Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently. However, most of the existing methods focus ...on addressing the task of nonoverlapping community detection, where each node must belong to one and only one community. In fact, communities are often overlapped with each other in many real-world networks, thus it is necessary to design overlapping community detection algorithms. To this end, this paper proposes a mixed representation-based MOEA (MR-MOEA) for overlapping community detection. In MR-MOEA, a mixed individual representation scheme is proposed to fast encode and decode the overlapping divisions of complex networks. Specifically, this mixed representation consists of two parts: one represents all potential overlapping nodes and the other delegates all nonoverlapping nodes. These two parts evolve together to detect the overlapping communities of networks based on different updating strategies suggested in MR-MOEA. We verify the effectiveness of the proposed algorithm MR-MOEA on ten real-world complex networks and the experimental results demonstrate that MR-MOEA is superior over six representative algorithms for overlapping community detection.
Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the ...exponential increase of search space. In this paper, we suggest a network reduction-based multiobjective evolutionary algorithm for community detection in large-scale networks, where the size of the networks is recursively reduced as the evolution proceeds. In each reduction of the network, the local communities found by the elite individuals in the population are identified as nodes of the reduced network for further evolution, thereby considerably reducing the search space. A local community repairing strategy is also suggested to correct the misidentified nodes after each network reduction during the evolution. Experimental results on synthetic and real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms for large-scale networks, in terms of both computational efficiency and detection performance.
For sparse ultra-wideband signals in frequency domain, the non-uniform adoption system is used to sample the signals at different rates. OMP algorithm, LASSO algorithm and BCS algorithm are used to ...recover the non-uniformly sampled signal. According to the analysis and comparison of the simulation results, the following conclusions are drawn: BSC algorithm has little change in time-consuming and robustness with the increase of the number of observations, and has better reconstruction performance when the number of observations is low and the noise is included.
Traditional Chinese solid-state fermented cereal starters contain highly complex microbial communities and enzymes. Very little is known, however, about the microbial dynamics related to ...environmental conditions, and cellulolytic communities have never been proposed to exist during cereal starter fermentation. In this study, we performed Illumina MiSeq sequencing combined with PCR-denaturing gradient gel electrophoresis to investigate microbiota, coupled with clone library construction to trace cellulolytic communities in both fermentation stages. A succession of microbial assemblages was observed during the fermentation of starters. Lactobacillales and Saccharomycetales dominated the initial stages, with a continuous decline in relative abundance. However, thermotolerant and drought-resistant Bacillales, Eurotiales, and Mucorales were considerably accelerated during the heating stages, and these organisms dominated until the end of fermentation. Enterobacteriales were consistently ubiquitous throughout the process. For the cellulolytic communities, only the genera Sanguibacter, Beutenbergia, Agrobacterium, and Erwinia dominated the initial fermentation stages. In contrast, stages at high incubation temperature induced the appearance and dominance of Bacillus, Aspergillus, and Mucor. The enzymatic dynamics of amylase and glucoamylase also showed a similar trend, with the activities clearly increased in the first 7 days and subsequently decreased until the end of fermentation. Furthermore, β-glucosidase activity continuously and significantly increased during the fermentation process. Evidently, cellulolytic potential can adapt to environmental conditions by changes in the community structure during the fermentation of starters.
Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30% of severely injured patients who are admitted ...to emergency departments (EDs). Given that conventional coagulation indicators usually require more than 1 hour after admission to yield results—a limitation that frequently prevents the ability for clinicians to make appropriate interventions during the optimal therapeutic window—it is clearly of vital importance to develop prediction models that can rapidly identify ATC; such models would also facilitate ancillary resource management and clinical decision support. Using the critical care Emergency Rescue Database and further collected data in ED, a total of 1385 patients were analyzed and cases with initial international normalized ratio (INR) values >1.5 upon admission to the ED met the defined diagnostic criteria for ATC; nontraumatic conditions with potentially disordered coagulation systems were excluded. A total of 818 individuals were collected from Emergency Rescue Database as derivation cohorts, then were split 7:3 into training and test data sets. A Pearson correlation matrix was used to initially identify likely key clinical features associated with ATC, and analysis of data distributions was undertaken prior to the selection of suitable modeling tools. Both machine learning (random forest) and traditional logistic regression were deployed for prediction modeling of ATC. After the model was built, another 587 patients were further collected in ED as validation cohorts. The ATC prediction models incorporated red blood cell count, Shock Index, base excess, lactate, diastolic blood pressure, and potential of hydrogen. Of 818 trauma patients filtered from the database, 747 (91.3%) patients did not present ATC (INR ≤ 1.5) and 71 (8.7%) patients had ATC (INR > 1.5) upon admission to the ED. Compared to the logistic regression model, the model based on the random forest algorithm showed better accuracy (94.0%, 95% confidence interval CI: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95), precision (93.3%, 95% CI: 0.914-0.948 to 93.1%, 95% CI: 0.912-0.946), F1 score (93.4%, 95% CI: 0.915-0.949 to 92%, 95% CI: 0.9-0.937), and recall score (94.0%, 95% CI: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95) but yielded lower area under the receiver operating characteristic curve (AU-ROC) (0.810, 95% CI: 0.673-0.918 to 0.849, 95% CI: 0.732-0.944) for predicting ATC in the trauma patients. The result is similar in the validation cohort. The values for classification accuracy, precision, F1 score, and recall score of random forest model were 0.916, 0.907, 0.901, and 0.917, while the AU-ROC was 0.830. The values for classification accuracy, precision, F1 score, and recall score of logistic regression model were 0.905, 0.887, 0.883, and 0.905, while the AU-ROC was 0.858. We developed and validated a prediction model based on objective and rapidly accessible clinical data that very confidently identify trauma patients at risk for ATC upon their arrival to the ED. Beyond highlighting the value of ED initial laboratory tests and vital signs when used in combination with data analysis and modeling, our study illustrates a practical method that should greatly facilitates both warning and guided target intervention for ATC.
