Macrophage IRX3 promotes diet-induced obesity and metabolic inflammation.

Metabolic inflammation is closely linked to obesity, and is implicated in the pathogenesis of metabolic diseases. FTO harbors the strongest genetic association with polygenic obesity, and IRX3 mediates the effects of FTO on body weight. However, in what cells and how IRX3 carries out this control are poorly understood. Here we report that macrophage IRX3 promotes metabolic inflammation to accelerate the development of obesity and type 2 diabetes. Mice with myeloid-specific deletion of Irx3 were protected against diet-induced obesity and metabolic diseases via increasing adaptive thermogenesis. Mechanistically, macrophage IRX3 promoted proinflammatory cytokine transcription and thus repressed adipocyte adrenergic signaling, thereby inhibiting lipolysis and thermogenesis. JNK1/2 phosphorylated IRX3, leading to its dimerization and nuclear translocation for transcription. Further, lipopolysaccharide stimulation stabilized IRX3 by inhibiting its ubiquitination, which amplified the transcriptional capacity of IRX3. Together, our findings identify a new player, macrophage IRX3, in the control of body weight and metabolic inflammation, implicating IRX3 as a therapeutic target.

Is coffee, tea, and red wine consumption beneficial for individuals with hypertension?

OBJECTIVE: To investigate the associations of tea, coffee, and red wine intakes with health risks among individuals with hypertension. METHODS: This prospective cohort study included participants with hypertension from the UK Biobank cohort. Study exposures included self-reported intakes of coffee, tea, and red wine. The primary outcome was all-cause mortality, and the secondary outcomes were cardiovascular mortality and cardiovascular disease. The associations of beverage intake with outcomes were analyzed using Cox regression models. The hazard ratios and 95% confidence intervals were estimated. RESULTS: A total of 187 708 participants with hypertension were included. The median follow-up period was 13.8 years. In individuals with hypertension, drinking one to two cups/day of coffee or three to four cups/day of tea was significantly associated with the lowest risk of all-cause mortality compared with less than one cup/day [hazard ratio for coffee, 0.943 (95% confidence interval, 0.908-0.979); hazard ratio for tea, 0.882 (95% confidence interval, 0.841-0.924)]. Red wine intake was inversely associated with all-cause mortality risk. Dose-response analysis revealed that high coffee intake (approximately greater than or equal to six cups/day) was significantly associated with increased risks of cardiovascular mortality and cardiovascular disease, but high tea and red wine intakes were not. Furthermore, replacing plain water with tea, but not coffee, significantly reduced the risks of all-cause mortality and cardiovascular disease. Replacing other alcoholic beverages with red wine also significantly reduced the risks of all three outcomes. CONCLUSIONS: These findings suggest that tea and red wine, but not coffee, can be part of a healthy diet for the hypertensive population.

High-precision detection method for an object edge based on a position-sensitive detector.

The paper proposed and verified a method of object edge detection based on the innovative defect spot working mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, edge-detection sensitivity could be improved. Calibration experiments with the piezoelectric transducer (PZT) and object edge-detection experiments indicate that the object edge-detection sensitivity and accuracy of our method could reach 1 and 20 nm, respectively. Therefore, this method can be widely used in high-precision alignment, geometric parameters measurement, and other fields.

An Improved Sensorless Hybrid Control Method of Permanent Magnet Synchronous Motor Based on I/F Startup.

To realize permanent magnet synchronous motor (PMSM) in the full speed domain without speed sensor operation, a hybrid control method combining I/F startup and extended Kalman filter (EKF) is proposed in this paper. This method employs I/F startup to transition at low speed, effectively resolving the issue that the position estimation method based on the back electromotive force (EMF) model fails at zero speed and low speed, and converts to EKF for speed closed-loop vector control at medium and high speed. Moreover, a new feedback regulation mechanism as a solution to the problem of smooth switching between the two methods is proposed. First, the power angle is determined based on the relationship between the given I/F frequency and the estimated EKF position angle. Using the information of power angle, the damping torque of the system is increased to reduce velocity fluctuations during I/F startup. In addition, the balance point of current and position error angle is adjusted using the closed-loop information of position error angle to reduce the torque abrupt change before and after switching, thereby making the motor switching process to EKF speed closed-loop control more stable. Finally, simulation results are used to verify the effectiveness of the proposed scheme.

