Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.

BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. METHODS: A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. RESULTS: Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. CONCLUSION: This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Specific capture and determination of glycoprotein using a hybrid epitopes and monomers-mediated molecular-imprinted polymer enzyme-free electrochemical biosensor.

A novel molecular-imprinted polymer (MIP)-based enzyme-free biosensor was created for the selective detection of glycoprotein transferrin (Trf). For this purpose, MIP-based biosensor for Trf was prepared by electrochemical co-polymerization of novel hybrid monomers 3-aminophenylboronic acid (M-APBA) and pyrrole on a glassy carbon electrode (GCE) modified with carboxylated multi-walled carbon nanotubes (cMWCNTs). Hybrid epitopes of Trf (C-terminal fragment and glycan) have been selected as templates. The produced sensor exhibited great selective recognition ability toward Trf under optimal preparation conditions, offering good analytical range (0.125-1.25 µM) with a detection limit of 0.024 µM. The proposed hybrid epitope in combination with hybrid monomer-mediated imprinting strategy was successfully applied to detect Trf in spiked human serum samples, with recoveries and relative standard deviations ranging from 94.7 to 106.0% and 2.64 to 5.32%, respectively. This study provided a reliable protocol for preparing hybrid epitopes and monomers-mediated MIP for the synergistic and effective determination of glycoprotein in complicated biological samples.

Berberine-Based Carbon Quantum Dots Improve Intestinal Barrier Injury and Alleviate Oxidative Stress in C57BL/6 Mice with 5-Fluorouracil-Induced Intestinal Mucositis by Enhancing Gut-Derived Short-Chain Fatty Acids Contents.

This study aims to evaluate the effect of berberine-based carbon quantum dots (Ber-CDs) on improving 5-fluorouracil (5-FU)-induced intestinal mucositis in C57BL/6 mice, and explored the mechanisms behind this effect. Thirty-two C57BL/6 mice were divided into four groups: normal control (NC), 5-FU-induced intestinal mucositis model (5-FU), 5-FU + Ber-CDs intervention (Ber-CDs), and 5-FU + native berberine intervention (Con-CDs). The Ber-CDs improved body weight loss in 5-FU-induced mice with intestinal mucositis compared to the 5-FU group. The expressions of IL-1ß and NLRP3 in spleen and serum in Ber-CDs and Con-Ber groups were significantly lower than those in the 5-FU group, and the decrease was more significant in the Ber-CDs group. The expressions of IgA and IL-10 in the Ber-CDs and Con-Ber groups were higher than those in the 5-FU group, but the up-regulation was more significant in the Ber-CDs group. Compared with the 5-FU group, the relative contents of Bifidobacterium, Lactobacillus and the three main SCFAs in the colon contents were significantly increased the Ber-CDs and Con-Ber groups. Compared with the Con-Ber group, the concentrations of the three main short-chain fatty acids in the Ber-CDs group were significantly increased. The expressions of Occludin and ZO-1 in intestinal mucosa in the Ber-CDs and Con-Ber groups were higher than those in the 5-FU group, and the expressions of Occludin and ZO-1 in the Ber-CDs group were more higher than that in the Con-Ber group. In addition, compared with the 5-FU group, the damage of intestinal mucosa tissue in the Ber-CDs and Con-Ber groups were recovered. In conclusion, berberine can attenuate intestinal barrier injury and oxidative stress in mice to mitigate 5-fluorouracil-induced intestinal mucositis, moreover, the above effects of Ber-CDs were more significant than those of native berberine. These results suggest that Ber-CDs may be a highly effective substitute for natural berberine.

Lactiplantibacillus plantarum NKK20 Alleviates High-Fat-Diet-Induced Nonalcoholic Fatty Liver Disease in Mice through Regulating Bile Acid Anabolism.

