Hydroxyphenylpyruvate Dioxygenase Is a Metabolic Immune Checkpoint for UTX-deficient Colorectal Cancer.

BACKGROUND & AIMS: Aberrant epigenetic events mediated by histone methyltransferases and demethylases contribute to malignant progression of colorectal cancer (CRC). However, the role of the histone demethylase ubiquitously transcribed tetratricopeptide repeat on chromosome X (UTX) in CRC remains poorly understood. METHODS: UTX conditional knockout mice and UTX-silenced MC38 cells were used to investigate UTX function in tumorigenesis and development of CRC. We performed time of flight mass cytometry to clarify the functional role of UTX in remodeling immune microenvironment of CRC. To investigate metabolic interaction between myeloid-derived suppressor cells (MDSCs) and CRC, we analyzed metabolomics data to identify metabolites secreted by UTX-deficient cancer cells and taken up by MDSCs. RESULTS: We unraveled a tyrosine-mediated metabolic symbiosis between MDSC and UTX-deficient CRC. Loss of UTX in CRC resulted in methylation of phenylalanine hydroxylase, preventing its degradation and subsequently increasing tyrosine synthesis and secretion. Tyrosine taken up by MDSCs was metabolized to homogentisic acid by hydroxyphenylpyruvate dioxygenase. Homogentisic acid modified protein inhibitor of activated STAT3 via carbonylation of Cys 176, and relieved the inhibitory effect of protein inhibitor of activated STAT3 on signal transducer and activator of transcription 5 transcriptional activity. This in turn, promoted MDSC survival and accumulation, enabling CRC cells to acquire invasive and metastatic traits. CONCLUSIONS: Collectively, these findings highlight hydroxyphenylpyruvate dioxygenase as a metabolic checkpoint to restrict immunosuppressive MDSCs and to counteract malignant progression of UTX-deficient CRC.

Ectopic invasive ACTH-secreting pituitary adenoma mimicking chordoma: a case report and literature review.

BACKGROUND: Ectopic pituitary adenoma (EPA) is defined as a special type of pituitary adenoma that originates outside of the sellar region, is extra- or intra-cranially located, and without connection to normal pituitary tissue. EPA is extremely rare, with most cases presented as case reports or small case series. Due to nonspecific symptoms and laboratory indicators, the preoperative diagnosis, treatment and management for EPA remain challenging. CASE PRESENTATION: Here, we report the imaging phenotype and pathological findings of a case of invasive EPA in a 47-year-old woman. A preoperative non-contrast CT scan revealed a 5.8 × 3.6 × 3.7 cm soft tissue mass located in the sphenoid sinus and clivus. MRI showed an ill-defined solid mass with heterogeneous signals on T1-weighted and T2-weighted images. The mass displayed infiltrative growth pattern, destroying bone of the skull base, invading adjacent muscles and encasing vessels. The patient underwent partial tumor resection via transsphenoidal endoscopic surgery. Pathological examination led to diagnosis of ectopic ACTH-secreting pituitary adenoma. Post-surgery, the patient received external beam radiotherapy. CONCLUSION: EPA with invasive growth pattern has rarely been reported. The imaging phenotype displays its relationship to the pituitary tissue and surrounding structures. Immunohistochemical examination acts as a crucial role in differentiating EPA from other skull base tumors. This case report adds to the literature on EPA by summarizing its characteristics alongside a review of the literature.

Discovery and identification of a novel PI3K inhibitor with enhanced CDK2 inhibition for the treatment of triple negative breast cancer.

Blocking the PI3K pathway has been recognized as a promising strategy for cancer therapy. Herein, we report the discovery of novel PI3K inhibitors utilizing 7-azaindole-based fragment-oriented growth. Among them, compound FD2056 stands out as the most promising candidate, maintaining potent inhibitory activity against PI3K and enhanced CDK2 inhibition, and showing moderate selectivity among 108 kinases. In cellular assays, the inhibitor FD2056 demonstrated superior anti-proliferative profiles over reference compounds against TNBC cells and significantly increased apoptosis of MDA-MB-231 cells in a dose-dependent manner. Moreover, FD2056 showed more efficacious anti-TNBC activity than the corresponding drugs BKM120 and CYC202 at an oral dose of 15 mg/kg in the MDA-MB-231 xenograft model, inhibiting tumor growth by 43% with no observable toxic effects. All these results suggest that FD2056 has potential for further development as a promising anticancr compound, and co-targeting PI3K and CDK2 pathways may provide an alternative therapeutic strategy for the treatment of TNBC.

