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Objective To analyze the clinical and molecular genetic features in one pedigree with nail-patella syndrome（NPS）， and provide evidence for genetic counseling and reproductive guidance for this family. Methods Whole-exon sequencing （WES）was performed to detect the gene variant of the proband and the whole family. Candidate variants were verified by Sanger sequencing and bioinformatic analysis. Using the key words of “nail-patella syndrome”and “prenatal diagnosis/antenatal diagnosis” as the searching words in both Chinese and English，relevant literatures were searched from PubMed， SinoMed， CNKI， Wanfang Data and Chongqing VIP databases up to December， 2022. Clinical data of NPS cases were collected and analyzed. Results The proband and aborted fetus carried a heterozygous variant c.736C>T （p.Arg246*） of the LMX1B. The same heterozygous variant was found in father rather than in mother. This variant has been proven to be pathogenic. Bioinformatic analysis predicted that the 246th amino acid was highly conserved among different species and p.Arg246* variant altered the structure and function of LMX1B protein. According to literature review， six cases of NPS confirmed by prenatal diagnosis were searched. All cases were diagnosed by ultrasound and/or molecular genetic analysis. Conclusion The heterozygous variant c.736C>T （p.Arg246*） of LMX1B gene is the pathogenic mutation in this NPS pedigree.

Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron's own spike history and the cross-coupling parameters of other neurons' spike history. We use simulation data to test the estimation algorithm's effectiveness and analyze the main factors that evoke neuronal responding activity. Finally, we use the coupling generalized linear model to encode neuronal spiking activity evoked by two acupuncture manipulations, and estimate the coupling parameters of stimulus, the self-coupling parameters and the cross-coupling parameters. The results show that in acupuncture experiments, acupuncture stimulus is the inducing factor of neuronal spiking activity, and the cross-coupling of other neurons' spike history is the main factor of neuronal spiking activity. Additionally, the higher the amplitude of the neuronal spiking waveform, the greater the cross-coupling parameter. This lays a theoretical foundation for the scientific application of acupuncture therapy. [ABSTRACT FROM AUTHOR]

The humanoid torso is crucial for the overall performance of a humanoid robot. Developing an effective humanoid spine is essential for enhancing this mechanism. This paper introduces a one-vertebral-disc unit inspired by human spine anatomy. A prototype was created using 3D-printed parts and commercially available components. Two general human-like motions are achieved using two servo motors and two pulleys, reducing the number of servo motors needed. The results indicate that a one-vertebral-disc unit can bend up to 15 degrees. The proposed mechanism functions effectively and successfully mimics human movements. It holds potential for integration into humanoid torsos, enabling efficient performance in human-like tasks in the future. [ABSTRACT FROM AUTHOR]

The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into the backbone and neck networks to improve the detection speed. Then, the triplet attention mechanism was embedded into the last two detection heads to enhance the network's feature extraction for strawberries and improve the detection accuracy. Lastly, the focal loss function was utilized to enhance the model's recognition capability for challenging strawberry targets, which thereby improves the model's recall rate. The experimental results demonstrated that the RTF-YOLO model achieved a detection speed of 145 FPS (frames per second), a precision of 91.92%, a recall rate of 81.43%, and an m A P (mean average precision) of 90.24% on the test dataset. Relative to the baseline of YOLOv5s, it showed improvements of 19%, 2.3%, 4.2%, and 3.6%, respectively. The RTF-YOLO model performed better than other mainstream models and addressed the problems of false positives and false negatives in strawberry detection caused by variations in illumination and occlusion. Furthermore, it significantly enhanced the speed of detection. The proposed model can offer technical assistance for strawberry yield estimation and automated harvesting. [ABSTRACT FROM AUTHOR]

Alertness is the state of attention by high sensory awareness. A lack of alertness is one of the main reasons of serious accidents. Traffic accidents caused by driver’s drowsy driving have a high fatality rate. This paper presents an EEG-based alertness detection system. In order to ensure the convenience and long-term wearing comfort of EEG recordings, the wearable electrode cap will be the principal choice in the future, and the selection of channels will be limited. We first built a 3-D simulated driving platform using Unity3D. Then, we perform an experiment with driving drift task. EEG signals are recorded form frontal and occipital regions. We select data segments using the driving reaction time, classify the state of alertness with a support vector machine (SVM), and select the optimal combination of channels with minimum number of channels. Our results demonstrate that alertness can be classified efficiently with one channel (PO6) at accuracy of 93.52%, with two channels (FP1+PO6) at 95.85% and with three channels (FP1+PO6+PO5 and FP1+PO6+POZ) at 96.11%.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection. [ABSTRACT FROM AUTHOR]

