MULTILEVEL ANT COLONY OPTIMIZATION TO SOLVE CONSTRAINED FOREST TRANSPORTATION PLANNING PROBLEMS

In this dissertation, we focus on solving forest transportation planning related problems, including constraints that consider negative environmental impacts and multi-objective optimizations that provide forest managers and road planers alternatives for making informed decisions. Along this line of study, several multilevel techniques and mataheuristic algorithms have been developed and investigated. The forest transportation planning problem is a fixed-charge problem and known to be NP-hard. The general idea of utilizing multilevel approach is to solve the original problem of which the computational cost maybe prohibitive by using a set of increasingly smaller problems of which the computational cost is cheaper. The multilevel techniques are devised consisting of two parts. The first part is to recursively apply a graph coarsening heuristic to the original problem to produce a set of coarser level problems of which the sizes in terms of number of problem components such as edges and nodes are in decreasing order. The second part is to solve the set of the coarser level problems including the original problem bottom up, starting with the coarsest level. We propose that if coarser level problems inherit important properties (such as attribute value distribution) from their ancestor during the coarsening process, they can be treated as smaller versions of the original problem. Based on this hypothesis, the multilevel techniques use solutions obtained for the coarser level problems to solve the finer level problems. Mainly, we develop multilevel techniques to address three problems, namely a constrained fixed-charge problem, parameter configuration problem, and a multi-objective transportation optimization problem in this study. The performance of the multilevel techniques is compared with other commonly used approaches. The statistical analyses on the experimental results indicate that the multilevel approach can reduce computing time significantly without sacrificing the solution quality

A Framework for Consistency Based Feature Selection

Feature selection is an effective technique in reducing the dimensionality of features in many applications where datasets involve hundreds or thousands of features. The objective of feature selection is to find an optimal subset of relevant features such that the feature size is reduced and understandability of a learning process is improved without significantly decreasing the overall accuracy and applicability. This thesis focuses on the consistency measure where a feature subset is consistent if there exists a set of instances of length more than two with the same feature values and the same class labels. This thesis introduces a new consistency-based algorithm, Automatic Hybrid Search (AHS) and reviews several existing feature selection algorithms (ES, PS and HS) which are based on the consistency rate. After that, we conclude this work by conducting an empirical study to a comparative analysis of different search algorithms

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure

Non-invasive intravital imaging of cellular differentiation with a bright red-excitable fluorescent protein

A method for non-invasive visualization of genetically labelled cells in animal disease models with micron-level resolution would greatly facilitate development of cell-based therapies. Imaging of fluorescent proteins (FPs) using red excitation light in the “optical window” above 600 nm is one potential method for visualizing implanted cells. However, previous efforts to engineer FPs with peak excitation beyond 600 nm have resulted in undesirable reductions in brightness. Here we report three new red-excitable monomeric FPs obtained by structure-guided mutagenesis of mNeptune, previously the brightest monomeric FP when excited beyond 600 nm. Two of these, mNeptune2 and mNeptune2.5, demonstrate improved maturation and brighter fluorescence, while the third, mCardinal, has a red-shifted excitation spectrum without reduction in brightness. We show that mCardinal can be used to non-invasively and longitudinally visualize the differentiation of myoblasts and stem cells into myocytes in living mice with high anatomical detail

Pedestrian Guiding Signs Optimization for Airport Terminal

The pedestrian guiding sign (PGS) is used to lead people within the transportation terminal to their directions efficiently and without boundaries. In this paper, we aim to optimize the guiding signs for people in the comprehensive transportation terminal with a mathematical model, which describes the pedestrian's reaction, judgment, and perception of the outline about the guiding signs, as well as pedestrian's moving status through self-organized characteristic behavior. Furthermore, the model also reflects the information intensity of the guiding signs within the pedestrian's visual field which is taken as the influence level score of PGS. In order to solve the model, cellular automation (CA) is employed to simulate the characteristics of the pedestrians such as crowd moving and sign selection

The Dynamical Decision Model of Intersection Congestion Based on Risk Identification

The paper focuses on the problem of traffic congestion at intersection based on the mechanism of risk identification. The main goal of this study is to explore a new methodology for identifying and predicting the intersection congestion. Considering all the factors influencing the traffic status of intersection congestion, an integrated evaluation index system is constructed. Then, a detailed dynamic decision model is proposed for identifying the risk degree of the traffic congestion and predicting its influence on future traffic flow, which combines the traffic flow intrinsic properties with the basic model of the Risking Dynamic Multi-Attribute Decision-Making theory. A case study based on a real-world road network in Baoji, China, is implemented to test the efficiency and applicability of the proposed modeling. The evaluation result is in accord with the actual condition and shows that the approach proposed can determine the likelihood and risk degree of the traffic congestion occurring in the intersection, which can be used as a tool to help transport managers make some traffic control measures in advance. Document type: Articl

Comparison of Nasopharyngeal MR, 18 F-FDG PET/CT, and 18 F-FDG PET/MR for Local Detection of Natural Killer/T-Cell Lymphoma, Nasal Type.

