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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%.