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Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.
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Mannan is a predominant constituent of cork hemicellulose and is widely distributed in various plant tissues. ß-Mannanase is the principal mannan-degrading enzyme, which breaks down the ß-1,4-linked mannosidic bonds in mannans in an endo-acting manner. Microorganisms are a valuable source of ß-mannanase, which exhibits catalytic activity in a wide range of pH and temperature, making it highly versatile and applicable in pharmaceuticals, feed, paper pulping, biorefinery, and other industries. Here, the origin, classification, enzymatic properties, molecular modification, immobilization, and practical applications of microbial ß-mannanases are reviewed, the future research directions for microbial ß-mannanases are also outlined.
Assuntos
Mananas , beta-Manosidase , beta-Manosidase/genética , TemperaturaRESUMO
Soft robots have received a great deal of attention from both academia and industry due to their unprecedented adaptability in unstructured environment and extreme dexterity for complicated operations. Due to the strong coupling between the material nonlinearity due to hyperelasticity and the geometric nonlinearity due to large deflections, modeling of soft robots is highly dependent on commercial finite element software packages. An approach that is accurate and fast, and whose implementation is open to designers, is in great need. Considering that the constitutive relation of the hyperelastic materials is commonly expressed by its energy density function, we present an energy-based kinetostatic modeling approach in which the deflection of a soft robot is formulated as a minimization problem of its total potential energy. A fixed Hessian matrix of strain energy is proposed and adopted in the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, which significantly improves its efficiency for solving the minimization problem of soft robots without sacrificing prediction accuracy. The simplicity of the approach leads to an implementation of MATLAB with only 99-line codes, which provides an easy-to-use tool for designers who are designing and optimizing the structures of soft robots. The efficiency of the proposed approach for predicting kinetostatic behaviors of soft robots is demonstrated by seven pneumatic-driven and cable-driven soft robots. The capability of the approach for capturing buckling behaviors in soft robots is also demonstrated. The energy-minimization approach, as well as the MATLAB implementation, could be easily tailored to fulfill various tasks, including design, optimization, and control of soft robots.
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Poly-ADP-ribose-polymerase (PARP) inhibitors have achieved regulatory approval in oncology for homologous recombination repair deficient tumors including BRCA mutation. However, some have failed in combination with first-line chemotherapies, usually due to overlapping hematological toxicities. Currently approved PARP inhibitors lack selectivity for PARP1 over PARP2 and some other 16 PARP family members, and we hypothesized that this could contribute to toxicity. Recent literature has demonstrated that PARP1 inhibition and PARP1-DNA trapping are key for driving efficacy in a BRCA mutant background. Herein, we describe the structure- and property-based design of 25 (AZD5305), a potent and selective PARP1 inhibitor and PARP1-DNA trapper with excellent in vivo efficacy in a BRCA mutant HBCx-17 PDX model. Compound 25 is highly selective for PARP1 over other PARP family members, with good secondary pharmacology and physicochemical properties and excellent pharmacokinetics in preclinical species, with reduced effects on human bone marrow progenitor cells in vitro.
Assuntos
DNA , Poli(ADP-Ribose) Polimerase-1 , Inibidores de Poli(ADP-Ribose) Polimerases , Poli(ADP-Ribose) Polimerases , Humanos , Cristalografia por Raios X , DNA/química , Poli(ADP-Ribose) Polimerase-1/antagonistas & inibidores , Inibidores de Poli(ADP-Ribose) Polimerases/química , Inibidores de Poli(ADP-Ribose) Polimerases/farmacologia , Poli(ADP-Ribose) Polimerases/metabolismo , Especificidade por SubstratoRESUMO
JAK1, JAK2, JAK3, and TYK2 belong to the JAK (Janus kinase) family. They play critical roles in cytokine signaling. Constitutive activation of JAK/STAT pathways is associated with a wide variety of diseases. Particularly, pSTAT3 is observed in response to the treatment with inhibitors of oncogenic signaling pathways such as EGFR, MAPK, and AKT and is associated with resistance or poorer response to agents targeting these pathways. Among the JAK family kinases, JAK1 has been shown to be the primary driver of STAT3 phosphorylation and signaling; therefore, selective JAK1 inhibition can be a viable means to overcome such treatment resistances. Herein, an account of the medicinal chemistry optimization from the promiscuous kinase screening hit 3 to the candidate drug 21 (AZD4205), a highly selective JAK1 kinase inhibitor, is reported. Compound 21 has good preclinical pharmacokinetics. Compound 21 displayed an enhanced antitumor activity in combination with an approved EGFR inhibitor, osimertinib, in a preclinical non-small-cell lung cancer (NSCLC) xenograft NCI-H1975 model.