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic disease in modern society. It is characterized by an accumulation of lipids in the liver and an excessive inflammatory response. Clinical trials have provided evidence that probiotics may prevent the onset and relapse of NAFLD. The aim of this study was to explore the effect of Lactiplantibacillus plantarum NKK20 strain (NKK20) on high-fat-diet-induced NAFLD in an ICR murine model and propose the underlying mechanism whereby NKK20 protects against NAFLD. The results showed that the administration of NKK20 ameliorated hepatocyte fatty degeneration, reduced total cholesterol and triglyceride concentrations, and alleviated inflammatory reactions in NAFLD mice. In addition, the 16S rRNA sequencing results indicated that NKK20 could decrease the abundance of Pseudomonas and Turicibacter and increase the abundance of Akkermansia in NAFLD mice. LC-MS/MS analysis showed that NKK20 could significantly increase the concentration of short-chain fatty acids (SCFAs) in the colon contents of mice. The obtained non-targeted metabolomics results revealed a significant difference between the metabolites in the colon contents of the NKK20 administration group and those in the high-fat diet group, in which a total of 11 different metabolites that were significantly affected by NKK20 were observed, and these metabolites were mainly involved in bile acid anabolism. UPLC-MS technical analysis revealed that NKK20 could change the concentrations of six conjugated and free bile acids in mouse liver. After being treated with NKK20, the concentrations of cholic acid, glycinocholic acid, and glycinodeoxycholic acid in livers of the NAFLD mice were significantly decreased, while the concentration of aminodeoxycholic acid was significantly increased. Thus, our findings indicate that NKK20 can regulate bile acid anabolism and promote the production of SCFA, which can inhibit inflammation and liver damage and thus prevent the development of NAFLD.

An edible molecularly imprinted material prepared by a new environmentally friendly deep eutectic solvent for removing oxalic acid from vegetables and human blood.

A novel deep eutectic solvent-magnetic molecularly imprinted polymer (DES-MMIP) for the specific removal of oxalic acid (OA) was prepared by an environmentally friendly deep eutectic solvent, consisting of betaine, citric acid, and glycerol, which acted as the functional monomer for polymerization. The structure and morphology of DES-MMIPs were studied by X-ray diffraction, scanning and transmission electron microscopy, thermal gravimetric analysis, Fourier transform infrared spectroscopy, and vibrating sample magnetometer. DES-MMIPs had a core-shell structure, with magnetic iron oxide as the core, and showed good thermal stability and high adsorption capacity (18.73 mg/g) for OA. The adsorption process of OA by DES-MMIPs followed the pseudo-second-order kinetic model and Langmuir isotherm model. DES-MMIPs had significant selectivity for OA and their imprinting factor was 3.26. When applied to real samples, high performance liquid chromatography analysis showed that DES-MMIPs could remove OA from both spinach and blood serum. These findings provide potential methods for removal of OA from vegetables and for specific removal of OA in renal dialysis.

Molecular Insights into the Effect of Nitrogen Bubbles on the Formation of Tetrahydrofuran Hydrates.

In this work, a molecular dynamics simulation was conducted to study the microscopic mechanism of how nitrogen bubbles affect the formation of THF hydrates at the molecular level. The results obtained reveal that the nitrogen bubble can promote the formation of THF hydrates. In the system with a nitrogen bubble, more THF-filled cages were generated, and the crystal structure was more orderly. The promotion of nitrogen bubbles on hydrate crystallization comes from the dissolution of nitrogen molecules. Some of dissolved nitrogen molecules can be enclosed in small hydrate cages near the nitrogen bubble, which can serve as stable sites for hydrate crystal growth, resulting in the fact that THF-filled cages connected with N2-filled cages are much more stable and have a long lifetime. The results in this work can help to understand the promotion effect of micro- and nano-air bubbles on the crystallization of THF hydrates.

Sodium Butyrate Attenuates AGEs-Induced Oxidative Stress and Inflammation by Inhibiting Autophagy and Affecting Cellular Metabolism in THP-1 Cells.

