Dual detection of human motion and glucose in sweat with polydopamine and glucose oxidase doped self-healing nanocomposite hydrogels.

The detection of human motion and sweat composition are important for human health or sports training, so it is necessary to develop flexible sensors for monitoring exercise processes and sweat detection. Mussel secretion of adhesion proteins enables self-healing of byssus and adhesion to surfaces. We prepared Au nanoparticles@polydopamine (AuNPs@PDA) nanomaterials based on mussel-inspired chemistry and compounded them with polyvinyl alcohol (PVA) hydrogels to obtain PVA/AuNPs@PDA self-healing nanocomposite hydrogels. Dopamine (DA) was coated on the surface of AuNPs to obtain AuNPs based composite (AuNPs@PDA) and the AuNPs@PDA was implanted into the PVA hydrogels to obtain nanocomposite hydrogel through facile freeze-thaw cycle. Glucose oxidase (GOD) was added to the hydrogel matrix to achieve specific detection of glucose in sweat. The obtained hydrogels exhibit high deformability (573.7 %), excellent mechanical strength (550.3 KPa) and self-healing properties (85.1 %). The PVA/AuNPs@PDA hydrogel sensors exhibit quick response time (185.0 ms), wide strain sensing range (0-500 %), superior stability and anti-fatigue properties in motion detection. The detection of glucose had wide concentration detection range (1.0 µmol/L-200.0 µmol/L), low detection limits (0.9 µmol/L) and high sensitivity (24.4 µA/mM). This work proposes a reference method in dual detection of human exercise and sweat composition analysis.

Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.

Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks.

The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge-Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around 180× ( 20× ) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than 300× ( 240× ) improvement on speed and 180× ( 250× ) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain 2× training speedup over the Euler method, which makes it suitable for online training in real-time applications.

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems.

Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs' constraints and considerations in neuromorphic systems.

Selective Toluene Detection with Mo2CTx MXene at Room Temperature.

MXenes are a promising class of two-dimensional materials with several potential applications, including energy storage, catalysis, electromagnetic interference shielding, transparent electronics, and sensors. Here, we report a novel Mo2CTx MXene sensor for the successful detection of volatile organic compounds (VOCs). The proposed sensor is a chemiresistive device fabricated on a Si/SiO2 substrate using photolithography. The impact of various MXene process conditions on the performance of the sensor is evaluated. The VOCs, such as toluene, benzene, ethanol, methanol, and acetone, are studied at room temperature with varying concentrations. Under optimized conditions, the sensor demonstrates a detection limit of 220 ppb and a sensitivity of 0.0366 Ω/ppm at a toluene concentration of 140 ppm. It exhibits an excellent selectivity toward toluene against the other VOCs. Ab initio simulations demonstrate selectivity toward toluene in line with the experimental results.

Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems.

To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve energy efficiency. The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during training. The proposed adaptation scheme allows the network to effectively identify critical weights associated with each neuron by changing the pruning threshold dynamically over time and neurons. It balances the connection strength of neurons with the previous layer with adaptive thresholds and prevents weak neurons from failure after pruning. We also evaluated improvement in the energy efficiency of SNNs with our method by computing synaptic operations (SOPs). Simulation results and detailed analyses have revealed that applying adaptation in the pruning threshold can significantly improve network performance and reduce the number of SOPs. The pruned SNN with 800 excitatory neurons can achieve a 30% reduction in SOPs during training and a 55% reduction during inference, with only 0.44% accuracy loss on MNIST dataset. Compared with a previously reported online soft pruning method, the proposed adaptive pruning method shows 3.33% higher classification accuracy and 67% more reduction in SOPs. The effectiveness of our method was confirmed on different datasets and for different network sizes. Our evaluation showed that the implementation overhead of the adaptive method regarding speed, area, and energy is negligible in the network. Therefore, this work offers a promising solution for effective network compression and building highly energy-efficient neuromorphic systems in real-time applications.

A stretchable laminated GNRs/BNNSs nanocomposite with high electrical and thermal conductivity.

