Gate-bias stability of triple-gated feedback field-effect transistors with silicon nanosheet channels.

In this study, we investigate the gate-bias stability of triple-gated feedback field-effect transistors (FBFETs) with Si nanosheet channels. The subthreshold swing (SS) of FBFETs increases from 0.3 mV dec-1to 60 and 80 mV dec-1inp- andn-channel modes, respectively, when a positive bias stress (PBS) is applied for 1000 s. In contrast, the SS value does not change even after a negative bias stress (NBS) is applied for 1000 s. The difference in the switching characteristics under PBS and NBS is attributed to the ability of the interface traps to readily gain electrons from the inversion layer. The switching characteristics deteriorated by PBS are completely recovered after annealing at 300 °C for 10 min, and the characteristics remain stable even after PBS is applied again for 1000 s.

Minimum clinically important difference and substantial clinical benefit in patients with chronic temporomandibular disorders.

BACKGROUND: Research on temporomandibular disorder (TMD) responsiveness is scarce and limited regarding patients' representativeness. OBJECTIVE(S): This study aimed to estimate minimum clinically important difference (MCID) and substantial clinical benefit (SCB) among a large and diverse patient population regarding sex and age. METHODS: In this study, 162 patients participated from five hospitals. MCID and SCB in pain, functional disability and quality of life were examined with anchor-based methods. Patients' global impression of change was used as the anchor. Area under the curve (AUC) values were determined for testing accuracy. Changes from baseline and coefficient of variation by responsiveness status were calculated to explain the results of accuracy. RESULTS: SCB was estimated to be 2.18 for the numeric rating scale (NRS) for pain (AUC: 0.80 [95% CI: 0.72-0.88]) in all patients and 2.50 in women (AUC: 0.81 [95% CI: 0.71-0.89]). The estimated SCB of NRS for discomfort (1.50) and Jaw Functional Limitation Scale for mastication (1.35) had wide CIs for AUCs. Likewise, the estimated MCIDs of NRS for pain (0.80) and NRS for discomfort (1.50) had wide CIs for AUCs. Among non-responders who did not achieve the MCID of NRS for pain, the coefficient of variation was very high for all outcomes other than the NRS for pain. CONCLUSION: This study investigated the responsiveness of patients with TMD using a large and diverse patient sample. SCB in pain decrease can be used to assess the responsiveness of patients with TMD. Composite outcomes should be developed to estimate MCID.

Reconfiguration of operation modes in silicon nanowire field-effect transistors by electrostatic virtual doping.

In this study, we perform reconfigurable n- and p-channel operations of a tri-top-gate field-effect transistor (FET) made of a p+-i-n+silicon nanowire (SiNW). In the reconfigurable FET (RFET), two polarity gates and one control gate induce virtual electrostatic doping in the SiNW channel. The polarity gates are electrically connected to each other and program the channel type, while the control gate modulates the flow of charge carriers in the SiNW channel. The SiNW RFET features simple device design, symmetrical electrical characteristics in the n- and p-channel operation modes using p+-i-n+diode characteristics, and both operation modes exhibit high ON/OFF ratios (∼106) and high ON currents (∼1µAµm-1). The proposed device is demonstrated experimentally using a fully CMOS-compatible top-down processes.

Inverting logic-in-memory cells comprising silicon nanowire feedback field-effect transistors.

In this paper, we propose inverting logic-in-memory (LIM) cells comprising silicon nanowire feedback field-effect transistors with steep switching and holding characteristics. The timing diagrams of the proposed inverting LIM cells under dynamic and static conditions are investigated via mixed-mode technology computer-aided design simulation to verify the performance. The inverting LIM cells have an operating speed of the order of nanoseconds, an ultra-high voltage gain, and a longer retention time than that of conventional dynamic random access memory. The disturbance characteristics of half-selected cells within an inverting LIM array confirm the appropriate functioning of the random access memory array.

Prescription rate and treatment patterns for allergic rhinitis from 2010 to 2018 in South Korea: a retrospective study.

BACKGROUND: There has been little investigation on how guidelines for allergic rhinitis (AR) treatment are applied in current clinical practice. We aimed to analyze prescription trends and patterns for AR treatment according to patient characteristics over a 9-year period in Korea. METHODS: We used cross-sectional data from the Korean Health Insurance Review & Assessment Service National Patient Sample from 2010 to 2018. We analyzed 1,719,194 patients with AR as the principal diagnosis. Prescription rates of antihistamines, steroids, and other drugs; combination prescriptions; and first-choice prescriptions were analyzed. RESULTS: The prescription rate of first-generation antihistamines decreased over the years (2010: 29.13; 2018: 23.41). By contrast, the prescription rate of systemic steroids (2010: 23.60; 2018: 28.70), nasal steroids (2010: 9.70; 2018: 14.67), and leukotriene receptor antagonists (LTRAs) (2010: 11.13; 2018: 26.56) increased. The prescription rate of steroids was lower in patients aged 0-5 years and ≥ 65 years than in other age groups and that of LTRAs was the highest in patients aged 0-5 years. The rate of combination prescribing antihistamines and nasal steroids increased (2010: 7.99; 2018: 12.09). The rate of first-choice prescriptions with antihistamines and nasal steroids also increased (2010: 4.72; 2018: 7.24). CONCLUSIONS: The results confirmed a decrease in antihistamine prescriptions, especially with first-generation, and an increase in steroid and LTRA prescriptions in patients with AR in Korea. Regarding prescription patterns, steroids were increasingly prescribed in combination with antihistamines. However, the trend was opposite in the 0-5 years and ≥ 65 years groups.

Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.

PURPOSE: To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images. DESIGN: Cross-sectional study. PARTICIPANTS: For the development and testing of deep learning models, 309 786 readings from 103 262 images were used. Two additional external datasets (the Indian Diabetic Retinopathy Image Dataset and e-ophtha) were used for testing. A third external dataset (Messidor) was used for comparison of the models with human experts. METHODS: Macula-centered retinal fundus images from the Seoul National University Bundang Hospital Retina Image Archive, obtained at the health screening center and ophthalmology outpatient clinic at Seoul National University Bundang Hospital, were assessed for 12 major findings (hemorrhage, hard exudate, cotton-wool patch, drusen, membrane, macular hole, myelinated nerve fiber, chorioretinal atrophy or scar, any vascular abnormality, retinal nerve fiber layer defect, glaucomatous disc change, and nonglaucomatous disc change) with their regional information using deep learning algorithms. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve and sensitivity and specificity of the deep learning algorithms at the highest harmonic mean were evaluated and compared with the performance of retina specialists, and visualization of the lesions was qualitatively analyzed. RESULTS: Areas under the receiver operating characteristic curves for all findings were high at 96.2% to 99.9% when tested in the in-house dataset. Lesion heatmaps highlight salient regions effectively in various findings. Areas under the receiver operating characteristic curves for diabetic retinopathy-related findings tested in the Indian Diabetic Retinopathy Image Dataset and e-ophtha dataset were 94.7% to 98.0%. The model demonstrated a performance that rivaled that of human experts, especially in the detection of hemorrhage, hard exudate, membrane, macular hole, myelinated nerve fiber, and glaucomatous disc change. CONCLUSIONS: Our deep learning algorithms with region guidance showed reliable performance for detection of multiple findings in macula-centered retinal fundus images. These interpretable, as well as reliable, classification outputs open the possibility for clinical use as an automated screening system for retinal fundus images.

Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks.

Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for accurate geometric analysis and reliable automated diagnosis. In recent years, Convolutional Neural Networks (CNN) have shown outstanding performance compared to the conventional approaches in the segmentation tasks. In this paper, we experimentally measure the performance gain for Generative Adversarial Networks (GAN) framework when applied to the segmentation tasks. We show that GAN achieves statistically significant improvement in area under the receiver operating characteristic (AU-ROC) and area under the precision and recall curve (AU-PR) on two public datasets (DRIVE, STARE) by segmenting fine vessels. Also, we found a model that surpassed the current state-of-the-art method by 0.2 - 1.0% in AU-ROC and 0.8 - 1.2% in AU-PR and 0.5 - 0.7% in dice coefficient. In contrast, significant improvements were not observed in the optic disc segmentation task on DRIONS-DB, RIM-ONE (r3) and Drishti-GS datasets in AU-ROC and AU-PR.