A Compact RF Energy Harvesting Wireless Sensor Node with an Energy Intensity Adaptive Management Algorithm.

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of -24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

Bifunctional MoS2/Al2O3-Zeolite Catalysts in the Hydroprocessing of Methyl Palmitate.

A series of bifunctional catalysts, MoS2/Al2O3 (70 wt.%), zeolite (30 wt.%) (zeolite-ZSM-5, ZSM-12, and ZSM-22), and silica aluminophosphate SAPO-11, were synthesized for hydroconversion of methyl palmitate (10 wt.% in dodecane) in a trickle-bed reactor. Mo loading was about 7 wt.%. Catalysts and supports were characterized by different physical-chemical methods (HRTEM-EDX, SEM-EDX, XRD, N2 physisorption, and FTIR spectroscopy). Hydroprocessing was performed at a temperature of 250-350 °C, hydrogen pressure of 3.0-5.0 MPa, liquid hourly space velocity (LHSV) of 36 h-1, and an H2/feed ratio of 600 Nm3/m3. Complete conversion of oxygen-containing compounds was achieved at 310 °C in the presence of MoS2/Al2O3-zeolite catalysts; the selectivity for the conversion of methyl palmitate via the 'direct' hydrodeoxygenation (HDO) route was over 85%. The yield of iso-alkanes gradually increases in order: MoS2/Al2O3 < MoS2/Al2O3-ZSM-12 < MoS2/Al2O3-ZSM-5 < MoS2/Al2O3-SAPO-11 < MoS2/Al2O3-ZSM-22. The sample MoS2/Al2O3-ZSM-22 demonstrated the highest yield of iso-alkanes (40%). The hydroisomerization activity of the catalysts was in good correlation with the concentration of Brønsted acid sites in the synthesized supports.

Flipped superposed constellation design for MIMO visible-light communication systems.

Multiple-input multiple-output (MIMO) visible-light communication (VLC) systems based on the concept of superposed constellation have attracted increasing attention because multiplexing gains can be achieved even in a highly correlated MIMO channel. In this paper, a novel superposed constellation scheme is proposed for 2×2 MIMO VLC systems, where a 2n-order quadrature amplitude modulation (QAM) signal is obtained by superposing 4QAM and processed 2n-2-order QAM signals. Based on the original 2n-2-order QAM signal, the processed signal is generated by adding an optimal offset, power normalization, and flipping according to the value of the 4QAM signal. Thus, the required power ratio between the two superposed signals is equal to 1, which not only avoids the power competition in the receiver, but also reduces the risk of the nonlinear distortion of the light-emitting diode at the transmitter. Moreover, benefitting from the flipped superposition method, two additional performance gains are provided. First, thorough Gray coding can be achieved. Second, the received power can be improved because the two transmitted signals are correlated. An algorithm for solving the optimal offset is proposed based on the target of the equal power ratio, and the expression of the received power is derived as well. Then, the performance of the proposed scheme is investigated using detailed simulations in an additive white Gaussian noise channel, where the superposed 64QAM constellation is assumed as an example. Further, we perform an experimental demonstration and examine the performance of the proposed system when a practical optical channel is considered, where nonideal impacts, such as nonlinearity and power competition, may occur. The experimental results confirm that the proposed scheme achieves a lower bit error rate (BER) and a larger dynamic range of driving peak-to-peak voltage compared with the existing superposed constellation schemes. Considering the 7% pre-forward error correction BER threshold of 3.8 × 10-3, the proposed superposed 64QAM constellation system can achieve a maximum transmission rate of 3 Gb/s.

Deep learning based end-to-end visible light communication with an in-band channel modeling strategy.

Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED-VLC systems. We present a deep learning-based autoencoder to deal with this challenge. A novel modeling strategy is proposed to bypass the influence of the LFN and other low signal-to-noise ratio data when training the channel model of our E2E framework. The deep learning-based autoencoder then embeds the differentiable channel model and learns to combat the majority of channel impairments. In the E2E LED-VLC experiment, 1.875 Gbps transmission is achieved under the 7% HD-FEC threshold, 0.325 Gbps faster than the baseline. The E2E framework is robust to signal bias and amplitude variations, implying dimming support in the indoor environment.

Low spatial complexity adaptive artificial neural network post-equalization algorithms in MIMO visible light communication systems.

In this paper, we experimentally propose a feasible and low spatial complexity adaptive artificial neural network (AANN) post-equalization algorithm in MIMO visible light communication (VLC) systems. By introducing the power ratio and the MIMO least mean square (MIMO-LMS) post-equalization algorithm into the structure design process of the artificial neural network (ANN) post-equalization algorithm, we reduced the spatial complexity of the post-ANN equalization algorithm to less than 10%. At the same time, the bit error rate (BER) performance of AANNs did not decrease. Finally, we achieved a data rate of 2.1Gbps in the AANN equalized 16QAM superposition coding modulation (SCM) and carrier-less amplitude-phase (CAP) single-receiver MIMO (SR-MIMO) VLC system.

Hybrid frequency domain aided temporal convolutional neural network with low network complexity utilized in UVLC system.

Deep neural network has been used to compensate the nonlinear distortion in the field of underwater visible light communication (UVLC) system. Considering the tradeoff between the equalization performance and the network complexity is the priority in practical applications. In this paper, we propose a novel hybrid frequency domain aided temporal convolutional neural network (TFCNN) with attention scheme as the post-equalizer in CAP modulated UVLC system. Experiments illustrate that the proposed TFCNN can achieve better equalization performance and remain the bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8×10-3 when other equalizers loss effectiveness under serious distortion condition. Compared with the standard deep neural network, TFCNN shows 76.4% network parameters complexity reduction.

Adaptive deep-learning equalizer based on constellation partitioning scheme with reduced computational complexity in UVLC system.

Visible light communication (VLC) system has emerged as a promising solution for high-speed underwater data transmission. To tackle with the linear and nonlinear impairments, deep learning inspired equalization is introduced into VLC. Despite their success in accuracy, deep learning approaches often come with high computational budget. In this paper, we propose an adaptive deep-learning equalizer based on complex-valued neural network and constellation partitioning scheme for 64 QAM-CAP modulated underwater VLC (UVLC) system. Inspired by the fact that symbols modulated at different levels experience various extent of nonlinear distortion, we adaptively partition the received symbols in constellation and design compact equalization networks for specific regions to reduce computation consumption. Experiments demonstrate that the partitioned equalizer can achieve the bit error rate below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10-3 at 2.85 Gbps similar to the standard complex-valued network, yet with 56.1% total computational complexity reduction. This work paves the path for online data processing in high speed UVLC system.

Unraveling the mechanism of efficient adsorption of riboflavin onto activated biochar derived from algal blooms.

Riboflavin is commercially produced primarily by bio-fermentation. Nonetheless, purification and separation are particularly complex and costly. Adsorption from the fermentation liquor is an alternative riboflavin separation technology during which a cost-efficient adsorbent is highly desired. In this study, a low-cost activated algal biomass-derived biochar (AABB) was applied as an adsorbent to efficiently adsorb riboflavin from an aqueous solution. The adsorption capacity of riboflavin on AABB increased with the increase in pyrolysis temperature and initial riboflavin concentration. The adsorption isotherms were well described by the Freundlich and Langmuir models. The AABB displayed excellent adsorption performance and its maximum adsorption capacity was 476.9 mg/g, which was 6.8, 6.8, and 5.2 times higher than that of laboratory-prepared activated rape straw biochar, activated broadbean shell biochar and commercial activated carbon, respectively, which was mainly ascribed to its larger specific surface area and abundant functional groups. The mass transfer model results showed that mass transfer resistance was dependent on both the film mass transfer and porous diffusion. Raman and Fourier transform-infrared spectra confirmed the presence of π-π interactions and hydrogen bonding between riboflavin and the AABB. The adsorption of riboflavin onto AABB was a spontaneous process, which was dominated by van der Waals forces. These results will be beneficial for developing effective riboflavin recovery technologies and simultaneously utilizing waste algal blooms.

