Preparation of surface molecular imprinting fluorescent sensor based on magnetic porous silica for sensitive and selective determination of catechol.

A magnetic fluorescent molecularly imprinted sensor was successfully prepared and implemented to determine catechol (CT). Fe3O4 nanoparticles were synthesized by the solvothermal technique and mesoporous Fe3O4@SiO2@mSiO2 imprinted carriers were prepared by coating nonporous and mesoporous SiO2 shells on the surface of the Fe3O4 subsequently. The magnetic surface molecularly imprinted fluorescent sensor was created after the magnetic mesoporous carriers were modified with γ-methacryloxyl propyl trimethoxy silane to introduce double bonds on the surface of the carries and the polymerization was carried out in the presence of CT and fluorescent monomers. The magnetic mesoporous carriers were modified with γ-methacryloxyl propyl trimethoxy silane and double bonds were introduced on the surface of the carriers. After CT binding with the molecularly imprinted polymers (MIPs), the fluorescent intensity of the molecularly imprinted polymers (Ex = 400 nm, Em = 523 nm) increased significantly. The fluorescent intensity ratio (F/F0) of the sensor demonstrated a favorable linear correlation with the concentration of CT between 5 and 50 µM with a detection limit of 0.025 µM. Furthermore, the sensor was successfully applied to determine CT in actual samples with recoveries of 96.4-105% and relative standard deviations were lower than 3.5%. The results indicated that the research of our present work provided an efficient approach for swiftly and accurately determining organic pollutant in water.

Reflections on the strategy of hospital development based on the characteristics of new hospitals.

BACKGROUND: The disease spectrum is constantly changing, meaning patients' medical characteristics are complex and varied, and hospital operations are facing great challenges. OBJECTIVE: To provide a basis for formulating relevant policies, promoting the continuous improvement of medical quality, improving the efficiency of medical services and proposing corresponding countermeasures. METHODS: Information on the first page of the medical cases of patients discharged from 2020-2021 in the case management system of The Second People's Hospital of Zhangye City was collected, and statistical analysis was performed in terms of the origin, age, gender, occupation and main diagnosis of the patients. The database was established using Excel software, and statistical description was performed using SPSS 23.0 software, in which the main indicators were the number of cases and relative numbers (%). RESULTS: The total number of inpatients in the hospital exhibited an overall upward trend, with the number in 2021 increasing by 40.53% compared with the previous year. Among them, 0.75% of the inpatients resided outside the province and 99.25% resided within the province. The proportion of inpatients within the city was 98.86%, including 85.50% in Ganzhou District and 14.5% in the five surrounding counties (districts). In 2021, the number of inpatients in the five surrounding districts increased by 60.67% compared with the previous year. The occupational structure of the inpatients was mainly farmers, accounting for 64.68%, which was higher than all other occupational groups, while public employees accounted for 17.9%. Inpatients aged 60 years and above accounted for 37.75%. In terms of disease spectrum ranking, circulatory, digestive, respiratory, injury and oncological system diseases ranked in the top five of the presented diseases, accounting for 64.47% of the total number of inpatients. CONCLUSION: The patients of the new hospital are mainly elderly patients and local farmers. The hospital development should be based on the disease characteristics of this group, improving the diagnosis and treatment capacity of the departments and strengthening the capacity building of the hospital and the level of the medical services.

Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury.

Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI.

Preparation of molecularly imprinted ratiometric fluorescence sensor for visual detection of tetrabromobisphenol A in water samples.

A sensitive molecularly imprinted ratiometric fluorescence sensor was constructed for the first time to visually detect tetrabromobisphenol A (TBBPA). The blue fluorescent carbon quantum dots (CQDs) were coated with SiO2 through the reverse microemulsion method to obtain a stable internal reference signal CQDs@SiO2. The ratiometric fluorescence sensor was finally prepared using red fluorescent CdTe QDs as the response signal in the presence of CQDs@SiO2. When the molecularly imprinted polymers were combined with TBBPA, the fluorescence of CdTe QDs (Ex = 365 nm, Em = 665 nm) was rapidly quenched, while that of CQDs (Ex = 365 nm, Em = 441 nm) remained stable, resulting in a noticeable fluorescence color change. Moreover, the fluorescence intensity ratio (I665/I441)0/(I665/I441) of the sensor showed a linear response to TBBPA in the concentration range 0.1 to 10 µM with a low detection limit of 3.8 nM. The prepared sensor was successfully applied to detect TBBPA in water samples. The recoveries were in the range 98.2-103%, with relative standard deviations lower than 2.5%. Furthermore, a fluorescent test strip for visual monitoring of TBBPA was constructed to streamline the procedure. The excellent results demonstrate that the prepared test strip has a broad prospect for the offline detection of pollutants.

Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury.

Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI .

A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs.

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveillance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at https://github.com/Zhizhen- Zhao/Age-Risk-Identification.

A FAIR and AI-ready Higgs boson decay dataset.

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

Efficacy of ultrasound-accelerated versus traditional catheter-directed thrombolysis in treatment of lower extremity deep venous thrombosis: A protocol for systematic review and meta-analysis.

BACKGROUND: There is no meta-analysis or review in the literature to compare and evaluate the difference and effectiveness of ultrasonic-accelerated thrombolysis (UAT) and catheter directed thrombolysis (CDT) in lower extremity deep vein thrombosis (DVT) patients. Therefore, we conducted this protocol of systematic review and meta-analysis to evaluate the efficacy between UAT and CDT for patients with lower extremity DVT. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols reporting guidelines to conduct this study. Reviewers will search the PubMed, Cochrane Library, Web of Science, and EMBASE online databases using the key phrases "deep venous thrombosis," "thrombolysis," and "ultrasound-accelerated" for all cohort studies published up to July 22, 2021. There is no restriction in the dates of publication or language in the search for the current review. The primary outcome is major bleeding. Secondary outcomes include health-related quality of life and complications such as recurrent venous thromboembolism, pulmonary embolism, in-stent thrombosis, and death. Review Manager software (v 5.4; Cochrane Collaboration) will be used for the meta-analysis. A P value of < .05 is considered to be statistically significant. RESULTS: We hypothesized that these two methods would provide similar therapeutic benefits. OSF REGISTRATION NUMBER: 10.17605/OSF.IO/YZB3H.

A Tubular Flexible Triboelectric Nanogenerator with a Superhydrophobic Surface for Human Motion Detecting.

The triboelectric nanogenerator (TENG) is a newly arisen technology for mechanical energy harvesting from the environment, such as raindrops, wind, tides, and so on. It has attracted widespread attention in flexible electronics to serve as self-powered sensors and energy-harvesting devices because of its flexibility, durability, adaptability, and multi-functionalities. In this work, we fabricated a tubular flexible triboelectric nanogenerator (TF-TENG) with energy harvesting and human motion monitoring capabilities by employing polydimethylsiloxane (PDMS) as construction material, and fluorinated ethylene propylene (FEP) films coated with Cu as the triboelectric layer and electrode, serving in a free-standing mode. The tube structure has excellent stretchability that can be stretched up to 400%. Modifying the FEP films to obtain a superhydrophobic surface, the output performance of TF-TENG was increased by at least 100% compared to an untreated one. Finally, as the output of TF-TENG is sensitive to swing angle and frequency, demonstration of real-time monitoring of human motion state was realized when a TF-TENG was worn on the wrist.

Steerable ePCA: Rotationally Invariant Exponential Family PCA.

In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications require an accurate estimation of the covariance of the underlying 2-D clean images. For example, in X-ray free electron laser (XFEL) single molecule imaging, the covariance matrix of 2-D diffraction images is used to reconstruct the 3-D molecular structure. Accurate estimation of the covariance from low-photon-count images must take into account that pixel intensities are Poisson distributed, hence the classical sample covariance estimator is highly biased. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this paper we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflections. Our procedure, steerable ePCA, combines in a novel way two recently introduced innovations. The first is a methodology for principal component analysis (PCA) for Poisson distributions, and more generally, exponential family distributions, called ePCA. The second is steerable PCA, a fast and accurate procedure for including all planar rotations when performing PCA. The resulting principal components are invariant to the rotation and reflection of the input images. We demonstrate the efficiency and accuracy of steerable ePCA in numerical experiments involving simulated XFEL datasets and rotated face images from Yale Face Database B.

Joint Angular Refinement and Reconstruction for Single-Particle Cryo-EM.

Single-particle cryo-electron microscopy (cryo-EM) reconstructs the three-dimensional (3D) structure of biomolecules from a large set of 2D projection images with random and unknown orientations. A crucial step in the single-particle cryo-EM pipeline is 3D refinement, which resolves a highresolution 3D structure from an initial approximate volume by refining the estimation of the orientation of each projection. In this work, we propose a new approach that refines the projection angles on the continuum. We formulate the optimization problem over the density map and the orientations jointly. The density map is updated using the efficient alternating-direction method of multipliers, while the orientations are updated through a semicoordinate- wise gradient descent for which we provide an explicit derivation of the gradient. Our method eliminates the requirement for a fine discretization of the orientation space and does away with the classical but computationally expensive templatematching step. Numerical results demonstrate the feasibility and performance of our approach compared to several baselines.

