Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface

Voice disorders affect millions of people worldwide. Surface electromyography-based Silent Speech Interfaces (sEMG-based SSIs) have been explored as a potential solution for decades. However, previous works were limited by small vocabularies and manually extracted features from raw data. To address these limitations, we propose a lightweight deep learning knowledge-distilled ensemble model for sEMG-based SSI (KDE-SSI). Our model can classify a 26 NATO phonetic alphabets dataset with 3900 data samples, enabling the unambiguous generation of any English word through spelling. Extensive experiments validate the effectiveness of KDE-SSI, achieving a test accuracy of 85.9\%. Our findings also shed light on an end-to-end system for portable, practical equipment.Comment: 6 pages, 5 figure

FedTP: Federated Learning by Transformer Personalization

Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner. Personalized federated learning extends this paradigm to overcome heterogeneity across clients by learning personalized models. Recently, there have been some initial attempts to apply Transformers to federated learning. However, the impacts of federated learning algorithms on self-attention have not yet been studied. This paper investigates this relationship and reveals that federated averaging algorithms actually have a negative impact on self-attention where there is data heterogeneity. These impacts limit the capabilities of the Transformer model in federated learning settings. Based on this, we propose FedTP, a novel Transformer-based federated learning framework that learns personalized self-attention for each client while aggregating the other parameters among the clients. Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP. Specifically, the learn-to-personalize is realized by learning a hypernetwork on the server that outputs the personalized projection matrices of self-attention layers to generate client-wise queries, keys and values. Furthermore, we present the generalization bound for FedTP with the learn-to-personalize mechanism. Notably, FedTP offers a convenient environment for performing a range of image and language tasks using the same federated network architecture - all of which benefit from Transformer personalization. Extensive experiments verify that FedTP with the learn-to-personalize mechanism yields state-of-the-art performance in non-IID scenarios. Our code is available online

Exosomes Derived From Bone Mesenchymal Stem Cells Ameliorate Early Inflammatory Responses Following Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of mortality and disability worldwide. Although treatment guidelines have been developed, no best treatment option or medicine for this condition exists. Recently, mesenchymal stem cells (MSCs)-derived exosomes have shown lots of promise for the treatment of brain disorders, with some results highlighting the neuroprotective effects through neurogenesis and angiogenesis after TBI. However, studies focusing on the role of exosomes in the early stages of neuroinflammation post-TBI are not sufficient. In this study, we investigated the role of bone mesenchymal stem cells (BMSCs)-exosomes in attenuating neuroinflammation at an early stage post-TBI and explored the potential regulatory neuroprotective mechanism. We administered 30 μg protein of BMSCs-exosomes or an equal volume of phosphate-buffered saline (PBS) via the retro-orbital route into C57BL/6 male mice 15 min after controlled cortical impact (CCI)-induced TBI. The results showed that the administration of BMSCs-exosomes reduced the lesion size and improved the neurobehavioral performance assessed by modified Neurological Severity Score (mNSS) and rotarod test. In addition, BMSCs-exosomes inhibited the expression of proapoptosis protein Bcl-2-associated X protein (BAX) and proinflammation cytokines, tumor necrosis factor-α (TNF-α) and interleukin (IL)-1β, while enhancing the expression of the anti-apoptosis protein B-cell lymphoma 2 (BCL-2). Furthermore, BMSCs-exosomes modulated microglia/macrophage polarization by downregulating the expression of inducible nitric oxide synthase (INOS) and upregulating the expression of clusters of differentiation 206 (CD206) and arginase-1 (Arg1). In summary, our result shows that BMSCs-exosomes serve a neuroprotective function by inhibiting early neuroinflammation in TBI mice through modulating the polarization of microglia/macrophages. Further research into this may serve as a potential therapeutic strategy for the future treatment of TBI

Prediction of musculoskeletal pain after the first intravenous zoledronic acid injection in patients with primary osteoporosis: development and evaluation of a new nomogram

Abstract Objective To construct a new prediction nomogram to predict the risk of musculoskeletal pain in patients with primary osteoporosis who receive zoledronic acid intravenously for the first time. Method Clinical data of 368 patients with primary osteoporosis who received the first intravenous injection of zoledronic acid in our hospital from December 2019 to December 2022 were studied. Patients were divided into a musculoskeletal pain group (n = 258) and a non-musculoskeletal pain group (n = 110) based on the presence or absence of musculoskeletal pain 3 days after injection. Statistically significant predictors were screened by logistic regression analysis and the minimum absolute contraction and selection operator (LASSO) to construct a nomogram. The nomogram was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, the C-index, and the decision curve analysis (DCA) and verified in a validation cohort. Results The independent predictors of the nomogram were age, serum 25-hydroxyvitamin D, NSAIDs, prior Vitamin D intake, and BMI. The area under the ROC curve (AUC) was 0.980 (95% CI, 0.915–0.987), showing excellent predictive performance. The nomogram c index was 0.980, and the nomogram c index for internal verification remained high at 0.979. Moreover, calibration curves show that the nomogram has good consistency. Finally, the DCA showed that the net benefit of the nomogram was 0.20–0.49. Conclusion Musculoskeletal pain is a common symptom of APR in OP patients treated with intravenous zoledronic acid. Risk factors for musculoskeletal pain after zoledronic acid injection in OP patients were: non-use of NSAIDs, youth (<80 years old), serum 25 (OH) D<30ng /mL, no prior intake of vitamin D, BMI<24 kg /m2. A nomogram constructed from the above predictors can be used to predict musculoskeletal pain after the first zoledronic acid injection