Partial pruning strategy for a dual-branch multilayer perceptron-based post-equalizer in underwater visible light communication systems.

We demonstrate a partial pruning strategy for a post-equalizer based on a dual-branch multilayer perceptron-based post-equalizer (DBMLP PE) in an underwater visible light communication (UVLC) system. The partial pruning strategy produces a sparse DBMLP PE (SDBMLP PE) with less space complexity than the Volterra PE and bit error rate (BER) performance similar to the DBMLP PE. We experimentally prove the effectiveness and necessity of the partial pruning algorithm in multilayer perceptron-based (MLP) PE. The partial pruning strategy consists of two parts: (i) preventing the pruning of connections to the output nodes, and (ii) avoiding the linear mapping branches of the DBMLP during the pruning process. Our experiments prove that the SDBMLP PE further reduces the BER of the UVLC system by 36.5%, with only 33.8% parameters of the Volterra PE. To the best of our knowledge, this paper presents the first deep neural network-based PE with lower complexity and higher BER performance than the Volterra PE in the UVLC system, which dramatically increases the applicability of artificial neural network-based (ANN) PE in the field of UVLC systems.

Underwater visible light communication at 3.24 Gb/s using novel two-dimensional bit allocation.

Underwater visible light communication (UVLC) systems suffer from a strong nonlinear effect and high inter-symbol interference (ISI). In this study, to improve the performance of a UVLC system under such conditions, we propose a novel nonlinear hybrid modulation scheme named two-dimensional bit allocation (2DBA). By comparing the performance of 2DBA with the famous Levin-Campello (LC) algorithm and the quadrature amplitude modulation (QAM)-based time-domain hybrid modulation (TDHQ) algorithm, we have proved by analysis and experiment that 2DBA can outperform the power allocation-based LC algorithm and the TDHQ algorithm below the 3.8×10-3 hard decision forward error correction threshold (HD-FEC) when the system has a severe nonlinear effect and ISI. The data rate 3.24 Gb/s of 2DBA is measured after 1.2 m underwater transmission; as far as we know, this is the highest data rate reported in a blue LED chip based UVLC system.

Enhanced performance of MIMO multi-branch hybrid neural network in single receiver MIMO visible light communication system.

For the single receiver multiple-input-multiple-output (SR-MIMO) visible light communication (VLC) system, the superposing of two transmitters will introduce severe distortion in the time-domain and frequency-domain. In this paper, we first proposed a MIMO multi-branch hybrid neural network (MIMO-MBNN) as the post-equalizer in the SR-MIMO pulse amplitude magnitude eight levels (PAM8) VLC system. Compared with the traditional single-input-single-output least mean square equalizer with Volterra series (SISO-LMS) and SISO deep neural network (SISO-DNN), MIMO-MBNN can achieve at most 3.35 dB Q factor improvement. Furthermore, the operation range of MIMO-MBNN is at least 2.33 times of SISO-DNN and SISO-LMS among the measured signal peak to peak voltage. At last, 2.1 Gbps data rate is achieved by MIMO-MBNN below the 7% hard-decision forward error correction (HD-FEC) threshold. As far as we know, this is the highest data rate in the SR-MIMO VLC system.

Comparison of nonlinear equalizers for high-speed visible light communication utilizing silicon substrate phosphorescent white LED.