Tunable, Ultrasensitive, and Flexible Pressure Sensors Based on Wrinkled Microstructures for Electronic Skins.

Flexible pressure sensors play an important role in electronic skins (E-Skins), which mimic the mechanical forces sensing properties of human skin. A rational design for a pressure sensor with adjustable characteristics is in high demand for different application scenarios. Here, we present tunable, ultrasensitive, and flexible pressure sensors based on compressible wrinkled microstructures. Modifying the morphology of polydimethylsiloxane (PDMS) microstructure enables the device to obtain different sensitivities and pressure ranges for different requirements. Furthermore, by intentionally introducing hollow structures in the PDMS wrinkles, our pressure sensor exhibits an ultrahigh sensitivity of 14.268 kPa-1. The elastic microstructure-based capacitive sensor also possesses a very low detectable pressure limit (1.5 Pa), a fast response time (<50 ms), a wide pressure range, and excellent cycling stability. Implementing respiratory monitoring and vocalization recognition is realized by attaching the flexible pressure sensor onto the chest and throat, respectively, showing its great application potential for disease diagnosis, monitoring, and other advanced clinical/biological wearable technologies.

Bispectrum Inversion with Application to Multireference Alignment.

We consider the problem of estimating a signal from noisy circularly-translated versions of itself, called multireference alignment (MRA). One natural approach to MRA could be to estimate the shifts of the observations first, and infer the signal by aligning and averaging the data. In contrast, we consider a method based on estimating the signal directly, using features of the signal that are invariant under translations. Specifically, we estimate the power spectrum and the bispectrum of the signal from the observations. Under mild assumptions, these invariant features contain enough information to infer the signal. In particular, the bispectrum can be used to estimate the Fourier phases. To this end, we propose and analyze a few algorithms. Our main methods consist of non-convex optimization over the smooth manifold of phases. Empirically, in the absence of noise, these non-convex algorithms appear to converge to the target signal with random initialization. The algorithms are also robust to noise. We then suggest three additional methods. These methods are based on frequency marching, semidefinite relaxation and integer programming. The first two methods provably recover the phases exactly in the absence of noise. In the high noise level regime, the invariant features approach for MRA results in stable estimation if the number of measurements scales like the cube of the noise variance, which is the information-theoretic rate. Additionally, it requires only one pass over the data which is important at low signal-to-noise ratio when the number of observations must be large.

MAHALANOBIS DISTANCE FOR CLASS AVERAGING OF CRYO-EM IMAGES.

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images. We introduce a new affinity measure, akin to the Mahalanobis distance, to compare cryo-EM images belonging to different defocus groups. The new similarity measure is employed to detect similar images, thereby leading to an improved algorithm for class averaging. We evaluate the performance of the proposed class averaging procedure on synthetic datasets, obtaining state of the art classification.

Machine-Washable Textile Triboelectric Nanogenerators for Effective Human Respiratory Monitoring through Loom Weaving of Metallic Yarns.

Textile triboelectric nanogenerators for human respiratory monitoring with machine washability are developed through loom weaving of Cu-PET and PI-Cu-PET yarns. Triboelectric charges are generated at the yarn crisscross intersections to achieve a maximum short circuit current density of 15.50 mA m-2 . By integrating into a chest strap, human respiratory rate and depth can be monitored.

Fast Steerable Principal Component Analysis.

Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL3 + L4), while existing algorithms take O(nL4). The new algorithm computes the expansion coefficients of the images in a Fourier-Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA.

Rotationally invariant image representation for viewing direction classification in cryo-EM.

We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations.

Fourier-Bessel rotational invariant eigenimages.

We present an efficient and accurate algorithm for principal component analysis (PCA) of a large set of two-dimensional images and, for each image, the set of its uniform rotations in the plane and its reflection. The algorithm starts by expanding each image, originally given on a Cartesian grid, in the Fourier-Bessel basis for the disk. Because the images are essentially band limited in the Fourier domain, we use a sampling criterion to truncate the Fourier-Bessel expansion such that the maximum amount of information is preserved without the effect of aliasing. The constructed covariance matrix is invariant to rotation and reflection and has a special block diagonal structure. PCA is efficiently done for each block separately. This Fourier-Bessel-based PCA detects more meaningful eigenimages and has improved denoising capability compared to traditional PCA for a finite number of noisy images.