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Renal cancer displays a high metastatic potential and a poor response to chemotherapy. However, the critical contributors to renal cancer development remain elusive. This study focused on acetylcholine (ACh) signaling. We identified the vesicular acetylcholine transporter (SLC18A3) that upregulates in patients with renal cancer. We further discovered that SLC18A3 enhanced the uptake of ACh, a classical neurotransmitter mediating synaptic transmission. The elevated ACh activated the protein kinase A (PKA)/cAMP-response element binding protein (CREB) pathway, which contributed to renal cancer cell proliferation and invasive migration. Consistently, SLC18A3 overexpression caused sustained tumor growth and increased lung metastases in A489-bearing mice. In summary, our study demonstrated that SLC18A3 contributed to cancer spread in an ACh/PKA/CREB-dependent manner, which may drive the design of efficacious treatment strategies.

Background: Inflammatory cells play an important role in the occurrence and development of cancer. In recent years, the value of inflammatory cell count and its ratio in the diagnosis of prostate cancer has been controversial.Methods: A retrospective analysis of 475 patients with transrectal prostate puncture with TPSA>4 ng/ml in the Third Affiliated Hospital of Xi’an Jiaotong University. Univariate analysis, multivariate analysis and Receiver Operating Characteristics curve analysis were performed to analysis the factor of age, TPSA, FPSA, PV, NC, LC, PC, NLR, PLR in the diagnosis value of prostate cancer, and further analysis the value of inflammatory cell count and its ratio in different TPSA groups of prostate cancer (4 ng/ml Results: The results of Univariate analysis in the overall data showed that Age, TPSA, PV, NC, PLC were influencing factors in the diagnosis of prostate cancer. The Area Under the Curve (AUC) of NC was 56.2% with a sensitivity of 72.2% and specificity of 41.5% in the cut -off point of 4.52 (p = 0.021). The PLC cut-off point of 205.5 gives 55.6% AUC with 67.2% sensitivity and 4.2% specificity. Multivariate binary logistic regression analysis results showed that Age, TPSA, PV, NC were independent influencing factors for the diagnosis of prostate cancer; In different TPSA group studies, it was found that NC, NLR, and PLR were valuable for the diagnosis of prostate cancer when TPSA>20 ng/ml，at the NC cut-off point of 4.52 with 67.3% AUC, 71.2% sensitivity and 62.5% specificity(p＜0.001), at the NLR cut-off point of 3.14 with 65.4% AUC, 67.2% sensitivity and 61.1% specificity (p＜0.001), and at the PLR cut-off point of 135.8 with 62.8% AUC , 57.6% sensitivity and 66.7% specificity (p＜0.001). Multivariate binary logistic regression analysis results showed that when TPSA>20 ng/ml, the higher Age, TPSA , Low PV and NC are independent risk factors affecting the diagnosis of prostate cancer. Conclusion: NC has promising value in predicting prostate cancer, especially when TPSA>20 ng/ml. A further prospective study in validating its diagnostic value was needed.

Alternative to the traditional force cooling technologies, daytime radiative cooling (DRC) has drawn widespread attention for its zero-power. A porous polymer coating based on poly (vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) has been reported as it has excellent DRC capacity. However, performance of the PVDF-HFP coating is affected substantially by its preparation conditions, restricting its application. To resolve the issue, we utilize an artificial neural network (ANN) to predict its DRC capacity and obtain the best preparation condition by siftings. In this work, the predicted solar reflectance ( R ¯ s o l a r ) and emittance of the atmospheric transmittance window ( ε ¯ a t w ), under the optimal preparation condition, reach 0.983 and 0.932, with a 1.865% and 0.107% error from the experimental value, correspondingly. Noticeably, the optimal PVDF-HFP coating achieves about 6℃ temperature drops below ambient temperature during daytime. In addition, to extend its applications in space, we conduct the extreme environmental experiment on the PVDF-HFP coating. After exposing in the extreme environment, R ¯ s o l a r of the coating has degradation rate over 11%. Consequently, these simulative methods and experimental results provide a positive direction for fabricating the high-performance DRCs.

