunction, stability, and reusability of the BCZ. VB-XPS, Mott-Schottky plots, and UV–Vis DRS defined the band structure and electron transfer mechanism of BCZ. The effects of ten operating condition factors, including reaction time, initial concentration of CFX, the dosage of photocatalyst, reaction temperature, initial pH of the reaction, visible and UV intensity, and concentration of Na2SO4, NaOH, and NaCl in the reaction solution were experimentally investigated. These factors were then modeled through artificial neural networks (ANN). The number of neurons, training algorithm, and the type of activation functions in the ANN were optimized by mean squared error metric followed by the visualizations of the ANN predictions. Two cost functions (the ratio of instantaneous CFX concentration to initial CFX concentration (C/C0) and the ratio of photocatalyst dosage to the amount of CFX removed) were employed to optimize the value of these operating condition factors separately and simultaneously through single and multi-objective genetic algorithms. Coupling LC-MS results and DFT calculations, a degradation pathway for Cefixime was proposed and then analyzed by QSAR. The toxicity of the treated solution was investigated using the wheat seed culture, along with MIC and MBC testing conducted by E. coli and S. aureus, and its eco-friendliness was confirmed by TOC and COD. Furthermore, ICP-OES confirmed that BCZ is a green photocatalyst.

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families

We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is fixed and the second layer has a single neuron, our approach resembles previous uses of squared Gaussian process or kernel methods, but allowing the hidden layer to be learnt allows for additional flexibility. In many cases of interest, the integrated intensity function admits a closed form and can be computed in quadratic time in the number of hidden neurons. We enumerate a far more extensive number of such cases than has previously been discussed. Our approach is more memory and time efficient than naive implementations of squared or exponentiated kernel methods or Gaussian processes. Maximum likelihood and maximum a posteriori estimates in a reparameterisation of the final layer of the intensity function can be obtained by solving a (strongly) convex optimisation problem using projected gradient descent. We demonstrate SNEPPPs on real, and synthetic benchmarks, and provide a software implementation.

On the Expressivity of Recurrent Neural Cascades

Recurrent Neural Cascades (RNCs) are the recurrent neural networks with no cyclic dependencies among recurrent neurons. This class of recurrent networks has received a lot of attention in practice. Besides training methods for a fixed architecture such as backpropagation, the cascade architecture naturally allows for constructive learning methods, where recurrent nodes are added incrementally one at a time, often yielding smaller networks. Furthermore, acyclicity amounts to a structural prior that even for the same number of neurons yields a more favourable sample complexity compared to a fully-connected architecture. A central question is whether the advantages of the cascade architecture come at the cost of a reduced expressivity. We provide new insights into this question. We show that the regular languages captured by RNCs with sign and tanh activation with positive recurrent weights are the star-free regular languages. In order to establish our results we developed a novel framework where capabilities of RNCs are assessed by analysing which semigroups and groups a single neuron is able to implement. A notable implication of our framework is that RNCs can achieve the expressivity of all regular languages by introducing neurons that can implement groups.

Postnatal survival of phrenic motor neurons is promoted by BDNF/TrkB.FL signaling.

The number of motor neurons (MNs) declines precipitously during the final trimester before birth. Thereafter, the number of MNs remains relatively stable, with their connections to skeletal muscle dependent on neurotrophins, including brain-derived neurotrophic factor (BDNF) signaling through its high-affinity full-length tropomyosin-related kinase receptor subtype B (TrkB.FL) receptor. As a genetic knockout of BDNF leads to extensive MN loss and postnatal death within 1-2 days after birth, we tested the hypothesis that postnatal inhibition of BDNF/TrkB.FL signaling is important for postnatal phrenic MN (PhMN) survival. In the present study, we used a 1NMPP1-sensitive TrkBF616A mutant mouse to evaluate the effects of inhibition of TrkB kinase activity on phrenic MN (PhMN) numbers and diaphragm muscle (DIAm) fiber cross-sectional area (CSA). Pups were exposed to 1NMPP1 or vehicle (DMSO) from birth to 21 days old (weaning) via the mother's ingestion in the drinking water. Following weaning, the right phrenic nerve was exposed in the neck and the proximal end dipped in a rhodamine solution to retrogradely label PhMNs. After 24 h, the cervical spinal cord and DIAm were excised. Labeled PhMNs were imaged using confocal microscopy, whereas DIAm strips were frozen at ∼1.5× resting length, cryosectioned, and stained with hematoxylin and eosin to assess CSA. We observed an ∼34% reduction in PhMN numbers and increased primary dendrite numbers in 1NMPP1-treated TrkBF616A mice. The distribution of PhMN size (somal surface area) DIAm fiber cross-sectional areas did not differ. We conclude that survival of PhMNs during early postnatal development is sensitive to BDNF/TrkB.FL signaling.NEW & NOTEWORTHY During early postnatal development, BDNF/TrkB signaling promotes PhMN survival. Inhibition of BDNF/TrkB signaling in early postnatal development does not impact PhMN size. Inhibition of BDNF/TrkB signaling in early postnatal development does not impact the number or CSA of DIAm fibers.