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AbstractWe assembled the complete mitochondrial genome (mitogenome) of Mastigias papua (Scyphozoa: Rhizostomeae: Mastigiidae) by the data generated from the next-generation sequencing platform. The complete mitogenome of M. papua was 16,560 bp in length, containing 14 protein-coding genes, two transfer RNA genes, and two ribosomal RNA genes. The base compositions were A 30.65%, C 15.16%, G 16.34%, and T 37.86%, with a gene arrangement similar to the mitogenomes derived from other representatives of Scyphozoa. Based on the 13 common protein-coding genes of 16 species within Scyphozoa, we constructed the phylogenetic tree and found that M. papua has a close relationship with Cassiopea andromeda and Cassiopea xamachana. All these species belong to an order of jellyfish Rhizostomeae, which have similar morphological characteristics. This is agreement with the conclusion we got by the phylogenetic relationship analysis using molecular data. This research has practical implications for advancing understanding of the phylogenetic relationships, taxonomic classifications, and phylogeography within Scyphozoa.

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community “QC”, as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) “Air health management (AHM)” could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.

Given their good antitumor effects, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are standard first-line therapy for EGFR-sensitive mutations, including exon 19 deletions and exon 21 L858R mutations. EGFR fusion mutations and EGFR amplification are very rare in non-small cell lung cancer (NSCLC). We describe 2 patients with NSCLC harboring EGFR fusion mutations (EGFR-MACF1 and EGFR-GNAT3) combined with EGFR amplification. Both patients received EGFR-TKI treatment, and 1 of them showed an antitumor response.

In this study, enhanced pyridine bio-photodegradation with assistance of electricity was achieved. Meanwhile, photoelectron-hole played a vital role in accelerating pyridine biomineralization. The significant separation of photoelectron-hole was achieved with an external electric field, which provided sufficient electron donors and acceptors for pyridine biodegradation. The enhanced electron transport system activity also revealed the full utilization of photoelectron-hole by microbes at semiconductor-microbe interface with assistance of electricity. Microbial community analysis confirmed the enrichment of functional species related to pyridine biodegradation and electron transfer. Microbial function analysis and microbial co-occurrence networks analysis indicated that upregulated functional genes and positive interactions of different species were the important reasons for enhanced pyridine bio-photodegradation with external electric field. A possible mechanism of enhanced pyridine biodegradation was proposed, i.e., more photoelectrons and holes of semiconductors were utilized by microbes to accelerate reduction and oxidation of pyridine with the assistance of electrical stimulation. The excellent performance of the photoelectrical biodegradation system showed a potential alternative for recalcitrant organic wastewater treatment.

Parkinson's disease (PD) is recognized as a degenerative and debilitating neurodegenerative disorder. The novel protective role of icariside II (ICS II) as a plant-derived flavonoid compound in neurodegenerative diseases has aroused much attention. Herein, the definite impacts of ICS II on the process of PD and the relevant action mechanism were studied. Human neuroblastoma SK-N-SH cells were challenged with 1-methyl-4-phenylpyridinium ion (MPP+) to construct the PD cell model. MTT assay and flow cytometry analysis, respectively, appraised cell viability and apoptosis. Caspase 3 Activity Assay examined caspase 3 activity. Corresponding kits examined oxidative stress levels. BODIPY 581/591 C11 assay evaluated lipid reactive oxygen species. Iron Assay Kit assessed iron content. Western blot tested the expression of apoptosis-, ferroptosis- and Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) signaling-associated proteins. Molecular docking verified the binding of ICS II with Keap1. The existing experimental results unveiled that ICS II elevated the viability whereas reduced the apoptosis, oxidative stress, and ferroptosis in MPP+-treated SK-N-SH cells in a concentration-dependent manner. Furthermore, ICS II declined Keap1 expression while raised Nrf2, heme oxygenase 1, and GPX4 expression. In addition, ICS II had a strong binding with Keap1 and Nrf2 inhibitor ML385 partially abolished the suppressive role of ICS II in MPP+-triggered apoptosis, oxidative stress, and ferroptosis in SK-N-SH cells. To summarize, ICS II might inhibit apoptosis, oxidative stress, and ferroptosis in the MPP+-stimulated PD cell model, which might be due to the activation of Keap1/Nrf2/GPX4 signaling.

Background and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.Materials and methodsA total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.ResultsThe radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.ConclusionWe established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features.

