Reducing contact resistance of MoS2-based field effect transistors through uniform interlayer insertion via atomic layer deposition.

Molybdenum disulfide (MoS2), a semiconducting two-dimensional layered transition metal dichalcogenide (2D TMDC), with attractive properties enables the opening of a new electronics era beyond Si. However, the notoriously high contact resistance (RC) regardless of the electrode metal has been a major challenge in the practical applications of MoS2-based electronics. Moreover, it is difficult to lower RC because the conventional doping technique is unsuitable for MoS2 due to its ultrathin nature. Therefore, the metal-insulator-semiconductor (MIS) architecture has been proposed as a method to fabricate a reliable and stable contact with low RC. Herein, we introduce a strategy to fabricate MIS contact based on atomic layer deposition (ALD) to dramatically reduce the RC of single-layer MoS2 field effect transistors (FETs). We utilize ALD Al2O3 as an interlayer for the MIS contact of bottom-gated MoS2 FETs. Based on the Langmuir isotherm, the uniformity of ALD Al2O3 films on MoS2 can be increased by modulating the precursor injection pressures even at low temperatures of 150 °C. We discovered, for the first time, that film uniformity critically affects RC without altering the film thickness. Additionally, we can add functionality to the uniform interlayer by adopting isopropyl alcohol (IPA) as an oxidant. Tunneling resistance across the MIS contact is lowered by n-type doping of MoS2 induced by IPA as the oxidant in the ALD process. Through a highly uniform interlayer combined with strong doping, the contact resistance is improved by more than two orders of magnitude compared to that of other MoS2 FETs fabricated in this study.

Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry.

This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classification of the entire liver region into the right and left lobes, and estimation of the right lobe graft weight from the CT-measured right lobe graft volume using a volume-to-weight conversion formula. The first two steps were performed with a transformer-based deep learning model. To train and evaluate the model, a total of 248 CT datasets (188 for training, 40 for validation, and 20 for testing and clinical evaluation) were used. The Dice similarity coefficient (DSC), mean surface distance (MSD), and the 95th percentile Hausdorff distance (HD95) were used for evaluating the segmentation accuracy of the full liver region and the right liver lobe. The correlation coefficient (CC), percentage error (PE), and percentage absolute error (PAE) were used for the clinical evaluation of the estimated right lobe graft weight. The proposed method achieved high accuracy in segmentation for DSC, MSD, and HD95 (95.9% ± 1.0%, 1.2 ± 0.4 mm, and 5.2 ± 1.9 mm for the entire liver region; 92.4% ± 2.7%, 2.0 ± 0.7 mm, and 8.8 ± 2.9 mm for the right lobe) and in clinical evaluation for CC, PE, and PAE (0.859, - 1.8% ± 9.6%, and 8.6% ± 4.7%). For the right lobe graft weight estimation, the present study underestimated the graft weight by - 1.8% on average. A mean difference of - 21.3 g (95% confidence interval: - 55.7 to 13.1, p = 0.211) between the estimated graft weight and the actual graft weight was achieved in this study. The proposed method is effective for clinical application.

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer.

Significance: When developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties. Aim: The aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer. Approach: The generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs. Results: To advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast. Conclusions: The proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

CRISPR-Cas9-based precise engineering of SlHyPRP1 protein towards multi-stress tolerance in tomato.

Recently, CRISPR-Cas9-based genome editing has been widely used for plant breeding. In our previous report, a tomato gene encoding hybrid proline-rich protein 1 (HyPRP1), a negative regulator of salt stress responses, has been edited using a CRISPR-Cas9 multiplexing approach that resulted in precise eliminations of its functional domains, proline-rich domain (PRD) and eight cysteine-motif (8CM). We subsequently demonstrated that eliminating the PRD domain of HyPRP1 in tomatoes conferred the highest level of salinity tolerance. In this study, we characterized the edited lines under several abiotic and biotic stresses to examine the possibility of multiple stress tolerance. Our data reveal that the 8CM removal variants of HK and the KO alleles of both HK and 15T01 cultivars exhibited moderate heat stress tolerance. Similarly, plants carrying either the domains of the PRD removal variant (PR1v1) or 8CM removal variants (PR2v2 and PR2v3) showed better germination under osmosis stress (up to 200 mM mannitol) compared to the WT control. Moreover, the PR1v1 line continuously grew after 5 days of water cutoff. When the edited lines were challenged with pathogenic bacteria of Pseudomonas syringae pv. tomato (Pto) DC3000, the growth of the bacterium was significantly reduced by 2.0- to 2.5-fold compared to that in WT plants. However, the edited alleles enhanced susceptibility against Fusarium oxysporum f. sp. lycopersici, which causes fusarium wilt. CRISPR-Cas9-based precise domain editing of the SlHyPRP1 gene generated multi-stress-tolerant alleles that could be used as genetic materials for tomato breeding.

Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast.

SIGNIFICANCE: In three-dimensional (3D) functional optoacoustic tomography (OAT), wavelength-dependent optical attenuation and nonuniform incident optical fluence limit imaging depth and field of view and can hinder accurate estimation of functional quantities, such as the vascular blood oxygenation. These limitations hinder OAT of large objects, such as a human female breast. AIM: We aim to develop a measurement-data-driven method for normalization of the optical fluence distribution and to investigate blood vasculature detectability and accuracy for estimating vascular blood oxygenation. APPROACH: The proposed method is based on reasonable assumptions regarding breast anatomy and optical properties. The nonuniform incident optical fluence is estimated based on the illumination geometry in the OAT system, and the depth-dependent optical attenuation is approximated using Beer-Lambert law. RESULTS: Numerical studies demonstrated that the proposed method significantly enhanced blood vessel detectability and improved estimation accuracy of the vascular blood oxygenation from multiwavelength OAT measurements, compared with direct application of spectral linear unmixing without optical fluence compensation. Experimental results showed that the proposed method revealed previously invisible structures in regions deeper than 15 mm and/or near the chest wall. CONCLUSIONS: The proposed method provides a straightforward and computationally inexpensive approximation of wavelength-dependent effective optical attenuation and, thus, enables mitigation of the spectral coloring effect in functional 3D OAT imaging.

3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulation studies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI.

OBJECTIVE: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. METHOD & MATERIALS: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). RESULTS: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per [Formula: see text].

Bone-Like Apatite Formation on the Plasma Electrolytic Oxidation-Treated Ti-6Al-4V Alloy in Solution Containing Si and Mg Ions.

The bon-like apatite formation on the plasma electrolytic oxidation (PEO)-treated Ti-6Al-4V alloy in solution containing Si and Mg ions was studied using various experimental techniques. A Ti-6Al-4V ELI disk and implant was used as a substrate for PEO. A pulsed DC power supply was used to apply a potential of 280 V in the electrolyte for 3 min. To examine the bioactivity, the PEO films formed implant specimens were immersed in a simulated body fluid (SBF) for 12 h. PEO-treated surface has large micro-pores and small micro-pores, and 5 Mg/Si and 20 Mg/Si coated surfaces showed the more small micro-pores than that of CaP coated surface. The peaks of the anatase and the hydroxyapatite phases after SBF immersion shifted to the left as compared to before SBF immersion. Numbers of cells increased, as Mg content increased on the PEO treated surface. Bone-like apatite is well formed on the Mg and Si contained surface.

Surface functionalization-specific binding of coagulation factors by zinc oxide nanoparticles delays coagulation time and reduces thrombin generation potential in vitro.

Zinc oxide nanoparticles (ZnO NPs) have many biomedical applications such as chemotherapy agents, vaccine adjuvants, and biosensors but its hemocompatibility is still poorly understood, especially in the event of direct contact of NPs with blood components. Here, we investigated the impact of size and surface functional groups on the platelet homeostasis. ZnO NPs were synthesized in two different sizes (20 and 100 nm) and with three different functional surface groups (pristine, citrate, and L-serine). ZnO NPs were incubated with plasma collected from healthy rats to evaluate the coagulation time, kinetics of thrombin generation, and profile of levels of coagulation factors in the supernatant and coronated onto the ZnO NPs. Measurements of plasma coagulation time showed that all types of ZnO NPs prolonged both active partial thromboplastin time and prothrombin time in a dose-dependent manner but there was no size- or surface functionalization-specific pattern. The kinetics data of thrombin generation showed that ZnO NPs reduced the thrombin generation potential with functionalization-specificity in the order of pristine > citrate > L-serine but there was no size-specificity. The profile of levels of coagulation factors in the supernatant and coronated onto the ZnO NPs after incubation of platelet-poor plasma with ZnO NPs showed that ZnO NPs reduced the levels of coagulation factors in the supernatant with functionalization-specificity. Interestingly, the pattern of coagulation factors in the supernatant was consistent with the levels of coagulation factors adsorbed onto the NPs, which might imply that ZnO NPs simply adsorb coagulation factors rather than stimulating these factors. The reduced levels of coagulation factors in the supernatant were consistent with the delayed coagulation time and reduced potential for thrombin generation, which imply that the adsorbed coagulation factors are not functional.

