Resonantly hybridized excitons in moiré superlattices in van der Waals heterostructures.

Atomically thin layers of two-dimensional materials can be assembled in vertical stacks that are held together by relatively weak van der Waals forces, enabling coupling between monolayer crystals with incommensurate lattices and arbitrary mutual rotation1,2. Consequently, an overarching periodicity emerges in the local atomic registry of the constituent crystal structures, which is known as a moiré superlattice3. In graphene/hexagonal boron nitride structures4, the presence of a moiré superlattice can lead to the observation of electronic minibands5-7, whereas in twisted graphene bilayers its effects are enhanced by interlayer resonant conditions, resulting in a superconductor-insulator transition at magic twist angles8. Here, using semiconducting heterostructures assembled from incommensurate molybdenum diselenide (MoSe2) and tungsten disulfide (WS2) monolayers, we demonstrate that excitonic bands can hybridize, resulting in a resonant enhancement of moiré superlattice effects. MoSe2 and WS2 were chosen for the near-degeneracy of their conduction-band edges, in order to promote the hybridization of intra- and interlayer excitons. Hybridization manifests through a pronounced exciton energy shift as a periodic function of the interlayer rotation angle, which occurs as hybridized excitons are formed by holes that reside in MoSe2 binding to a twist-dependent superposition of electron states in the adjacent monolayers. For heterostructures in which the monolayer pairs are nearly aligned, resonant mixing of the electron states leads to pronounced effects of the geometrical moiré pattern of the heterostructure on the dispersion and optical spectra of the hybridized excitons. Our findings underpin strategies for band-structure engineering in semiconductor devices based on van der Waals heterostructures9.

Author Correction: Resonantly hybridized excitons in moiré superlattices in van der Waals heterostructures.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mitochondrial fission enhances IL-6-induced metastatic potential in ovarian cancer via ERK1/2 activation.

Ovarian cancer is a lethal gynecologic cancer mostly diagnosed in an advanced stage with an accumulation of ascites. Interleukin-6 (IL-6), a pro-inflammatory cytokine is highly elevated in malignant ascites and plays a pleiotropic role in cancer progression. Mitochondria are dynamic organelles that undergo fission and fusion in response to external stimuli and dysregulation in their dynamics has been implicated in cancer progression and metastasis. Here, we investigate the effect of IL-6 on mitochondrial dynamics in ovarian cancer cells (OVCs) and its impact on metastatic potential. Treatment with IL-6 on ovarian cancer cell lines (SKOV3 and PA-1) led to an elevation in the metastatic potential of OVCs. Interestingly, a positive association was observed between dynamin-related protein 1 (Drp1), a regulator of mitochondrial fission, and IL-6R in metastatic ovarian cancer tissues. Additionally, IL-6 treatment on OVCs was linked to the activation of Drp1, with a notable increase in the ratio of the inhibitory form p-Drp1(S637) to the active form p-Drp1(S616), indicating enhanced mitochondrial fission. Moreover, IL-6 treatment triggered the activation of ERK1/2, and inhibiting ERK1/2 mitigated IL-6-induced mitochondrial fission. Suppressing mitochondrial fission through siRNA transfection and a pharmacological inhibitor reduced the IL-6-induced migration and invasion of OVCs. This was further supported by 3D invasion assays using patient-derived spheroids. Altogether, our study suggests the role of mitochondrial fission in the metastatic potential of OVCs induced by IL-6. The inhibition of mitochondrial fission could be a potential therapeutic approach to suppress the metastasis of ovarian cancer.

Rural Health Disparities in Skin Cancer Amplified Among Skin of Color.

Limited studies explore the role social determinants of health have on urban-rural health disparities, particularly for Skin of Color. To further evaluate this relationship, a cross-sectional study was conducted on data from five states using the 2018 to 2021 Behavior Risk Factor Surveillance Survey, a national state-run health survey. Prevalence of skin cancer history and urban/rural status were evaluated across these social determinants of health: sex, age, race, insurance status, number of personal healthcare providers, and household income. Overall, rural counterparts were significantly more likely to have a positive skin cancer history across most social determinants of health. Rural populations had a higher prevalence of skin cancer history across all races (P<.001). Rural non-Hispanic Whites had greater odds than their urban counterparts (OR=1.40; 95% CI 1.34 - 1.46). The odds were approximately twice as high for rural Black (OR=1.74; 95% CI 1.14 - 2.65), Hispanic (OR=2.31; 95% CI 1.56 - 3.41), and Other Race, non-Hispanic (OR=1.99; 95% CI 1.51 - 2.61), and twenty times higher for Asians (OR=20.46; 95% CI 8.63 - 48.54), although no significant difference was seen for American Indian/Alaskan Native (OR=1.5; 95% CI 0.99 - 2.28). However, when household income exceeded $100,000 no significant difference in prevalence or odds was seen between urban and rural settings. Despite increasing awareness of metropolitan-based health inequity, urban-rural disparities in skin cancer prevalence continue to persist and may be magnified by social determinants such as income and race. J Drugs Dermatol. 2024;23(6):480-484.    doi:10.36849/JDD.8094.