Understanding the spatial patterns and the maintenance of biodiversity is a central target in ecology and biogeography, which provide important insights into community assembly processes. Mountain ecosystems provide informative systems for examining how biodiversity is distributed and identifying the mechanisms underlying those patterns. However, most of the existed studies only focused on plants, birds, and microbes, while little attention has been paid on amphibians, especially tadpoles. In the present study, we explored the elevational patterns of multiple facets of tadpole diversity and the community assembly mechanisms in temperate montane streams of Mount Emei, China. The taxonomic, functional, and phylogenetic diversity of tadpole assemblages in montane streams were quantified. Their elevational patterns were investigated using first and second-order polynomial regression analyses. The microhabitat determinants of these patterns were assessed by ordinary least squares models and hierarchical partitioning analyses. The phylogenetic tree of tadpoles and the Eucliden distance of traits between tadpoles were constructed. They were subsequently used to calculate the standardized effect size of mean pairwise phylogenetic and functional distances, and to speculate the tadpole assembly rules. Our results indicated that the Faith’s PD and the standardized effect size of Faith’s PD had hump-shaped responses to elevation, while the relationships between elevation and observed taxonomic diversity, observed functional diversity, and the standardized effect size of functional diversity were not significant. Interestingly, these patterns were determined by microhabitat variables, such as water conductivity, river width, water depth, and substrate type. Mean pairwise phylogenetic distance also showed hump-shaped correlations with the elevational gradient. Tadpole functional and phylogenetic structures were more clustered in low and high elevational streams, suggesting that environmental filtering was the main driver. At mid-elevations, functional and phylogenetic structures were overdispersal, indicating that limiting similarity plays a dominant role in shaping the tadpole assemblages.

Measurement technology based on machine vision has been widely used in various industries. The development of vision measurement technology mainly depends on the process of photosensitive components and the algorithm of processing a target image. In the high-precision dimension measurement of machined metal parts, the high-resolution imaging device usually exposes the cutting texture of the metal surface and affects the accuracy of measurement algorithm. At the same time, the edges of machined metal parts are often chamfered, which makes the edges of objects in the picture overexposed in the lighting measurement environment. These factors reduce the accuracy of dimensioning metal parts using visual measurements. The traditional vision measurement method based on color/gray image makes it difficult to analyze the physical quantities in the light field except for the light intensity, which limits the measurement accuracy. Polarization information can more carefully depict the edge contour edge information in the scene and increase the contrast between the foreground and the background. This paper presents a method to improve the measurement accuracy of machined metal parts by using polarization vision. The incident angle of the light source is optimized according to the complex refractive index of the metal material, and the degree of polarization image with enhanced edge contour features of the ROI (region of interest) is obtained. The high-precision measurement of cylindrical brass motor components is realized by using the method of reprojection transformation correction and maximum correlation template matching (NCC) for rough positioning, as well as the method of edge extraction and optimal fitting. The experimental results show that for copper parts with a tolerance range of ±0.1 mm, the average measurement error and maximum measurement error are within 0.01 mm, which are higher than the existing color/gray image measurement methods.

As a widely present form of bimodal transit systems, the trunk-feeder transit corridor has received less attention in the academic research compared to the conventional single-modal systems, partly due to the difficulty in the two-dimensional modeling. The schedule coordination of this corridor has been largely ignored, which can potentially strengthen the mutually reinforcing relationship between the trunk transit (e.g., rail) and the feeder transit (e.g., bus). In light of this, the paper proposes a novel two-stage programming model with the objective of minimizing the total cost to design a coordinated trunk-feeder corridor. In stage one, the continuous approximation approach and the discrete method are incorporated to obtain an optimized uncoordinated scheme and a feasible layout. The screened layout parameters such as the specific station location, feeder line location, length of the feeder line segments parallel to the trunk line, and the passenger flow distribution, are recruited into the coordination design in stage two. A nested two-phase optimization algorithm integrated with the analytic method and adaptive genetic algorithm is proposed to find the solutions to the mixed integer nonlinear program issue. Results show that the proposed method is suitable for heterogeneous schedule coordination design and the coordination strategy is more preferable at a lower-level heterogeneous demand. Meanwhile, different from the conventional approaches, this method can generate a rational cost-saving coordination scheme with a feasible layout under the two-dimensional heterogeneous demand pattern.

Abstract This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as “credit”) are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. information cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total generalized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small transaction cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.

Based on the daily data of 24 meteorological warfare points in Ningxia, the potential evaporation (ET0), the Mann–Kendall test (MK), and inverse distance weight interpolation were used to analyze the temporal and spatial changes in the potential evaporation at different scales, and the Pearson correlation coefficient was used to determine the growth rate in Ningxia. The atmospheric circulation influencing factors of seasonal potential evaporation were established, and the prediction model of potential evaporation in the growing season in Ningxia was established by stepwise linear regression, using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The error test was carried out, and the results show the following: (1) The areas with larger potential evaporation during the growing season are mainly the central area of Ningxia and the northern side of the northern area. The presence on the north side tends to decrease. (2) On the monthly scale, the potential evaporation continued to increase from April to June, reached the maximum in June, continued to decrease from July to October, and was the smallest in October. (3) During the MK test in the growing season, the change in potential evaporation in the growing season showed an “increase–decrease” trend. The results of the monthly MK test show that the potential evaporation in April, May, and June has shown an increasing trend over the past 30 years, and the UF value in July, August, and October has been relatively high. The change trend of potential evaporation has been relatively stable, and the potential evaporation in September has shown a downward trend over the past 20 years. (4) The prediction model of potential evaporation in the growing season established by the circulation factor has shown a good effect in Ningxia and can be used to establish the scheduling prediction of irrigation water.