Greater flood risks in response to slowdown of tropical cyclones over the coast of China.

The total amount of rainfall associated with tropical cyclones (TCs) over a given region is proportional to rainfall intensity and the inverse of TC translation speed. Although the contributions of increase in rainfall intensity to larger total rainfall amounts have been extensively examined, observational evidence on impacts of the recently reported but still debated long-term slowdown of TCs on local total rainfall amounts is limited. Here, we find that both observations and the multimodel ensemble of Global Climate Model simulations show a significant slowdown of TCs (11% in observations and 10% in simulations, respectively) from 1961 to 2017 over the coast of China. Our analyses of long-term observations find a significant increase in the 90th percentile of TC-induced local rainfall totals and significant inverse relationships between TC translation speeds and local rainfall totals over the study period. The study also shows that TCs with lower translation speed and higher rainfall totals occurred more frequently after 1990 in the Pearl River Delta in southern China. Our probability analysis indicates that slow-moving TCs are more likely to generate heavy rainfall of higher total amounts than fast-moving TCs. Our findings suggest that slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.

Targeting oxidative pentose phosphate pathway prevents recurrence in mutant Kras colorectal carcinomas.

Recurrent tumors originate from cancer stem cells (CSCs) that survive conventional treatments. CSCs consist of heterogeneous subpopulations that display distinct sensitivity to anticancer drugs. Such a heterogeneity presents a significant challenge in preventing tumor recurrence. In the current study, we observed that quiescent CUB-domain-containing protein 1 (CDCP1)+ CSCs are enriched after chemotherapy in mutant Kirsten rat sarcoma viral oncogene homolog (Kras) colorectal carcinomas (CRCs) and serve as a reservoir for recurrence. Mechanistically, glucose catabolism in CDCP1+ CSCs is routed to the oxidative pentose phosphate pathway (PPP); multiple cycling of carbon backbones in the oxidative PPP potentially maximizes NADPH reduction to counteract chemotherapy-induced reactive oxygen species (ROS) formation, thereby allowing CDCP1+ CSCs to survive chemotherapeutic attack. This is dependent on silent mating type information regulation 2 homolog 5 (Sirt5)-mediated inhibition of the glycolytic enzyme triosephosphate isomerase (TPI) through demalonylation of Lys56. Blocking demalonylation of TPI at Lys56 increases chemosensitivity of CDCP1+ CSCSs and delays recurrence of mutant Kras CRCs in vivo. These findings pinpoint a new therapeutic approach for combating mutant Kras CRCs.

Bromodomain-containing protein 9 is a prognostic biomarker associated with immune infiltrates and promotes tumor malignancy through activating notch signaling pathway in negative HIF-2α clear cell renal cell carcinoma.

HIF-2α selective inhibitor showed successful efficacy in sensitive clear cell renal cell carcinoma (ccRCC) presenting higher levels of HIF-2α compared to resistant tumors with low level of HIF-2α (negative HIF-2α ccRCC). Currently, negative HIF-2α ccRCC lacks truly effective therapeutic agents to improve the outcomes. Bromodomain-containing protein 9 (BRD9) plays a critical role in human hepatocellular carcinoma, squamous cell lung cancer, acute myeloid leukemia, and so on. However, expression and biological role of BRD9 in negative HIF-2α ccRCC is poorly understood. Clinically, we demonstrated that expression of BRD9 in negative HIF-2α ccRCC tissues was higher than that in positive HIF-2α ccRCC. Moreover, high BRD9 expression was correlated with unfavorable clinicopathological features and predicted the poor overall survival of negative HIF-2α ccRCC patients. Functionally, BRD9 knockout resulted in reduced proliferation, migration and invasion of negative HIF-2α ccRCC cells (Caki-2). In addition, BRD9 was related to the TIIC infiltration level in negative HIF-2α ccRCC tissues. Mechanistically, Gene set enrichment analysis (GSEA) showed that BRD9 was closely related to Notch signaling pathway. BRD9 knockout resulted in reduced mRNA level of Hes1 and Notch1 in negative HIF-2α ccRCC in vitro. The overexpression of NICD (Notch intracellular domain) enhanced malignant behaviors of Caki-2 cells with BRD9 knockout. And Notch inhibition led to attenuation of cell growth and reduced migration and invasion in Caki-2 cells. Overall, our results identified that BRD9 promotes the proliferation, migration and invasion of negative HIF-2α ccRCC cells by targeting Notch signaling pathway and serve as a promising biomarker for negative HIF-2α ccRCC.

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories.

Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.