The rapid development of modern electronics has accelerated the demand for stretchable components with high thermal management capability because increasing the power density and miniaturization of electronic devices generate greater heat. However, stretchable electronics with enhanced heat dissipation have been rarely reported. In this study, a stretchable laminated nanocomposite-based conductor with both robust electric conductivity and enhanced thermal management capability was fabricated. With the optimized GNRs and BNNS contents, this conductor exhibited a thermal conductivity enhancement of 266%, leading to a decrease in the working temperature from 57.4 °C to 29.2 °C. Even under 100% strain, the fluctuation of the equilibrium operational temperature was within 10%. Moreover, the conductor showed outstanding electric performance under 200% strain with an R/R0 value of 1.46. Whether stretched and tested in a Moebius-belt shape or under hard-environmental conditions such as in seawater, crude oil, and even integrated in a wireless charging circuit, the significant reliability of this conductor was recorded. Thus, our results are promising to provide a practical approach for the fabrication of stretchable electronic devices working in high temperature environments associated with extreme thermal stresses and under extreme circumstances such as sea rescue operations and marine oil pollution remediation.

An Ultra-Area-Efficient 1024-Point In-Memory FFT Processor.

Current computation architectures rely on more processor-centric design principles. On the other hand, the inevitable increase in the amount of data that applications need forces researchers to design novel processor architectures that are more data-centric. By following this principle, this study proposes an area-efficient Fast Fourier Transform (FFT) processor through in-memory computing. The proposed architecture occupies the smallest footprint of around 0.1 mm 2 inside its class together with acceptable power efficiency. According to the results, the processor exhibits the highest area efficiency ( FFT / s / area ) among the existing FFT processors in the current literature.

Wireless piezoelectric devices based on electrospun PVDF/BaTiO3 NW nanocomposite fibers for human motion monitoring.

Real-time personalized motion monitoring and analysis are important for human health. Thus, to satisfy the needs in this area and the ever-increasing demand for wearable electronics, we design and develop a wireless piezoelectric device consisting of a piezoelectric pressure sensor based on electrospun PVDF/BaTiO3 nanowire (NW) nanocomposite fibers and a wireless circuit system integrated with a data conversion control module, a signal acquisition and amplification module, and a Bluetooth module. Finally, real-time piezoelectric signals of human motion can be displayed by an App on an Android mobile phone for wireless monitoring and analysis. This wireless piezoelectric device is proven to be sensitive to human motion such as squatting up and down, walking, and running. The results indicate that our wireless piezoelectric device has potential applications in wearable medical electronics, particularly in the fields of rehabilitation and sports medicine.

Silencing LDHA inhibits proliferation, induces apoptosis and increases chemosensitivity to temozolomide in glioma cells.

Glioblastoma multiforme (GBM) is a prevalent and aggressive disease, and the development of a novel therapy to better treat advanced GBM is urgently required. Lactate dehydrogenase A (LDHA), which functions as a key enzyme in transforming pyruvate into lactate, has attracted more attention in recent years due to its critical role in various types of advanced cancer. Previous data derived from the Oncomine database have shown that the expression of LDHA is higher in GBM tissues than that in corresponding normal control tissues. However, the association of LDHA levels with glioma clinical grades and the possible mechanisms of LDHA in GBM progression have not been investigated. The present study showed that there is a significant positive correlation between LDHA expression levels and tumor clinical stages. The knockdown of LDHA inhibited cell growth by inhibiting cell cycle progression and inducing apoptosis in glioma cell lines. Upon investigating the molecular mechanism, LDHA knockdown via siRNA treatment was associated with decreased cyclin D1 expression, increased cleavage of PARP, and altered B-cell lymphoma 2 and B-cell lymphoma 2-associated protein X expression. In addition, LDHA knockdown led to the marked downregulation of matrix metalloproteinase (MMP)-2, MMP-9, VE-Cadherin and vascular endothelial growth factor expression levels. Furthermore, knock down of LDHA enhanced the chemosensitivity of glioma cells to temozolomide (TMZ), a second-generation alkylating agent with activity against recurrent high-grade glioma. These findings support LDHA as a novel target for developing effective therapeutic strategies to treat GBM.

