Glyphosate resistance and no fitness cost in backcross offspring of wild soybean and transgenic soybean with epsps gene.

BACKGROUND: The commercial utilization of genetically modified soybeans has yielded substantial economic advantages. Nevertheless, the genetic drift towards wild soybeans is one of the main ecological risks that needs to be addressed. Previous experiments demonstrated the absence of fitness cost or florescence overlap in hybrid offspring resulting from the crossbreeding of transgenic soybean GTS40-3-2 and Zhengzhou wild soybeans. In this study, hybrid progeny was systematically crossed with wild soybeans to establish a backcross progeny system. This system was employed to evaluate the ecological risk associated with the backcross progeny of transgenic and wild soybeans. RESULTS: The findings indicated that the offspring from the backcross exhibited glyphosate tolerance. Furthermore, the expression of foreign proteins in the backcross offspring was notably lower than in the transgenic soybean, and there was no significant difference when compared to the hybrid progeny. Parameters such as germination rate, aboveground biomass, pods per plant, full seeds per plant, and 100-grain weight exhibited no significant differences between the negative and positive lines of the backcross progenies, and no fitness cost was identified in comparison to wild soybeans. These results underscore the potential for foreign genes to propagate within other wild soybeans, which requires continuous attention. CONCLUSIONS: The widespread adoption of genetically modified soybeans has undeniably led to substantial economic gains. However, the research findings emphasize the critical importance of addressing the ecological risks posed by genetic drift towards wild soybeans. The backcross progeny system established in this study indicates that the potential for foreign gene dissemination to wild soybean populations warrants continued attention and mitigation strategies.

The relationship between air pollution and the occurrence of hypertensive disorders of pregnancy: Evidence from a study in Wuhan, China.

Ambient air pollution has been reported to be a risk factor for hypertensive disorders of pregnancy (HDP). Past studies have reported supportive evidence, but evidence from China is scarce and does not integrate the different periods of the pregnancy course. In this study, 1945 pregnant women with HDP and healthy pregnancies between 2016 and 2022 from the Renmin Hospital of Wuhan University registry network database were analysed. The geographic information, biological information and demographic information of the case were fused in the analysis. Machine learning methods were used to obtain the weight of the variable. Then, we used the generalized linear mixed model to evaluate the relationship between increased exposure to each pollutant at different periods of HDP and examined it in different groups. The results showed that SO2 had the predominate impact (12.65 %) on HDP compared with other air pollutants. SO2 exposure was associated with an increased risk of HDP. Increased unit SO2 concentrations were accompanied by an increased risk of HDP (OR = 1.33, 95 % CI: 1.13, 1.566), and the susceptible window for this effect was mainly in the first trimester (OR = 1.242, 95 % CI: 1.092, 1.412). In addition, SO2 exposure was associated with an increased risk of HDP in urban maternity (OR = 1.356, 95 % CI: 1.112, 1.653), obese maternity (OR = 3.58, 95 % CI: 1.608, 7.971), no higher education maternity (OR = 1.348, 95 % CI: 1.065, 1.706), nonzero delivery maternity (OR = 1.981, 95 % CI: 1.439, 2.725), maternal with first time maternity (OR = 1.247, 95 % CI: 1.007, 1.544) and other groups. In summary, SO2 exposure in early pregnancy is one of the risk factors for HDP, and the increased risk of HDP due to increased SO2 exposure may be more pronounced in obese, urban, low-education, and nonzero delivery populations.

Metabolic profiling and spatial metabolite distribution in wild soybean (G. soja) and cultivated soybean (G. max) seeds.

Wild soybeans retain many substances significantly reduced or lost in cultivars during domestication. This study utilized LC-MS to analyze metabolites in the seed coats and embryos of wild and cultivated soybeans. 866 and 815 metabolites were identified in the seed extracts of both soybean types, with 35 and 10 significantly differing metabolites in the seed coat and embryos, respectively. The upregulated metabolites in wild soybeans are linked to plant defense, stress responses, and nitrogen cycling. MALDI-MSI results further elucidated the distribution of these differential metabolites in the cotyledons, hypocotyls, and radicles. In addition to their role in physiological processes like growth and response to environmental stimuli, the prevalent terpenoids, lipids, and flavonoids present in wild soybeans exhibit beneficial bioactivities, including anti-inflammatory, antibacterial, anticancer, and cardiovascular disease prevention properties. These findings underscore the potential of wild soybeans as a valuable resource for enhancing the nutritional and ecological adaptability of cultivated soybeans.