External Deformation Monitoring and Improved Partial Least Squares Data Analysis Methods of High Core Rock-Fill Dam (HCRFD).

External deformation monitoring of high core rock-fill dams (HCRFDs) is an important and difficult part of safety monitoring. The traditional method of external deformation monitoring and data analysis for HCRFDs is to use a total station for small angle observations and establish a regression model to analyze the results. However, the small angle method has low accuracy and a low automation degree, and there is multicollinearity between the independent variables, which affects the parameter estimation and leads to the failure of model establishment. The angle forward intersection method is adopted in this paper for observation, and an improved partial least squares method (IPLS) is proposed to eliminate the multicollinearity of the independent variables. Compared to the traditional method, the improved observation method exhibits high accuracy and a high automation degree. The new data analysis method can not only eliminate multicollinearity but also improve the interpretation ability of the model. The data from the initial stage of water storage shows that the displacement increases with the increase in the upstream water level and time, and the speed of water storage is proportional to the displacement. The water level and time are the main influencing factors. This conclusion provides a theoretical basis for reservoir management departments to control water levels and gate opening and closing. The method in this paper can be applied to arch dams, gravity dams, and other types of waterpower engineering systems.

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network.

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.

Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping.

Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and geocoding procedures, these images can be processed into a mosaic orthoimage for rapid ground disaster mapping. Notable, due to the relatively low flight latitude, small clouds can be easily removed by stacking multi-angle tilt images in the VPARS method. A case study on the 2019 Guangdong flood monitoring validates these advantages. The frequent aircraft revisit time, intensive flight coverage, multi-angle images and low cost of the VPARS make it a potential way to complement traditional remote sensing methods in emergency response.

Evaluating the effectiveness of the pollutant discharge permit program in China: A case study of the Nenjiang River Basin.

China is continually seeking to improve river water quality. Implemented in 1996, the total pollutant load control system (TPLCS) is a regulatory strategy to reduce total pollutant loads, under which a Pollutant Discharge Permit (PDP) program tracks and regulates nutrient inputs from point source polluters. While this has been promising, the input-response relationship between discharge permits and water quality targets is largely unclear - especially in China's large and complex river basins. In response, this study involved a quantitative analysis method to combine the water quality targets of the 12th Five-Year Plan (2011-2015) with allocated PDPs in the Nenjiang River Basin, China. We demonstrated our approach by applying the Soil and Water Assessment Tool (SWAT) to the Nenjiang River Basin for hydrological and water quality simulation. Ammonia nitrogen (NH3-N) was used as the primary water quality indicator. Modelling indicated that only one control section in the wider river basin did not achieve the water quality target, suggesting that the TPLCS is largely effective. The framework should be applied in other basins to study the effectiveness of PDP policies, advise further updates to the TPLCS, and ultimately aim to achieve freshwater quality targets nationally.

A Peak Traffic Congestion Prediction Method Based on Bus Driving Time.

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses' GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( M A P E ¯ ) and root mean square error ( R M S E ¯ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.

A Review of GPS Trajectories Classification Based on Transportation Mode.

GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Robust Lane-Detection Method for Low-Speed Environments.

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

A Data Cleaning Method for Big Trace Data Using Movement Consistency.

Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method.

Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning.

The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors.

Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5⁻3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms.

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What's more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

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.

A 3D Laser Profiling System for Rail Surface Defect Detection.

Rail surface defects such as the abrasion, scratch and peeling often cause damages to the train wheels and rail bearings. An efficient and accurate detection of rail defects is of vital importance for the safety of railway transportation. In the past few decades, automatic rail defect detection has been studied; however, most developed methods use optic-imaging techniques to collect the rail surface data and are still suffering from a high false recognition rate. In this paper, a novel 3D laser profiling system (3D-LPS) is proposed, which integrates a laser scanner, odometer, inertial measurement unit (IMU) and global position system (GPS) to capture the rail surface profile data. For automatic defect detection, first, the deviation between the measured profile and a standard rail model profile is computed for each laser-imaging profile, and the points with large deviations are marked as candidate defect points. Specifically, an adaptive iterative closest point (AICP) algorithm is proposed to register the point sets of the measured profile with the standard rail model profile, and the registration precision is improved to the sub-millimeter level. Second, all of the measured profiles are combined together to form the rail surface through a high-precision positioning process with the IMU, odometer and GPS data. Third, the candidate defect points are merged into candidate defect regions using the K-means clustering. At last, the candidate defect regions are classified by a decision tree classifier. Experimental results demonstrate the effectiveness of the proposed laser-profiling system in rail surface defect detection and classification.