Electrically Conductive TPU Nanofibrous Composite with High Stretchability for Flexible Strain Sensor.

Highly stretchable and electrically conductive thermoplastic polyurethane (TPU) nanofibrous composite based on electrospinning for flexible strain sensor and stretchable conductor has been fabricated via in situ polymerization of polyaniline (PANI) on TPU nanofibrous membrane. The PANI/TPU membrane-based sensor could detect a strain from 0 to 160% with fast response and excellent stability. Meanwhile, the TPU composite has good stability and durability. Besides, the composite could be adapted to various non-flat working environments and could maintain opportune conductivity at different operating temperatures. This work provides an easy operating and low-cost method to fabricate highly stretchable and electrically conductive nanofibrous membrane, which could be applied to detect quick and tiny human actions.

Comparative analysis of conventional and biological treatment in healing of bone disease.

The healing of Bone tissue consists of a complex process. Hence, we designed our study to evaluate chondrial diseases, which are as they have a very low healing capacity. Seventy two elderly osteoarthritis (OA) and 54-paediatric juvenile idiopathic arthritis (JIA) patients were included. The group was divided as 24 OA patients and 18 JIA patients in each group. Group I received Hyualuronic acid and glucocorticoides. Group II received platelet rich plasma and fibrin glue. Group III received PRP, fibrin glue, and MSC. 40 control patients received only PRP treatment. Out of 72 OA patients 35 (48.6%) male and 37 (51.4%) female with mean age of 48 ± 6.5 years. 64 (88.9%) Patients had pain and swelling. 52 (72.2%) lacked flexibility. 42 (58.3%) had hypertrophy. 28 (38.9%) had less cartilage thickness. 34 (47.2%) were in grade 3, grade 2 has 28 (38.9%) and grade 1 has 10 (13.9%) patients respectively. Among 54 JIA patients 28 (51.9%) male and 26 (48.1%) female patients with mean, age 4.6 ± 3.8 years. 39 (72.2%) had pain and swelling. 32 (59.3%) lacked flexibility. 29 (53.7%) children's had functional disability. Group I patients showed 30% improvement with no statistical significance (P < 0.21). Group II showed 45% improvement with statistical significance (P < 0.01). In Group III 80%, improvement was observed with statistical significance (P < 0.001). In 40 control patients, 60% improvement was observed. In conclusion, use of these MSC, PRP, and PPP are safe and less cost effective for treating OA and JIA.

[Experimental study of the effect of chitosan on the capsule inside the expanded flap].

OBJECTIVE: To investigate the effect of chitosan on the capsule inside the expanded flap. METHODS: The expanders were implanted in animals with the treatment of chitosan(experimental group, n = 15) or without (control group, n = 15). After taking out the expanders, the flap contraction rate was calculated. The samples were observed through HE, Masson dyeing and CD34 immunohistochemical study. The thickness of capsule inside the expanded flap was measured under microscope. The samples were also studied under electron microscope. RESULTS: The thickness of capsule was 516.000 +/- 128.491 microm in the experimental group, and 833.000 +/- 227.379 microm in the control group (P < 0.05). The number of microvessels was 8.200 +/- 2.150 per visual in experimental group, and 7.900 +/- 1.729 per visual in control group (P > 0.05). Under the electron microscope, the rough endoplasmic reticulum (RER) in the capsule in experimental group decreased and enlarged with degranulation. The mitochondria emerged or disappeared. The number of ribosome was reduced. In the control group, the RER enlarged without degranulation, the mitochondria was intact. The number of ribosome was not reduced. CONCLUSIONS: The chitosan can effectively reduce the contraction of expanded flap through collagen secretion of fibroblast, delaying the differentiation from fibroblast to fiber cell, inhibiting thansform from fibroblast to myofibroblast. It has no effect on the microvascular generation and expansion, so the flap blood supply will not be affected with thicker capsule.