Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis

Research on Vehicle Swing Model based on Road Structure: Driving Safety and Comfort

The vehicle swing is a major factor on driving safety and comfort, while the acceleration noise is exactly a description of vehicle swing. Thus, acceleration noise can be a good evaluation indicator of driving safety and comfort. First, the vehicle forces are analyzed in three dimensions, and six vehicle swing models (VSMs) are established based on different road structure by combining acceleration noise theory. Then, the time discrete method is used to further discretize these models for easy calculation and application in practice projects. Finally, a large number of simulation experiments are performed with appropriate roads. The simulation results show that curvature radius, ramp angle, and superelevation slope angle are the major influence factors to driving safety and comfort. This paper not only provides mathematic expression in driving safety and comfort evaluation, but also has certain reference to the geometric design during the design of new highway

Study on Autonomous Path Planning by Mobile Robot for Road Nondestructive Testing Based on GPS

International audienceThis paper analyses the statements of video imaging, CT, infrared thermography and radiography applied in the road nondestructive testing, and design a path planning mobile robot with GPS positioning which can remarkably increase the efficient of road nondestructive testing. Besides, appropriate algorithm for nondestructive testing on the road autonomous mobile robot path planning is given. This method is simplicity, versatility, and efficiency. The mobile robot are selected for example and the simulation results represent the effectiveness of this method

Urban Expressway Travel Time Prediction Method Based on Fuzzy Adaptive Kalman Filter

Abstract: According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the interest changes, which changed the trust and utilization degree of the model for the observation information, and this made the filter eventually tend to be stable. To guarantee the real-time performance of system, a direct input- output fuzzy membership function matching method was put forward to take the place of fuzzy reasoning. The method was tested on the urban expressway in Guangzhou by using real-time detection data, and the result show that the traffic state estimation model had better tracking ability than conventional Kalman filter, and results of travel time prediction show that there was a slight difference between the prediction value and that of actual observation in free traffic flow state, and the relative error was under 15 % in traffic congested state. The precision and applicability of this method were acceptable, and it can be used to provide a basis for travel time of urban expressway in traffic control and guidance system

Universal and Energy‐Efficient Approach to Synthesize Pt‐Rare Earth Metal Alloys for Proton Exchange Membrane Fuel Cell

Abstract Traditional synthesis methods of platinum‐rare earth metal (Pt‐RE) alloys usually involve harsh conditions and high energy consumption because of the low standard reduction potentials and high oxophilicity of RE metals. In this work, a one‐step strategy is developed by rapid Joule thermal‐shock (RJTS) to synthesize Pt‐RE alloys within tens of seconds. The method can not only realize the regulation of alloy size, but also a universal method for the preparation of a family of Pt‐RE alloys (RE = Ce, La, Gd, Sm, Tb, Y). In addition, the energy consumption of the Pt‐RE alloy preparation is only 0.052 kW h, which is 2–3 orders of magnitude lower than other reported methods. This method allows individual Pt‐RE alloy to be embedded in the carbon substrate, endowing the alloy catalyst excellent durability for oxygen reduction reaction (ORR). The performance of alloy catalyst shows negligible decay after 20k accelerated durability testing (ADT) cycles. This strategy offers a new route to synthesize noble/non‐noble metal alloys with diversified applications besides ORR

Pt Nanoparticles Densely Coated on SnO₂‑Covered Multiwalled Carbon Nanotubes with Excellent Electrocatalytic Activity and Stability for Methanol Oxidation

A new electrocatalyst exhibiting enhanced activity and stability is designed from SnO₂-covered multiwalled carbon nanotubes coated with 85 wt % ratio Pt nanoparticles (NPs). This catalyst showed a mass activity 6.2 times as active as that of the commercial Pt/C for methanol oxidation, owing to the unique one-dimensional structure. Moreover, the durability and antipoisoning ability were also improved greatly. The enhanced intrinsic performance was ascribed to the densely connected networks of Pt NPs on the SnO₂ NPs

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance

ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting

Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF model consisting of the Compensated Residual Matrix Factorization with Spatial-Temporal regularization for graph-based traffic time series forecasting. Our model inherits the benefits of MF and regularizer optimization and further carries out the compensatory modeling of the spatial-temporal correlations through a well-designed bi-directional residual structure. Of particular concern is that MF modeling and later residual learning share and synchronize iterative updates as equal training parameters, which considerably alleviates the error propagation problem that associates with rolling forecasting. Besides, most of the existing prediction models have neglected the difficult-to-avoid issue of missing traffic data; the ST-CRMF model can repair the possible missing value while fulfilling the forecasting tasks. After testing the effects of key parameters on model performance, the numerous experimental results confirm that our ST-CRMF model can efficiently capture the comprehensive spatial-temporal dependencies and significantly outperform those state-of-the-art models in the short-to-long terms (5-/15-/30-/60-min) traffic forecasting tasks on the open Seattle-Loop and METR-LA traffic datasets