Evolution of Local Structural Motifs in Colloidal Quantum Dot Semiconductor Nanocrystals Leading to Nanofaceting.

Colloidal nanocrystals (NCs) have shown remarkable promise for optoelectronics, energy harvesting, photonics, and biomedical imaging. In addition to optimizing quantum confinement, the current challenge is to obtain a better understanding of the critical processing steps and their influence on the evolution of structural motifs. Computational simulations and electron microscopy presented in this work show that nanofaceting can occur during nanocrystal synthesis from a Pb-poor environment in a polar solvent. This could explain the curved interfaces and the olivelike-shaped NCs observed experimentally when these conditions are employed. Furthermore, the wettability of the PbS NCs solid film can be further modified via stoichiometry control, which impacts the interface band bending and, therefore, processes such as multiple junction deposition and interparticle epitaxial growth. Our results suggest that nanofaceting in NCs can become an inherent advantage when used to modulate band structures beyond what is traditionally possible in bulk crystals.

Increased Urination Urgency Exacerbates Sexual Risk-Taking Through Heightened Sexual Arousal.

Increased urination urgency has been shown to facilitate impulse control in cognitive domains, but its effects in other areas are unknown. We examined whether inhibitory spillover effects would replicate and extend to close relationships-specifically, influencing decision making related to sexual risk-taking. Across three studies, we either measured (Studies 1 and 3) or manipulated (Study 2) participants' bladder pressure and assessed sexual self-control using a questionnaire of sexual risk-taking intentions (Study 1) or a simulated semi-behavioral sexual risk-taking (Choose Your Own Sexual Adventure) task (Studies 2 and 3). Study 1 (N = 44 men, 59 women) showed greater urination urgency was associated with greater sexual risk-taking. Study 2 (N = 65 men, 91 women) showed that increasing urination urgency led to greater sexual risk-taking, but only among men. Study 3 (N = 86 men, 183 women) showed elevated urination urgency was associated with an increase in sexual arousal, which accounted for the greater sexual risk-taking.

Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model.

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.

Imaging Quantum Interference in Stadium-Shaped Monolayer and Bilayer Graphene Quantum Dots.

Experimental realizations of graphene-based stadium-shaped quantum dots (QDs) have been few and have been incompatible with scanned probe microscopy. Yet, the direct visualization of electronic states within these QDs is crucial for determining the existence of quantum chaos in these systems. We report the fabrication and characterization of electrostatically defined stadium-shaped QDs in heterostructure devices composed of monolayer graphene (MLG) and bilayer graphene (BLG). To realize a stadium-shaped QD, we utilized the tip of a scanning tunneling microscope to charge defects in a supporting hexagonal boron nitride flake. The stadium states visualized are consistent with tight-binding-based simulations but lack clear quantum chaos signatures. The absence of quantum chaos features in MLG-based stadium QDs is attributed to the leaky nature of the confinement potential due to Klein tunneling. In contrast, for BLG-based stadium QDs (which have stronger confinement) quantum chaos is precluded by the smooth confinement potential which reduces interference and mixing between states.

Computational modeling of malignant ascites reveals CCL5-SDC4 interaction in the immune microenvironment of ovarian cancer.

Fluid accumulation in the abdominal cavity is commonly found in advanced-stage ovarian cancer patients, which creates a specialized tumor microenvironment for cancer progression. Using single-cell RNA sequencing (scRNA-seq) of ascites cells from five patients with ovarian cancer, we identified seven cell types, including heterogeneous macrophages and ovarian cancer cells. We resolved a distinct polarization state of macrophages by MacSpectrum analysis and observed subtype-specific enrichment of pathways associated with their functions. The communication between immune and cancer cells was predicted through a putative ligand-receptor pair analysis using NicheNet. We found that CCL5, a chemotactic ligand, is enriched in immune cells (T cells and NK cells) and mediates ovarian cancer cell survival in the ascites, possibly through SDC4. Moreover, SDC4 expression correlated with poor overall survival in ovarian cancer patients. Our study highlights the potential role of T cells and NK cells in long-term survival patients with ovarian cancer, indicating SDC4 as a potential prognostic marker in ovarian cancer patients.