A Novel Method of Cone Beam CT Projection Binning Based on Image Registration.

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.

Manganese Coatings on Hydroxyapatite-Deposited Ti­29Nb­xHf Alloys After Nanomesh Formation.

The purpose of this study was to investigate manganese coatings on the hydroxyapatite-deposited Ti­29Nb­xHf alloy after nanomesh formation. The Ti alloy substrates for nanomesh formation were immersed in 5 M NaOH aqueous solution at 60 °C for 12 h. Electrochemical deposition of HA was carried out using cyclic voltammetry at 80 °C in 2.5 mM Ca(NO3)2 + 1.5 mM NH4H2PO4. The manganese coating was obtained by a radio frequency magnetron sputtering technique. The microstructure, composition, and phase structure of the coated alloys were examined by optical microscopy, field emission scanning electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy. The microstructures of the Ti­29Nb­xHf alloys were changed from an α″ + ß phase to a ß phase, and needle-like structure to equiaxed structure with Hf content. The bulk surfaces of Ti­29Nb alloy were partially covered with a rod-like hydroxyapatite layer and exhibited rod-like and round edge shape. In contrast, the hydroxyapatite coating after mesh formation on the Ti­29Nb­15Hf alloy showed plate-like precipitates. Manganese coatings on the Ti­29Nb­xHf alloys with mesh formation exhibited rod-like particles at the tip of plate-like precipitates by RF-sputtering. The Ca/P ratios of the coatings were 1.4 and 1.68 without and with mesh formation, respectively.

Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

Dietary evaluation of a low-iodine diet in Korean thyroid cancer patients preparing for radioactive iodine therapy in an iodine-rich region.

BACKGROUND/OBJECTIVES: Despite the importance of a low-iodine diet (LID) for thyroid cancer patients preparing for radioactive iodine (RAI) therapy, few studies have evaluated dietary intake during LID. This study evaluated the amount of dietary iodine intake and its major food sources during a typical diet and during LID periods for thyroid cancer patients preparing for RAI therapy, and examined how the type of nutrition education of LID affects iodine intake. SUBJECTS/METHODS: A total of 92 differentiated thyroid cancer patients with total thyroidectomy were enrolled from Seoul National University Hospital. All subjects completed three days of dietary records during usual and low-iodine diets before (131)I administration. RESULTS: The median iodine intake was 290 µg/day on the usual diet and 63.2 µg/day on the LID. The major food groups during the usual diet were seaweed, salted vegetables, fish, milk, and dairy products and the consumption of these foods decreased significantly during LID. The mean energy intake on the LID was 1,325 kcal, which was 446 kcal lower than on the usual diet (1,771 kcal). By avoiding iodine, the intake of most other nutrients, including sodium, was significantly reduced during LID (P < 0.005). Regarding nutritional education, intensive education was more effective than a simple education at reducing iodine intake. CONCLUSION: Iodine intake for thyroid cancer patients was significantly reduced during LID and was within the recommended amount. However, the intake of most other nutrients and calories was also reduced. Future studies are needed to develop a practical dietary protocol for a LID in Korean patients.

Inhibition of cell cycle progression via p27Kip1 upregulation and apoptosis induction by an ethanol extract of Rhus verniciflua Stokes in AGS gastric cancer cells.

Botanical preparations are widely used by patient with cancer in Korea, Japan and China. Rhus verniciflua Stokes (RVS) has traditionally been used as a medicinal ingredient for the therapy of stomach and uterine cancer. In this study, we showed that exposure to an ethanol extract of RVS (50 microg/ml) resulted in a synergistic inhibitory effect on cell growth in AGS cells. Growth inhibition was related with the inhibition of proliferation and induction of apoptosis. The extract induces G1-cell cycle arrest through the regulation of cyclins, the induction of p27Kip1, and decrease the CDK2 kinase activity. The upregulated p27Kip1 level is caused by protein stability increment by the reduction of Skp2, a key molecule related with p27Kip1 ubiquitination and degradation, and de novo protein synthesis. RVS extract induces apoptosis through the expression of Bax, poly(ADP-ribose) polymerase (PARP) and activation of caspase-3. RVS extract induces G1-cell cycle arrest via accumulation of p27Kip1 controlled by Skp2 reduction and apoptosis passing through an intrinsic pathway in human gastric cancer cells but not in normal cells, therefore we suggest that this extract could be a candidate medicine or compound for the development of novel class of anti-cancer drugs.