TransC-ac4C: Identification of N4-acetylcytidine (ac4C) sites in mRNA using deep learning.

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA that is critical in mRNA translation in terms of stability and regulation. In the past few years, numerous approaches employing convolutional neural networks (CNN) and Transformer have been proposed for the identification of ac4C sites, with each variety of approaches processing distinct characteristics. CNN-based methods excels at extracting local features and positional information, whereas Transformer-based ones stands out in establishing long-range dependencies and generating global representations. Given the importance of both local and global features in mRNA ac4C sites identification, we propose a novel method termed TransC-ac4C which combines CNN and Transformer together for enhancing the feature extraction capability and improving the identification accuracy. Five different feature encoding strategies (One-hot, NCP, ND, EIIP, and K-mer) are employed to generate the mRNA sequence representations, in which way the sequence attributes and physical and chemical properties of the sequences can be embedded. To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the CNN is employed to process five single features, stitch them together and feed them to the Transformer layer. Then, our approach employs CNN to extract local features and Transformer subsequently to establish global long-range dependencies among extracted features. We use 5-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 81.42.

Effect of resistance circuit training on comprehensive health indicators in older adults: a systematic review and meta-analysis.

The aging process leads to the degeneration of body structure and function. The objective of this study is to conduct a systematic review and meta-analysis of the effects of resistance circuit training (RCT) on comprehensive health indicators of older adults. PubMed, Embase, and Web of Science were searched until August 2023. Primary outcomes were body composition, muscle strength, cardiorespiratory endurance, blood pressure, and functional autonomy. Muscle function and exercise intensity subgroups were analyzed. RCT reduces body fat (MD = - 5.39 kg, 95% CI - 10.48 to - 0.29), BMI (MD = - 1.22, 95% CI - 2.17 to - 0.26), and body weight (MD = - 1.28 kg, 95% CI - 1.78 to - 0.78), and increases lean body mass (MD = 1.42 kg, 95% CI 0.83-2.01) in older adults. It improves upper limb strength (SMD = 2.09, 95% CI 1.7-2.48), lower limb strength (SMD = 2.03, 95% CI 1.56-2.51), cardiorespiratory endurance (MD = 94 m, 95% CI 25.69-162.67), and functional autonomy (MD = - 1.35, 95% CI - 1.73 to - 0.96). High-intensity RCT benefits BMI and body weight, while low-intensity exercise reduces blood pressure. RCT improves muscle function in push, pull, hip, and knee movements in older adults. RCT improves body composition, muscle strength, cardiorespiratory endurance, blood pressure, and functional autonomy in older adults. High-intensity training is superior for body composition, while moderate to low intensity training is more effective for lowering blood pressure.

Mechanisms Underlying a Quantum Superposition Microscope Based on THz-Driven Coherent Oscillations in a Two-Level Molecular Sensor.

We report pump-probe measurements of a hydrogen molecule (H_{2}) in the tunnel junction of a scanning tunneling microscope coupled to ultrashort terahertz (THz) pulses. The coherent oscillation of the THz-induced dc tunneling current at a frequency of ∼0.5 THz fingerprints the absorption by H_{2} as a two-level system (TLS). Two components of the oscillatory signal are observed and point to both photon and field aspects of the THz pulses. A few loosely bound states with similar energies for the upper state of the TLS are evidenced by the coherent revival of oscillatory signal. Furthermore, the comparison of spectroscopic features of H_{2} with different tips provides an understanding of the TLS for H_{2}.

A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing.

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.

Avoided Level Crossing and Entangled States of Interacting Hydrogen Molecules Detected by the Quantum Superposition Microscope.

Pump-probe measurements by ultrashort THz pulses can be used to excite and follow the coherence dynamics in the time domain of single hydrogen molecules (H2) in the junction of a scanning tunneling microscope (STM). By tailoring the resonance frequency through the sample bias, we identified two spectral signatures of the interactions among multiple H2 molecules. First, the avoided level crossing featured by energy gaps ranging from 20 to 80 GHz was observed because of the level repulsion between two H2 molecules. Second, the tip can sense the signal of H2 outside the junction through the projective measurement on the H2 inside the junction, owing to the entangled states created through the interactions. A dipolar-type interaction was integrated into the tunneling two-level system model of H2, enabling accurate reproduction of the observed behaviors. Our results obtained by the quantum superposition microscope reveal the intricate quantum mechanical interplay among H2 molecules and additionally provide a 2D platform to investigate unresolved questions of amorphous materials.

Uncovering the Role of Crystal Phase in Determining Nonvolatile Flash Memory Device Performance Fabricated from MoTe2-Based 2D van der Waals Heterostructures.