In recent years, sodium butyrate has gained increased attention for its numerous beneficial properties. However, whether sodium butyrate could alleviate inflammatory damage by macrophage activation and its underlying mechanism remains unclear. The present study used an advanced glycosylation products- (AGEs-) induced inflammatory damage model to study whether sodium butyrate could alleviate oxidative stress, inflammation, and metabolic dysfunction of human monocyte-macrophage originated THP-1 cells in a PI3K-dependent autophagy pathway. The results indicated that sodium butyrate alleviated the AGEs-induced oxidative stress, decreased the level of reactive oxygen species (ROS), increased malondialdehyde (MDA) and mRNA expression of pro-inflammatory cytokines of interleukin (IL)-1ß and tumor necrosis factor (TNF)-α, and increased the content of superoxide dismutase (SOD). Sodium butyrate reduced the protein expression of the NLR family, pyrin domain-containing protein 3 (NLRP3) and Caspase-1, and decreased the nucleus expression of nuclear factor-kappaB (NF-κB). Sodium butyrate decreased the expression of light-chain-associated protein B (LC3B) and Beclin-1, and inhibited autophagy. Moreover, sodium butyrate inhibited the activation of the PI3K/Akt pathway in AGEs-induced THP-1 cells. In addition, the metabolomics analysis showed that sodium butyrate could affect the production of phosphatidylcholine, L-glutamic acid, UDP-N-acetylmuraminate, biotinyl-5'-AMP, and other metabolites. In summary, these results revealed that sodium butyrate inhibited autophagy and NLRP3 inflammasome activation by blocking the PI3K/Akt/NF-κB pathway, thereby alleviating oxidative stress, inflammation, and metabolic disorder induced by AGEs.

Engineering Peptide-Functionalized Biomimetic Nanointerfaces for Synergetic Capture of Circulating Tumor Cells in an EpCAM-Independent Manner.

Broad-spectrum detection and long-term monitoring of circulating tumor cells (CTCs) remain challenging due to the extreme rarity, heterogeneity, and dynamic nature of CTCs. Herein, a dual-affinity nanostructured platform was developed for capturing different subpopulations of CTCs and monitoring CTCs during treatment. Stepwise assembly of fibrous scaffolds, a ligand-exchangeable spacer, and a lysosomal protein transmembrane 4 ß (LAPTM4B)-targeting peptide creates biomimetic, stimuli-responsive, and multivalent-binding nanointerfaces, which enable harvest of CTCs directly from whole blood with high yield, purity, and viability. The stable overexpression of the target LAPTM4B protein in CTCs and the enhanced peptide-protein binding facilitate the capture of rare CTCs in patients at an early stage, detection of both epithelial-positive and nonepithelial CTCs, and tracking of therapeutic responses. The reversible release of CTCs allows downstream molecular analysis and identification of specific liver cancer genes. The consistency of the information with clinical diagnosis presents the prospect of this platform for early diagnosis, metastasis prediction, and prognosis assessment.

Recurrent neural network FPGA hardware accelerator for delay-tolerant indoor optical wireless communications.

The optical wireless communication (OWC) system has been widely studied as a promising solution for high-speed indoor applications. The transmitter diversity scheme has been proposed to improve the performance of high-speed OWC systems. However, the transmitter diversity is vulnerable to the delay of multiple channels. Recently neural networks have been studied to realize delay-tolerant indoor OWC systems, where long-short term memory (LSTM) and attention-augmented LSTM (ALSTM) recurrent neural networks (RNNs) have shown their capabilities. However, they have high computation complexity and long computation latency. In this paper, we propose a low complexity delay-tolerant RNN scheme for indoor OWC systems. In particular, an RNN with parallelized structure is proposed to reduce the computation cost. The proposed RNN schemes show comparable capability to the more complicated ALSTM, where a bit-error-rate (BER) performance within the forward-error-correction (FEC) limit is achieved for up to 5.5 symbol periods delays. In addition, previously studied LSTM/ALSTM schemes are implemented using high-end GPUs, which have high cost, high power consumption, and long processing latency. To solve these practical limitations, in this paper we further propose and demonstrate the FPGA-based RNN hardware accelerator for delay-tolerant indoor OWC systems. To optimize the processing latency and power consumption, we also propose two optimization methods: the parallel implementation with triple-phase clocking and the stream-in based computation with additive input data insertion. Results show that the FPGA-based RNN hardware accelerator with the proposed optimization methods achieves 96.75% effective latency reduction and 90.7% lower energy consumption per symbol compared with the FPGA-based RNN hardware accelerator without optimization. Compared to the GPU implementation, the latency is reduced by about 61% and the power consumption is reduced by about 58.1%.

Calcium, conformational selection, and redox-active tyrosine YZ in the photosynthetic oxygen-evolving cluster.

