Prevalence and characteristics of comorbid stroke and traumatic brain injury in a real-world population: findings from a nationally representative cross-sectional survey in China.

BACKGROUND: In China, data on the prevalence and characteristics of comorbid stroke and traumatic brain injury (TBI) in real-world populations are still lacking but of paramount importance for the evidence-based prevention and control of the comorbidity of the two diseases. This study aimed to investigate the prevalence and characteristics of comorbid stroke and TBI in a real-world population. METHODS: In 2013, a nationally representative, door-to-door survey on stroke and TBI using a complex, multistage, probability sampling design was conducted among approximately 600,000 participants from 155 urban and rural areas in China (Ethic ID: KY2013-006-01). The weighted prevalence of comorbid stroke and TBI was estimated using individuals' final weight. A Poisson regression analysis was used to compare the rate ratio of the comorbidity prevalence among different subgroups of the population, including age, sex, place of residence, and geographic location subgroups. For analyses of associations between the comorbidities and predictors of interest, all other variables were adjusted for in a multinomial logistic regression model. RESULTS: Among the 596,536 people, 219 patients with comorbid stroke and TBI were identified. The point prevalence of comorbid stroke and TBI weighted to the China 2010 census population was 29.30 (95% CI: 22.69-37.84) per 100,000 population in China. The adjusted prevalence of post-TBI stroke in patients with previous TBI was significantly higher than that of post-stroke TBI in patients with previous stroke (6021.3 vs. 811.1 per 100,000 people; rate ratio: 11.001; 95% CI: 8.069-14.998). Patients with nonconcussion had significantly higher rates of both pre-stroke TBI (odds ratio: 4.694; 95% CI: 3.296-6.687) and post-stroke TBI (odds ratio: 6.735; 95% CI: 3.719-12.194) than patients with concussion. Compared to patients with ischaemic stroke, patients with subarachnoid haemorrhage (odds ratio: 2.044; 95% CI: 1.097-3.809) and intracerebral haemorrhage (odds ratio: 1.903; 95% CI: 1.296-2.795) had significantly higher rates of post-TBI stroke. CONCLUSIONS: The high prevalence of stroke among TBI patients is becoming a new public health issue. TBI patients, especially those with nonconcussion TBI, are more likely to develop comorbid stroke and TBI than stroke patients, especially ischaemic stroke patients.

Recent Advances in Underwater Signal Processing.

The ocean, covering 71% of the Earth's surface, is integral to human life [...].

Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications. Firstly, a VAMP framework with a non-loopy factor graph for index detection is formulated. Secondly, by utilizing the sparsity inherently existing in OFDM-IM symbols, a novel shrinkage function is derived based on the minimum mean square error criterion, which guarantees better posterior estimation. To reduce the errors from estimated non-existing indices, one trick is utilized to search the elements from the look-up table with the minimal Euclidean distance for the replacement of erroneously estimated indices. Experiments verify the advantages of the proposed detector in terms of low complexity, robustness and effectiveness compared with the state-of-art benchmarks.

Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems.

Non-orthogonal multiple access (NOMA) is considered a promising multiple access technique for fifth generation (5G) mobile networks and tactical internet due to its high spectral efficiency. Thanks to the high spectral efficiency of NOMA, it can be a strong candidate suitable for the limited channel bandwidth of underwater acoustic communication. The NOMA transmitter is employing superposition coding (SC). The NOMA receiver is based on the successive interference cancellation (SIC) technique. The multicarrier NOMA adopts orthogonal frequency division multiplexing (OFDM) as a multicarrier modulation (MCM) technique; however, conventional cyclic prefix OFDM (CP-OFDM) and zero padding (ZP-OFDM) have inefficient spectral efficiency. Thanks to efficient synchronization and high energy-spectral efficiency of the time-division synchronization OFDM (TDS-OFDM), it is a significant attractive candidate for underwater multicarrier communication. However, wasting the power transmission of long guard intervals in the battery-based underwater communication is represented as one of the TDS-OFDM main drawbacks. Harvesting energy and improving the energy efficiency of acoustic-based TDS-OFDM-NOMA represent high achievement goal battery recharging challenges due to the ocean environment. This paper proposes time switching simultaneous wireless information and power transfer (TS-SWIPT) to harvest the energy of transmitted power over the guard interval in the TDS-OFDM-NOMA scheme. The proposed energy harvested scheme harvests the energy from the wasted power in the long guard interval and improves the energy efficiency of the TDS-OFDM multicarrier scheme. This study demonstrates the superiority of the proposed TDS-OFDM-NOMA over the underwater acoustic channel by revealing high energy efficiency, high spectral efficiency, better bit error rate performance, and high system data throughput.

Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction.

Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.

Mortality due to primary brain tumours in China and detection rate in people with suspected symptoms: a nationally representative cross-sectional survey.

BACKGROUND AND PURPOSE: Epidemiological data on primary brain tumours (PBTs) are lacking due to the difficulty in case ascertainment among the population. Thus, we aimed to estimate mortality due to PBTs in China nationwide and the detection rate in people with suspected symptoms. METHODS: A multistage, complex sampling survey regarding mortality due to PBTs in Chinese individuals was carried out by reviewing all causes of death within a year. The detection rates in people with suspected symptoms were estimated based on PBT symptom screening and neurologist reviews and compared between groups by logistic regression analysis. RESULTS: Weighted mortality due to PBT was 1.6 (0.8-3.3) per 100,000 population in Chinese individuals, 1.8 (0.7-4.6) per 100,000 population in men, and 1.5 (0.5-4.5) per 100,000 population in women. Among 14,990 people with suspected symptoms, the PBT detection rate was 306.9 (95% CI 224.7-409.3) per 100,000 population in the total population, 233.0 (95% CI 135.7-373.1) per 100,000 population in men, and 376.9 (95% CI 252.4-546.3) per 100,000 population in women. People with an unsteady gait (OR 2.46; 95% CI 1.09-5.51; P=0.029), visual anomalies (3.84; 1.88-7.85; P<0.001), and headache (2.06; 1.10-3.86; P=0.023) were more likely to have a brain tumour than those without corresponding symptoms, while people with dizziness/vertigo were less likely to have a brain tumour than those without corresponding symptoms (0.45; 0.23-0.87; P=0.017). CONCLUSIONS: Mortality due to PBT in China was low, with a nationwide estimate of 21,215 (10,427-43,165) deaths attributable to PBTs annually. However, the detection rate of PBTs can be greatly improved based on symptom screening in the population.

Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise.

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

Prevalence and Altered Causes of Traumatic Brain Injury in China: A Nationwide Survey in 2013.

BACKGROUND: Traumatic brain injury (TBI) is one of the leading causes of death and disability annually worldwide. However, the epidemiology of TBI had not been established in China. We conducted a nationally representative door-to-door survey in the general population across all age groups in 31 provinces in mainland China in 2013. METHODS: All participants were reviewed for a history of physician-diagnosed TBI by trained investigators using a structured questionnaire. TBI survivors were considered as prevalent cases at the prevalent time. The present study also examined the odds of TBI as a function of sex, age, and other demographical variables using logistic regression model. RESULTS: Of 583,870 participants, 2,673 individuals had suffered from a TBI during their past life, yielding a weighted prevalence of being 442.4 (95% CI 342.2-542.6) per 100,000 person. The TBI prevalence increased with increasing age. The present study observed the multiadjusted ORs of TBI were 1.9 (95% CI 1.8-2.1) for the male, 1.9 (95% CI 1.2-3.1) for the farmers, 1.9 (95% CI 1.2-3.3) for the retiree or homemakers, 3.4 (95% CI 1.5-7.7), and 2.8 (95% CI 1.1-6.6) for those whose education were primary school and high school, respectively. The most common external cause was road traffic accidents among those who were aged 18-34 years old and those whose educational levels were middle school in both genders. CONCLUSIONS: Our results indicate TBI was substantially prevalent among Chinese population and underscore the need to develop national strategies to improve the safe education on road and traffic of TBI in rural residents and some subgroup population.

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization.

Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication.

The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial's communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver's computational complexity.

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise.

Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy.

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things.

