Correlations between IGF-IR Expression and Clinicopathological Factors and Prognosis in Patients with Lung Adenocarcinoma

Background and objective The incidence of lung adenocarcinoma increases rapidly, and IGF-IR is the key mediator of several growth factors signal transduction, therefore it plays an important role in the proliferation and differentiation of cancer cell. The aim of this study is to detect the expression of IGF-IR in lung adenocarcinoma and to evaluate its implication for the clinicopathological factors and prognosis of patients with this disease. Methods The IGF-IR expression was detected by immunohistochemical staining. Correlations between IGF-IR expression with clinicopathological factors were analyzed using the Chi-squared test. The Kaplan-Meier method was used to calculate the overall patient survival rate, and the difference in survival curves was evaluated using a Log-rank test. Univariate and multivariate analysis was carried out using the Cox proportional-hazard model. Results In 126 cases of tumor sections tested, IGF-IR were detected in 89 cases. Statistical analysis revealed that the IGF-IR expression was related to tumor size and T stage, while there were no relations between IGFIR expression and age, gender, smoking, pathological stages, and differentiation. Cox analysis indicated that metastasis and chemotherapy efficacy were the prognostic factors in these patients, while IGF-IR expression was not the independent prognostic factor. Conclusion The IGF-IR expression is related to tumor size and T stage, while there is no relation between IGF-IR expression and prognosis

Inverse Design for Coating Parameters in Nano-Film Growth Based on Deep Learning Neural Network and Particle Swarm Optimization Algorithm

The NN (neural network)-PSO (particle swarm optimization) method is demonstrated to be able to inversely extract the coating parameters for the multilayer nano-films through a simulation case and two experimental cases to verify its accuracy and robustness. In the simulation case, the relative error (RE) between the average layer values and the original one was less than 3.45% for 50 inverse designs. In the experimental anti-reflection (AR) coating case, the mean thickness values of the inverse design results had a RE of around 5.05%, and in the Bragg reflector case, the RE was less than 1.03% for the repeated inverse simulations. The method can also be used to solve the single-solution problem of a tandem neural network in the inverse process

Inverse Design for Coating Parameters in Nano-Film Growth Based on Deep Learning Neural Network and Particle Swarm Optimization Algorithm

The NN (neural network)-PSO (particle swarm optimization) method is demonstrated to be able to inversely extract the coating parameters for the multilayer nano-films through a simulation case and two experimental cases to verify its accuracy and robustness. In the simulation case, the relative error (RE) between the average layer values and the original one was less than 3.45% for 50 inverse designs. In the experimental anti-reflection (AR) coating case, the mean thickness values of the inverse design results had a RE of around 5.05%, and in the Bragg reflector case, the RE was less than 1.03% for the repeated inverse simulations. The method can also be used to solve the single-solution problem of a tandem neural network in the inverse process

Simultaneous characterization of the atmospheres, surfaces, and exomoons of nearby rocky exoplanets

International audienceAtmospheric composition is an important indicator of habitability and life. The presence or absence of a large exomoon around an Earth‐size exoplanet could have important consequences for planet climate stability. Thus the detection of exomoons and retrieval of information regarding atmospheric composition of Earth‐size exoplanets are important goals of future exoplanet observations. Here a data analysis method is developed to achieve both goals simultaneously, based on reflection spectra of exoplanet‐exomoon systems. We show that the existence of exomoons, the size of exomoons, and the concentrations of some atomic and molecular species in the atmospheres of their hosting Earth‐like exoplanets can be retrieved with high levels of reliability. In addition, the method can provide well‐constrained fractions of basic surface types on the targets because of the characteristic spectral features of atmospheric species and surface types in the analyzed spectral range

Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions

Abstract The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlO x /Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times

CCR5 closes the temporal window for memory linking

Real-world memories are formed in a particular context and are often not acquired or recalled in isolation1-5. Time is a key variable in the organization of memories, as events that are experienced close in time are more likely to be meaningfully associated, whereas those that are experienced with a longer interval are not1-4. How the brain segregates events that are temporally distinct is unclear. Here we show that a delayed (12-24 h) increase in the expression of C-C chemokine receptor type 5 (CCR5)-an immune receptor that is well known as a co-receptor for HIV infection6,7-after the formation of a contextual memory determines the duration of the temporal window for associating or linking that memory with subsequent memories. This delayed expression of CCR5 in mouse dorsal CA1 neurons results in a decrease in neuronal excitability, which in turn negatively regulates neuronal memory allocation, thus reducing the overlap between dorsal CA1 memory ensembles. Lowering this overlap affects the ability of one memory to trigger the recall of the other, and therefore closes the temporal window for memory linking. Our findings also show that an age-related increase in the neuronal expression of CCR5 and its ligand CCL5 leads to impairments in memory linking in aged mice, which could be reversed with a Ccr5 knockout and a drug approved by the US Food and Drug Administration (FDA) that inhibits this receptor, a result with clinical implications. Altogether, the findings reported here provide insights into the molecular and cellular mechanisms that shape the temporal window for memory linking

CCR5 closes the temporal window for memory linking

Real-world memories are formed in a particular context and are often not acquired or recalled in isolation(1-5) . Time is a key variable in the organization of memories, as events that are experienced close in time are more likely to be meaningfully associated, whereas those that are experienced with a longer interval are not(1-4). How the brain segregates events that are temporally distinct is unclear. Here we show that a delayed (12-24 h) increase in the expression of C-C chemokine receptor type 5 (CCR5)-an immune receptor that is well known as a co-receptor for HIV infection(6-7)- after the formation of a contextual memory determines the duration of the temporal window for associating or linking that memory with subsequent memories. This delayed expression of CCR5 in mouse dorsal CA1 neurons results in a decrease in neuronal excitability, which in turn negatively regulates neuronal memory allocation, thus reducing the overlap between dorsal CA1 memory ensembles. Lowering this overlap affects the ability of one memory to trigger the recall of the other, and therefore closes the temporal window for memory linking. Our findings also show that an age-related increase in the neuronal expression of CCR5 and its ligand CCL5 leads to impairments in memory linking in aged mice, which could be reversed with a Ccr5 knockout and a drug approved by the US Food and Drug Administration (FDA) that inhibits this receptor, a result with clinical implications. Altogether, the findings reported here provide insights into the molecular and cellular mechanisms that shape the temporal window for memory linking.11Nsciescopu