Flavonoid localization in soybean seeds: Comparative analysis of wild (Glycine soja) and cultivated (Glycine max) varieties.

Wild soybean (Glycine soja) is known for its high flavonoid contents, yet the distribution of flavonoids in the seeds is not well understood. Herein, we utilized matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and metabolomics methods to systematically investigate flavonoid differences in the seed coats and embryos of G. soja and G. max. The results of flavonoid profiles and total flavonoid content analyses revealed that flavonoid diversity and abundance in G. soja seed coats were significantly higher than those in G. max whereas the levels were similar in embryos. Specifically, 23 unique flavonoids were identified in the seed coats of G. soja, including procyanidins, epicatechin derivatives, and isoflavones. Using MALDI-MSI, we further delineated the distribution of the important flavonoids in the cotyledons, hypocotyls, and radicles of the two species. These findings imply that G. soja holds considerable breeding potential to enhance the nutritional and stress resistance traits of G. max.

Seed coat colour and structure are related to the seed dormancy and overwintering ability of crop-to-wild hybrid soybean.

The possible persistence of genetically modified (GM) crop-to-wild hybrid seeds in the soil seed bank is a major concern in risk assessment and is closely related to seed characteristics such as dormancy. In the present study, we generated F3 hybrids via crosses between GM soybean accessions and wild soybean and evaluated the dormancy, overwintering ability and inheritance of foreign genes in different-coloured hybrid seeds (yellow, green, brown and black). The results revealed that the 5-enolpyruvylshikimate-3-phosphate synthase transgene may have no influence on crop wild hybrid seed dormancy and overwintering ability, and the dormancy of the hybrid seeds was closely related to seed coat colour. F3 hybrid seeds with light colours (yellow and green) were relatively nondormant, while seeds that were dark (brown and black) in colour were relatively dormant. Moreover, the hybrid seeds that were dark in colour had a much stronger overwintering ability than the lighter-coloured seeds, with 21.33 % of the black seeds and 33.33 % of the brown seeds remaining viable after 240 days of soil burial. In contrast, almost all the F3 yellow and green seeds were no longer viable during winter. Scanning electron microscopy revealed that the lighter-coloured seeds had a thin palisade layer and very few surface deposits, while the darker-coloured seeds had a thicker palisade layer and a large area of honeycomb-like surface deposits similar to those of wild soybean seeds. Thus, the physical dormancy and overwintering ability of the darker-coloured seeds may be related to the seed coat. Our results suggest that transgenes of GM soybean might disperse into wild populations and persist in seed banks.

Fitness changes in wild soybean caused by gene flow from genetically modified soybean.

BACKGROUND: Crop-wild hybridization has generated great concerns since gene flow can be an avenue for transgene escape. However, a rather limited number of studies on risk assessment regarding the dispersion of transgenes from GM soybean to populations of its wild relatives have been previously conducted. RESULTS: The results of the 3-year experiment demonstrated that hybrids between GM soybeans and wild soybean had lower seed germination and higher seed productivity than GM soybean. Both of these features of hybrid (especially F2 and F3) were similar to those of wild soybean. Furthermore, the foreign protein was stably expressed in hybrid EPSPS positive plants; however, no difference was observed in agronomic measurements between hybrids that are glyphosate sensitive or resistant, homozygous or heterozygous for the transgene, indicating that the presence of the EPSPS transgene does not affect the vigor of hybrid. In contrast, hybridization between GM soybean and wild soybean may have more impact on hybrid growth and fecundity, this increase in biomass and yield confers a potential competition benefit to hybrids. CONCLUSIONS: Gene flow from GM soybean to wild soybean has the potential to promote the adaptability of hybrids and may increase the possibility of dispersal of transgenes in wild soybean relatives.

Estimating Ulva prolifera green tides of the Yellow Sea through ConvLSTM data fusion.