Fibrinogen and platelet, as the two main components of hemostatic resuscitation, are frequently administered in traumatic massive hemorrhage patients. It is reasonable to infer that they may have an ...impact on post-traumatic sepsis as more and more recognition of their roles in inflammation and immunity. This study aims to determine the association between the fibrinogen/platelet transfusion ratio during the first 24 h after trauma and the risk of the post- traumatic sepsis.
We analyzed the data from the National Trauma Data Bank (NTDB). Subjects included the critically injured adult patients admitted to Level I/II trauma center from 2013 to 2017 who received fibrinogen and platelet supplementation and more than 10 units (about 4000 ml) packed red blood cells (pRBCs) during the first 24 h after trauma. Two parts of analyses were performed: (1) multivariable stepwise regression was used to determine the variables that influence the risk of post-traumatic sepsis; (2) propensity score matching (PSM), to compare the influences of different transfusion ratio between fibrinogen and platelet on the risk of sepsis and other outcomes after trauma.
8 features were screened out by bi-directional multivariable stepwise logistic regression to predict the post-traumatic sepsis. They are age, sex, BMI, ISSabdomen, current smoker, COPD, Fib4h/24h and Fib/PLT24h. Fib/PLT24h was negatively related to sepsis (p < 0.05). A total of 1601 patients were included in the PSM cohort and grouped by Fib/PLT24h = 0.025 according to the fitting generalized additive model (GAM) model curve. The incidence of sepsis was significantly decreased in the high Fib/PLT group 3.3 % vs 9.4 %, OR = 0.33, 95 %CI (0.17–0.60); the length of stay in ICU and mechanical ventilation were both shortened as well 8 (IQR 2.00,17.00) vs 9 (IQR 3.00,19.25), p = 0.006 and 4 (IQR 2.00,10.00) vs 5 (IQR 2.00,14.00), p = 0.003, respectively.
Early and sufficient supplementation of fibrinogen was a convenient way contribute to reduce the risk of sepsis after trauma.
Cardiovascular autonomic neuropathy (CAN) is a debilitating condition occurring among diabetic patients especially those with long duration of disease. Whereas incidences and treatment of CAN has ...been well described for Western populations, fewer studies have been conducted among the Chinese. This study, therefore, aimed to assess the prevalence of CAN among sampled Chinese diabetic patients. Accordingly, 2,048 participants with a history of type 1 diabetes mellitus (T1DM, 73) and type 2 diabetes mellitus (T2DM, 1975) were randomly sampled from 13 hospitals. Patients’ biodata were recorded, and autonomic nervous system function tests performed to aid in the preliminary diagnosis of CAN. The final CAN diagnosis was based on the Ewing's test in which heart rate variation (HRV) values were evaluated through deep-breathing (DB), lying-to-standing (LS) and Valsalva (V) tests. Systolic blood pressure (SBP) variation values were also evaluated through LS. In the T1DM group, 61.6% patients were diagnosed with CAN and no differences were observed in the baseline and clinical data between this group and those without CAN (P>0.05). In the T2DM group, 62.6% patients were diagnosed with CAN and statistically significant differences were found between the CAN and non- CAN group with regards to age, duration of diabetes, metformin treatment, retinopathy and hypertension history (P<0.05). The most common manifestations of CAN included weakness (28.6%), dizziness (23.4%), frequent urination (19.6%), upper body sweating (18.3%) and nocturia (15.9%). Additionally, duration of disease and age were independent risk factors for CAN in T1DM and T2DM, respectively. On diagnosis, a combination of the V test + LS test provided the highest sensitivity of detecting CAN among T1DM group (sensitivity=97.6%, AUC=0.887) while for T2DM category, DB test had the highest sensitivity (83.6%), and maximal AUC (0.856) was found with V test + DB test. The overall prevalence of diabetes with CAN in the study was up to 63%.
Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes, and its progression significantly worsens the patient's quality of life. This study investigated the prevalence and ...risk factors associated with DPN in a large sample of Beijing individuals with type 1 and 2 diabetes, as well as compared the diagnostic methods for DPN. A total of 2,048 diabetic patients from 13 centers in Beijing were assessed for DPN through questionnaires and examination. Patients were divided into DPN group and suspected DPN/non-DPN group. The demographic, clinical and biological characteristics between the two groups were compared. Binary logistic regression analysis was performed to identify potential variables associated with DPN in diabetic patients. The diagnostic methods for DPN were also compared. Among the 2,048 diabetic patients, 73 cases of type 1 diabetes mellitus, 1,975 cases of type 2 diabetes were included in this study. Among them, 714 (34.86%) were identified with DPN, 537 (26.22%) were suspected of having DPN, and 797 (38.92%) were identified without DPN. Patient's age, duration of diabetes, and diabetic retinopathy were the significant independent risk factor for DPN among patients with type 2 diabetes. The odds ratio (OR) was 1.439 (95% confidence interval (CI): 1.282-1.616,
< 0.001), 1.297 (95% CI: 1.151-1.462,
< 0.001), and 0.637 (95% CI: 0.506-0.802,
< 0.001), respectively. Ankle reflex, temperature sensation plus vibration sensation are the best screening test for patients with type 1 and 2 diabetes. The Youden indexes were 62.2 and 69.8%, respectively. The prevalence rates of DPN in the Chinese patients with type 1 and type 2 diabetes in Beijing were 21.92 and 35.34%, respectively. Patient's age, duration of diabetes, and diabetic retinopathy are the independent risk factors for DPN.
In the present study, the dynamics of microbial communities and their abundance associated with each stage of a tannery wastewater treatment process were investigated by polymerase chain ...reaction-denaturing gradient gel electrophoresis (PCR-DGGE) combined with high throughput sequencing. Both PCR-DGGE and high throughput sequencing results reflected the bacterial succession in the integrated treatment process and phyla of Proteobacteria, Bacteroidetes and Firmicutes dominated throughout the integrated treatment process. However, Actinobacteria, Planctomycetes and Chlofoflexi only predominant the anoxic/oxic (A/O) process. The bacterial richness during the A/O process was higher than that in the pre-treatment process. Moreover, quantitative polymerase chain reaction (qPCR) analysis indicated that the absolute abundance of 16S rRNA genes in the biological treatment stages were higher than in other stages. Finally, redundancy analysis suggested that Thermi should be involved in NH4+-N removal and ammonia concentration had positive effects on the bacterial diversity. Overall, this study provided insight into the evolution of the bacterial community structure and diversity in integrated wastewater treatment processes and identified the correlations between the physicochemical characteristics of wastewater and bacterial community structures.
Background: Epidermal growth factor receptor (EGFR) is an essential target for cancer treatment. However, EGFR inhibitor erlotinib showed limited clinical benefit in pancreatic cancer therapy. Here, ...we showed the underlying mechanism of tumor microenvironment suppressing the sensitivity of EGFR inhibitor through the pancreatic stellate cell (PSC). Methods: The expression of alpha-smooth muscle actin (α-SMA) and hypoxia marker in human pancreatic cancer tissues were detected by immunohistochemistry, and their correlation with overall survival was evaluated. Human immortalized PSC was constructed and used to investigate the potential effect on pancreatic cancer cell lines in hypoxia and normoxia. Luciferase reporter assay and Chromatin immunoprecipitation were performed to explore the potential mechanisms in vitro. The combined inhibition of EGFR and Met was evaluated in an orthotopic xenograft mouse model of pancreatic cancer. Findings: We found that high expression levels of α-SMA and hypoxia markers are associated with poor prognosis of pancreatic cancer patients. Mechanistically, we demonstrated that hypoxia induced the expression and secretion of HGF in PSC via transcription factor HIF-1α. PSC-derived HGF activates Met, the HGF receptor, suppressing the sensitivity of pancreatic cancer cells to EGFR inhibitor in a KRAS-independent manner by activating the PI3K-AKT pathway. Furthermore, we found that the combination of EGFR inhibitor and Met inhibitor significantly suppressed tumor growth in an orthotopic xenograft mouse model. Interpretation: Our study revealed a previously uncharacterized HIF1α-HGF-Met-PI3K-AKT signaling axis between PSC and cancer cells and indicated that EGFR inhibition plus Met inhibition might be a promising strategy for pancreatic cancer treatment. Funding: This study was supported by The National Natural Science Foundation of China.
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