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training.

BACKGROUND: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. METHODS: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. RESULTS: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. CONCLUSION: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

Laterality Classification of Fundus Images Using Interpretable Deep Neural Network.

In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images). Also, activation maps were generated to mark important regions in the image for the classification. Then, uncertainties were quantified to support explanations as to why certain images were incorrectly classified under the proposed model. Our model achieved a mean training accuracy of 99%, which is comparable to the performance of clinicians. Strong activations were detected at the location of optic disc and retinal blood vessels around the disc, which matches to the regions that clinicians attend when deciding the laterality. Uncertainty analysis discovered that misclassified images tend to accompany with high prediction uncertainties and are likely ungradable. We believe that visualization of informative regions and the estimation of uncertainty, along with presentation of the prediction result, would enhance the interpretability of neural network models in a way that clinicians can be benefitted from using the automatic classification system.

Generation and Storage of Random Voltage Values via Ring Oscillators Comprising Feedback Field-Effect Transistors.

In this study, we demonstrate the generation and storage of random voltage values using a ring oscillator consisting of feedback field-effect transistors (FBFETs). This innovative approach utilizes the logic-in-memory function of FBFETs to extract continuous output voltages from oscillatory cycles. The ring oscillator exhibited uniform probability distributions of 51.6% for logic 0 and 48.4% for logic 1. The generation of analog voltages provides binary random variables that are stored for over 5000 s. This demonstrates the potential of the ring oscillator in advanced physical functions and true random number generator technologies.

Binary and ternary logic-in-memory using nanosheet feedback field-effect transistors with triple-gated structure.

In this study, we demonstrate binary and ternary logic-in-memory (LIM) operations of inverters and NAND and NOR gates comprising nanosheet (NS) feedback field-effect transistors (FBFETs) with a triple-gated structure. The NS FBFETs are reconfigured in p- or n-channel modes depending on the polarity of the gate bias voltage and exhibit steep switching characteristics with an extremely low subthreshold swing of 1.08 mV dec-1 and a high ON/OFF current ratio of approximately 107. Logic circuits consisting of NS FBFETs perform binary and ternary logic operations of the inverters and NAND and NOR gates in each circuit and store their outputs under zero-bias conditions. Therefore, NS FBFETs are promising components for next-generation LIM.

Frequency-Dependent Dielectric Permittivity and Water Permeability in Ordered Mesoporous Silica-Grafted Fluorinated Polyimides.

Polymers with a low dielectric constant (Dk) are promising materials for high-speed communication networks, which demand exceptional thermal stability, ultralow Dk and dissipation factor, and minimum moisture absorption. In this paper, we prepared a series of novel low-Dk polyimide films containing an MCM-41-type amino-functionalized mesoporous silica (AMS) via in situ polymerization and subsequent thermal imidization and investigated their morphologies, thermal properties, frequency-dependent dielectric behaviors, and water permeabilities. Incorporating 6 wt.% AMS reduced the Dk at 1 MHz from 2.91 of the pristine fluorinated polyimide (FPI) to 2.67 of the AMS-grafted FPI (FPI-g-AMS), attributed to the free volume and low polarizability of fluorine moieties in the backbone and the incorporation of air voids within the mesoporous AMS particles. The FPI-g-AMS films presented a stable dissipation factor across a wide frequency range. Introducing a silane coupling agent increased the hydrophobicity of AMS surfaces, which inhibited the approaching of the water molecules, avoiding the hydrolysis of Si-O-Si bonds of the AMS pore walls. The increased tortuosity caused by the AMS particles also reduced water permeability. All the FPI-g-AMS films displayed excellent thermooxidative/thermomechanical stability, including a high 5% weight loss temperature (>531 °C), char residue at 800 °C (>51%), and glass transition temperature (>300 °C).

Clinical Utility of Deep Learning Assistance for Detecting Various Abnormal Findings in Color Retinal Fundus Images: A Reader Study.