In Photosystem II (PSII), YZ (Tyr161D1) participates in radical transfer between the chlorophyll donor and the Mn4CaO5 cluster. Under flashing illumination, the metal cluster cycles among five Sn states, and oxygen is evolved from water. The essential YZ is transiently oxidized and reduced on each flash in a proton-coupled electron transfer (PCET) reaction. Calcium is required for function. Of reconstituted divalent ions, only strontium restores oxygen evolution. YZ is predicted to hydrogen bond to calcium-bound water and to His190D1 in PSII structures. Here, we report a vibrational spectroscopic study of YZ radical and singlet in the presence of the metal cluster. The S2 state is trapped by illumination at 190 K; flash illumination then generates the S2YZ radical. Using reaction-induced FTIR spectroscopy and divalent ion depletion/substitution, we identify calcium-sensitive tyrosyl radical and tyrosine singlet bands in the S2 state. In calcium-containing PSII, two CO stretching bands are detected at 1,503 and 1,478 cm-1 These bands are assigned to two different radical conformers in calcium-containing PSII. At pH 6.0, the 1,503-cm-1 band shifts to 1,507 cm-1 in strontium-containing PSII, and the band is reduced in intensity in calcium-depleted PSII. These effects are consistent with a hydrogen-bonding interaction between the calcium site and one conformer of radical YZ. Analysis of the amide I region indicates that calcium selects for a PCET reaction in a subset of the YZ conformers, which are trapped in the S2 state. These results support the interpretation that YZ undergoes a redox-coupled conformational change, which is calcium dependent.

FPGA-based neural network accelerators for millimeter-wave radio-over-fiber systems.

With rapidly developing high-speed wireless communications, the 60 GHz millimeter-wave (mm-wave) frequency range has attracted extensive interests, and radio-over-fiber (RoF) systems have been widely investigated as a promising solution to deliver mm-wave signals. Neural networks have been proposed and studied to improve the mm-wave RoF system performances at the receiver side by suppressing both linear and nonlinear impairments. However, previous studies of neural networks in mm-wave RoF systems all focus on the use of off-line processing with high-end GPUs or CPUs, which are not practical for low power-consumption, low-cost and limited computation platform applications. To solve this issue, in this paper we investigate neural network hardware accelerator implementations for mm-wave RoF systems for the first time using the field programmable gate array (FPGA), taking advantage of the low power consumption, parallel computation, and reconfigurablity features of FPGA. Both the convolutional neural network (CNN) and binary convolutional neural network (BCNN) hardware accelerators are demonstrated. In addition, to satisfy the low-latency requirement in mm-wave RoF systems and to enable the use of low-cost compact FPGA devices, a novel inner parallel computation optimization method for implementing CNN and BCNN on FPGA is proposed. It is shown that compared with the popular embedded processor (ARM Cortex A9) execution latency, the proposed FPGA-based hardware accelerator reduces the processing delay in mm-wave RoF systems by about 99.45% and 92.79% for CNN and BCNN, respectively. Compared with non-optimized FPGA implementations, results show that the proposed inner parallel computation method reduces the processing latency by about 44.93% and 45.85% for CNN and BCNN, respectively. In addition, compared with the GPU implementation, the latency of CNN implementation with the proposed optimization method is reduced by 85.49%, while the power consumption is reduced by 86.91%. Although the latency of BCNN implementation with the proposed optimization method is larger compared with the GPU implementation, the power consumption is reduced by 86.14%. The demonstrated FPGA-based neural network hardware accelerators provide a promising solution for mm-wave RoF systems.

Recurrent neural network (RNN) for delay-tolerant repetition-coded (RC) indoor optical wireless communication systems.

Indoor optical wireless communications have been widely studied to provide high-speed connections to users, where the use of repetition-coded (RC) multiple transmitters has been proposed to improve both the system robustness and capacity. To exploit the benefits of the RC system, the multiple signals received after transmission need to be precisely synchronized, which is challenging in high-speed wireless communications. To overcome this limit, we propose and demonstrate a recurrent neural network (RNN)-based symbol decision scheme to enable a delay-tolerant RC indoor optical wireless communication system. The experiments show that the proposed RNN can improve the bit-error-rate by about one order of magnitude, and the improvement is larger for longer delays. The results also show that the RNN outperforms previously studied fully connected neural network schemes.

From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition.

Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on the wrist is more comfortable for general consumers because of its unobtrusiveness and incorporation with the existing wrist-based wearables. Recently, deep learning methods have gained attention for myoelectric control but their performance is unclear on wrist myoelectric signals. This study compared the gesture recognition performance of myoelectric signals from the wrist and forearm between a state-of-the-art method, TDLDA, and four deep learning models, including convolutional neural network (CNN), temporal convolutional network (TCN), gate recurrent unit (GRU) and Transformer. It was shown that with forearm myoelectric signals, the performance between deep learning models and TDLDA was comparable, but with wrist myoelectric signals, the deep learning models outperformed TDLDA significantly with a difference of at least 9%, while the performance of TDLDA was close between the two signal modalities. This work demonstrated the potential of deep learning for wrist-based myoelectric control and would facilitate its application into more sections.

Variation Minimization Based Electrocardiogram Artifacts Removal for Local Field Potentials From Neurostimulator.

Local field potential (LFP) recorded by sensing-enabled neurostimulators provided chronic observation of deep brain activities for the research of brain disorders. However, the contamination from the electrocardiogram (ECG) deteriorated the extraction of effective information from LFP. This study proposed a novel algorithm based on minimizing the variance combining template subtraction to improve the performance of ECG artifact removal for LFP. Four patients with implanted electrodes were recruited, and eight real LFP records were collected from their left and right hemispheres, respectively. The results showed that the proposed method improved the accuracy of artifact peak detection in LFP, and the subsequent signal quality after template subtraction compared to the traditional Pan-Tompkins (PT) method. The outcome of this study benefited the LFP-based brain research, promoting the application of sensing-enabled neurostimulators in more areas.

Using Quercetin to Construct Molecularly Imprinting Polymer in the Preparation and Enrichment of Flavonol and Flavonoid Compounds.

Flavonol and flavonoid compounds are important natural compounds with various biomedical activities. Therefore, it is of great significance to develop a strategy for the specific extraction of flavonol and flavonoid compounds. Quercetin is a well-studied flavonoid possessing many health benefits. This compound is a versatile antioxidant known to possess protective abilities against body tissue injury induced by pathological situations and various drug toxicities. Although quercetin is widely distributed in many plants, its content generally is not very high. Therefore, the specific extraction of quercetin as well as other flavonol and flavonoid compounds has profound significance. In this work, the quercetin molecularly imprinting polymer (QMIP) was successfully prepared, in which a typical flavonol quercetin was selected as the template molecule. QMIP was synthesized by performing the surface molecular imprinting technology on the surface of NH2-MIL-101(Fe). Our study results showed that QMIP exhibited quick binding kinetic behavior, a high adsorption capacity (57.04[Formula: see text]mg/g), and the specific recognition ability toward quercetin compared with structurally distinct compounds (selective [Formula: see text]). The specific adsorption ability of quercetin by QMIP was further explained using computation simulation that molecules with non-planar 3D conformations hardly entered the molecularly imprinted cavities on QMIP. Finally, QMIP was successfully used for the specific extraction of quercetin and five other flavonol and flavonoid compounds in the crude extracts from Sapium sebiferum. This study proposes a new strategy to synthesize the molecularly imprinted polymer based on a single template for enriching and loading a certain class of active ingredients with similar core structures from variable botanicals.

Inulin Reduces Kidney Damage in Type 2 Diabetic Mice by Decreasing Inflammation and Serum Metabolomics.

This study is aimed at assessing the impact of soluble dietary fiber inulin on the treatment of diabetes-related chronic inflammation and kidney injury in mice with type 2 diabetes (T2DM). The T2DM model was created by feeding the Institute of Cancer Research (ICR) mice a high-fat diet and intraperitoneally injecting them with streptozotocin (50 mg/kg for 5 consecutive days). The thirty-six ICR mice were divided into three dietary groups: the normal control (NC) group, the T2DM (DM) group, and the DM + inulin diet (INU) group. The INU group mice were given inulin at the dose of 500 mg/kg gavage daily until the end of the 12th week. After 12 weeks, the administration of inulin resulted in decreased serum levels of fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (CRE). The administration of inulin not only ameliorated renal injury but also resulted in a reduction in the mRNA expressions of inflammatory factors in the spleen and serum oxidative stress levels, when compared to the DM group. Additionally, inulin treatment in mice with a T2DM model led to a significant increase in the concentrations of three primary short-chain fatty acids (SCFAs) (acetic acid, propionic acid, and butyric acid), while the concentration of advanced glycation end products (AGEs), a prominent inflammatory factor in diabetes, exhibited a significant decrease. The results of untargeted metabolomics indicate that inulin has the potential to alleviate inflammatory response and kidney damage in diabetic mice. This beneficial effect is attributed to its impact on various metabolic pathways, including glycerophospholipid metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, and tryptophan metabolism. Consequently, oral inulin emerges as a promising treatment option for diabetes and kidney injury.

