An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval

The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN graph size due to the high-quantity of new connections between nodes in the graph, resulting in long computation times. Several strategies have been proposed to address this, but none are effective and efficient. Our novel technique, based on LSH projections, obtains the same performance as the exact kNN graph after diffusion, but in less time (approximately 18 times faster on a dataset of a hundred thousand images). The proposed method was validated and compared with other state-of-the-art on several public image datasets, including Oxford5k, Paris6k, and Oxford105k

Genetic variation at Exon2 of TLR4 gene and its association with resistant traits in chicken

This study was conducted to analyze the polymorphisms of chicken Toll-like receptors 4(TLR4) gene and aimed to provide a theoretical foundation for a further research on correlation between chicken TLR4 gene and disease resistance. Genetic variations at exon 2 of TLR4 gene in 14 chicken breeds and the red jungle fowl were detected by PCR-SSCP method and two alleles and three genotypes were found, Tibetan chicken and red jungle fowl only had BB genotype, while the others presented three genotypes of AA, BB and AB. Sequencing results showed two mutations, G114A and G142A, located at exon 2 of TLR4 gene. The results of Chi square test showed that all populations, except Xianju chicken, were in accordance with Hardy-Weinberg equilibrium at this locus (P > 0.05). According to analysis of population genetic variation, all the populations were at moderate polymorphism (0.25 < PIC < 0.5) except red jungle fowl and Tibetan chicken (PIC = 0). The study demonstrated that there were differences of normal anti-disease ability in Chinese indigenous chicken breeds and appeared no significant correlation with body size, product type and geographical location. The associated analysis of results showed that the SNPs of TLR4 gene in the study were not linked with potential major loci or genes affecting some resistant traits.Key words: Chicken, TLR4 gene, polymorphism, resistant traits

A factorial Bayesian copula framework for partitioning uncertainties in multivariate risk inference

National Key Research and Development Plan; Natural Sciences Foundation of China; Natural Science and Engineering Research Council of Canad

Development of clustered polynomial chaos expansion model for stochastic hydrological prediction

Data availability: The data that support the findings of this study are available from https://www.researchgate.net/publication/342065388_Yuanjiaan1981-1987. The code used in this paper are available from the corresponding author upon reasonable request.Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S002216942100069X?via%3Dihub#s0075 .This study introduced a clustered polynomial chaos expansion (CPCE) model to reveal random propagation and dynamic sensitivity of uncertainty parameters in hydrologic prediction. In the CPCE model, the random characteristics of the streamflow simulations resulting from parameter uncertainties are characterized through the polynomial chaos expansion (PCE) model based on the probabilistic collocation method. At the same time, a multivariate discrete non-functional relationship between PCE coefficients and hydrological model inputs is established based on stepwise cluster analysis. Therefore, compared with traditional PCE method, the developed CPCE model cannot only reflect uncertainty propagation in stochastic hydrological simulation, but also have the capability of random forecasting. Moreover, the dynamic sensitivities of model parameters are investigated through the multilevel factorial analyses. The developed approach was applied for streamflow forecasting for the Ruihe watershed, China. Results showed that with effective quantification for the random characteristics of hydrological processes, the CPCE model can directly predict runoff series and generate the associated probability distributions at different time periods. The dynamic sensitivity analysis indicates that the maximum soil moisture capacity within the catchment plays a key role in the accuracy of the low-flow forecasting, while the degree of spatial variability in soil moisture capacities has a remarkable impact on the accuracy of the high-flow forecasting in the studied watershed.National Key Research and Development Plan (2016YFC0502800), the Natural Sciences Foundation (51520105013, 51679087), the 111 Program (B14008) and the Natural Science and Engineering Research Council of Canada

Quantum dissipation and broadening mechanisms due to electron-phonon interactions in self-formed InGaN quantum dots

Quantum dissipation and broadening mechanisms in Si-doped InGaN quantum dots are studied via the photoluminescence technique. It is found that the dissipative thermal bath that embeds the quantum dots plays an important role in the photon emission processes. Observed spontaneous emission spectra are modeled with the multimode Brownian oscillator model achieving an excellent agreement between experiment and theory for a wide temperature range. The dimensionless Huang-Rhys factor characterizing the strength of electron-LO-phonon coupling and damping constant accounting for the LO-phonon-bath interaction strength are found to be ∼0.2 and 200 cm-1, respectively, for the InGaN QDs. © 2006 American Institute of Physics.published_or_final_versio

Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study

Guang-Hong Xie,1,* Jun Shen,2,* Fan Li,2 Huan-Huan Yan,2 Ying Qian3 1Department of Operating Room, The First People’s Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People’s Republic of China; 2Department of Breast Surgery, The First People’s Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People’s Republic of China; 3Department of Operating Room, Wuxi People’s Hospital, Wuxi, Jiangsu, 214063, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Qian Department of Operating Room, Wuxi People’s Hospital, No. 299 Qingyang Road, Liangxi District, Wuxi, 214063, Jiangsu, People’s Republic of China, Tel +8615190209358, Email [email protected]: We aimed to analyze the factors related to delay in transfer of patients in the post-anesthesia care unit (PACU) and to develop and validate a prediction model for understanding these factors to guide precise clinical intervention.Methods: We collected data from two cohorts of 1153 and 297 patients who underwent surgery and were treated in the PACU at two time points. We examined their clinical features and anesthesia care data using analytical methods such as logistic regression, Random Forest, and eXtreme Gradient Boosting (Xgboost) to screen out variables and establish a prediction model. We then validated and simplified the model and plotted a nomogram. Using LASSO regression, we reduced the dimensionality of the data. We developed multiple models and plotted receiver operating characteristic (ROC) and calibration curves. We then constructed a simplified model by pooling the identified variables, which included hemoglobin (HB), alanine transaminase (ALT), glucose levels, duration of anesthesia, and the minimum bispectral index value (BIS_min).Results: The model had good prediction performance parameters in the training and validation sets, with an AUC of 0.909 (0.887– 0.932) in the training set and 0.939 (0.919– 0.959) in the validation set. When we compared model 6 with other models, the net reclassification index (NRI) and the integrated discriminant improvement (IDI) index indicated that it did not differ significantly from the other models. We developed a scoring system, and it showed good prediction performance when verified with the training and validation sets as well as external data. Additionally, both the decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the potential clinical efficacy of the model in guiding patient interventions.Conclusion: Predicting transfer delays in the post-anesthesia care unit using predictive models is feasible; however, this merits further exploration.Keywords: delayed transfer, machine learning, nomogram, post-anesthesia care unit, predictive mode

Different definition of sarcopenia and mortality in cancer: A meta-analysis

Objectives: Sarcopenia has been an emerging theme in clinical oncology. Various definitions of sarcopenia have been proposed, but their prognostic performance have yet to be evaluated and compared. The aim of this meta-analysis is to comprehensively evaluate the performance of different cutoff definitions of sarcopenia in cancer mortality prognostication. / Methods: This is a meta-analysis. Cohort studies on lean mass and mortality published before December 20, 2017 were obtained by systematic search on PubMed, Cochrane Library, and Embase. Inclusion criteria were cohort studies reporting binary lean mass categorized according to clearly defined cutoffs, and with all-cause mortality as study outcome. Studies were stratified according to the cutoff(s) used in defining low lean mass. The cutoff-specific hazard ratios (HRs) and 95% confidence intervals (CIs) of low lean mass on cancer mortality were pooled with a random-effects model and compared. / Results: Altogether 81 studies that studied binary lean mass were included. The pooled HRs on cancer mortality using the 3 most used definitions were: 1.74 (95% CI, 1.46–2.07) using the definition proposed by International Consensus of Cancer Cachexia, 1.45 (95% CI, 1.21–1.75) using that by Martin, and 1.58 (95% CI, 1.35–1.84) using that by Prado. The associations between sarcopenia and cancer mortality using other definitions were all statistically significant, despite different estimates were observed. / Conclusions: The association of low lean mass with increased mortality was consistent across different definitions; this provides further evidence on the poorer survival in cancer patients with sarcopenia. However, further studies evaluating the performance of each definition are warranted

The Malagarasi River Does Not Form an Absolute Barrier to Chimpanzee Movement in Western Tanzania

The Malagarasi River has long been thought to be a barrier to chimpanzee movements in western Tanzania. This potential geographic boundary could affect chimpanzee ranging behavior, population connectivity and pathogen transmission, and thus has implications for conservation strategies and government policy. Indeed, based on mitochondrial DNA sequence comparisons it was recently argued that chimpanzees from communities to the north and to the south of the Malagarasi are surprisingly distantly related, suggesting that the river prevents gene flow. To investigate this, we conducted a survey along the Malagarasi River. We found a ford comprised of rocks that researchers could cross on foot. On a trail leading to this ford, we collected 13 fresh fecal samples containing chimpanzee DNA, two of which tested positive for SIVcpz. We also found chimpanzee feces within the riverbed. Taken together, this evidence suggests that the Malagarasi River is not an absolute barrier to chimpanzee movements and communities from the areas to the north and south should be considered a single population. These results have important consequences for our understanding of gene flow, disease dynamics and conservation management

Tuning a Circular p-n Junction in Graphene from Quantum Confinement to Optical Guiding

The motion of massless Dirac-electrons in graphene mimics the propagation of photons. This makes it possible to control the charge-carriers with components based on geometrical-optics and has led to proposals for an all-graphene electron-optics platform. An open question arising from the possibility of reducing the component-size to the nanometer-scale is how to access and understand the transition from optical-transport to quantum-confinement. Here we report on the realization of a circular p-n junction that can be continuously tuned from the nanometer-scale, where quantum effects are dominant, to the micrometer scale where optical-guiding takes over. We find that in the nanometer-scale junction electrons are trapped in states that resemble atomic-collapse at a supercritical charge. As the junction-size increases, the transition to optical-guiding is signaled by the emergence of whispering-gallery modes and Fabry-Perot interference. The creation of tunable junctions that straddle the crossover between quantum-confinement and optical-guiding, paves the way to novel design-architectures for controlling electronic transport.Comment: 16 pages, 4 figure