Purpose: To evaluate the clinical usefulness of a deep learning-based detection device for multiple abnormal findings on retinal fundus photographs for readers with varying expertise. Methods: Fourteen ophthalmologists (six residents, eight specialists) assessed 399 fundus images with respect to 12 major ophthalmologic findings, with or without the assistance of a deep learning algorithm, in two separate reading sessions. Sensitivity, specificity, and reading time per image were compared. Results: With algorithmic assistance, readers significantly improved in sensitivity for all 12 findings (P < 0.05) but tended to be less specific (P < 0.05) for hemorrhage, drusen, membrane, and vascular abnormality, more profoundly so in residents. Sensitivity without algorithmic assistance was significantly lower in residents (23.1%∼75.8%) compared to specialists (55.1%∼97.1%) in nine findings, but it improved to similar levels with algorithmic assistance (67.8%∼99.4% in residents, 83.2%∼99.5% in specialists) with only hemorrhage remaining statistically significantly lower. Variances in sensitivity were significantly reduced for all findings. Reading time per image decreased in images with fewer than three findings per image, more profoundly in residents. When simulated based on images acquired from a health screening center, average reading time was estimated to be reduced by 25.9% (from 16.4 seconds to 12.1 seconds per image) for residents, and by 2.0% (from 9.6 seconds to 9.4 seconds) for specialists. Conclusions: Deep learning-based computer-assisted detection devices increase sensitivity, reduce inter-reader variance in sensitivity, and reduce reading time in less complicated images. Translational Relevance: This study evaluated the influence that algorithmic assistance in detecting abnormal findings on retinal fundus photographs has on clinicians, possibly predicting its influence on clinical application.

An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship.

The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis systems (CADs) have demonstrated their potential to reduce reading time and discrepancy amongst readers. However, the obscure reasoning of deep neural networks (DNNs) has been the leading cause to reluctance in its clinical use as CAD systems. Here, we present a novel architectural and algorithmic design of DNNs to comprehensively identify 15 abnormal retinal findings and diagnose 8 major ophthalmic diseases from macula-centered fundus images with the accuracy comparable to experts. We then define a notion of counterfactual attribution ratio (CAR) which luminates the system's diagnostic reasoning, representing how each abnormal finding contributed to its diagnostic prediction. By using CAR, we show that both quantitative and qualitative interpretation and interactive adjustment of the CAD result can be achieved. A comparison of the model's CAR with experts' finding-disease diagnosis correlation confirms that the proposed model identifies the relationship between findings and diseases similarly as ophthalmologists do.

New ternary inverter with memory function using silicon feedback field-effect transistors.

In this study, we present a fully complementary metal-oxide-semiconductor-compatible ternary inverter with a memory function using silicon feedback field-effect transistors (FBFETs). FBFETs operate with a positive feedback loop by carrier accumulation in their channels, which allows to achieve excellent memory characteristics with extremely low subthreshold swings. This hybrid operation of the switching and memory functions enables FBFETs to implement memory operation in a conventional CMOS logic scheme. The inverter comprising p- and n-channel FBFETs in series can be in ternary logic states and retain these states during the hold operation owing to the switching and memory functions of FBFETs. It exhibits a high voltage gain of approximately 73 V/V, logic holding time of 150 s, and reliable endurance of approximately 105. This ternary inverter with memory function demonstrates possibilities for a new computing paradigm in multivalued logic applications.

Virtual object sizes for efficient and convenient mid-air manipulation.

It has been taken for granted that the sizes of virtual objects affect the efficiency and convenience of mid-air manipulation in immersive virtual environments. If a virtual object is too small or too large, for example, manipulating it becomes a difficult task. Nevertheless, the virtual object sizes that are optimal and convenient have rarely been studied. In this paper, we select a virtual object with many distinct geometric features and conduct user studies via docking tasks. Through the user studies, the optimal and convenient sizes for mid-air manipulation are estimated. In order to verify the results, a proxy-based manipulation method is designed and implemented, where the proxy is created with the estimated optimal size. The test based on the method shows that the optimal-size proxy enables users to manipulate efficiently virtual objects and the estimated range of convenient sizes is also preferred by the users. Supplementary Information: The online version contains supplementary material available at 10.1007/s00371-022-02555-6.