Understanding mechanisms determining the coexistence between different species is one of the key issues in community ecology and biodiversity conservation. Microhabitat segregation is a way for species to coexist, which reflects the specific habitat selection of coexisting species in a finer spatial scale. Despite quantitative studies have been conducted to investigate the microhabitat segregation of coexisting species, this type of studies was not often performed on tadpoles. In this study, we assessed the habitat selection of two coexisting tadpoles (Quasipaa boulengeri and Leptobrachium boringii) in a stream on Emei Mountain, China. Our results demonstrated that L. boringii and Q. boulengeri tadpoles occupied different microhabitats. Specifically, Q. boulengeri tadpoles preferred deep, narrow, and weak acid stream segments with slow current velocity and low value of conductivity, while L. boringii tadpoles tended to occur in a wide, shallow water bodies with relatively higher pH, conductivity, and current velocity. Overall, our study supported the Hutchinson’s niche concept, showing that at least one dimension of niche differentiation (i.e., microhabitat) occurred between coexisting tadpole species.

Xinjiang has a serious wind erosion problem due to its fragile ecological condition and sensitivity to climate change. Wind erosion climatic erosivity is a measure of climatic factors influencing wind erosion; evaluating its spatiotemporal variations and relationship with the large-scale circulation pattern can contribute to the understanding of the climate change effect on wind erosion risk. Thus, this study quantified the wind erosion climatic erosivity and examined the connections between climatic erosivity and climate indices using trend analysis, geo-statistical analysis, and cross-wavelet analysis based on the observed daily meteorological data from 64 weather stations in Xinjiang, China during 1969–2019 (50 years). The results indicated that the climatic erosivity showed a significant downward trend at seasonal and annual scales over the past 50 years. Strong seasonality in the C-factor was found, with its highest values in the spring and summer and its lowest values in the winter. The average climatic erosivity was weaker during El Niño events than during La Niña events. The impact of El Niño events on climatic erosivity in Xinjiang continued from the beginning of the event to two months after the end of the events. The La Niña events had a lag effect on the climatic erosivity in Xinjiang, with a lag period of 4 months. From a statistical perspective, the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) indices showed relationships to the climatic erosivity in Xinjiang in terms of their correlation and periodicity. The relationships between the climatic erosivity and ENSO were not clearly positive or negative, with many correlations advanced or delayed in phase. The NAO and AO indices showed a consistent in-phase relationship with climatic erosivity on significant bands, whereas the profound mechanisms involved in this require further study. The results of this study provide a preliminary perspective on the effect of large-scale atmospheric circulation on wind erosion risk in arid and semi-arid regions.

Although transit stop location problem has been extensively studied, the two main categories of modeling methodologies, i.e., discrete models and continuum approximation (CA) ones, seem have little intersection. Both have strengths and weaknesses, respectively. This study intends to integrate them by taking the advantage of CA models’ parsimonious property and discrete models’ fine consideration of practical conditions. In doing so, we first employ the state-of-the-art CA models to yield the optimal design, which serves as the input to the next discrete model. Then, the stop location problem is formulated into a multivariable nonlinear minimization problem with a given number of stop location variables and location constraint. The interior-point algorithm is presented to find the optimal design that is ready for implementation. In numerical studies, the proposed model is applied to a variety of scenarios with respect to demand levels, spatial heterogeneity, and route length. The results demonstrate the consistent advantage of the proposed model in all scenarios as against its counterparts, i.e., two existing recipes that convert CA model-based solution into real design of stop locations. Lastly, a case study is presented using real data and practical constraints for the adjustment of a bus route in Chengdu (China). System cost saving of 15.79% is observed by before-and-after comparison.

With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users' preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.

Abstract To promptly detect abnormal events in surveillance videos, this article designs a video anomaly detection method based on multiple instance learning. Generally, abnormal events occur less frequently compared to normal events. Traditional video surveillance relies on manual operation to monitor scenes and detect abnormal events by watching surveillance videos. However, watching surveillance footage is a labor‐intensive task, and prolonged observation can lead to visual fatigue and lack of concentration, which in turn results in missed detections and false positives [1]. Therefore, it is crucial to develop intelligent algorithms for video anomaly detection. The method can detect whether segments of a video contain abnormal events. First, the I3D network is used as a feature extractor to capture spatiotemporal features from the input video. Then, the spatiotemporal information is processed and input into a segment‐level anomaly detector based on multiple instance learning for detection. The authors treat abnormal videos as positive bags and normal videos as negative bags, and automatically learn a deep anomaly ranking model that can predict abnormal segments. Finally, the results of the training were tested and analyzed, demonstrating that the model is capable of detecting abnormal traffic segments.