Indoor air quality improvement with filtration and UV-C on mitigation of particulate matter and airborne bacteria: Monitoring and modeling.

Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts people's daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 µg/m3) and ON days (49 µg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.

Coupled Ferroelectric-Photonic Memory in a Retinomorphic Hardware for In-Sensor Computing.

The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Ultrasound propagation characteristics within the bone tissue of miniature ultrasound probes: implications for the spinal navigation of pedicle screw placement.

Background: The accuracy of pedicle screw fixation is crucial for patient safety. Traditional navigation methods based on computed tomography (CT) imaging have several limitations. Therefore, this study aimed to investigate the ultrasonic propagation characteristics of bone tissue and their relationship with CT imaging results, as well as the potential application of ultrasound navigation in pedicle screw fixation. Methods: The study used three bovine spine specimens (BSSs) and five human vertebral allograft bones (HABs) to progressively decrease the thickness of the cancellous bone layer, simulating the process of pedicle screw perforation. Five unfocused miniature ultrasound probes with frequencies of 2.2, 2.5, 3, 12, and 30 MHz were employed for investigating the ultrasonic propagation characteristics of cancellous and cortical bone through ultrasound transmission and backscatter experiments. The CT features of the bone tissue was obtained with the Skyscan 1174 micro-CT scanner (Bruker, Billerica, MA, USA). Results: The experimental results demonstrated that low-frequency (2-3 MHz) ultrasound effectively penetrated the cancellous bone layer up to a depth of approximately 5 mm, with an attenuation coefficient below 10 dB/cm. Conversely, high-frequency (12 MHz) ultrasound exhibited significant signal attenuation in cancellous bone, reaching up to 55.8 dB/cm. The amplitude of the backscattered signal at the cancellous bone interface exhibited a negative correlation with the bone sample thickness (average r=-0.84), meaning that as the thickness of the cancellous bone layer on the cortical bone decreases, the backscattered signal amplitude gradually increases (P<0.05). Upon reaching the cortical bone interface, there was a rapid surge in echo signal amplitude, up to 8 times higher. Meanwhile, the statistical results indicated a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabecular structure. Conclusions: Theoretically, using multiple ultrasonic probes (≥3) and regions of interest (ROIs) (≥5) has the potential to provide surgeons with early warning signals for pedicle perforation based on three or more successive increases in echo signal amplitude or a sudden substantial increase. The statistical results indicate a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabeculae, suggesting the potential use of ultrasound as opposed to CT for real-time intraoperative bone navigation.

PolyphyllinVI alleviates the spared nerve injury-induced neuropathic pain based on P2X3 receptor-mediated the release of inflammatory mediators.

ETHNOPHARMACOLOGICAL RELEVANCE: PolyphyllinVI (PPⅥ) is the main bioactive component of Chonglou which is a traditional Chinese herbal with various effects, including antitumor, anti-inflammatory, and analgesia. AIM OF THE STUDY: This study aimed to investigate the properties and mechanisms of the analgesia of PPⅥ by using neuropathic pain (NPP) mice. MATERIALS AND METHODS: The potential targets and mechanisms of PPⅥ in alleviating NPP were excavated based on the network pharmacology. Subsequently, the construction of a spared nerve injury (SNI) mice model was used to evaluate the effect of PPⅥ on NPP and the expression of the P2X3 receptor. We identified the signaling pathways of PPⅥ analgesia by RNA sequencing. RESULTS: The results of network pharmacology showed that BCL2, CASP3, JUN, STAT3, and TNF were the key targets of the analgesic effect of PPⅥ. PPⅥ increased the MWT and TWL of SNI mice and decreased the level of P2X3 receptors in the dorsal root ganglion (DRG) and spinal cord (SC). Additionally, PPⅥ reduced the release of pro-inflammatory mediators (TNF-α, IL-1ß, and IL-6) in the DRG, SC, and serum. Based on the KEGG enrichment of differentially expressed genes (DEGs) identified by RNA-Seq, PPVI may relieve NPP by regulating the AMPK/NF-κB signaling pathway. Western blotting results showed that the AMPK signaling pathway was activated, followed by inhibition of the NF-κB signaling pathway. CONCLUSION: PPⅥ increased the MWT and TWL of SNI mice maybe by inhibiting the expression of the P2X3 receptor and the release of inflammatory mediators. The properties of the analgesia of PPⅥ may be based on the AMPK/NF-κB pathway.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology.

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN) inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.