A full design of the Internet of Underwater Things (IoUT) with a high data rate is one of the greatest underwater communication difficulties due to the unavailability of a sustainable power source for the battery supplies of sensor nodes, electromagnetic spread weakness, and limited acoustic waves channel bandwidth. This paper presents a new energy-efficient communication scheme named Enhanced Fully Generalized Spatial Modulation (EFGSM) for the underwater acoustic channel, where the different number of active antennas used in Fully Generalized Spatial Modulation (FGSM) is combined with multiple signal constellations. The proposed EFGSM enhances energy efficiency over conventional schemes such as spatial modulation, generalized spatial modulation, and FGSM. In order to increase energy and spectral performance, the proposed technique conveys data bits not just by the number of active antenna's index as in the existing traditional FGSM, but also using the type of signal constellation to increase the data bit rate and improve power saving without increasing the receiver's complexity. The proposed EFGSM uses primary and secondary constellations as indexes to carry information, they are derived from others by geometric interpolation signal space. The performance of the suggested EFGSM is estimated and demonstrated through Monte Carlo simulation over an underwater acoustic channel. The simulation results confirm the advantage of the suggested EFGSM scheme not just regarding energy and spectral efficiency but also concerning the average bit error rate (ABER).

Underwater Image Transmission Using Spatial Modulation Unequal Error Protection for Internet of Underwater Things.

A spatial modulation (SM) scheme has been developed as a hopeful candidate for spectral and energy-efficient wireless communication systems, as it provides a great judgment for the system performance, data transmission rate, receiver complexity, and energy/spectrum efficiency. In SM, the data is conveyed by both habitual M-ary signal constellations and the transmit antennas indices. Therefore, the system data rate improvement due to the side information bits transmitted, encapsulated in indices of the transmit antennas, improves the SM transmission efficiency compared to the different MIMO players. The information bits transmitted over the antenna index and data symbol constellation using M-ary signal performance have different levels of bit error rate (BER) performance. This paper proposes unequal error protection (UEP) scheme for image transmission over the Internet of Underwater Things (IoUTs) using SM. The Set Partitioning in Hierarchical Trees (SPIHT) coders encode the underwater image and classify the encoded bits in two categories: critical and uncritical bits. The critical bits are transmitted over the SM index bits and have a low BER while the uncritical bits are transmitted over high order M-ary signal constellation to resolve the underwater acoustic channel bandwidth limitation problem. The proposed SM-UEP technique has been developed carefully with enough justification and evaluation over the measured underwater acoustic channel and the simulated channel. The simulation results show that the proposed SM-UEP can increase the average peak signal-to-noise ratio (PSNR) of the reconstructed received image considerably, and significantly.

Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults.

BACKGROUND: China bears the biggest stroke burden in the world. However, little is known about the current prevalence, incidence, and mortality of stroke at the national level, and the trend in the past 30 years. METHODS: In 2013, a nationally representative door-to-door survey was conducted in 155 urban and rural centers in 31 provinces in China, totaling 480 687 adults aged ≥20 years. All stroke survivors were considered as prevalent stroke cases at the prevalent time (August 31, 2013). First-ever strokes that occurred during 1 year preceding the survey point-prevalent time were considered as incident cases. According to computed tomography/MRI/autopsy findings, strokes were categorized into ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, and stroke of undetermined type. RESULTS: Of 480 687 participants, 7672 were diagnosed with a prevalent stroke (1596.0/100 000 people) and 1643 with incident strokes (345.1/100 000 person-years). The age-standardized prevalence, incidence, and mortality rates were 1114.8/100 000 people, 246.8 and 114.8/100 000 person-years, respectively. Pathological type of stroke was documented by computed tomography/MRI brain scanning in 90% of prevalent and 83% of incident stroke cases. Among incident and prevalent strokes, ischemic stroke constituted 69.6% and 77.8%, intracerebral hemorrhage 23.8% and 15.8%, subarachnoid hemorrhage 4.4% and 4.4%, and undetermined type 2.1% and 2.0%, respectively. Age-specific stroke prevalence in men aged ≥40 years was significantly greater than the prevalence in women (P<0.001). The most prevalent risk factors among stroke survivors were hypertension (88%), smoking (48%), and alcohol use (44%). Stroke prevalence estimates in 2013 were statistically greater than those reported in China 3 decades ago, especially among rural residents (P=0.017). The highest annual incidence and mortality of stroke was in Northeast (365 and 159/100 000 person-years), then Central areas (326 and 154/100 000 person-years), and the lowest incidence was in Southwest China (154/100 000 person-years), and the lowest mortality was in South China (65/100 000 person-years) (P<0.002). CONCLUSIONS: Stroke burden in China has increased over the past 30 years, and remains particularly high in rural areas. There is a north-to-south gradient in stroke in China, with the greatest stroke burden observed in the northern and central regions.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise.

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.

Mortality of Stroke and Its Subtypes in China: Results from a Nationwide Population-Based Survey.