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM

Optical Polarization Division Multiplexing Transmission System Based on Simplified Twin-SSB Modulation

Optical twin-single sideband (Twin-SSB) modulation, due to the left sideband (LSB) and right sideband (RSB) signal carrying individual data, has become an attractive technique in fiber transmission because it satisfies the demand of the explosive increase in data traffic. This paper focuses on reducing the complexity of Twin-SSB system and further enhancing the spectral efficiency by proposing a polarization division multiplexing (PDM) Twin-SSB modulation scheme. LSB and RSB signals are extracted using de-mapping algorithm instead of optical bandpass filters (OBPFs) to reduce system complexity. To further improve spectral efficiency, PDM is employed to meet the polarization multiplexing transmission and achieve a higher transmission capacity. Based on the PDM Twin-SSB system, the LSB is 3-arr phase-shift-keying (3PSK) modulated, while RSB is quadrature phase-shift keying (QPSK) modulated. We simulated that the bit error ratio (BER) performance of LSB and RSB of X-polarization (X-Pol) and Y-polarization (Y-Pol) at 8-Gbaud, 10-Gbaud, 12-Gbaud, 14-Gbaud, and 16-Gbaud in the case of back-to-back (BTB) and 2 km standard single-mode fiber (SSMF) transmission. The simulation results verify the effectiveness and practical feasibility of the proposed PDM Twin-SSB scheme for future short-distance transmission owing to low cost, simplified structure, low algorithm complexity, and high data transmission capacity

Membrane Crystallization of Sodium Carbonate for Carbon Dioxide Recovery: Effect of Impurities on the Crystal Morphology

Membrane contactors have been proposed as an advanced tool for CO2 capture from flue gases by absorption in alkaline solutions. However, regeneration of the alkaline reagent and further CO2 sequestration are pending issues. In this paper, membrane-assisted crystallization is proposed for crystallizing Na2CO3, which allows its reuse, after CO2 absorption from flue gases. Due to the presence of compounds other than CO2 in flue gases (i.e., SO2, NOx), other compounds (Na2SO4 and NaNO3) may interfere with Na2CO3 crystallization. This was evaluated by measuring the flux through the membrane and the morphology, crystallography, and purity of the crystals. Furthermore, the presence of NaCl possibly transferred from the osmotic solution to the feed solution was evaluated. The experimental results indicate that the presence of impurities decreases the flux through the membrane due to the decrease of water activity, although there is no influence on the overall mass transfer coefficient. The presence of Na2SO4 affected the morphology of the Na2CO3 crystals while NaNO3 and NaCl had no apparent effect on the crystalline products. It was confirmed that Na2CO3•10H2O was formed during the crystallization. Moreover, the purity of Na2CO3 crystals reaches up to ca. 99.5%. Membrane-assisted crystallization was concluded to be feasible in recovering CO2 as a carbonate salt, which can possibly be reused in the industry

Review on the Millimeter-Wave Generation Techniques Based on Photon Assisted for the RoF Network System

With the development trend of wireless and broadband in the communication link and even the whole information industry, the demand of high-frequency microwave bandwidth has been increasing. The RoF network system solves the problem of spectrum congestion in low-frequency band by providing an effective technology for the distribution of high-frequency microwave signals over optical fiber links. However, the traditional mm-wave generation technique is limited by the bandwidth of electronic devices. It is difficult to generate high-frequency and low-phase noise mm-wave signals with pure electrical components. The mm-wave communication technology based on photon assisted can overcome the bandwidth bottleneck of electronic devices and provide the potential for developing the low-cost infrastructure demand of broadband mobile services. This paper will briefly explain the characteristics of the RoF network system and the advantages of high-frequency mm-wave. Then we, respectively, introduce the modulation schemes of RoF mm-wave generation based on photon assisted including directly modulated laser (DML), external modulation, and optical heterodyne. The review mainly focuses on a variety of different mm-wave generation technologies including multifrequency vector mm-wave. Furthermore, we list several approaches to realize the large capacity data transmission techniques and describe the digital signal processing (DSP) algorithm flow in the receiver. In the end, we summarize the RoF network system and look forward to the future

Strain modulated nanostructure patterned AlGaN-based deep ultraviolet multiple-quantum-wells for polarization control and light extraction efficiency enhancement

Strain modulated nanostructure patterned AlGaN-based deep ultraviolet multiple-quantum-wells for polarization control and light extraction efficiency enhancemen