Design and Simulation of Logic-In-Memory Inverter Based on a Silicon Nanowire Feedback Field-Effect Transistor.

In this paper, we propose a logic-in-memory (LIM) inverter comprising a silicon nanowire (SiNW) n-channel feedback field-effect transistor (n-FBFET) and a SiNW p-channel metal oxide semiconductor field-effect transistor (p-MOSFET). The hybrid logic and memory operations of the LIM inverter were investigated by mixed-mode technology computer-aided design simulations. Our LIM inverter exhibited a high voltage gain of 296.8 (V/V) when transitioning from logic '1' to '0' and 7.9 (V/V) when transitioning from logic '0' to '1', while holding calculated logic at zero input voltage. The energy band diagrams of the n-FBFET structure demonstrated that the holding operation of the inverter was implemented by controlling the positive feedback loop. Moreover, the output logic can remain constant without any supply voltage, resulting in zero static power consumption.

HM-Chromanone Ameliorates Hyperglycemia and Dyslipidemia in Type 2 Diabetic Mice.

The effects of (E)-5-hydroxy-7-methoxy-3-(2-hydroxybenzyl)-4-chromanone (HMC) on hyperglycemia and dyslipidemia were investigated in diabetic mice. Mice were separated into three groups: db/db, rosiglitazone and HMC. Blood glucose or glycosylated hemoglobin values in HMC-treated mice were significantly lower compared to db/db mice. Total cholesterol, LDL-cholesterol, and triglyceride values were lower, and HDL-C levels were higher, in the HMC group compared to the diabetic and rosiglitazone groups. HMC markedly increased IRS-1Tyr612, AktSer473 and PI3K levels and plasma membrane GLUT4 levels in skeletal muscle, suggesting improved insulin resistance. HMC also significantly stimulated AMPKThr172 and PPARα in the liver, and ameliorated dyslipidemia by inhibiting SREBP-1c and FAS. Consequently, HMC reduced hyperglycemia by improving the expression of insulin-resistance-related genes and improved dyslipidemia by regulating fatty acid synthase and oxidation-related genes in db/db mice. Therefore, HMC could ameliorate hyperglycemia and dyslipidemia in type 2 diabetic mice.

NAND and NOR logic-in-memory comprising silicon nanowire feedback field-effect transistors.

The processing of large amounts of data requires a high energy efficiency and fast processing time for high-performance computing systems. However, conventional von Neumann computing systems have performance limitations because of bottlenecks in data movement between separated processing and memory hierarchy, which causes latency and high power consumption. To overcome this hindrance, logic-in-memory (LIM) has been proposed that performs both data processing and memory operations. Here, we present a NAND and NOR LIM composed of silicon nanowire feedback field-effect transistors, whose configuration resembles that of CMOS logic gate circuits. The LIM can perform memory operations to retain its output logic under zero-bias conditions as well as logic operations with a high processing speed of nanoseconds. The newly proposed dynamic voltage-transfer characteristics verify the operating principle of the LIM. This study demonstrates that the NAND and NOR LIM has promising potential to resolve power and processing speed issues.

Polymer Concentration and Liquid-Liquid Demixing Time Correlation with Porous Structure of Low Dielectric Polyimide in Diffusion-Driven Phase Separation.

Porous polyimide (PI) films are a promising low-k dielectric material for high-frequency data transmission with low signal attenuation. Pores are generated by non-solvent induced phase separation (NIPS) during phase inversion of polymer solution via non-solvent accumulation and solvent diffusion. In this study, aromatic PI was employed as a matrix for NIPS, and the influence of polymer concentration and liquid­liquid demixing time on the morphology of pores in the PI films was investigated. This ensured control over the porous structure of the PI film and provided desirable dielectric properties in a broad frequency range of 100 Hz−30 MHz (1.99 at 30 MHz) and thermal stability (Td5% > 576 °C, Tg > 391 °C). This study addresses the effect of polymer concentration and coagulation time on the morphology and physical properties of PI sponge films and provides guidance on the design and optimization of architectures for polymeric materials requiring pore modification.