Heterogeneous temporal representation for diabetic blood glucose prediction

Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for personalized monitoring of blood glucose levels in diabetic patients, providing valuable support for physicians in diagnosis and treatment planning. Despite the remarkable success achieved, applying BGP in multi-patient scenarios remains problematic, largely due to the inherent heterogeneity and uncertain nature of continuous glucose monitoring (CGM) data obtained from diverse patient profiles.Methodology: This study proposes the first graph-based Heterogeneous Temporal Representation (HETER) network for multi-patient Blood Glucose Prediction (BGP). Specifically, HETER employs a flexible subsequence repetition method (SSR) to align the heterogeneous input samples, in contrast to the traditional padding or truncation methods. Then, the relationships between multiple samples are constructed as a graph and learned by HETER to capture global temporal characteristics. Moreover, to address the limitations of conventional graph neural networks in capturing local temporal dependencies and providing linear representations, HETER incorporates both a temporally-enhanced mechanism and a linear residual fusion into its architecture.Results: Comprehensive experiments were conducted to validate the proposed method using real-world data from 112 patients in two hospitals, comparing it with five well-known baseline methods. The experimental results verify the robustness and accuracy of the proposed HETER, which achieves the maximal improvement of 31.42%, 27.18%, and 34.85% in terms of MAE, MAPE, and RMSE, respectively, over the second-best comparable method.Discussions: HETER integrates global and local temporal information from multi-patient samples to alleviate the impact of heterogeneity and uncertainty. This method can also be extended to other clinical tasks, thereby facilitating efficient and accurate capture of crucial pattern information in structured medical data

Molecular Orbital Gating Surface-Enhanced Raman Scattering

One of the promising approaches to meet the urgent demand for further device miniaturization is to create functional devices using single molecules. Although various single-molecule electronic devices have been demonstrated recently, single-molecule optical devices which use external stimulations to control the optical response of a single molecule have rarely been reported. Here, we propose and demonstrate a field-effect Raman scattering (FERS) device with a single molecule, an optical counterpart to field-effect transistors (a key component of modern electronics). With our devices, the gap size between electrodes can be precisely adjusted at subangstrom accuracy to form single molecular junctions as well as to reach the maximum performance of Raman scattering via plasmonic enhancement. Based on this maximum performance, we demonstrated that the intensity of Raman scattering can be further enhanced by an additional ∼40% if the orbitals of the molecules bridged two electrodes were shifted by a gating voltage. This finding not only provides a method to increase the sensitivity of Raman scattering beyond the limit of plasmonic enhancement, but also makes it feasible to realize addressable functional FERS devices with a gate electrode array

DataSheet1_Heterogeneous temporal representation for diabetic blood glucose prediction.PDF

Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for personalized monitoring of blood glucose levels in diabetic patients, providing valuable support for physicians in diagnosis and treatment planning. Despite the remarkable success achieved, applying BGP in multi-patient scenarios remains problematic, largely due to the inherent heterogeneity and uncertain nature of continuous glucose monitoring (CGM) data obtained from diverse patient profiles.Methodology: This study proposes the first graph-based Heterogeneous Temporal Representation (HETER) network for multi-patient Blood Glucose Prediction (BGP). Specifically, HETER employs a flexible subsequence repetition method (SSR) to align the heterogeneous input samples, in contrast to the traditional padding or truncation methods. Then, the relationships between multiple samples are constructed as a graph and learned by HETER to capture global temporal characteristics. Moreover, to address the limitations of conventional graph neural networks in capturing local temporal dependencies and providing linear representations, HETER incorporates both a temporally-enhanced mechanism and a linear residual fusion into its architecture.Results: Comprehensive experiments were conducted to validate the proposed method using real-world data from 112 patients in two hospitals, comparing it with five well-known baseline methods. The experimental results verify the robustness and accuracy of the proposed HETER, which achieves the maximal improvement of 31.42%, 27.18%, and 34.85% in terms of MAE, MAPE, and RMSE, respectively, over the second-best comparable method.Discussions: HETER integrates global and local temporal information from multi-patient samples to alleviate the impact of heterogeneity and uncertainty. This method can also be extended to other clinical tasks, thereby facilitating efficient and accurate capture of crucial pattern information in structured medical data.</p

Shaping the Atomic-Scale Geometries of Electrodes to Control Optical and Electrical Performance of Molecular Devices

A straightforward method to generate both atomic‐scale sharp and atomic‐scale planar electrodes is reported. The atomic‐scale sharp electrodes are generated by precisely stretching a suspended nanowire, while the atomic‐scale planar electrodes are obtained via mechanically controllable interelectrodes compression followed by a thermal‐driven atom migration process. Notably, the gap size between the electrodes can be precisely controlled at subangstrom accuracy with this method. These two types of electrodes are subsequently employed to investigate the properties of single molecular junctions. It is found, for the first time, that the conductance of the amine‐linked molecular junctions can be enhanced ≈50% as the atomic‐scale sharp electrodes are used. However, the atomic‐scale planar electrodes show great advantages to enhance the sensitivity of Raman scattering upon the variation of nanogap size. The underlying mechanisms for these two interesting observations are clarified with the help of density functional theory calculation and finite‐element method simulation. These findings not only provide a strategy to control the electron transport through the molecule junction, but also pave a way to modulate the optical response as well as to improve the stability of single molecular devices via the rational design of electrodes geometries

Atomic switches of metallic point contacts by plasmonic heating

Plasmonics: Tripping the light fantastic switch Scientists have shown how light can be used to control a nano-switch, laying the groundwork for atomic-sized devices for use in a range of nanotechnologies. The fabrication of switches with nanoscale dimensions is a fundamental step forward in the further miniaturization of electronic devices, and the realization of fully-integrated nanotechnologies. Now, Dong Xiang and colleagues from Nankai University in China, working with fellow Chinese and international researchers, has developed a method that uses light to control the electrical conductance of the junction between gold nano-electrodes. By heating electrons at the surface of the electrodes with light, a technique known as plasmonic heating, the team was able to expand the electrodes and close the gap between them, turning the switch on. The work could pave the way for new nanotechnologies, including single-molecule transistors and nanopore-based biosensors