Although the crystal phase of two-dimensional (2D) transition metal dichalcogenides (TMDs) has been proven to play an essential role in fabricating high-performance electronic devices in the past decade, its effect on the performance of 2D material-based flash memory devices still remains unclear. Here, we report the exploration of the effect of MoTe2 in different phases as the charge-trapping layer on the performance of 2D van der Waals (vdW) heterostructure-based flash memory devices, where a metallic 1T'-MoTe2 or semiconducting 2H-MoTe2 nanoflake is used as the floating gate. By conducting comprehensive measurements on the two kinds of vdW heterostructure-based devices, the memory device based on MoS2/h-BN/1T'-MoTe2 presents much better performance, including a larger memory window, faster switching speed (100 ns), and higher extinction ratio (107), than that of the device based on the MoS2/h-BN/2H-MoTe2 heterostructure. Moreover, the device based on the MoS2/h-BN/1T'-MoTe2 heterostructure also shows a long cycle (>1200 cycles) and retention (>3000 s) stability. Our study clearly demonstrates that the crystal phase of 2D TMDs has a significant impact on the performance of nonvolatile flash memory devices based on 2D vdW heterostructures, which paves the way for the fabrication of future high-performance memory devices based on 2D materials.

CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones.

As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m2 (SNpM2) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM2 and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.

Electronic/Optoelectronic Memory Device Enabled by Tellurium-based 2D van der Waals Heterostructure for in-Sensor Reservoir Computing at the Optical Communication Band.

Although 2D materials are widely explored for data storage and neuromorphic computing, the construction of 2D material-based memory devices with optoelectronic responsivity in the short-wave infrared (SWIR) region for in-sensor reservoir computing (RC) at the optical communication band still remains a big challenge. In this work, an electronic/optoelectronic memory device enabled by tellurium-based 2D van der Waals (vdW) heterostructure is reported, where the ferroelectric CuInP2 S6 and tellurium channel endow this device with both the long-term potentiation/depression by voltage pulses and short-term potentiation by 1550 nm laser pulses (a typical wavelength in the conventional fiber optical communication band). Leveraging the rich dynamics, a fully memristive in-sensor RC system that can simultaneously sense, decode, and learn messages transmitted by optical fibers is demonstrated. The reported 2D vdW heterostructure-based memory featuring both the long-term and short-term memory behaviors using electrical and optical pulses in SWIR region has not only complemented the wide spectrum of applications of 2D materials family in electronics/optoelectronics but also paves the way for future smart signal processing systems at the edge.

Electrical Manipulation of Quantum Coherence in a Two-Level Molecular System.

We report the manipulation of ultrafast quantum coherence of a two-level single hydrogen molecular system by employing static electric field from the sample bias in a femtosecond terahertz scanning tunneling microscope. A H_{2} molecule adsorbed on the polar Cu_{2}N surface develops an electric dipole and exhibits a giant Stark effect. An avoided crossing of the quantum state energy levels is derived from the resonant frequency of the single H_{2} two levels in a double-well potential. The dephasing time of the initial wave packet can also be changed by applying the electric field. The electrical manipulation for different tunneling gaps in three dimensions allows quantification of the surface electrostatic fields at the atomic scale. Our work demonstrated the potential application of molecules as controllable two-level molecular systems.

Atomic-Scale Rectification and Inelastic Electron Tunneling Spectromicroscopy.

The phenomenon of rectification describes the emergence of a DC current from the application of an oscillating voltage. Although the origin of this effect has been associated with the nonlinearity in the current-voltage I(V) relation, a rigorous understanding of the microscopic mechanisms for this phenomenon remains challenging. Here, we show the close connection between rectification and inelastic electron tunneling spectroscopy and microscopy for single molecules with a scanning tunneling microscope. While both techniques are based on nonlinear features in the I(V) curve, comprehensive line shape analyses reveal notable differences that highlight the two complementary techniques of nonlinear conductivity spectromicroscopy for probing nanoscale systems.

Atomic-scale quantum sensing based on the ultrafast coherence of an H2 molecule in an STM cavity.

A scanning tunneling microscope (STM) combined with a pump-probe femtosecond terahertz (THz) laser can enable coherence measurements of single molecules. We report THz pump-probe measurements that demonstrate quantum sensing based on a hydrogen (H2) molecule in the cavity created with an STM tip near a surface. Atomic-scale spatial and femtosecond temporal resolutions were obtained from this quantum coherence. The H2 acts as a two-level system, with its coherent superposition exhibiting extreme sensitivity to the applied electric field and the underlying atomic composition of the copper nitride (Cu2N) monolayer islands grown on a Cu(100) surface. We acquired time-resolved images of THz rectification of H2 over Cu2N islands for variable pump-probe delay times to visualize the heterogeneity of the chemical environment at sub-angstrom scale.

Growth of Tellurium Nanobelts on h-BN for p-type Transistors with Ultrahigh Hole Mobility.

