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A multilayer depth-of-interaction positron emission tomography (DOI-PET) detector with an independent readout structure has a potential advantage as a time-of-flight (TOF)-PET detector. To evaluate the potential TOF capabilities of a multilayer DOI-PET detector, which consists of thin layers of a cerium-doped lutetium-yttrium oxyorthosilicate (LYSOCe) scintillator coupled to a multi-pixel photon counter (MPPC) array, we examined the detector's CTR performance via Monte Carlo simulations. We used several types of scintillator structures a monolithic plate, laser-processing array with 3.2-mm pitch, fine laser-processing array with 1.6-mm pitch, and pixelated array with 3.2-mm pitch, with 2-, 4-, 6-, and 8-mm thickness values. Here, we note that the CTR performance also significantly depends on the timing-detection method, which generates a timing trigger signal for coincidence detection. Thus, we evaluated the CTRs for each scintillator structure by adopting four timing-detection methods using the total sum signal of MPPC chips (T_sum), the maximum signal in the MPPC chips (Max), the sum signal of a partial number of MPPC chips located at and in the vicinity of the -ray interaction position (P_sum), and the average of the timestamps generated at several MPPC chips (Ave). When using the T_sum for timing detection, the CTR full width at half-maximum (FWHM) values were ~100 ps regardless of the scintillator structure. [https://www.selleckchem.com/products/tdi-011536.html TDI-011536 inhibitor] However, when using the Max signal approach, the CTRs of the monolithic plates, laser-processing arrays, and fine-pitch laser-processing arrays were drastically degraded with increasing thickness. On the other hand, the CTRs of the pixelated arrays exhibited almost no degradation. To improve the CTRs of the monolithic plate and the (fine pitch) laser-processing array that exhibit a large light spread in the scintillator block, we applied the P_sum and Ave methods. The resulting CTRs significantly improved upon using P_sum; however, the Ave approach only worked for thicknesses of &gt;6 mm.Objectives.To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.Methods.CT images with ≤ 1 mm ST in the public dataset were converted to low-resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm ST and 5 mm ST to 1 mm ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.Results.CT images of 108 patients were used for training (n = 63), validation (n = 11) and testing (n = 34). The DNN method showed significantly higher PSNR and SSIM values (p less then 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3-1 mm, and from 305(64%) to 353(74%) for the conversion of 5-1 mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.Conclusions.The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.Objective.To evaluate the cerebral autoregulation (CA) in idiopathic intracranial hypertension (IIH) patients with transfer function analysis, and to explore its improvement after venous sinus stenting.Approach. In total, 15 consecutive IIH patients with venous sinus stenosis and 15 controls were recruited. All the patients underwent digital subtraction angiography and venous manometry. Venous sinus stenting was performed for IIH patients with a trans-stenosis pressure gradient ≥8 mmHg. CA was assessed before and after the operation with transfer function analysis, by using the spontaneous oscillations of the cerebral blood flow velocity in the bilateral middle cerebral artery and blood pressure.Main results. Compared with controls, the autoregulatory parameters, phase shift and rate of recovery, were both significantly lower in IIH patients [(57.94° ± 23.22° versus 34.59° ± 24.15°,p less then 0.001; (39.87 ± 21.95) %/s versus (20.56 ± 46.66) %/s,p= 0.045, respectively). In total, six patients with bilateral transverse or sigmoid sinus stenosis received venous sinus stenting, in whom, the phase shift significantly improved after venous sinus stenting (39.62° ± 20.26° versus 22.79° ± 19.96°,p = 0.04).Significance. The study revealed that dynamic CA was impaired in IIH patients and was improved after venous sinus stenting. CA assessment has the potential to be used for investigating the hemodynamics in IIH patients.Herein, FePS3/reduced graphene oxide (rGO) heterostructure has been prepared via a typical hydrothermal process, and flexible photodetectors based on hybrids have been subsequently fabricated. The photoresponse measurement results demonstrate that the photodetector exhibits obvious photoelectric conversion behavior without applied potential, indicating the device possesses capability of self-powered. In addition, the photocurrent density of as-fabricated photodetectors reaches up to 125 nA/cm2 under 90 mW/cm2 of illumination intensity without external power source, which is 5.86 times higher