Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks

MicroRNAs (miRNAs) have been demonstrated to play significant biological roles in many human biological processes. Inferring the functions of miRNAs is an important strategy for understanding disease pathogenesis at the molecular level. In this paper, we propose an integrated model, PmiRGO, to infer the gene ontology (GO) functions of miRNAs by integrating multiple data sources, including the expression profiles of miRNAs, miRNA-target interactions, and protein-protein interactions (PPI). PmiRGO starts by building a global network consisting of three networks. Then, it employs DeepWalk to learn latent representations as network features of the global heterogeneous network. Finally, the SVM-based models are applied to label the GO terms of miRNAs. The experimental results show that PmiRGO has a significantly better performance than existing state-of-the-art methods in terms of Fmax. A case study further demonstrates the feasibility of PmiRGO to annotate the potential functions of miRNAs

Abnormal Liver Function Tests Were Associated With Adverse Clinical Outcomes: An Observational Cohort Study of 2,912 Patients With COVID-19

Background and Aim: The impact of liver function test (LFTs) abnormality on adverse clinical outcomes in coronavirus disease 2019 (COVID-19) patients remains controversial. The aim of this study was to assess the impact of abnormal LFTs on clinical outcomes in a large cohort of hospitalized patients with COVID-19.Methods: We retrospectively collected data on 2,912 consecutive patients with COVID-19 who were admitted to a makeshift hospital in China between 5 February and 23 March 2020. The association between LFTs abnormalities (baseline and peak values) and clinical outcomes was measured by using Cox regression models.Results: On admission 1,414 patients (48.6%) had abnormal LFTs, with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), alkaline phosphatase (ALP), and gamma-glutamyltransferase (GGT) elevation in 662 (22.7%), 221 (7.6%), 52 (1.8%), 135 (4.6%), and 536 (18.5%) patients, respectively, and hypoalbuminemia in 737 (25.3%) patients. During a median 13 (IQR: 8–19) days of hospitalization, 61 patients (2.1%) died, 106 patients (3.6%) admitted to intensive care unit (ICU), and 75 patients (2.6%) required mechanical ventilation. After adjustment for confounders, baseline abnormal LFTs were independently associated with increased risks of mortality (adjusted HR 3.66, 95%CI 1.64–8.19, p = 0.002), ICU admission (adjusted HR 3.12 95%CI 1.86–5.23, p < 0.001), and mechanical ventilation (adjusted HR 3.00, 95%CI 1.63–5.52, p < 0.001), which was homogeneous across the severity of COVID-19 infection. Among the parameters of LTFs, the associations with the outcomes were more pronounced for AST and albumin abnormality. In contrast, ALT elevation was not significantly associated with those outcomes. Similar results were observed for peak values of LFTs during hospitalization.Conclusions: Abnormality of AST, albumin, TBIL, ALP, and GGT but not ALT were independently associated with adverse outcomes

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure

Uplink Resource Allocation in Device-to-Device Communication System

In this paper, we study uplink resource allocation problem to maximize the overall system capacity while guaranteeing the signal-to-noise ratio of both D2D users and cellular users (CUs). The optimization problem can be decomposed into two subproblems: power control and channel assignment. We first prove that the objective function of power control problem is a convex function to get the optimal transmit power. Then, we design an optimal selection algorithm for channel assignment. Numerical results reveal the proposed scheme is capable of improving the system’s performance compared with the random selection algorithm

Diverse responses of vegetation phenology to changes in temperature and precipitation in Northern China

The start and end of the vegetation growing season (SOS and EOS) determine the length of the vegetation growing season and are necessary parameters to assess the primary productivity and carbon stocks of vegetation as well as the key dates to determine the start and end of soil wind erosion in arid zones. However, how SOS and EOS of different types of vegetation respond to changes in temperature and precipitation is still unclear. In this study, Moderate-resolution imaging spectroradiometer (MODIS)-normalized difference vegetation index data from 2001 to 2015 were used to estimate SOS and EOS for each vegetation type, the temperature and precipitation data recorded by the meteorological stations in the study area were used to calculate the average temperature and precipitation in 19 periods from June of the previous year to November of the following year, Spearman's rank correlation analysis was used to analyze the response relationships of SOS and EOS to average temperature and precipitation in each period. The results showed that SOS, EOS, the average timing of SOS (AvSOS) and the average timing of EOS (AvEOS) all had strong spatial heterogeneity. Precipitation is the key factor controlling SOS and EOS, an increase causing SOS to advance and EOS to be delayed, while higher temperature has the opposite effects. Overall, the average advancement rate of SOS was 0.88 day/year, the average delay rate of EOS was 0.33 day/year. From southeast to northwest, AvSOS gradually changed from 121st day to 141st day, whereas AvEOS was the opposite, advancing from 271st day to 282nd day. We divided the vegetation into 12 types in the soil wind erosion area of northern China according to latitudinal zonation and dominant vegetation species, the results avoided t a shortcoming of previous studies in which SOS and EOS were identified without distinguishing vegetation types

