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The noise covariance matrices Q and R are set as constant values in the traditional extended Kalman filter (TEKF). They are determined by trial and error. This process is very complicated and the optimal matrices are difficult to determine. In addition, when the characteristic of noise changes, the matrices cannot be adjusted correspondingly, and the performance of the TEKF deteriorates. Therefore, an adaptive EKF algorithm based on the maximum likelihood estimation criterion with limited memory exponential weighting (EW-MLE-AEKF) is proposed in this paper. In the proposed EW-MLE-AEKF algorithm, the windowing method is adopted to save the posterior residual sequences in the previous N calculation periods. Then, these sequences are used to estimate and update the process noise covariance matrix Q in real time. To speed up the convergence speed of the estimation, a limited memory exponential weighting algorithm is added to the windowing method, which can increase the importance of recent data. Through real-time experiments, the superiority of the proposed EW-MLE-AEKF algorithm is verified.

Perivascular epithelioid cell tumor (PEComa) is a rare type of mesenchymal neoplasm, which occurs most commonly in uterus and gastrointestinal tract. PEComa with perirenal manifestation is an extremely rare entity. To the best of our knowledge, only four cases have been reported up to now. In this case, we reported a patient with both a pulmonary mass and a perirenal mass. Two resections were performed successively and postoperative pathology suggested pulmonary micro invasive adenocarcinoma (MIA) and perirenal PEComa. This is the first case of perirenal PEComa with pulmonary MIA. Combining the present case and prior literature, we summarized the crucial role of immunohistochemistry in the diagnosis and consider that complete operation might be conducive to patients with perirenal PEComa that presents a benign phenotype, regardless of complications with other tumors.

Background EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation training, but its effectiveness is controversial because some participants failed to learn self-regulation of FAA signals. It is desired to reveal neuroimaging predictors of learning ability in FAA neurofeedback, which cannot only improve our knowledge of the mechanisms of neurofeedback but can also enable personalized neurofeedback scheme. Previous studies have indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional baseline features. However, these studies only focused on regional measures of brain structure or function, and overlooked possible neural correlates of brain network measures. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. Methods The present study designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data (structural magnetic resonance imaging and diffusion tensor imaging, and resting-state functional MRI) from healthy participants before neurofeedback training. We assessed the learning performance for evoked EEG modulations during training (performance index L1) and at rest (performance index L2), and investigated performance related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. Results Participants from both real and sham groups could increase their FAA during training blocks by maintaining a positive emotional state, but only the real group showed a significant increase in FAA at rest. The predictors of learning performance were different for evoked EEG modulations during training blocks and at rests: L1 was correlated with the graph-theoretical metrics (normalized clustering coefficient and local efficiency) of the right hemispheric gray matter network and functional brain network, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left hemispheric functional brain network. Conclusions The individual differences in FAA neurofeedback learning could be explained with individual variation in structural/functional architecture, and the correlated global topological metrics of different learning performance indices showed different laterality of hemispheric brain networks. These results provided an insight into the neural correlates of inter-individual differences in neurofeedback learning.