Due to its high local heat transfer coefficients, spray cooling is a vital technique for thermal management of high heat flux electronic devices. In this study, an experimental system using R134a as the working fluid was designed and built to study the heat transfer characteristic of spray cooling. The influences of the liquid flow rate, the vapor phase mass fraction and the liquid subcooling of the two phase R134a spray cooling on the heat transfer performances were discussed. The experimental results show that the heat transfer coefficient increased with the increasing liquid flow rate when the liquid fluid rate was less than 1.6 L/min. However, the enhancement on heat transfer coefficient owing to the increase of liquid flow rate was insignificant while the flow rate was higher than 1.6 L/min. A maximum surface heat flux of 325.99 W/cm2 was achieved using R134a spray cooling while the temperature of the test surface was about 42.4 °C at the flow rate of 2.15 L/min.

In this study, nanofluids were introduced into jet arrays impingement as the working fluid. The heat transfer features of the nanofluids were experimentally investigated. Four different Cu-nanoparticle volume fractions ranged from 0.17 Vol.% to 0.64 Vol.% and two dispersant sodium dodecyl benzoic sulfate (SDBS) mass concentrations varied from 0.05 wt% to 0.1 wt% were involved. The influences of the nanoparticle volume fraction and the dispersant SDBS on the heat transfer of nanofluids were discussed. The experimental results show that the suspended nanoparticles increase the heat transfer performances of the jet arrays impingement cooling system. It had also been found that compared with the case of using nanofluids without any addition of dispersant, the nanofluid with dispersant led to a great deterioration on impingement heat transfer coefficient and even the heat transfer coefficients were smaller than that of the base liquid.

Surfactants or dispersants are vital for preparing stable nanofluids and their influences may often be neglected during estimating the enhancement effect of nanofluids. In this letter, we report the experimental investigation on the influence of surfactant–sodium dodecyl benzoic sulfate (SDBS) on impingement heat transfer performance of nanofluids. The Cu–water nanofluids with three volume fractions and two mass concentrations of SDBS are involved. The experimental investigation reveals that the surfactant remarkably affects transport properties and heat transfer performance of nanofluids and suppresses heat transfer enhancement effect of suspended nanoparticles. The deterioration mechanism of heat transfer induced by the surfactant is discussed.

The influence of jet arrays on heat transfer was experimentally and numerically studied. Four different arrays were tested using deionized water as the working liquid. The jets diameters ranged from 0.5 mm to 2.0 mm and the Reynolds number varied from 3500 to 12,000. Spacing of 3.5 mm between adjacent jets and 100 jets were used for the 0.5 mm diameter jet arrays while spacing of 7.0 mm between adjacent jets and 25 jets were employed for other jet arrays. The dimensionless parameter H/d n was fixed to 3.36. With an average impingement velocity of 11 m/s and the temperature of the inlet liquid being 20 °C, a maximum surface heat flux of 270 W/cm 2 was achieved using 1.0 mm diameter jet arrays. The average temperature of the heater surface reached 75.73 °C. The experimental data were corrected using four independent dimensionless parameters. With the application of the correction, the optimal main geometrical parameters of the jet arrays could be obtained. Based on the experimental setup, analysis models were established. The influences of dimensionless parameters H/d n and s/d n were studied. Studies of the Nusselt number in those models showed that jets impingement cooling established best with fixed s/d n of 7 and H/d n of 5.0.

The mean shift algorithm equipped with the intersection of confidence intervals (ICI) rule for image smoothing and segmentation is proposed. Firstly, the ICI rule for bandwidth selection in a multi-dimensional feature space is studied. In the ICI rule, the kernel function is used to estimate the probability density intervals of the pixel feature and find its bandwidth close to the optimal parameter. Secondly, the mean shift algorithm is used for image smoothing and segmentation with the bandwidth determined by the ICI rule. Experimental results show that the structures of the objects in images are preserved and over-segmentation caused by noises and texture can be eliminated effectively. In addition, a comparison between the smoothing results with adaptive bandwidths determined by the ICI rule and with fixed bandwidths is done. The results show that the proposed method is better in the field of image smoothing and segmentation.