Dalyknowles3501

Multi-enzyme immobilization onto a capacitive field-effect biosensor by nano-spotting technique is presented. The nano-spotting technique allows to immobilize different enzymes simultaneously on the sensor surface with high spatial resolution without additional photolithographical patterning. Chlorine6 The amount of applied enzymatic cocktail on the sensor surface can be tailored. Capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors with Ta2O5 as pH-sensitive transducer layer have been chosen to immobilize the three different (pL droplets) enzymes penicillinase, urease, and glucose oxidase. Nano-spotting immobilization is compared to conventional drop-coating method by defining different geometrical layouts on the sensor surface (fully, half-, and quarter-spotted). The drop diameter is varying between 84 µm and 102 µm, depending on the number of applied drops (1 to 4) per spot. For multi-analyte detection, penicillinase and urease are simultaneously nano-spotted on the EIS sensor. Sensor characterization was performed by C/V (capacitance/voltage) and ConCap (constant capacitance) measurements. Average penicillin, glucose, and urea sensitivities for the spotted enzymes were 81.7 mV/dec, 40.5 mV/dec, and 68.9 mV/dec, respectively.In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user's intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users' eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%-8.9% under low and dark light conditions, which is a promising step toward further research.Atopic dermatitis (AD) is a complex, often lifelong allergic disease with severe pruritus affecting around 10% of both humans and dogs. To investigate the role of mast cells (MCs) and MC-specific proteases on the immunopathogenesis of AD, a vitamin D3-analog (MC903) was used to induce clinical AD-like symptoms in c-kit-dependent MC-deficient Wsh-/- and the MC protease-deficient mMCP-4-/-, mMCP-6-/-, and CPA3-/- mouse strains. MC903-treatment on the ear lobe increased clinical scores and ear-thickening, along with increased MC and granulocyte infiltration and activity, as well as increased levels of interleukin 33 (IL-33) locally and thymic stromal lymphopoietin (TSLP) both locally and systemically. The MC-deficient Wsh-/- mice showed significantly increased clinical score and ear thickening albeit having lower ear tissue levels of IL-33 and TSLP as well as lower serum levels of TSLP as compared to the WT mice. In contrast, although having significantly increased IL-33 ear tissue levels the chymase-deficient mMCP-4-/- mice showed similar clinical score, ear thickening, and TSLP levels in ear tissue and serum as the WT mice, whereas mMCP-6 and CPA3 -deficient mice showed a slightly reduced ear thickening and granulocyte infiltration. Our results suggest that MCs promote and control the level of MC903-induced AD-like inflammation.Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.The composition of the extracellular matrix (ECM) plays a pivotal role in tumour initiation, metastasis and therapy resistance. Until now, the ECM composition of salivary gland carcinomas (SGC) has not been studied. We quantitatively analysed the mRNA of 28 ECM-related genes of 34 adenoid cystic (AdCy; n = 11), mucoepidermoid (MuEp; n = 14) and salivary duct carcinomas (SaDu; n = 9). An incremental overexpression of six collagens (including COL11A1) and four glycoproteins from MuEp and SaDu suggested a common ECM alteration. Conversely, AdCy and MuEp displayed a distinct overexpression of COL27A1 and LAMB3, respectively. Nonhierarchical clustering and principal component analysis revealed a more specific pattern for AdCy with low expression of the common gene signature. In situ studies at the RNA and protein level confirmed these results and indicated that, in contrast to MuEp and SaDu, ECM production in AdCy results from tumour cells and not from cancer-associated fibroblasts (CAFs). Our findings reveal different modes of ECM production leading to common and distinct RNA signatures in SGC.