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Graphene research so far has focused on electronic 1 , 2 , 3 , 4 , 5 , 6 rather than photonic applications, in spite of its impressive optical properties 7 , 8 . These include its ability to absorb ∼2% of incident light over a broad wavelength range despite being just one atom thick 7 . Here, we demonstrate ultrafast transistor-based photodetectors made from single- and few-layer graphene. The photoresponse does not degrade for optical intensity modulations up to 40 GHz, and further analysis suggests that the intrinsic bandwidth may exceed 500 GHz. The generation and transport of photocarriers in graphene differ fundamentally from those in photodetectors made from conventional semiconductors as a result of the unique photonic and electronic properties of the graphene. This leads to a remarkably high bandwidth, zero source–drain bias and dark current operation, and good internal quantum efficiency. Field-effect transistors made from graphene act as photodetectors at frequencies up to 40 GHz, demonstrating the advantage offered by graphene for photonic applications.

A high-quality junction between graphene and metallic contacts is crucial in the creation of high-performance graphene transistors. In an ideal metal–graphene junction, the contact resistance is determined solely by the number of conduction modes in graphene. However, as yet, measurements of contact resistance have been inconsistent, and the factors that determine the contact resistance remain unclear. Here, we report that the contact resistance in a palladium–graphene junction exhibits an anomalous temperature dependence, dropping significantly as temperature decreases to a value of just 110 ± 20 Ω µm at 6 K, which is two to three times the minimum achievable resistance. Using a combination of experiment and theory we show that this behaviour results from carrier transport in graphene under the palladium contact. At low temperature, the carrier mean free path exceeds the palladium–graphene coupling length, leading to nearly ballistic transport with a transfer efficiency of ~75%. As the temperature increases, this carrier transport becomes less ballistic, resulting in a considerable reduction in efficiency. The contact resistance of a junction between graphene and palladium is shown to be strongly affected by carrier transport in graphene underneath the palladium, and is measured to be just two to three times larger than the minimum resistance achievable.

A wafer-scale graphene circuit was demonstrated in which all circuit components, including graphene field-effect transistor and inductors, were monolithically integrated on a single silicon carbide wafer. The integrated circuit operates as a broadband radio-frequency mixer at frequencies up to 10 gigahertz. These graphene circuits exhibit outstanding thermal stability with little reduction in performance (less than 1 decibel) between 300 and 400 kelvin. These results open up possibilities of achieving practical graphene technology with more complex functionality and performance.

The three-dimensional organization of chromatin is essential for gene regulation and cellular function, with epigenome playing a key role. Hi-C methods have expanded our understanding of chromatin interactions, but their high cost and complexity limit their use. Existing models for predicting chromatin interactions rely on limited ChIP-seq inputs, reducing their accuracy and generalizability. In this work, we present a computational approach, EpiVerse, which leverages imputed epigenetic signals and advanced deep learning techniques. EpiVerse significantly improves the accuracy of cross-cell-type Hi-C prediction, while also enhancing model interpretability by incorporating chromatin state prediction within a multitask learning framework. Moreover, EpiVerse predicts Hi-C contact maps across an array of 39 human tissues, which provides a comprehensive view of the complex relationship between chromatin structure and gene regulation. Furthermore, EpiVerse facilitates unprecedented in silico perturbation experiments at the “epigenome-level” to unveil the chromatin architecture under specific conditions. EpiVerse is available on GitHub: https://github.com/jhhung/EpiVerse . EpiVerse is a deep-learning framework that integrates imputed epigenetic signals to improve cross-cell-type Hi-C prediction, enhance interpretability, and enable in silico perturbation of chromatin architecture.

Background Taiwan became an aged society in March 2018, and it is expected to become a super-aged society by 2025. The trend of increasing proportions of older adults continuing to work is inevitable. However, few studies have been conducted to investigate the effects of employment on the mental health of older adults. Therefore, we longitudinally explored the relationship between employment status and depressive symptoms in Taiwanese older adults. Methods The study included 5,131 individuals aged 50 and above, of which 55.6% were men, who had participated in the national-wide Taiwan Longitudinal Study of Aging in 1996, 1999, 2003, and 2007. Of them, 1,091 older adults had completed all four surveys. Depressive symptoms were assessed using the Center for Epidemiological Studies of Depression scale; the total score on this scale ranges from 0 to 30. Employment status was assessed during each survey wave. Logistic regression was performed using a cross-sectional design. The effects of unemployment on depressive symptoms were analyzed using a generalized estimating equation model with a repeated measures design. Results In each survey wave, employed older adults exhibited better mental health than did unemployed ones. After adjustments for potential confounders, unemployment was found to exert a significant adverse effect on depressive symptoms. The repeated measures analysis revealed that employment protected against depressive symptoms, as noted in the subsequent surveys conducted after 3 to 4 years (aOR [95% CI] = 0.679 [0.465–0.989]). Conclusion Employment may reduce the risk of depressive symptoms in community-dwelling older adults in Taiwan.

