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This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

In this paper, we study a robust beamforming design for multi-user multiple-input multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet of Things devices under the non-Linear energy harvesting model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve energy harvesting fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method outperforms the traditional AN-aided method and CJ-aided method.

Background The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population. Methods The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age Results Compared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P Conclusions It is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Background/Aims: Chronic cerebral hypoperfusion (CCH) is a high-risk factor for vascular dementia and Alzheimer’s disease. Autophagy plays a critical role in the initiation and progression of CCH. However, the underlying mechanisms remain unclear. In this study, we identified the effect of a microRNA (miR) on autophagy under CCH. Methods: A CCH rat model was established by two-vessel occlusion (2VO). Learning and memory abilities were assessed by the Morris water maze. The protein levels of LC3, beclin-1, and mTOR were detected by western blotting and immunofluorescence assays, miR-96 expression was assessed by real-time PCR, luciferase assays were used to determine the effect of miR-96 on the 3′ untranslated region (UTR) of mTOR, and the number of autophagosomes was examined by electron microscopy. Results: The level of miR-96 was significantly increased in 2VO rats, and inhibition of miR-96 ameliorated the cognitive impairment induced by 2VO. Furthermore, the number of LC3- and beclin-1-positive autophagosomes was increased in 2VO rats, and was decreased after miR-96 antagomir injection. However, the protein level of mTOR was reduced in 2VO rats, and it was down-regulated by miR-96 overexpression and up-regulated by miR-96 inhibition in 2VO rats and primary culture cells. Moreover, the luciferase activity of the 3′-UTR of mTOR was suppressed by miR-96, which was relieved by mutation of the miR-96 binding sites. Conclusion: Our study demonstrated that miR-96 may play a key role in autophagy under CCH by regulating mTOR; therefore, miR-96 may represent a potential therapeutic target for CCH.

Due to the random wave motions as well as the vehicle’s movement pattern changes, the attitude dynamics (including heading, pitch, and roll motions) of the vehicle induced degrades the accuracy of doppler velocity log (DVL), which is a key element in deciding the precision of strapdown inertial navigation system (SINS)/DVL integrated navigation system. In this paper, the effects of heading, pitch and roll dynamics are first analyzed separately. Then, the general error model for DVL measurement errors caused by attitude dynamics is established. In the modeling process, the changes of angular position and angular rate between the DVL signal transmission and reception instant are considered. Subsequently, by utilizing the established error model, a correction method based on the real-time attitude information from SINS/DVL system is presented. Finally, the performance of the proposed method is examined through numerical simulations in which typical scenarios with a series of attitude dynamics are used. Simulation results show that the errors can be effectively corrected by using the correction method. This contributes to an improved positioning accuracy of SINS/DVL navigation.

Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS.

Background: Venous thromboembolism (VTE) is an important complication in patients with malignant tumors. Its exact diagnosis and treatment are still lacking. We used a high-sensitive chemiluminescence method to detect thrombin–antithrombin III complex (TAT), plasmin-α2-plasmininhibitor complex (PIC), thrombomodulin (TM), and tissue plasminogen activator–inhibitor complex(t-PAIC) in combination with D-dimer and fibrin degradation product (FDP) to analyze their diagnostic and prognostic value in patients with malignant tumors. Methods: In total, 870 patients with confirmed malignant tumors were included, 82 of whom had diagnosed VTE; 200 healthy individuals were classified as the control group. The TAT, PIC, TM, and t-PAIC were detected using Sysmex HISCL5000 automated analyzers, whereas FDP and D-dimer were detected using Sysmex CS5100 coagulation analyzer. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficiency. Survival probabilities were determined using Kaplan–Meier analysis, and multivariate analyses were performed using a Cox regression model. Results: Compared with healthy controls, patients with malignant tumors showed significantly elevated TAT, PIC, TM, t-PAIC, D-dimer, and FDP. Similarly, compared with patients in the non-thrombosis group, those in the thrombosis group showed significantly elevated levels of the above mentioned markers. Logistic regression analysis showed that TAT, PIC, TM, t-PAIC, D-Dimer, and FDP were all associated with VTE. ROC analysis showed that “TAT+PIC+TM+t-PAIC+D-dimer+FDP”showed the highest sensitivity and specificity. Patients with elevated TAT, PIC, TM, and t-PAIC had a significantly shorter survival. Multivariate Cox survival analysis showed that TM and t-PAIC were significantly associated with poor prognosis. In addition, the incidence of VTE was significantly lower in patients with malignant tumors who were treated with low-molecular-weight heparin (LMWH), and their survival period was significantly longer than that of patients with malignant tumors who were not treated with LMWH. Conclusion: TAT, PIC, TM, and t-PAIC combined with D-dimer and FDP were better than the application of a single marker in the diagnosis of VTE in patients with malignant tumors. TAT and PIC can be used as sensitive markers in the diagnosis of VTE but not as prognostic markers. TM and t-PAIC might be independent prognostic indicators in patients with malignant tumors, regardless of the state of thrombus.

Carboxypeptidase E (CPE) is one of the most important carboxypeptidases involved in biosynthesis of numerous peptide hormones and neurotransmitters and has an important role in endocrine regulation. A splice variant of CPE (CPE-ΔN) has been detected and the mechanism of CPE-ΔN action in tumorigenesis has been studied in many different cancers. The aim of this study was to examine CPE-ΔN expression in human colorectal cancer (CRC) and to evaluate its possible use as a potential prognostic marker. Two hundred nineteen primary colorectal tumors and corresponding normal tissues were included in the study. We have analyzed CPE-ΔN isoform expression by qRT-PCR and Western blot in 219 CRC patients. Correlations between CPE-ΔN mRNA expression and clinicopathological variables were determined with chi-square tests. Survival probabilities were determined using Kaplan–Meier analysis, and univariate and multivariate analyses of the prognostic factors were performed with a Cox regression model. Our results show that CPE-ΔN is overexpressed in colorectal tumor tissue and that high CPE-ΔN mRNA expression is closely correlated with tumor differentiation, pT classification, pN classification, tumor recurrence, and lymph node metastasis (P = 0.042, 0.036, 0.031, 0.006, and 0.008, respectively). However, no correlation was observed between CPE-ΔN expression and age, gender, tumor localization, gross features, and the tumor size. In addition, patients with high CPE-ΔN expression had a significantly shorter survival (P