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Abstract Background Osteocytes are critical mechanosensory cells in bone, and mechanically stimulated osteocytes produce exosomes that can induce osteogenesis. MicroRNAs (miRNAs) are important constituents of exosomes, and some miRNAs in osteocytes regulate osteogenic differentiation; previous studies have indicated that some differentially expressed miRNAs in mechanically strained osteocytes likely influence osteoblastic differentiation. Therefore, screening and selection of miRNAs that regulate osteogenic differentiation in exosomes of mechanically stimulated osteocytes are important. Results A mechanical tensile strain of 2500 με at 0.5 Hz 1 h per day for 3 days, elevated prostaglandin E2 (PGE2) and insulin-like growth factor-1 (IGF-1) levels and nitric oxide synthase (NOS) activity of MLO-Y4 osteocytes, and promoted osteogenic differentiation of MC3T3-E1 osteoblasts. Fourteen miRNAs differentially expressed only in MLO-Y4 osteocytes which were stimulated with mechanical tensile strain, were screened, and the miRNAs related to osteogenesis were identified. Four differentially expressed miRNAs (miR-1930-3p, miR-3110-5p, miR-3090-3p, and miR-3058-3p) were found only in mechanically strained osteocytes, and the four miRNAs, eight targeted mRNAs which were differentially expressed only in mechanically strained osteoblasts, were also identified. In addition, the mechanically strained osteocyte-derived exosomes promoted the osteoblastic differentiation of MC3T3-E1 cells in vitro, the exosomes were internalized by osteoblasts, and the up-regulated miR-3110-5p and miR-3058-3p in mechanically strained osteocytes, were both increased in the exosomes, which was verified via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Conclusions In osteocytes, a mechanical tensile strain of 2500 με at 0.5 Hz induced the fourteen differentially expressed miRNAs which probably were in exosomes of osteocytes and involved in osteogenesis. The mechanically strained osteocyte-derived exosomes which contained increased miR-3110-5p and miR-3058-3p (two of the 14 miRNAs), promoted osteoblastic differentiation.

IntroductionIn the evolving field of neurophysiological research, visual light flicker stimulation is recognized as a promising non-invasive intervention for cognitive enhancement, particularly in sleep-deprived conditions.MethodsThis study explored the effects of specific flicker frequencies (40 Hz and 20–30 Hz random flicker) on alertness recovery in sleep-deprived rats. We employed a multidisciplinary approach that included behavioral assessments with the Y-maze, in vivo electrophysiological recordings, and molecular analyses such as c-FOS immunohistochemistry and hormone level measurements.ResultsBoth 40 Hz and 20–30 Hz flicker significantly enhanced behavioral performance in the Y-maze test, suggesting an improvement in alertness. Neurophysiological data indicated activation of neural circuits in key brain areas like the thalamus and hippocampus. Additionally, flicker exposure normalized cortisol and serotonin levels, essential for stress response and mood regulation. Notably, increased c-FOS expression in brain regions related to alertness and cognitive functions suggested heightened neural activity.DiscussionThese findings underscore the potential of light flicker stimulation not only to mitigate the effects of sleep deprivation but also to enhance cognitive functions. The results pave the way for future translational research into light-based therapies in human subjects, with possible implications for occupational health and cognitive ergonomics.

BackgroundEnvironmental noise pollution is serious, and there are few studies on the effects of long-term noise exposure during sleep on cognitive function and possible biological clock mechanism. ObjectiveTo explore the cognitive impairment induced by noise exposure during sleep in mice and possible biological clock mechanism, and to provide a theoretical basis for the protection against noise exposure. MethodsTwenty male C57BL/6J mice were randomly divided into a control group and a noise-exposed group, 10 mice in each group. The noise-exposed group was exposed to sleep-period noise using a noise generator for 12 h (08:00–20:00) per day for a total of 30 d. The calibrated noise intensity was set at 90 dB. No intervention was imposed on the control group. At the end of the noise exposure, cognitive function of mice was examined using the new object recognition experiment and the open field test, and the hippocampal tissue damage of mice were evaluated by Nissl staining, ionized calcium binding adaptor molecule 1 (Iba1) immunofluorescence staining, and real-time fluorescence quantitative PCR for inflammatory factors and biological clock genes. Oxidative stress indicators in the hippocampus of mice were also detected by assay kit. ResultsAfter noise exposure during sleep period, the results of new object recognition experiment showed that the discrimination index of mice in the noise-exposed group was 0.06±0.04, which was significantly lower than that of the control group (0.65±0.13) (P