Ginsenoside Rh2 (G-Rh2), a rare ginsenoside isolated from red ginseng, has considerable anti-cancer activity and induces apoptosis in a variety of cancer cells, but its activity in esophageal cancer cells is unclear. In this study, we examined the cytotoxic activity of (20S) G-Rh2 in highly differentiated esophageal squamous ECA109 cells and poorly differentiated esophageal squamous TE-13 cells. (20S) G-Rh2 exerted intense cytotoxicity in ECA109 and TE-13 cells with an IC50 of 2.9 and 3.7 μg/mL, respectively. After treatment with G-Rh2, Bcl-2, and Bcl-xL, the two main anti-apoptosis Bcl-2 family proteins upregulated, and Bax and Bak, the two key pro-apoptosis proteins translocated to mitochondria in both cell lines. At the same time, cytochrome c and Smac released from mitochondria, followed by caspase-9 activation, indicating that a mitochondria-mediated intrinsic apoptosis pathway was activated in both cell lines upon treatment with (20S) G-Rh2. It is noteworthy that (20S) G-Rh2 upregulated the transcription and protein expression of two death receptors, Fas and DR5, and subsequently activated Caspase-8 in the TE-13 cells but not in the ECA109 cells. Taken together, we demonstrated the potent anti-esophageal cancer cell activity of (20S) G-Rh2 and showed its working mechanism in two differentiated esophageal cancer cells, which can provide important evidence for developing an effective strategy for anti-esophageal cancer treatment. [ABSTRACT FROM AUTHOR]

Herein, an atomically dispersed palladium catalyst on a hierarchical porous structure of N-doped carbon (Pd1/N–C) is prepared using a facile freeze-drying-assisted strategy. Freeze-drying methods not only suppress the aggregation of Pd atoms but also successfully produce abundant nanopores. HAADF-STEM confirms that Pd single atoms are uniformly anchored on the N–C surface. The Pd1/N–C electrocatalyst enhances the ORR and OER activity and durability compared to N–C and Pd–NPs/N–C. Rechargeable Zn–air batteries (ZABs) based on novel Pd1/N–C exhibit a peak power density of 113.7 mW cm−2 and maintain a voltage efficiency of 64.0% after 495 cycles at a discharge current density of 5 mA cm−2. Besides, two ZABs in series can supply an LED light for at least 170 h. [ABSTRACT FROM AUTHOR]

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue. [ABSTRACT FROM AUTHOR]

Monitoring environmental pollutants has become a popular topic because of the serious adverse effects of environmental pollutants on human beings and ecosystems. In the present work, a 3D flower-like nickel oxide (NiO)-decorated PDDA (poly-(dimethyl diallyl ammonium chloride))-functionalized RGO nanocomposite (NiO/PDDA-RGO) was synthesized via a hydrothermal route combined with an ultrasonic process. The nanocomposite integrates the large surface area and remarkable electrical properties of RGO with the good catalytic activity of NiO and shows great electrochemical signal enhancement toward the electrooxidation of nitrite. In optimal experimental conditions, the as-prepared sensor exhibited excellent nitrite sensing performances, with a wide operating range (5 μM–8 mM), low detection limit (0.98 μM), and high sensitivity (0.0184 μA mM–1 cm–2). Furthermore, the proposed strategy can perform real-time monitoring of nitrite in tap water with satisfactory results. A NiO/PDDA-RGO-based electrochemical sensor also has the advantages of simple preparation, environmental friendliness, low cost, outstanding reproducibility, and stability, which have profound significance for environmental pollution monitoring. [ABSTRACT FROM AUTHOR]