Background/Aims Rheumatoid arthritis (RA) is an inflammatory joint disease that can lead to disability if inflammation is not controlled. Biologic disease modifying anti-rheumatic drugs (bDMARDs), including tumour necrosis factor inhibitors (TNFi), are an effective line of therapy for moderate to severe RA. However, treatment response to TNFi is not universal and cannot be predicted from clinical factors. In this study, a protein quantitative trait loci (pQTL) study and genetic risk score (GRS) analysis were combined to identify protein-based biomarkers that are predictive of TNFi treatment response. Methods Protein expression data were generated using Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) in serum samples taken from 80 RA patients about to commence therapy with etanercept. Protein expression was log2 transformed and the k-nearest neighbour model was used to impute missing values. A pQTL study was performed in patients with available imputed genome-wide genetic variation data to detect cis-acting genetic markers (p < 1E-05). GRS for pQTLs were subsequently tested in 1,430 RA patients with available genome-wide genetic and TNFi treatment response data (improvement in DAS28 between pre-treatment and 3/6 months on drug). The GRS analysis was adjusted for baseline disease activity, sex, conventional synthetic DMARD co-therapy and the first 10 principal components, calculated from the genetic datasets. Results Following imputation, expression levels for 271 proteins were analysed in 69 RA patients with available genotype data. 514 cis-pQTLs were found for 16 proteins. GRS for the proteins Apolipoprotein(a) (UniProt ID P08519, p = 0.017) and Carboxypeptidase N subunit 2 (P22792, p = 0.027) were found to be modestly associated with treatment response; however, scores for both proteins explained less than 1% of the variance in DAS28 difference between time-points. Conclusion This study identified two protein-based genetic biomarkers of treatment response to TNFi. However, genetic scores based on these proteins are unlikely to be useful predictors, explaining little variance in on-treatment disease activity. Disclosure Z. Li: None. S.F. Ling: None. N. Nair: None. J.D. Isaacs: None. K.L. Hyrich: Honoraria; Abbvie. A.W. Morgan: None. A.G. Wilson: None. A. Barton: None. D. Plant: None.

Background: Polycystic ovarian syndrome (PCOS) is one of the most common endocrine disease affecting the reproductive age women. Objective: To investigate the relationship between PCOS and breast, ovarian, and endometrial cancer. Search Strategy: Systematic search of PubMed, EMBASE using keywords ‘PCOS’, ‘ovarian cancer’, ‘breast cancer’ and ‘endometrial cancer’. Selection Criteria: The study providing the relative risk (RR) in the cohort study, odds risk(OR) in the case-control study, and 95%confidence interval (95%CI) were included. The single-case report and the non-English study were excluded. Data Collection and Analysis: This meta-analysis was performed by calculating RR, OR and 95%CI using random-effect models. Main Result: A total of 21 studies (8 cohort studies,13 case-control studies) involving 3831083 participants fulfilled the inclusion criteria. Based on the results of cohort studies and case-control studies, the prevalence of breast cancer among PCOS and non-PCOS women was not significant increased, the RR and OR were 0.959 (95%CI, 0.806-1.112) and 0.991 (95%CI, 0.626-1.35), respectively. Evidence from case-control studies showed that PCOS did not increase the risk of endometrial and ovarian cancer, the summary ORs of case-control studies were 1.288 (95%CI,0.763-1.814) and 1.219 (95%CI, 0.816-1.623). The risk of endometrial and ovarian cancer was significantly increased based on cohort studies, the overall RRs of cohort studies were 2.542 (95%CI, 1.755-3.328) and 1.818 (95%CI, 1.222-2.414). Conclusion: The meta-analysis demonstrate that PCOS will not increase the risk of breast cancer. Evidence from the cohort studies suggests that PCOS significantly increases the risk of endometrial and ovarian cancer, although the case-control studies did not.

The paper studies two moving vehicles exchanging messages assisted by another vehicle which is used as a relay. All the vehicle-to-vehicle (V2V) links are modeled as flat double-Rayleigh fading. Based on this particular channel model, the OPA (optimal power allocation) solutions are derived through maximizing source-to-destination channel capacity on the assumptions of amplify-and-forward relaying strategy. Then, the outage probability is discussed and the lower bound on it is acquired. Simulation results manifest that the OPA method improves the average throughput of the IVC (inter-vehicular communication) system by about 2 dB at low SNR (signal-to-noise ratio) and has no less than 2 dB gains on outage probability at medium to high SNR over equal power allocation (EPA) scheme. They also show that the lower bound on outage probability approximates to the Monte Carlo numerical calculations at low to medium SNR region and, as the exact outage probability is not easy to obtain, it can be used to estimate the outage performance of the relay-assisted IVC system roughly.