than sole FePS3-based devices. Furthermore, the maximum attenuation in photocurrent density of as-fabricated flexible photodetectors measured at -0.3 V after different bending cycles and bending angles are 29.8 % and 17.7 %, respectively. These results demonstrate that as-fabricated photodetectors have excellent flexibility and provide a simple and effective strategy for the construction of flexible photodetector.Lithium-rich layered Li2MnO3is regarded as a new generation cathode material for lithium-ion batteries because of its high energy density. Due to the different preparation methods and technological parameters, there are a lot of intrinsic defects in Li2MnO3. One frequently observed defect in experiments is Mn antisite defect (MnLi). In this work, we study the energetics and electronic properties involving MnLiin Li2MnO3through first-principles calculations. We find that MnLican reduce the formation energy of Li vacancies around it, but increase that of O vacancies, indicating that MnLicould suppress the release of O around it and facilitate capacity retention. Both O and Mn near the MnLican participate in charge compensation in the delithiation process. Furthermore, the effect of MnLion the migration of Li and Mn is investigated. All possible migration paths are considered and it is found that MnLimakes the diffusion energy barrier of Li increased, but the diffusion energy barriers of Mn from transition metal layer to Li layer are decreased, especially for the migration of the defect Mn. The insight into the defect properties of MnLimakes further contribution to understand the relationship between intrinsic defects and electrochemical properties of Li2MnO3.Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR 0.750-0.810) and average surface distance of 5.92 mm (IQR 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.Plants translate wind energy into leaf fluttering and branch motion by reversible tissue deformation. Simultaneously, the outermost structure of the plant, i.e. the dielectric cuticula, and the inner ion-conductive tissue can be used to convert mechanical vibration energy, such as that produced during fluttering in the wind, into electricity by surface contact electrification and electrostatic induction. Constraining a tailored artificial leaf to a plant leaf can enhance oscillations and transient mechanical contacts and thereby increase the electricity outcome. We have studied the effects of wind-induced mechanical interactions between the leaf of a plant (Rhododendron) and a flexible silicone elastomer-based artificial leaf fixed at the petiole on power output and whether performance can be further tuned by altering the vibrational behavior of the artificial leaf. The latter is achieved by modifying a concentrated mass at the tip of the artificial leaf and observing plant-generated current and voltage signaind speed of only 1.9 m s-1.We report the investigation of spin-to-charge current interconversion process in hybrid structures of yttrium iron garnet (YIG)/metallic bilayers by means of two different experimental techniques spin pumping effect (SPE) and spin Hall magnetoresistance (SMR). We demonstrate the evidence of a correlation between spin-to-charge conversion and SMR in bilayers of YIG/Pd, YIG/Pt, and YIG/IrMn. The correlation was verified directly in the spin Hall angles and the amplitudes of the voltage signals measured by the SPE and SMR techniques. The detection of SMR was carried out using the modulated magnetoresistance technique and lock-in amplifier detection. For these measurements, we present a simple model for the interpretation of the results. The results allow us to conclude that indeed the interface in the YIG/metallic bilayers has a dominant role in the spin-to-charge current conversion and SMR.Morphotropic phase boundaries (MPBs) show substantial piezoelectric and dielectric responses, which have practical applications. The predicted existence of MPB in HfO2-ZrO2solid solution thin film has provided a new way to increase the dielectric properties of a silicon-compatible device. Here, we present a new fabrication design by which the density of MPBρMPBand consequently the dielectric constantϵrof HfO2-ZrO2thin film was considerably increased. TheρMPBwas controlled by fabrication of a 10 nm [1 nm Hf0.5Zr0.5O2(ferroelectric)/1 nm ZrO2(antiferroelectric)] nanolaminate followed by an appropriate annealing process. The coexistence of orthorhombic and tetragonal structures, which are the origins of ferroelectric (FE) and antiferroelectric (AFE) behaviors, respectively, was structurally confirmed, and a double hysteresis loop that originates from AFE ordering, with some remnant polarization that originates from FE ordering, was observed inP-Ecurve. A remarkable increase inϵrcompared to the conventional HfO2-ZrO2thin film was achieved by controlling the FE-AFE ratio.