Increasing serum gamma-glutamyltransferase level accompanies a rapid increase in the incidence of endometrial cancer in Korea: A nationwide cohort study.

OBJECTIVE: This study aimed to determine the association of serum GGT levels with the risk of developing endometrial cancer. Women's obesity and menopausal status were also taken into account in our analysis. METHODS: We used a nationwide cohort to examine the association between serum GGT levels and endometrial cancer development in Korean women. Data were retrieved from the Korean National Health Insurance Service (NHIS) healthcare system. Women aged over 19 years who participated in the Korea National Health Screening Examination in 2009 and were not diagnosed with endometrial cancer 1-year post-examination were included in our study (n = 2,736,588). RESULTS: Obese (BMI, ≥25 kg/m2) women with increased GGT levels were at high risk of endometrial cancer (HR = 1.415, 95% CI: 1.236-1.621). Interestingly, in pre-menopausal women, high GGT level (Q4) was associated with the increased endometrial cancer risk only for obese women (HR = 1.482, 95% CI: 1.205-1.821). In post-menopausal women, only a high GGT level (Q4) was also associated with the increased cancer risk for obese women (HR = 1.313, 95% CI: 1.096-1.573). We observed a significant association between high GGT levels and increased risk of endometrial cancer in pre-menopausal women with abdominal obesity (WC, ≥85 cm) (HR = 1.647, 95% CI: 1.218-2.227). CONCLUSIONS: Increased GGT level is an independent risk factor of endometrial cancer, especially for post-menopausal women and obese pre-menopausal women. These results may suggest that serum GGT levels might be useful in the risk stratification of endometrial cancer. Adopting a healthy lifestyle for lowering serum GGT level is warranted, especially for women with a higher risk of developing endometrial cancer.

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.

Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents.

The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.

A caffeic acid-ferulic acid hybrid compound attenuates lipopolysaccharide-mediated inflammation in BV2 and RAW264.7 cells.

In the present study, we synthesized and evaluated the anti-inflammatory effects of the two component hybrids, caffeic acid (CA)-ferulic acid (FA), FA-Tryptamine (Trm), CA-Piperonyl Triazol (PT) and FA-PT. Of these five hybrids, CA-FA had the most potent inhibitory effect on butyrylcholinesterase (BuChE) activity. The CA containing hybrids, CA-FA, CA-Trm, and CA-PT, dose-dependently inhibited LPS-induced nitric oxide (NO) generation in BV2 cells, whereas FA-PT, FA-Trm, CA, FA, Trm, and PT did not. Although CA-FA, CA-Trm and CA-PT had similar inhibitory effects on LPS-induced NO generation, CA-FA best protected BV2 cells from LPS-induced cell death. CA-FA, but not CA or FA, dose-dependently inhibited LPS-induced up-regulations of NO synthase (iNOS) and cyclooxygenase-2 (COX-2) protein expressions in BV2 and RAW264.7 cells. Furthermore, CA-FA inhibited LPS-induced iNOS, COX-2, interleukin-6, and interleukin-1ß mRNA expressions in BV2 cells. CA-FA also inhibited the LPS-induced phosphorylations of STAT3, Akt, and IκB and selectively inhibited LPS-induced NF-κB activation. Overall, our data suggest that CA-FA has BuChE inhibitory effects and down-regulates inflammatory responses by inhibiting NF-κB, which indicates CA-FA be viewed as a potential therapeutic agent for the treatment of inflammatory diseases of the peripheral system and central nervous systems.

Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors.

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as 'two-stage latent dynamics modeling and filtering' (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

Visualization and Control of Single-Electron Charging in Bilayer Graphene Quantum Dots.