PPP2R5D-related neurodevelopmental disorder, which is mainly caused by de novo missense variants in the PPP2R5D gene, is a rare autosomal dominant genetic disorder with about 100 patients and a total of thirteen pathogenic variants known to exist globally so far. Here, we present a 24-month-old Chinese boy with developmental delay and other common clinical characteristics of PPP2R5D-related neurodevelopmental disorder including hypotonia, macrocephaly, intellectual disability, speech impairment, and behavioral abnormality. Trio-whole exome sequencing (WES) and Sanger sequencing were performed to identify the causal gene variant. The pathogenicity of the variant was evaluated using bioinformatics tools. We identified a novel pathogenic variant in the PPP2R5D gene (c.620G>T, p.Trp207Leu). The variant is located in the variant hotspot region of this gene and is predicted to cause PPP2R5D protein dysfunction due to an increase in local hydrophobicity and unstable three-dimensional structure. We report a novel pathogenic variant of PPP2R5D associated with PPP2R5D-related neurodevelopmental disorder from a Chinese family. Our findings expanded the phenotypic and mutational spectrum of PPP2R5D-related neurodevelopmental disorder. [ABSTRACT FROM AUTHOR]

This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application. [ABSTRACT FROM AUTHOR]

We introduce a method that combines the unscented Kalman filter (UKF) and the adaptive lag synchronization (ALS) to estimate the unknown parameters of a neuron model with seizure-like activity using only the heavily noise-corrupted time series of membrane potentials. Although both UKF and ALS are able to estimate the parameters, UKF performs worse when the number of unknown parameters increases, while ALS requires system states that cannot be measured in practice. Therefore, we incorporate UKF as an observer of the unmeasured states into ALS method to estimate multiple parameters. The effectiveness of the combined method is guaranteed by Lyapunov stability theorem and Barbalat's lemma in theory. Numerical simulations demonstrate that, when two parameters are estimated simultaneously, the combined approach has better performance and higher accuracy than only using UKF or ALS method. This exploration of the proposed approach may play an important role in studying new treatments in seizure control. [ABSTRACT FROM AUTHOR]

Synchronous oscillations in certain frequencies of the sub-thalamic nucleus (STN) neurons are closely related to the physical symptoms of Parkinson's disease (PD). Recent results have highlighted the importance of calcium channels in the synchronization properties and regulation of STN neurons. In this paper, a novel hybrid neuron model which can capture the electrophysiological signature of neurons with low or high density of calcium channels is used to explore the synchronization propensity and regulation by firing frequencies of neurons. Numerical simulations show that the synchronization propensity of networks consisting of the novel hybrid neurons is quite distinguishing in low and high calcium conductance modes, especially, the synchronization can be differentially modulated by network frequencies in the two modes. By analyzing the firing frequency and phase response curve of the individual neuron, we find that a single parameter of the hybrid neuron, which is a direct image of the calcium conductance, is crucial in determining the excitability and response properties of the neuron. Different phase response properties of single neurons in different calcium conductance modes lead to network synchronization discrepancies. [ABSTRACT FROM AUTHOR]

This paper investigates the propagation of the weak aperiodic signal in a parallel array of FitzHugh–Nagumo (FHN) neurons with heterogenous aperiodic high-frequency (HAHF) disturbances. Different from the traditional vibrational resonance, where the optimal amplitudes of the HF driving signal should be tuned for individual elements, the ability of the proposed averaging parallel array network for weak signal detection can be optimized at a fixed amplitude of the HAHF disturbance, regardless of the nature of the input signal. Local connections in the parallel array network are found to be important for the propagation of weak signal in parallel array. Besides, the characteristics of high-frequency signal such as heterogeneity and frequency, can also modulate the propagation of aperiodic signal in parallel array. • Parallel vibrational resonance (VR) occurs with aperiodic high-frequency disturbances. • VR enhances weak aperiodic signal detection in a parallel array of neural networks. • Local connections are important for weak signal propagation. • Disturbances' heterogeneity and frequency modulate propagation of aperiodic signals. [ABSTRACT FROM AUTHOR]

In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the 'Zusanli' point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dynamic delayed feedback control approach or desynchronization of chaotic-bursting synchronous activities in an ensemble of globally coupled neuronal oscillators. We demonstrate that the difference signal between an ensemble's mean field and its time delayed state, filtered and fed back to the ensemble, can suppress the self-synchronization in the ensemble. These individual units are decoupled and stabilized at the desired desynchronized states while the stimulation signal reduces to the noise level. The effectiveness of the method is illustrated by examples of two different populations of globally coupled chaotic-bursting neurons. The proposed method has potential for mild, effective and demand-controlled therapy of neurological diseases characterized by pathological synchronization. [ABSTRACT FROM AUTHOR]