Clustering data streams has become a hot topic and has been extensively applied to many real-world applications. Compared with traditional clustering, data stream clustering is more challenging. Adaptive Resonance Theory (ART) is a powerful (online) clustering method, it can automatically adjust to learn both abstract and concrete information, and can respond to arbitrarily large non-stationary databases while having fewer parameters, low computational complexity, and less sensitivity to noise, but its limited feature representation hinders its application to complex data streams. In this paper, considering its advantages and disadvantages, we present its flexible extension for stream clustering, called fractional adaptive resonance theory (FRA-ART). FRA-ART enhances data representation by fractionally exponentiating input features using self-interactive basis functions (SIBFs) and incorporating feature interaction through cross-interactive basis functions (CIBFs) at the cost only of introducing an additionally adjustable fractional order. Both SIBFs and CIBFs can be precomputed using existing algorithms, making FRA-ART easily adaptable to any ART variant. Finally, comparative experiments on five data stream datasets, including artificial and real-world datasets, demonstrate FRA-ART’s superior robustness and comparable or improved performance in terms of accuracy, normalized mutual information, rand index, and cluster stability compared to ART and the state-of-the-art G-Stream algorithm.

Weeds remain one of the most important factors affecting the yield and quality of corn in modern agricultural production. To use deep convolutional neural networks to accurately, efficiently, and losslessly identify weeds in corn fields, a new corn weed identification model, SE-VGG16, is proposed. The SE-VGG16 model uses VGG16 as the basis and adds the SE attention mechanism to realize that the network automatically focuses on useful parts and allocates limited information processing resources to important parts. Then the 3 × 3 convolutional kernels in the first block are reduced to 1 × 1 convolutional kernels, and the ReLU activation function is replaced by Leaky ReLU to perform feature extraction while dimensionality reduction. Finally, it is replaced by a global average pooling layer for the fully connected layer of VGG16, and the output is performed by softmax. The experimental results verify that the SE-VGG16 model classifies corn weeds superiorly to other classical and advanced multiscale models with an average accuracy of 99.67%, which is more than the 97.75% of the original VGG16 model. Based on the three evaluation indices of precision rate, recall rate, and F1, it was concluded that SE-VGG16 has good robustness, high stability, and a high recognition rate, and the network model can be used to accurately identify weeds in corn fields, which can provide an effective solution for weed control in corn fields in practical applications.

Rice leaf diseases are important causes of poor rice yields, and accurately identifying diseases and taking corresponding measures are important ways to improve yields. However, rice leaf diseases are diverse and varied; to address the low efficiency and high cost of manual identification, this study proposes a stacking-based integrated learning model for the efficient and accurate identification of rice leaf diseases. The stacking-based integrated learning model with four convolutional neural networks (namely, an improved AlexNet, an improved GoogLeNet, ResNet50 and MobileNetV3) as the base learners and a support vector machine (SVM) as the sublearner was constructed, and the recognition rate achieved on a rice dataset reached 99.69%. Different improvement methods have different effects on the learning and training processes for different classification tasks. To investigate the effects of different improvement methods on the accuracy of rice leaf disease diagnosis, experiments such as comparison experiments between single models and different stacking-based ensemble model combinations and comparison experiments with different datasets were executed. The model proposed in this study was shown to be more effective than single models and achieved good results on a plant dataset, providing a better method for plant disease identification.

Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

Rhythmic light flickers have emerged as useful tools to modulate cognition and rescue pathological oscillations related to neurological disorders by entrainment. However, a mechanistic understanding of the entrainment for different brain oscillatory states and light flicker parameters is lacking. To address this issue, we proposed a biophysical neural network model for thalamocortical oscillations (TCOs) and explored the stimulation effects depending on the thalamocortical oscillatory states and stimulation parameters (frequency, intensity, and duty cycle) using the proposed model and electrophysiology experiments. The proposed model generated alpha, beta, and gamma oscillatory states (with main oscillation frequences at 9, 25, and 35 Hz, respectively), which were successfully transmitted from the thalamus to the cortex. By applying light flicker stimulation, we found that the entrainment was state-dependent and it was more prone to induce entrainment if the flicker perturbation frequency was closer to the endogenous oscillatory frequency. In addition, endogenous oscillation would be accelerated, whereas low-frequency oscillatory power would be suppressed by gamma (30–50 Hz) flickers. Notably, the effects of intensity and duty cycle on entrainment were complex; a high intensity of light flicker did not mean high entrainment possibility, and duty cycles below 50% could induce entrainment easier than those above 50%. Further, we observed entrainment discontinuity during gamma flicker stimulations with different frequencies, attributable to the non-linear characteristics of the network oscillations. These results provide support for the experimental design and clinical applications of the modulation of TCOs by gamma (30–50 Hz) light flicker.