Background It is unclear to familial screen time in early childhood is associated with the subsequent diagnosis of attention-deficit and hyperactivity disorder (ADHD). Our study is to evaluate the association between screen time during early childhood in families and the incidence of ADHD. Methods We conducted a population-based birth cohort study by using the Taiwan Birth Cohort Study, which recruited 24 200 mother–child pairs when children were 6 months old. Screen time exposure for children and parents were collected at the age of 18 and 36 months. Whether the child has ever been diagnosed with ADHD was determined at a follow-up interview at age 8. Factors including socioeconomic factors and screen time were analyzed using logistic regression to determine their association with the rate of ADHD. Results A total of 16 651 term singletons were included in the final analysis. Of them, 382 (2.3%) were diagnosed as having ADHD before the age of 8 years. No significant relationship between children’s or fathers’ screen time and ADHD was noted. When compared to children whose mothers spent less time on screens, those whose mothers spent more than 3 h a day on screens when the child was 3 years old exhibited a higher incidence of ADHD (adjusted OR [aOR]: 1.31, 95% CI: 1.03–1.66). Conclusion Higher maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in this population-based study. However, children’s screen time did not find related to ADHD. We found that it was the mother’s screen time, who typically serves as the primary caregiver in our study participants, not the child’s, that mattered. In addition to superficial screen use time, future research is needed to replicate the findings and clarify mechanisms underlying this association.

There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

Single-walled carbon nanotubes (SWCNTs) have been shown to exhibit excellent electrical properties, such as ballistic transport over several hundred nanometers at room temperature. Field-effect transistors (FETs) made from individual tubes show dc performance specifications rivaling those of state-of-the-art silicon devices. An important next step is the fabrication of integrated circuits on SWCNTs to study the high-frequency ac capabilities of SWCNTs. We built a five-stage ring oscillator that comprises, in total, 12 FETs side by side along the length of an individual carbon nanotube. A complementary metal-oxide semiconductor-type architecture was achieved by adjusting the gate work functions of the individual p-type and n-type FETs used.

Chronic kidney disease (CKD) has become a major issue in long-term healthcare. It is caused by recurrent kidney injury, which is possible induced by dehydration and heat stress. Therefore, it is important to access the dehydration diagnosis on fields. Conventional instruments for assessing dehydration from blood and urine samples are expensive and time-consuming. These disadvantages limit their applications in high-risk groups susceptible to kidney disease. To address this unmet need, this study presents a portable miniaturized device for dehydration diagnosis with clinical saliva samples. With co-plane coating-free gold electrodes, the dehydration diagnosis was achieved with a saliva specimen at low volumes (50–500 μL). To examine the characteristics, the developed device was assessed by using standard conductivity solutions and the examined variation was <5%. To validate the use for field applications, saliva samples were measured by the developed device and the measured results were compared with standard markers of serum osmolality (N = 30). These data indicate that the measured saliva conductivity is consistent with serum osmolality. And it shows significant difference between healthy adults and healthy farmers ( p  < 0.05), who typically suffer high risks of CKD. Based on this work, the proposed device and measurement offer a useful method to diagnosis dehydrations and indicate possible potential for CKD.

To evaluate efficacy of the AIxURO system, a deep learning-based artificial intelligence (AI) tool, in enhancing the accuracy and reliability of urine cytology for diagnosing upper urinary tract cancers. One hundred and eighty-five cytology samples of upper urine tract were collected and categorized according to The Paris System for Reporting Urinary Cytology (TPS), yielding 168 negative for High-Grade Urothelial Carcinoma (NHGUC), 14 atypical urothelial cells (AUC), 2 suspicious for high-grade urothelial carcinoma (SHGUC), and 1 high-grade urothelial carcinoma (HGUC). The AIxURO system, trained on annotated cytology images, was employed to analyze these samples. Independent assessments by a cytotechnologist and a cytopathologist were conducted to validate the initial AIxURO assessment. AIxURO identified discrepancies in 37 of the 185 cases, resulting in a 20% discrepancy rate. The cytotechnologist achieved an accuracy of 85% for NHGUC and 21.4% for AUC, whereas the cytopathologist attained accuracies of 95% for NHGUC and 85.7% for AUC. The cytotechnologist exhibited overcall rates of roughly 15% and undercall rates of greater than 50%, while the cytopathologist showed profoundly lower miscall rates from both undercall and overcall. AIxURO significantly enhanced diagnostic accuracy and consistency, particularly in complex cases involving atypical cells. AIxURO can improve the accuracy and reliability of cytology diagnosis for upper urine tract urothelial carcinomas by providing precise detection on atypical urothelial cells and reducing subjectivity in assessments. The integration of AIxURO into clinical practice can significantly ameliorate diagnostic outcomes, highlighting the synergistic potential of AI technology and human expertise in cytology.