BACKGROUND: In China, stroke is the leading cause of death and contributes to a heavy disease burden. However, a nationwide population-based survey of the mortality of stroke and its subtypes is lacking for this country. METHODS: Data derived from the National Epidemiological Survey of Stroke in China, which was a multistage, stratified clustering sampling-designed, cross-sectional survey, were analyzed. Mortality rate analyses were performed for 476,156 participants ≥20 years old from September 1, 2012 to August 31, 2013. RESULTS: Of the 476,156 participants in the investigated population, 364 died of ischemic stroke, 373 of hemorrhagic stroke, and 21 of stroke of undetermined pathological type. The age-standardized mortality rates per 100,000 person-years among those aged ≥20 years were 114.8 for total stroke, 56.5 for ischemic stroke, and 55.8 for hemorrhagic stroke. The age-standardized mortality rates of total stroke, ischemic stroke, and hemorrhagic stroke were all higher in rural areas than those in urban areas. The stroke mortality rate was higher in the northern regions than in the south. An estimated 1.12 million people aged ≥20 years in China died of stroke during the period from September 1, 2012 to August 31, 2013. CONCLUSIONS: The burden of stroke in China is still heavy. Greater attention should be paid to improve strategies for preventing stroke.

Difference of hospital charges for stroke inpatients between hospitals with different levels and therapeutic modes in Beijing, China.

OBJECTIVES: The present study analyzed the hospital charges for stroke patients in China and determined the factors associated with hospital costs. METHODS: Medical records of hospitalized patients with a primary diagnosis of acute stroke were collected from 121 hospitals in Beijing (2012). Distribution characteristics of hospital charges for different stroke types, hospital levels and types were studied. Factors influencing total hospital charges were analyzed. RESULTS: 60.8% of the 94 906 stroke patients were male and the mean age of these patients was 66.5 ± 13.2 years. The median length of hospital stay (LOHS) for these patients was 14 d (interquartile range, IQR 9-19). The mean hospital charge per patient was 19 270 Chinese Yuan. Forty-five percent of these charges were for medicine, 18% for laboratory and examination, 16% for material, 15% for therapy, 5% for service and 1% for blood product. The mean hospital charge for patients suffering from hemorrhagic stroke was significantly more than ischemic stroke (34 937 vs. 17 049, p < 0.001), and was significantly more for Level 3 than Level 2 hospitals (23 762 vs. 14 554, p < 0.001). LOHS, hospital level and stroke severity were key determinants of the hospital charge. CONCLUSIONS: Though hospital charges for stroke patients in China were low, it brought a heavy economic burden for the larger stroke population. Medicine accounted for the largest percentage of hospital charges in China. LOHS emerged to be the main predictor of the cost. Decreasing medicine charge and LOHS might be strategies to decrease hospital charges and reduce economic burden of stroke in China.

Modified particle filtering algorithm for single acoustic vector sensor DOA tracking.

The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms.

Natural Biomolecule Ovomucin-Chitosan Oligosaccharide Self-Assembly Nanogel for Lutein Application Enhancement: Characterization, Environmental Stability and Bioavailability.

As an essential nutrient, lutein (LUT) has the ability to aid in the prevention of eye diseases, cardiovascular diseases, and cancer. However, the application of LUT is largely restricted by its poor solubility and susceptibility to oxidative degradation. Thus, in this study, LUT-loaded nanogel (OVM-COS-LUT) was prepared by a self-assembly of ovomucin (OVM) and chitosan oligosaccharide (COS) to enhance the effective protection and bioavailability of LUT. The nanogel had excellent dispersion (PDI = 0.25) and an 89.96% LUT encapsulation rate. XRD crystal structure analysis confirmed that the encapsulated LUT maintained an amorphous morphology. In addition, the nanogel showed satisfactory stability with pH levels ranging from 2 to 9 and high ionic strengths (>100 mM). Even under long-term storage, the nanogel maintained an optimistic stabilization and protection capacity; its effective retention rates could reach 96.54%. In vitro, digestion simulation showed that the bioaccessibility and sustained release of OVM-COS-LUT nanogel was superior to that of free LUT. The nanogel provided significant antioxidant activity, and no significant harmful effects were detected in cytotoxicity analyses at higher concentrations. In summary, OVM-COS-LUT can be utilized as a potential safe oral and functional carrier for encapsulating LUT.