This paper studies the inter-vehicular communication (IVC) system where a vehicle transmits information to another vehicle assisted by an access point (AP), which may be mounted along a street or on a rooftop. The paper first presents the double-Rayleigh fading vehicle-to-vehicle (V2V) channel model and the amplify-and-forward (AF) relaying system. And then the optimal power allocation (OPA) solutions are derived through maximizing source-to-destination channel capacity. Based on mathematic analysis, the closed-form expression on outage probability is concluded finally. Simulation results show that the OPA scheme improves the average throughput by about 2 dB at low to high SNR (signal-to-noise ratio) and has no less than 2 dB gains on outage probability at medium to high SNR over equal power allocation (EPA) method. They also manifest that the theoretical analysis on average outage probability coincides very well with the Monte Carlo numerical calculations. The diversity order can be achieved as large as 2.7 dB in the relay-assisted IVC system when the relay approaches to the source and under EPA solution.

This paper studies the relay-assisted inter-vehicular communications (IVCs) with double Rayleigh fading vehicle-to-vehicle links. The OPA (optimal power allocation) solution is derived through maximizing source-to-destination channel capacity assuming amplify-and-forward relaying. The outage performance is discussed and the lower bound on outage probability at medium to high SNR (signal-to-noise ratio) is presented. Simulation results manifest that the ergodic capacity can be improved by about 2 dB at low SNR through OPA method over EPA (equal power allocation) solution, whereas the outage probability decreases more than 2dB at high SNR compared to EPA. They also show that the lower bound on average outage probability approximates to the exact calculation result at medium to high SNR in the relay-assisted IVC system.

The paper explores the relay-assisted IVC (inter-vehicular communication) systems with double-Rayleigh fading V2V (vehicle-to-vehicle) links for both APA (access point assisted) and VA (vehicle assisted) schemes. The OPA (optimal power allocation) solutions are derived through maximizing source-to-destination channel capacity assuming amplify-and-forward relaying. Furthermore, the outage performance is discussed and the lower bounds on outage probabilities at medium to high SNRs (signal-to-noise ratios) are presented for both APA and VA systems. Simulations manifest that the outage performance can be improved about 2dB gains through OPA method over EPA (equal power allocation) solution and the lower bounds coincide with the exact outage probabilities well at medium SNRs. These bounds can be used to evaluate the outage performance of the cooperative IVC systems roughly.

This paper explores the relay-assisted transmission system with mobile-to-mobile links undergo cascaded Rayleigh fading. The BER (bit error rate) bounds of the system are derived through mathematic analysis assuming amplify-and-forward relaying. And the OPA (optimal power allocation) method is discussed which is based on minimizing Chernoff bound on BER performance. The paper presents two classical cases based on geometrical topology and gets optimized power distribution parameters by numerical calculations. It further emphasizes useful insights which are as bellows: when the relay is closer to destination terminal (e.g., d R2 ≤d 12 /10), it should spend more than 97% power to source terminal for the first phase transmission, whereas the relay approaches to the source, an equal power method (EPA) is adopted between the source and the relay during two transmissions. Simulation results manifest that through OPA power savings can be achieved as large as 3dB.

This paper aims to study the reverse osmosis membranes which can desalinate seawater. Mechanism of water seepage has been studied by XRD analysis, scanning electron microscope and other methods. Effects outside temperatures and soil moisture conditions on the water infiltration rate of the membrane are explored by comparison. The experimental results reveal that the water seepage through the dense membrane layer is porous to filter out small ions in order to achieve the role of desalination. And the higher the outside temperature, water seepage rate of the membrane increased; the lower the outside temperature, water infiltration rate decreases; the higher the soil moisture, water seepage rate of the membrane increases; the lower the soil moisture, water infiltration rate decreases. In this thesis, the main innovation is combining the reverse osmosis membranes and afforestation, and analyzed by comparison of experimental factors. The results of this investigation can be applied to the actual afforestation, according to the different conditions to make better use of the reverse osmosis membranes in the actual use.