Power Efficient Secure Full-Duplex SWIPT Using NOMA and D2D with Imperfect CSI

The secure full-duplex (FD) simultaneous wireless information and power transfer (SWIPT) system and non-orthogonal multiple access (NOMA) have been deemed two promising technologies for the next generation of wireless communication. In this paper, the network is combined with device-to-device (D2D) and a practical bounded channel state information (CSI) estimation scheme. A system total transmit power minimization problem is studied and formulated as a multi-objective optimization (MOO) problem via the weighted Tchebycheff approach. A set of linear matrix inequalities (LMI) is used to transform the non-convex form of constraints into the convex form. Considering the imperfect CSI of the potential eavesdropper for robust power allocation, a bounded transmission beamforming vector design along with artificial noise (AN) is used, while satisfying the requirements from the secrecy rates as well as the energy harvesting (EH) task. Numerical simulation results validate the convergence performance and the trade-off between the uplink (UL) and downlink (DL) data transmit power. It is also shown that by FD and NOMA, the performance of the proposed algorithm is higher than that of half-duplex (HD) and orthogonal multiple access (OMA)

Mapping the fractional cover of non-photosynthetic vegetation and its spatiotemporal variations in the Xilingol grassland using MODIS imagery (2000−2019)

Obtaining accurate information on the fractional cover of non-photosynthetic vegetation (NPV) (fNPV) on grasslands is essential for monitoring soil erosion risk, assessing grassland productivity and managing grassland ecosystems. However, few studies have monitored fNPV in the Xilingol grassland region (XGR) and its spatiotemporal variations based on Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this study, we determined the upper dead fuel index (DFI) threshold for NPV in the XGR. Then, a remote-sensing inversion model for fNPV was established based on the ground-measured fNPV and the DFI derived from MODIS images. Based on the inversion model, we obtained the spatial distribution and spatiotemporal variations in fNPV from 2000 to 2019. The results indicated that the DFI can reflect the NPV in the XGR with an upper threshold of 27.2. The DFI-fNPV linear regression model showed a good performance, with a coefficient of determination (R2) of 0.60 and a root mean square error of leave-one-out cross-validation (RMSECV) of 0.1574. Furthermore, the spatial distribution of fNPV exhibited significant heterogeneity, and fNPV decreased from the northeastern XGR to the southwestern XGR. The overall trend of the interannual fNPV in the XGR increased in a fluctuating manner during 2000–2019. The fNPV increased in 66.92% of the XGR and decreased in a relatively small proportion (18.21%) of the XGR

Research on the characteristics of Ulva prolifera in Shandong Peninsula during 2008-2012 based on MODIS data

In the present study, the MODIS data were used to monitor the situation of Ulva. prolifera in the Shandong Peninsula waters during the period of 2008-2012. Those studies mainly calculate the area of NDVI, and get the information of the time, area, scope, floating path of Ulva. prolifera by using threshold segmentation method. The feasibility of monitoring Ulva. prolifera information based on MODIS data and the macroscopic regularity of the outburst of Ulva. prolifera was elementally studied. The results showed that Ulva. prolifera first generated in the middle of May or early June, the time, area, scope of Ulva. prolifera reached a maximum, but the relative crowding density was earlier or later when Ulva. prolifera developed into a outburst. Finally, Ulva. prolifera died away after existing for 71 days in the late July or the early August. Wholly, the floating path moved to the northwest from off the coast to offshore. Based on those aspects above, the outburst of Ulva. prolifera in 2008 and 2009 was more serious than others