The lack of stable p-type van der Waals (vdW) semiconductors with high hole mobility severely impedes the step of low-dimensional materials entering the industrial circle. Although p-type black phosphorus (bP) and tellurium (Te) have shown promising hole mobilities, the instability under ambient conditions of bP and relatively low hole mobility of Te remain as daunting issues. Here we report the growth of high-quality Te nanobelts on atomically flat hexagonal boron nitride (h-BN) for high-performance p-type field-effect transistors (FETs). Importantly, the Te-based FET exhibits an ultrahigh hole mobility up to 1370 cm2 V-1 s-1 at room temperature, that may lay the foundation for the future high-performance p-type 2D FET and metal-oxide-semiconductor (p-MOS) inverter. The vdW h-BN dielectric substrate not only provides an ultra-flat surface without dangling bonds for growth of high-quality Te nanobelts, but also reduces the scattering centers at the interface between the channel material and the dielectric layer, thus resulting in the ultrahigh hole mobility .

A Safe Flexible Self-Powered Wristband System by Integrating Defective MnO2-x Nanosheet-Based Zinc-Ion Batteries with Perovskite Solar Cells.

The booming market of portable and wearable electronics has aroused the requests for advanced flexible self-powered energy systems featuring both excellent performance and high safety. Herein, we report a safe, flexible, self-powered wristband system by integrating high-performance zinc-ion batteries (ZIBs) with perovskite solar cells (PSCs). ZIBs were first fabricated on the basis of a defective MnO2-x nanosheet-grown carbon cloth (MnO2-x@CC), which was obtained via the simple lithium treatment of the MnO2 nanosheets to slightly expand the interlayer spacing and generate rich oxygen vacancies. When used as a ZIB cathode, the MnO2-x@CC with a ultrahigh mass loading (up to 25.5 mg cm-2) exhibits a much enhanced specific capacity (3.63 mAh cm-2 at current density of 3.93 mA cm-2), rate performance, and long cycle stability (no obvious degradation after 5000 cycles) than those of the MnO2@CC. Importantly, the MnO2-x@CC-based quasi-solid-state ZIB not only achieves excellent flexibility and an ultrahigh energy density of 5.11 mWh cm-2 (59.42 mWh cm-3) but also presents a high safety under a wide temperature range and various severe conditions. More importantly, the flexible ZIBs can be integrated with flexible PSCs to construct a safe, self-powered wristband, which is able to harvest light energy and power a commercial smart bracelet. This work sheds light on the development of high-performance ZIB cathodes and thus offers a good strategy to construct wearable self-powered energy systems for wearable electronics.

Annular sector elemental image array generation method for tabletop integral imaging 3D display with smooth motion parallax.

One of the important features of tabletop 3D displays is the annular viewing area above the display system. In this paper, we propose an annular sector elemental image array (ASEIA) generation method for the tabletop integral imaging 3D display to form the annular viewing zone with smooth motion parallax. The effective pixels of the elemental images are distributed as annular sector, and they are mapped from the perspective images captured by the ring-shaped camera array. Correspondingly, the viewing sub-zones can be formed with an annular sector configuration and can be seamlessly stitched by using the time division scheme. Compared with the previous approach with rectangular elemental image array (EIA) distribution, the number of viewing sub-zones is decreased from 360 to 10 for the same effect of smooth motion parallax. Meanwhile, rendering efficiency is improved. The experimental results show that the proposed method is feasible to produce 360-degree continuous viewpoints in an annular viewing zone.

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.

BACKGROUND: Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data. OBJECTIVE: The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation. MATERIALS AND METHODS: A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score. RESULTS: In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk. CONCLUSIONS: Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.

Computational investigation of Zn-doped and undoped SrEu2Fe2O7 as potential mixed electron and proton conductors.

Understanding the electrode properties at the atomistic level is of great benefit to the evaluation of electrode performance and design of better electrode materials in solid oxide fuel cells. In this work, density functional theory (DFT) calculations are employed to investigate the formation and conducting behaviors of oxygen vacancies and proton defects in Ruddlesden-Popper oxide SrEu2Fe2O7 (SEFO), which has been experimentally characterized as a promising cathode. The calculation results suggest both oxygen vacancies and proton defects can be formed in SEFO, and especially, the formation of these defects is largely dependent on oxygen sites in the special crystal structure with alternative stacking of rock-salt layers and double-layered perovskite slabs. The oxygen vacancies within the perovskite slabs have very low formation energies, but demonstrate high energy barriers for migration and low hydration properties; while in the case of those in the rock salt layers, it's contrary. Interestingly, protons have similar migration abilities in the perovskite slabs and rock salt layers. And therefore, increasing the vacancy concentration of the rock salt layer is beneficial to increase the concentration of proton defects and to improve the proton conductivity. DFT calculations also indicate that substituting Zn for Fe in SEFO can largely depress the oxygen vacancy formation energy, which helps to increase the concentration of both defects. Importantly, the energy barriers for migration of both oxygen ions and protons are barely enhanced, implying a negligible trapping effect of the Zn dopant.