Compared with multicolor-chip integrated white LEDs, phosphor-based white LEDs are more attractive for daily illumination due to lower cost and complexity, and thus they are preferable for future commercial use of visible light communication (VLC) systems. However, the application of phosphorescent white LEDs has a lower data rate than multicolor-chip integrated LEDs because of severe nonlinear impairments and limited bandwidth caused by the slow-responding phosphor. In this paper, for the first time we propose to employ phosphorescent white LEDs based on silicon substrate with adaptive bit-loading discrete multitone (DMT) modulation and a memoryless polynomial based nonlinear equalizer to achieve a high-speed VLC system. We also present a comprehensive comparison among nonlinear equalizers based on the Volterra series model, memory polynomial model, memoryless polynomial model and deep neural network (DNN) with experimental results utilizing a silicon substrate phosphorescent white LED, and provide detailed suggestions on how to choose the most suitable nonlinear mitigation scheme considering different practical conditions and the tradeoff between complexity and performance. Beyond 3.00 Gb/s DMT VLC transmission over 1-m indoor free space is successfully demonstrated with bit error rate (BER) under the 7% forward error correction (FEC) limit of 3.8×10-3. As far as we know, this is the highest data rate ever reported for VLC systems based on a single high-power phosphorescent white LED.

Two tributaries heterogeneous neural network based channel emulator for underwater visible light communication systems.

This paper proposes a novel two tributaries heterogeneous neural network (TTHnet) based channel emulator, which is suitable for both estimating single-carrier and multi-carrier modulated channels of underwater visible light communication (UVLC). Compared to traditional neural networks, the TTHnet channel emulator has only 1932 trainable parameters, which is only 0.8% of multilayer perceptron (MLP) based channel emulator and 1% of a convolutional neural network (CNN) based channel emulator. Furthermore, it provides a more accurate estimation of the UVLC channel and greater interpretability than MLP and CNN. The experiments in this paper use carrier-less amplitude/phase modulation (CAP) and discrete multi-tone modulation (DMT) as representative examples of single-carrier and multi-carrier modulation, respectively. The experiment proves that the TTHnet based channel emulator could effectively emulate the channel response of UVLC systems both in time and frequency domain. To the best of our knowledge, this is the first time that the single-carrier and multi-carrier modulated UVLC channel is emulated by the deep neural networks based channel emulator, which will effectively accelerate the research progress of UVLC and reduce research costs of UVLC systems.

Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system.

In this paper, we demonstrate a novel Gaussian kernel-aided deep neural network (GK-DNN) equalizer that can effectively compensate for the high nonlinear distortion of underwater PAM8 visible light communication (VLC) channels. The application of a Gaussian kernel can reduce the necessary training iterations to 47.06%, enabling it to outperform the traditional DNN equalizer. At the same time, a novel design strategy with respect to the structure of the GK-DNN equalizer is proposed, which can effectively save computing resources and reduce the data volume of the necessary training data set. By using the GK-DNN equalizer, a 1.5 Gbps PAM8 VLC system over 1.2-m underwater transmission is successfully demonstrated.

[The Relationship Between Quality of Life and Psychological and Behavioral Factors in Patients With Heart Failure Following Cardiac Resynchronization Therapy].

BACKGROUND: While cardiac resynchronization therapy improves the quality of life of patients with heart failure, some psychological and behavioral factors still affect the quality of life of these patients. However, information on the factors that affect the quality of life of these patients is limited. PURPOSE: To describe the quality of life and investigate the relationship between quality of life and behavioral and psychological factors such as depression, smoking, drinking, water and sodium restrictions, exercise, and adherence in patients with chronic heart failure following cardiac resynchronization therapy. METHODS: This cross-sectional study was conducted using the Morisky Medication Adherence Scale, Minnesota Living With Heart Failure Questionnaire, and Cardiac Depression Scale. A convenience sample of 141 patients with heart failure following cardiac resynchronization therapy were recruited from a tertiary academic hospital in Chengdu. RESULTS: The mean overall score of the Minnesota Living With Heart Failure Questionnaire was 30.89 (out of a total possible score of 105). Water restrictions, sodium restrictions, depression, and exercise were all shown to significantly predict quality of life among the participants. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: This paper describes the quality of life and defines the behavioral factors that affect the quality of life of patients with heart failure following cardiac resynchronization therapy. The findings suggest that nurses should manage and conduct health education for patients in order to improve their quality of life.