Graphene p-n junctions provide an ideal platform for investigating novel behavior at the boundary between electronics and optics that arise from massless Dirac Fermions, such as whispering gallery modes and Veselago lensing. Bilayer graphene also hosts Dirac Fermions, but they differ from single-layer graphene charge carriers because they are massive, can be gapped by an applied perpendicular electric field, and have very different pseudospin selection rules across a p-n junction. Novel phenomena predicted for these massive Dirac Fermions at p-n junctions include anti-Klein tunneling, oscillatory Zener tunneling, and electron cloaked states. Despite these predictions there has been little experimental focus on the microscopic spatial behavior of massive Dirac Fermions in the presence of p-n junctions. Here we report the experimental manipulation and characterization of massive Dirac Fermions within bilayer graphene quantum dots defined by circular p-n junctions through the use of scanning tunneling microscopy-based (STM) methods. Our p-n junctions are created via a flexible technique that enables realization of exposed quantum dots in bilayer graphene/hBN heterostructures. These quantum dots exhibit sharp spectroscopic resonances that disperse in energy as a function of applied gate voltage. Spatial maps of these features show prominent concentric rings with diameters that can be tuned by an electrostatic gate. This behavior is explained by single-electron charging of localized states that arise from the quantum confinement of massive Dirac Fermions within our exposed bilayer graphene quantum dots.

LSTM-Guided Coaching Assistant for Table Tennis Practice.

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

Imaging of Interlayer Coupling in van der Waals Heterostructures Using a Bright-Field Optical Microscope.

Vertically stacked atomic layers from different layered crystals can be held together by van der Waals forces, which can be used for building novel heterostructures, offering a platform for developing a new generation of atomically thin, transparent, and flexible devices. The performance of these devices is critically dependent on the layer thickness and the interlayer electronic coupling, influencing the hybridization of the electronic states as well as charge and energy transfer between the layers. The electronic coupling is affected by the relative orientation of the layers as well as by the cleanliness of their interfaces. Here, we demonstrate an efficient method for monitoring interlayer coupling in heterostructures made from transition metal dichalcogenides using photoluminescence imaging in a bright-field optical microscope. The color and brightness in such images are used here to identify mono- and few-layer crystals and to track changes in the interlayer coupling and the emergence of interlayer excitons after thermal annealing in heterobilayers composed of mechanically exfoliated flakes and as a function of the twist angle in atomic layers grown by chemical vapor deposition. Material and crystal thickness sensitivity of the presented imaging technique makes it a powerful tool for characterization of van der Waals heterostructures assembled by a wide variety of methods, using combinations of materials obtained through mechanical or chemical exfoliation and crystal growth.

Strain-Mediated Interlayer Coupling Effects on the Excitonic Behaviors in an Epitaxially Grown MoS2/WS2 van der Waals Heterobilayer.

van der Waals heterostructures composed of two different monolayer crystals have recently attracted attention as a powerful and versatile platform for studying fundamental physics, as well as having great potential in future functional devices because of the diversity in the band alignments and the unique interlayer coupling that occurs at the heterojunction interface. However, despite these attractive features, a fundamental understanding of the underlying physics accounting for the effect of interlayer coupling on the interactions between electrons, photons, and phonons in the stacked heterobilayer is still lacking. Here, we demonstrate a detailed analysis of the strain-dependent excitonic behavior of an epitaxially grown MoS2/WS2 vertical heterostructure under uniaxial tensile and compressive strain that enables the interlayer interactions to be modulated along with the electronic band structure. We find that the strain-modulated interlayer coupling directly affects the characteristic combined vibrational and excitonic properties of each monolayer in the heterobilayer. It is further revealed that the relative photoluminescence intensity ratio of WS2 to MoS2 in our heterobilayer increases monotonically with tensile strain and decreases with compressive strain. We attribute the strain-dependent emission behavior of the heterobilayer to the modulation of the band structure for each monolayer, which is dictated by the alterations in the band gap transitions. These findings present an important pathway toward designing heterostructures and flexible devices.

Formation and magnetic properties of InFeP:Ag nanorods fabricated with noble metal Ag using an ion milling method.

The formation, including the density and height of the InFeP:Ag nanorods doped with noble metal Ag using an ion milling method, was preponderantly determined from transmission electron microscopy and x-ray diffraction analyses. We investigate, in particular, the enhanced ferromagnetism of the well-aligned InFeP:Ag nanorods. Auger electron spectroscopy and x-ray photoelectron spectroscopy measurements were carried out in order to investigate the incorporation of Ag and to verify the local chemical bonding of the InFeP:Ag nanorods. The variation of FWHM for the double-crystal x-ray rocking curve and triple-axis diffraction peaks demonstrates that noble metal Ag is incorporated into the InFeP:Ag nanorods. The noticeable ferromagnetic signature (M-H curve) of the InFeP:Ag nanorods is observed and T c persists up to almost 350 K (3.9 × 10-4 emu g-1), as determined by temperature-dependence magnetization (M-T curve) measurements. This study suggests that the InFeP:Ag nanorods should be a potential candidate for the application of spintronic devices.