Green tides, a worldwide problem, are harmful to aquaculture, tourism, marine ecosystems, and maritime traffic. Currently, green tide detection relies on remote sensing (RS) images, which are often missing or unusable. Thus, the observation and detection of green tides cannot be performed daily, which makes it difficult to improve environmental quality and ecological health. To address this problem, this study proposed a novel green tide estimation framework (GTEF) through convolutional long short-term memory, which learned the historical spatial-temporal seasonal and trend patterns of green tides from 2008 to 2021 and fused the previously observed or estimated data and biological (optional) and physical (optional) data over the preceding seven days when RS images were absent or unusable for daily observation and detection tasks. The results showed that the overall accuracy (OA), false-alarm rating (FAR), and missing-alarm rating (MAR) of the GTEF were 0.9592 ± 0.0375, 0.0885 ± 0.1877 and 0.4315 ± 0.2848, respectively. The estimated results described the green tides in terms of attributes, geometry and position features. Especially in the latitudinal features, the Pearson correlation coefficient of the predicted data and observed data were over 0.8 (P < 0.05), which showed a strong correlation. In addition, this study also discussed the role of biological and physical factors in the GTEF. Sea surface salinity may be the dominant factor in the early stages of green tides; in the late stage, solar irradiance may be the dominant factor. Sea surface winds and sea surface currents also played a significant role in green tide estimation. Results showed the OA, FAR and MAR of the GTEF which, with physical factors but without biological factors, were 0.9556 ± 0.0389, 0.1311 ± 0.3338 and 0.4297 ± 0.3180, respectively. In short, the proposed approach could generate a daily map of green tides, even if RS images were missing or unusable.

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach.

BACKGROUND: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. OBJECTIVE: The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. METHODS: In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. RESULTS: Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians' assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). CONCLUSIONS: The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

An urban crowd flow model integrating geographic characteristics.

Predicting urban crowd flow spatial distributions plays a critical role in optimizing urban public safety and traffic congestion management. The spatial dependency between regions and the temporal dynamics of the local crowd flow are two important features in urban crowd flow prediction. However, few studies considered geographic characteristic in terms of spatial features. To fill this gap, we propose an urban crowd flow prediction model integrating geographic characteristics (FPM-geo). First, three geographic characteristics, proximity, functional similarity, and road network connectivity, are fused by a residual multigraph convolution network to model the spatial dependency relationship. Then, a long short-term memory network is applied as a framework to integrate both the temporal dynamic patterns of local crowd flow and the spatial dependency between regions. A 4-day mobile phone dataset validates the effectiveness of the proposed method by comparing it with several widely used approaches. The result shows that the root mean square error decreases by 15.37% compared with those of the typical models with the prediction interval at the 15-min level. The prediction error increases with the crowd flow size in a local area. Moreover, the error reaches the top of the morning peak and the evening peak and slopes down to the bottom at night.

Assessment of global habitat suitability and risk of ocean green tides.

Green tides, which are widespread problems, are harmful issues that affect the protection of ocean ecosystems and natural resources. Scientific assessment and prevention of the green tides are essential for sustainable planning and the utilization of maritime traffic, tourism, and industry. However, the suitable or risk habitats and their dominant factors of green tides from global perspective are unknown. Here, this study proposed a novel framework to show the habitat suitability and risk of ocean green tides by considering marine environmental factors (i.e., sea surface temperature, sea surface salinity, solar irradiance, chlorophyll-a concentration, and sea surface wind). Through global remote sensing images and marine environmental factor data, this study found that (1) suitable and at-risk green tides areas are located in the north and south temperate zones; (2) marine physical factors are expected to weaken the green tide risk globally and enhance the green tide risk in coastal areas; (3) the green tides in the North Atlantic Ocean and the West Pacific Ocean are dominated by environmental factors and physical factors, respectively; and (4) when reducing carbon to promote sustainability, more potentially suitable green tide areas may appear at high latitudes. The results demonstrate the at-risk location and future trend of green tides, which are helpful for sustainable planning of ocean ecosystems.

Optimizing Living Material Delivery During the COVID-19 Outbreak.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

Fitness and Ecological Risk of Hybrid Progenies of Wild and Herbicide-Tolerant Soybeans With EPSPS Gene.

Exogenous genes of transgenic crops are usually transferred to their wild-type relatives through pollen-mediated gene flow, which may change the ecological fitness and ability to invade wild populations, resulting in the weeding of wild plants and other unpredictable environmental impacts. In this study, the F1 generation of herbicide-resistant soybeans and wild soybeans was obtained by artificial pollination, F2 generation seeds were obtained by self-crossing, and the fitness of the parents and their F1 and F2 generations were tested. The foreign protein EPSPS was expressed normally in the hybrid between transgenic and wild soybeans; however, the protein expression was significantly lower than that in transgenic soybeans. The fitness of the F1 hybrid between transgenic and wild soybeans was significantly lower than that of its parent. Compared with those of the wild soybeans, the F2 generation soybeans improved in some fitness indices, while the emergence rate, pollen germination rate, and number of full seeds per pod, pods per plant, and full seeds per plant did not significantly differ. The aboveground biomass and 100-seed weight of the F2 generation were higher than those of wild soybeans. Fitness among the F2-negative plants, homozygous, and heterozygous positive plants did not significantly vary. Improved fitness and presence of foreign genes in the F2 soybean were not significantly correlated. As the F2 generation of transgenic and wild soybeans had no fitness cost and the flowering stage were overlapped, the foreign gene might still spread in the wild soybean population.