Dose-effect of exercise intervention on heart rate variability of acclimatized young male lowlanders at 3,680 m.

This study investigated whether exercise could improve the reduced HRV in an environment of high altitude. A total of 97 young, healthy male lowlanders living at 3,680 m for >1 year were recruited. They were randomized into four groups, of which three performed-low-, moderate-, and high-intensity (LI, MI, HI) aerobic exercise for 4 weeks, respectively. The remaining was the control group (CG) receiving no intervention. For HI, compared to other groups, heart rate (p = 0.002) was significantly decreased, while standard deviation of RR intervals (p < 0.001), SD2 of Poincaré plot (p = 0.046) and the number of successive RR interval pairs that differ by > 50 ms divided by total number of RR (p = 0.032), were significantly increased after intervention. For MI, significantly increase of trigonometric interpolation in NN interval (p = 0.016) was observed after exercise. Further, a decrease in systolic blood pressure (SBP) after high-intensity exercise was found significantly associated with an increase in SD2 (r = - 0.428, p = 0.042). These results indicated that there was a dose effect of different intensities of aerobic exercise on the HRV of acclimatized lowlanders. Moderate and high-intensity aerobic exercise would change the status of the autonomic nervous system (ANS) and decrease the blood pressure of acclimatized lowlanders exposed to high altitude.

Signal-carrier interleaved optical OFDM for direct detection optical communication.

We propose signal-carrier interleaved (SCI) optical OFDM for direct detected transmission systems. Such a scheme can be considered as a variation of self-coherent detection where the carrier and signal are supplied at the transmitter and extracted at the receiver for coherent-like detection. This provides high OSNR sensitivity while maintaining very low carrier-to-signal power ratio (CSR). Our experiment results show that with 0 dB CSR, 43.2 Gb/s 16 QAM OFDM signal can be successfully delivered over 80 km standard single mode fiber (SSMF) with 24 dB OSNR requirements at 7% FEC limit.

Block-wise phase switching for double-sideband direct detected optical OFDM signals.

We propose phase switching for either main-carrier or subcarriers of two consecutive signal blocks to achieve fading-free double-sideband direct-detection (DD). The proposed approach has twice of the electrical spectral efficiency (SE) of offset OFDM, and the same electrical SE as single-side band (SSB) OFDM. With this scheme, 40-Gb/s DD-OOFDM is successfully received over 80-km SSMF with single polarization and single detector.

Cross-day analysis of Multicode Surface Electromyography based Biometrics for Personal Identification.

Recently, surface electromyography (sEMG) has emerged as a novel biometric trait for personal identification, potentially providing a superior spoof-resistant solution over existing traits. The sEMG possesses a unique dual-mode security: they differ between individuals (biometric-mode), and different gestures have different sEMG characteristics (knowledge-mode). To leverage the knowledge-mode facet of the dual-mode security, the previous studies have utilized a multicode framework involving the fusion of codes (gestures), however, the analysis involved data recorded on a single day and from a small subject-pool. In this study, wrist EMG data collected from 43 participants over three different days while performing static hand/wrist gestures was utilized in two cross-day analyses, where the training and testing data were from different days. Three levels of fusion, score, rank, and decision were investigated to determine the optimal fusion scheme. The results showed that the score-level fusion scheme resulted in a median rank-1 accuracy of 77.9% and rank-5 accuracy of 99.6%, all significantly higher (p<0.001) than the respective single-code gesture. Our results showed that the multicode sEMG biometric framework provides superior identification performance in a more realistic cross-day scenario.