Environmental noise is a common hazard in military operations. Military service members during long operations are often exposed to around-the-clock noise and suffer massive emotional and cognitive dysfunction related to an Alzheimer’s disease (AD)-like neuropathology. It is essential to clarify the mechanisms underlying the effects of around-the-clock noise exposure on the central nervous system. Here, Wistar rats were continuously exposed to white noise (95 dB during the on-duty phase [8:00–16:00] and 75 dB during the off-duty phase (16:00–8:00 the next day)) for 40 days. The levels of phosphorylated tau, amyloid-β (Aβ), and neuroinflammation in the cortex and hippocampus were assessed and autophagosome (AP) aggregation was observed by transmission electron microscopy. Dyshomeostasis of autophagic flux resulting from around-the-clock noise exposure was assessed at different stages to investigate the potential pathological mechanisms. Around-the-clock noise significantly increased Aβ peptide, tau phosphorylation at Ser396 and Ser404, and neuroinflammation. Moreover, the AMPK-mTOR signaling pathway was depressed in the cortex and the hippocampus of rats exposed to around-the-clock noise. Consequently, autophagosome–lysosome fusion was deterred and resulted in AP accumulation. Our results indicate that around-the-clock noise exposure has detrimental influences on autophagic flux homeostasis and may be associated with AD-like neuropathology in the cortex and the hippocampus.

High-intensity noise can cause permanent hearing loss; however, short-duration medium-intensity noise only induces a temporary threshold shift (TTS) and damages synapses formed by inner hair cells (IHCs) and spiral ganglion nerves. Synaptopathy is generally thought to be caused by glutamate excitotoxicity. In this study, we investigated the expression levels of vesicle transporter protein 3 (Vglut3), responsible for the release of glutamate; glutamate/aspartate transporter protein (GLAST), responsible for the uptake of glutamate; and Na+/K+-ATPase α1 coupled with GLAST, in the process of synaptopathy in the cochlea. The results of the auditory brainstem response (ABR) and CtBP2 immunofluorescence revealed that synaptopathy was induced on day 30 after 100 dB SPL noise exposure in C57BL/6J mice. We found that GLAST and Na+/K+-ATPase α1 were co-localized in the cochlea, mainly in the stria vascularis, spiral ligament, and spiral ganglion cells. Furthermore, Vglut3, GLAST, and Na+/K+-ATPase α1 expression were disrupted after noise exposure. These results indicate that disruption of glutamate release and uptake-related protein expression may exacerbate the occurrence of synaptopathy.

Rhythmic light flickers have emerged as useful tools to modulate cognition and rescue pathological oscillations related to neurological disorders by entrainment. However, a mechanistic understanding of the entrainment for different brain oscillatory states and light flicker parameters is lacking. To address this issue, we proposed a biophysical neural network model for thalamocortical oscillations (TCOs) and explored the stimulation effects depending on the thalamocortical oscillatory states and stimulation parameters (frequency, intensity, and duty cycle) using the proposed model and electrophysiology experiments. The proposed model generated alpha, beta, and gamma oscillatory states (with main oscillation frequences at 9, 25, and 35 Hz, respectively), which were successfully transmitted from the thalamus to the cortex. By applying light flicker stimulation, we found that the entrainment was state-dependent and it was more prone to induce entrainment if the flicker perturbation frequency was closer to the endogenous oscillatory frequency. In addition, endogenous oscillation would be accelerated, whereas low-frequency oscillatory power would be suppressed by gamma (30-50 Hz) flickers. Notably, the effects of intensity and duty cycle on entrainment were complex; a high intensity of light flicker did not mean high entrainment possibility, and duty cycles below 50% could induce entrainment easier than those above 50%. Further, we observed entrainment discontinuity during gamma flicker stimulations with different frequencies, attributable to the non-linear characteristics of the network oscillations. These results provide support for the experimental design and clinical applications of the modulation of TCOs by gamma (30-50 Hz) light flicker. [ABSTRACT FROM AUTHOR]