Fitness of Insect-resistant transgenic rice T1C-19 under four growing conditions combining land use and weed competition.

Transgene escape into natural ecosystems through seed spraying or transgene introgression may potentially cause environmental biosafety problems. In this study, we assessed the environmental risk of insect-resistant transgenic rice entering farmland margins or natural ecosystems adjacent to farmland. Transgenic Cry1C* rice (T1C-19) was used to study the effects of exogenous Cry1C* expression on vegetative and reproductive growth indices under different growing conditions using the following four combined treatments of land use and weeds: farmland and uncultivated land without weeds (F-NW and U-NW, respectively), and farmland and uncultivated land with weeds (F-W and U-W, respectively). The expression of Cry1C* protein under the U-NW, F-W, and U-W conditions was significantly lower than under the control condition, F-NW. Tiller number, biomass, filled grain number, filled grain weight, and other vegetative and reproductive indices were significantly lower in the rice line TIC-19 than in MH63 under F-NW and U-NW conditions, indicating a significant fitness cost. However, under F-W and U-W conditions, vegetative growth indices such as plant height, tiller number, and biomass, as well as reproductive growth indices such as filled grain number per plant, filled grain weight per plant, and seed setting rate in TIC-19 were similar to those in MH63, indicating a long-term coexistence. These results indicate a lower ecological risk of T1C-19 compared to MH63 under F-NW and U-NW, although their long-term coexistence may lead to potential ecological risks under F-W and U-W.

Effects of genetically modified maize expressing Cry1Ab and EPSPS proteins on Japanese quail.

A 49-d feeding study was conducted to evaluate the effects of the genetically modified (GM) maize strain C0030.3.5 on Japanese quails (Coturnix japonica) in terms of body performance and egg quality. Furthermore, the bodily fats of transgenic proteins in the Japanese quails were investigated. The results showed that the parameters body weight, hematology, serum chemistry, relative organ weight, and histopathological appearance were normal in male and female quails that consumed GM diets, and no differences could be attributed to the varying diets in regard to the laying performances or nutrient egg compositions between the groups. Furthermore, the transgenic Cry1Ab and EPSPS proteins were undetectable by Western blot in the blood, organ, fecal, and whole egg samples of quails fed a diet containing GM maize. The results obtained after 49 d suggested that consumption of C0030.3.5 transgenic feed did not adversely affect quail health or egg quality, and there was no evidence of transgenic protein translocation to the blood, tissues, feces, and eggs. Based on the different parameters assessed, C0030.3.5 transgenic maize is a safe food source for quails that does not differ in quality from non-GM maize.

The Development Tendency of 3D-Printed Bioceramic Scaffolds for Applications Ranging From Bone Tissue Regeneration to Bone Tumor Therapy.

Three-dimensional (3D) printing concept has been successfully employed in regenerative medicine to achieve individualized therapy due to its benefit of a rapid, accurate, and predictable production process. Traditional biocomposites scaffolds (SCF) are primarily utilised for bone tissue engineering; nevertheless, over the last few years, there has already been a dramatic shift in the applications of bioceramic (BCR) SCF. As a direct consequence, this study focused on the structural, degeneration, permeation, and physiological activity of 3D-printed BCR (3DP-B) SCF with various conformations and work systems (macros, micros, and nanos ranges), as well as their impacts on the mechanical, degeneration, porosity, and physiological activities. In addition, 3DP-B SCF are highlighted in this study for potential uses applied from bone tissue engineering (BTE) to bone tumor treatment. The study focused on significant advances in practical 3DP-B SCF that can be utilized for tumor treatment as well as bone tissue regeneration (BTR). Given the difficulties in treating bone tumors, these operational BCR SCF offer a lot of promise in mending bone defects caused by surgery and killing any remaining tumor cells to accomplish bone tumor treatment. Furthermore, a quick assessment of future developments in this subject was presented. The study not only summarizes recent advances in BCR engineering, but it also proposes a new therapeutic strategy focused on the extension of conventional ceramics' multifunction to a particular diagnosis.

Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road.

Automatic Identification System (AIS) data could support ship movement analysis, and maritime network construction and dynamic analysis. This study examines the global maritime network dynamics from multi-layers (bulk, container, and tanker) and multidimensional (e.g., point, link, and network) structure perspectives. A spatial-temporal framework is introduced to construct and analyze the global maritime transportation network dynamics by means of big trajectory data. Transport capacity and stability are exploited to infer spatial-temporal dynamics of system nodes and links. Maritime network structure changes and traffic flow dynamics grouping are then possible to extract. This enables the global maritime network between 2013 and 2016 to be investigated, and the differences between the countries along the 21st-century Maritime Silk Road and other countries, as well as the differences between before and after included by 21st-century Maritime Silk Road to be revealed. Study results indicate that certain countries, such as China, Singapore, Republic of Korea, Australia, and United Arab Emirates, build new corresponding shipping relationships with some ports of countries along the Silk Road and these new linkages carry significant traffic flow. The shipping dynamics exhibit interesting geographical and spatial variations. This study is meaningful to policy formulation, such as cooperation and reorientation among international ports, evaluating the adaptability of a changing traffic flow and navigation environment, and integration of the maritime economy and transportation systems.

Relative space-based GIS data model to analyze the group dynamics of moving objects.

The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

Fitness Cost of Transgenic cry1Ab/c Rice Under Saline-Alkaline Soil Condition.

The environmental release and biosafety of transgenic Bt crops have attracted global attention. China has a large area of saline-alkali land, which is ideal for large-scale production of Bt transgenic rice. Therefore an understanding of the fitness of Bt transgenic rice in saline-alkaline soils and the ability to predict its long-term environmental effects are important for the future sustainable use of these crops. In the present study, we aimed to evaluate the fitness of cry1Ab/c transgenic rice in both farmland and natural ecosystems. Transgenic cry1Ab/c rice Huahui1, for which a national biosafety certificate was obtained, was grown on normal farmland and saline-alkaline soils in a glass greenhouse. The expression pattern of exogenous Cry1Ab/c protein, and vegetative and reproductive fitness of rice were assessed. The expression of the exogenous Cry1Ab/c protein in the transgenic rice grown on saline-alkaline soil was lower than that in the strain grown on farmland soil. Under both the soil conditions, vegetative growth abilities, as evaluated by tiller number and biomass, and reproductive growth abilities, as measured by filled grain number and filled grain weight per plant, showed a significantly higher fitness cost for Huahui1 than that for the parental rice Minghui63 grown under the same soil conditions. In saline-alkaline soil, the fitness cost of Huahui1was moderately higher than that of Minghui63. Therefore, the ecological risk of cry1Ab/c transgenic rice is not expected to be higher than that of parental rice Minghui63 if the former escapes into natural saline-alkaline soil. The results of the present study provide a scientific basis to improve environmental safety assessment of the insect-resistant transgenic rice strain Huahui1 before commercialization.

An Invisible Salient Landmark Approach to Locating Pedestrians for Predesigned Business Card Route of Pedestrian Navigation.

Visual landmarks are important navigational aids for research into and design of applications for last mile pedestrian navigation, e.g., business card route of pedestrian navigation. The business card route is a route between a fixed origin (e.g., campus entrance) to a fixed destination (e.g., office). The changing characteristics and combinations of various sensors' data in smartphones or navigation devices can be viewed as invisible salient landmarks for business card route of pedestrian navigation. However, the advantages of these invisible landmarks have not been fully utilized, despite the prevalence of GPS and digital maps. This paper presents an improvement to the Dempster⁻Shafer theory of evidence to find invisible landmarks along predesigned pedestrian routes, which can guide pedestrians by locating them without using digital maps. This approach is suitable for use as a "business card" route for newcomers to find their last mile destinations smoothly by following precollected sensor data along a target route. Experiments in real pedestrian navigation environments show that our proposed approach can sense the location of pedestrians automatically, both indoors and outdoors, and has smaller positioning errors than purely GPS and Wi-Fi positioning approaches in the study area. Consequently, the proposed methodology is appropriate to guide pedestrians to unfamiliar destinations, such as a room in a building or an exit from a park, with little dependency on geographical information.

A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones.

Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g., Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments.