Explore the vast range of titles available.

Traditional consumption-based greenhouse gas emissions accounting attributed the gap between consumption-based and production-based emissions to international trade. Yet few attempts have analyzed the temporal deviation between current emissions and future consumption, which can be explained through changes in capital stock. Here we develop a dynamic model to incorporate capital stock change in consumption-based accounting. The new model is applied using global data for 1995–2009. Our results show that global emissions embodied in consumption determined by the new model are smaller than those obtained from the traditional model. The emissions embodied in global capital stock increased steadily during the period. However, capital plays very different roles in shaping consumption-based emissions for economies with different development characteristics. As a result, the dynamic model yields similar consumption-based emissions estimation for many developed countries comparing with the traditional model, but it highlights the dynamics of fast-developing countries. Traditional carbon accounting attributes gap between consumption- and production-based emissions to international trade. The authors develop a dynamic model that incorporates capital stock change and find it improves estimates for fast-developing countries.

Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.

Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

Background The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target. Methods We developed a deep learning-based model for DTIs prediction. The proteins evolutionary features are extracted via Position Specific Scoring Matrix (PSSM) and Legendre Moment (LM) and associated with drugs molecular substructure fingerprints to form feature vectors of drug-target pairs. Then we utilized the Sparse Principal Component Analysis (SPCA) to compress the features of drugs and proteins into a uniform vector space. Lastly, the deep long short-term memory (DeepLSTM) was constructed for carrying out prediction. Results A significant improvement in DTIs prediction performance can be observed on experimental results, with AUC of 0.9951, 0.9705, 0.9951, 0.9206, respectively, on four classes important drug-target datasets. Further experiments preliminary proves that the proposed characterization scheme has great advantage on feature expression and recognition. We also have shown that the proposed method can work well with small dataset. Conclusion The results demonstration that the proposed approach has a great advantage over state-of-the-art drug-target predictor. To the best of our knowledge, this study first tests the potential of deep learning method with memory and Turing completeness in DTIs prediction.

In this manuscript, we suggest a relativistic distorted wave approach for the prediction of structural properties and photoionization cross sections of highly charged ions in a non-ideal classical plasma (NICP) environment. The pseudopotential, obtained from a sequential solution of the Bogolyubov chain equations, is used to describe screened interactions in the plasma. We solve the Dirac equation to obtain wave functions and energies. Detailed calculations are carried out for the photoionization of the highly ionized H-like S15+ ions for an illustrative purpose. The NICP effects on the energies, transition rates, ionization potentials, and photoionization cross sections are investigated. Comparing our results with other available experimental and theoretical data, we find satisfactory agreement. Apart from its fundamental importance, the present study has implications for a range of fields, including astrophysics, nuclear fusion and laboratory plasma experiments.

Accurate estimation of the thermal conductivity of porous materials is crucial for the modeling of heat transfer and energy consumption calculation in energy, aerospace, biomedicine and chemical engineering, etc. The series-parallel model is a simple and direct method and is usually used in the prediction of the effective thermal conductivity (ETC) of porous materials. In this work, the weighted coefficients of the series and parallel section were obtained based on the tortuosity of the porous materials. Then, the physical model of the ETC of the porous materials was established. Furthermore, the ETC of the porous materials was developed using the fractal model to calculate the pore cross-sectional area of the porous materials. Finally, quantitative analysis of the characteristic parameters, e.g., porosity, tortuosity, tortuous fractal dimension and pore diameter distribution, of the ETC of the porous materials was conducted. The results show that the proposed model can provide an accurate prediction of the ETC of porous materials.

Oncogenic c-Myc is a master regulator of G1/S transition. Long non-coding RNAs (lncRNAs) emerge as new regulators of various cell activities. Here, we found that lncRNA SnoRNA Host Gene 17 (SNHG17) was elevated at the early G1-phase of cell cycle. Both gain- and loss-of function studies disclosed that SNHG17 increased c-Myc protein level, accelerated G1/S transition and cell proliferation, and consequently promoted tumor cell growth in vitro and in vivo. Mechanistically, the 1-150-nt of SNHG17 physically interacted with the 1035-1369-aa of leucine rich pentatricopeptide repeat containing (LRPPRC) protein, and disrupting this interaction abrogated the promoting role of SNHG17 in c-Myc expression, G1/S transition, and cell proliferation. The effect of SNHG17 in stimulating cell proliferation was attenuated by silencing c-Myc or LRPPRC. Furthermore, silencing SNHG17 or LRPPRC increased the level of ubiquitylated c-Myc and reduced the stability of c-Myc protein. Analysis of human hepatocellular carcinoma (HCC) tissues revealed that SNHG17, LRPPRC, and c-Myc were significantly upregulated in HCC, and they showed a positive correlation with each other. High level of SNHG17 or LRPPRC was associated with worse survival of HCC patients. These data suggest that SNHG17 may inhibit c-Myc ubiquitination and thus enhance c-Myc level and facilitate proliferation by interacting with LRPPRC. Our findings identify a novel SNHG17-LRPPRC-c-Myc regulatory axis and elucidate its roles in G1/S transition and tumor growth, which may provide potential targets for cancer therapy.

Based on the previous research on the preparation of solid waste ceramsite (SW-ceramsite), the phosphorus removal performance from aqueous solution by SW-ceramsite in a fixed-bed column was investigated. Characterization results showed that the S BET , pH pzc , pore volume, bulk density and void fraction values of pretreated SW-ceramsite were 4.18 × 10 4 cm 2 /g, 9.83, 3.05 × 10 3 cm 3 /g, 1.37 g/cm 3 and 69.8%, respectively. Column experiments indicated that under optimal operating conditions of an initial pH of 5, an initial phosphorus concentration of 5 mg/L, a reaction temperature of 323 K, and an initial flowrate of 40 mL/min, the breakthrough curve (BTC) exhibited an irregular “S” shape, and the breakthrough and saturation times were 80 h and 155 h, respectively. Kinetic analysis demonstrated that compared with the Adams-Bohart model, the Yoon-Nelson model better described the phosphorus removal behavior from water by SW-ceramsite in a fixed-bed column. Grey relation analysis (GRA) results suggested that except for initial flowrate, the assumed effects of initial pH, initial concentration, and reaction temperature on the BTC were consistent with the GRA outcomes, implying that the GRA method can be used to determine the relative influence of the aforementioned factors on the phosphorus removal performance from water in a fixed-bed column packed with SW-ceramsite. Furthermore, after eight regeneration cycles, the breakthrough and saturation times of SW-ceramsite packing decreased by 30% and 12.9%, respectively, suggesting that it has a certain regenerative ability.

Due to the non-uniform tension and compression strength of concrete, carbon fiber can be added to concrete to improve its static tensile behavior and increase the tension–compression ratio. In view of the destructive consequences of impacts and explosions, it is necessary to study the dynamic responses of carbon fiber reinforced concrete (CFRC) structures. Therefore, the effects of the stress rates and carbon fiber contents on the dynamic tension behavior of CFRC were investigated in this paper. The dynamic splitting tests of concrete with the fiber contents of 0, 0.1, 0.2, and 0.3% were carried out by using a split Hopkinson pressure bar (SHPB) device with a diameter of 74 mm. We found that with the increase of fiber content, the static tensile strength of CFRC increases obviously, but the increased amplitude tends to decrease. The dynamic tensile strength and dynamic increase factor (DIF) both increase with the increase of stress rate, but the growth rate slows down, showing an obvious rate effect. The rate sensitivity of ordinary concrete is higher than CFRC. There are significant differences in the influence of carbon fiber on the dynamic and static strength of concrete. In the design of concrete mixing proportion, the content of carbon fiber should be appropriately selected to meet the requirements of dynamic and static mechanical properties.

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.