Objectives The present study aims to compare the diagnostic efficacy of MR, 18F-FDG PET/CT, and 18F-FDG PET/MR for the local detection of early-stage extranodal natural killer/T-cell lymphoma, nasal type (ENKTL). Patients and Methods Thirty-six patients with histologically proven early-stage ENKTL were enrolled from a phase 2 study (Cohort A). Eight nasopharyngeal anatomical regions from each patient were imaged using 18F-FDG PET/CT and MR. A further nine patients were prospectively enrolled from a multicenter, phase 3 study; these patients underwent 18F-FDG PET/CT and PET/MR after a single 18F-FDG injection (Cohort B). Region-based sensitivity and specificity were calculated. The standardized uptake values (SUV) obtained from PET/CT and PET/MR were compared, and the relationship between the SUV and apparent diffusion coefficients (ADC) of PET/MR were analyzed. Results In Cohort A, of the 288 anatomic regions, 86 demonstrated lymphoma involvement. All lesions were detected by 18F-FDG PET/CT, while only 70 were detected by MR. 18F-FDG PET/CT exhibited a higher sensitivity than MR (100% vs. 81.4%, χ2 = 17.641, P < 0.001) for local detection of malignancies. The specificity of 18F-FDG PET/CT and MR were 98.5 and 97.5%, respectively (χ2 = 0.510, P = 0.475). The accuracy of 18F-FDG PET/CT was 99.0% and the accuracy of MR was 92.7% (χ2 = 14.087, P < 0.001). In Cohort B, 72 anatomical regions were analyzed. PET/CT and PET/MR have a sensitivity of 100% and a specificity of 92.5%. The two methods were consistent (κ = 0.833, P < 0.001). There was a significant correlation between PET/MR SUVmax and PET/CT SUVmax (r = 0.711, P < 0.001), and SUVmean (r = 0.685, P < 0.001). No correlation was observed between the SUV and the ADC. Conclusion In early-stage ENKTL, nasopharyngeal MR showed a lower sensitivity and a similar specificity when compared with 18F-FDG PET/CT. PET/MR showed similar performance compared with PET/CT

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

BackgroundRegulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD).MethodsTregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes.ResultsThe cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cellsConclusionTRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients

Phragmites australis

Aquatic plants play an essential role and are effective in mitigating lake eutrophication by forming complex plant-soil system and retaining total nitrogen (TN) and phosphorus (TP) in soils to ultimately reduce their quantities in aquatic systems. Two main vegetation types (Phragmites australis community and P. australis + Typha latifolia community) of Qin Lake wetland were sampled in this study for the analysis of TN and TP contents and reserves in the wetland soils. The results showed that (1) the consumption effect of Qin Lake wetland on soluble N was much more significant than on soluble P. (2) The efficiency of TN enrichment in wetland soil was enhanced by vegetation covering of P. australis and T. latifolia. (3) Wetland soil P was consumed by P. australis community and this pattern was relieved with the introduction of T. latifolia. (4) According to the grey relativity analysis, the most intensive interaction between plants and soil occurred in summer. In addition, the exchange of N in soil-vegetation system primarily occurred in the 0–15 cm soil layer. Our results indicated that vegetation covering was essential to the enrichment of TN and TP, referring to the biology-related fixation in the wetland soil

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk outcome pairs, and new data on risk exposure levels and risk outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017,34.1 million (95% uncertainty interval [UI] 33.3-35.0) deaths and 121 billion (144-1.28) DALYs were attributable to GBD risk factors. Globally, 61.0% (59.6-62.4) of deaths and 48.3% (46.3-50.2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10.4 million (9.39-11.5) deaths and 218 million (198-237) DALYs, followed by smoking (7.10 million [6.83-7.37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6.53 million [5.23-8.23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4.72 million [2.99-6.70] deaths and 148 million [98.6-202] DALYs), and short gestation for birthweight (1.43 million [1.36-1.51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4.9% (3.3-6.5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23.5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18.6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning