VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

Multi-dimensional microfluidic paper-based analytical devices (µPADs) for noninvasive testing: A review of structural design and applications.

The rapid emergence of microfluidic paper-based devices as point-of-care testing (POCT) tools for early disease diagnosis and health monitoring, particularly in resource-limited areas, holds immense potential for enhancing healthcare accessibility. Leveraging the numerous advantages of paper, such as capillary-driven flow, porous structure, hydrophilic functional groups, biodegradability, cost-effectiveness, and flexibility, it has become a pivotal choice for microfluidic substrates. The repertoire of microfluidic paper-based devices includes one-dimensional lateral flow assays (1D LFAs), two-dimensional microfluidic paper-based analytical devices (2D µPADs), and three-dimensional (3D) µPADs. In this comprehensive review, we provide and examine crucial information related to paper substrates, design strategies, and detection methods in multi-dimensional microfluidic paper-based devices. We also investigate potential applications of microfluidic paper-based devices for detecting viruses, metabolites and hormones in non-invasive samples such as human saliva, sweat and urine. Additionally, we delve into capillary-driven flow alternative theoretical models of fluids within the paper to provide guidance. Finally, we critically examine the potential for future developments and address challenges for multi-dimensional microfluidic paper-based devices in advancing noninvasive early diagnosis and health monitoring. This article showcases their transformative impact on healthcare, paving the way for enhanced medical services worldwide.

Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm.

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal-external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts.

Beyond 1D and oversimplified kinematics: A generic analytical framework for surrogate safety measures.

This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a spectrum of scenarios with varying degrees of complexity and dimensionality, facilitating the estimation of future vehicle trajectory evolution. It establishes a generic mathematical criterion to denote potential collisions, characterized by the spatial overlap between a vehicle and any other entity. A collision risk is present if the collision criterion is met at any non-negative estimated future time point, with the minimum threshold representing the remaining time to collision. The framework's proficiency spans from conventional one-dimensional (1D) SSMs to extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated through simulation experiments that assess the precision of the framework when linearization is performed on the vehicle movement model. The outcomes showcase remarkable accuracy in estimating vehicle trajectory evolution and the time remaining before potential collisions occur, comparing to high-accuracy numerical integration solutions. The necessity of higher-dimensional and higher-fidelity SSMs is highlighted through a comparison of conventional 1D SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D SSMs over 3D SSMs could be off by 300% for Time-to-Collision (TTC) values larger than 1.5 s and about 20% for TTC values below 1.5 s. In other words, conventional 1D SSMs can yield highly inaccurate and unreliable results when assessing collision proximity and substantially misjudge the count of conflicts with predefined threshold (e.g., below 1.5s). Furthermore, the framework's practical application is demonstrated through a case study that actively evaluates all potential conflicts, underscoring its effectiveness in dynamic, real-world traffic situations.

The ecological risk and fate of microplastics in the environmental matrices of marine ranching area in coastal water.

The debate surrounding "source" and "sink" of microplastics (MPs) in coastal water has persisted for decades. While the transportation of MPs is influenced by surface runoff and currents, the precise transport patterns remain inadequately defined. In this study, the typical coastal habitat - marine ranching in Haizhou Bay (Jiangsu Province, China) were selected as a case study to assess the ecological risk of MPs. An enhanced framework was employed to assess the entire community characteristics of MPs in various environmental compartments, including surface water (SW), middle water (MW), bottom water (BW), sea bottom sediment (SS), and intertidal sediment (IS). The results of the assessment showed a low risk in the water column and a high risk in the sediment. PERMANOVA based on size and polymer of MPs revealed significant differences between IS and other compartments (SW, MW, BW, and SS) (P < 0.001). The co-occurrence network analysis for MP size indicated that most sites occupied central positions, while the analysis for MP polymer suggested that sites near the marine ranching area held more central positions, with sites in MW, BW, and SS being somewhat related to IS. Generalized additive model (GAM) demonstrated that MP concentration in the water correlated with Chla and nutrients, whereas MPs in sediment exhibited greater susceptibility to dissolved oxygen (DO) and salinity. We believe that except for the natural sedimentation and re-suspension of MPs in the vertical direction, MPs in bottom water may migrate to the surface water due to upwelling mediated by artificial reefs. Additionally, under the combined influence of surface runoff, currents, and tides, MPs may migrate horizontally, primarily occurring between middle and bottom water and sediments. The study recommends limiting and reducing wastewater and sewage discharge, as well as regulating fishing and aquaculture activities to control the sources and sinks of MPs in coastal water. Moreover, it advocates the implementation and strengthening of marine monitoring activities to gain a better understanding of the factors driving MP pollution in marine ranching area.

Enhanced Storage Capacity via Anion Substitution for Advanced Delayed X-ray Detection.

X-ray radiation information storage, characterized by its ability to detect radiation with delayed readings, shows great promise in enabling reliable and readily accessible X-ray imaging and dosimetry in situations where conventional detectors may not be feasible. However, the lack of specific strategies to enhance the memory capability dramatically hampers its further development. Here, we present an effective anion substitution strategy to enhance the storage capability of NaLuF4:Tb3+ nanocrystals attributed to the increased concentration of trapping centers under X-ray irradiation. The stored radiation information can be read out as optical brightness via thermal, 980 nm laser, or mechanical stimulation, avoiding real-time measurement under ionizing radiation. Moreover, the radiation information can be maintained for more than 13 days, and the imaging resolution reaches 14.3 lp mm-1. These results demonstrate that anion substitution methods can effectively achieve high storage capability and broaden the application scope of X-ray information storage.

Superaerophobic/Superhydrophilic Multidimensional Electrode System for High-Current-Density Water Electrolysis.

Water electrolysis is emerging as a promising renewable-energy technology for the green production of hydrogen, which is a representative and reliable clean energy source. From economical and industrial perspectives, the development of earth-abundant non-noble metal-based and bifunctional catalysts, which can simultaneously exhibit high catalytic activities and stabilities for both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), is critical; however, to date, these types of catalysts have not been constructed, particularly, for high-current-density water electrolysis at the industrial level. This study developed a heterostructured zero-dimensional (0D)-one-dimensional (1D) PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF)-Ni3S2 as a self-supported catalytic electrode via interface and morphology engineering. This unique heterodimensional nanostructure of the PBSCF-Ni3S2 system demonstrates superaerophobic/superhydrophilic features and maximizes the exposure of the highly active heterointerface, endowing the PBSCF-Ni3S2 electrode with outstanding electrocatalytic performances in both HER and OER and exceptional operational stability during the overall water electrolysis at high current densities (500 h at 500 mA cm-2). This study provides important insights into the development of catalytic electrodes for efficient and stable large-scale hydrogen production systems.

Research on the"Package Payment"Problem of the Compact County Medical Community Based on Multi-Dimension-al Scale Analysis and Social Network Analysis / 中国卫生经济

Objective:To explore the core issues in the implementation of"packaged payment"in China's compact county medi-cal community,in order to provide useful references for the innovative reform of medical insurance payment methods in compact coun-ty medical community.Methods:By constructing the problem system through the macro model of the health system,analyzing the re-lated literature using multidimensional scale analysis and social network analysis,and comprehensively evaluating the results using the entropy-weighted TOPSIS method,it summarizes the core issues of"packaged payment"in compact county medical community.Results:There are core issues in China's compact county medical community,such as inadequate distribution of benefits and incen-tive and constraint mechanisms within the medical community(Ci= 1.000),lack of effective supervision and assessment mechanism for medical communities(Ci= 0.732),suppressed quality and efficiency of medical services(Ci= 0.652),lagging medical informatiza-tion construction(Ci= 0.595),and incomplete supporting policy measures(Ci= 0.579).Conclusion:The"packaged payment"of com-pact county medical community can be optimized from the following three aspects:a multi-level collaborative incentive mechanism should be improved to ensure the service quality and efficiency;optimize the total amount calculation method and improve the de-tailed supporting measures;accelerate information construction and strengthen supervision and assessment management.

A multi-dimension information fusion-based intelligent prediction approach for health literacy.

Health literacy refers to the ability of individuals to obtain and understand health information and use it to maintain and promote their own health. This paper manages to make predictions toward its development degree in society with use of a big data-driven statistical learning method. Actually, such results can be analyzed by discovering latent rules from massive public textual contents. As a result, this paper proposes a deep information fusion-based smart prediction approach for health literacy. Specifically, the latent Dirichlet allocation (LDA) and convolutional neural network (CNN) structures are utilized as the basic backbone to understand semantic features of textual contents. The feature learning results of LDA and CNN can be then mapped into prediction results via following multi-dimension computing structures. After constructing the CNN model, we can input health information into the model for feature extraction. The CNN model can automatically learn valuable features from raw health information through multi-layer convolution and pooling operations. These characteristics may include lifestyle habits, physiological indicators, biochemical indicators, etc., reflecting the patient's health status and disease risk. After extracting features, we can train the CNN model through a training set and evaluate the performance of the model using a test set. The goal of this step is to optimize the parameters of the model so that it can accurately predict health information. We can use common evaluation indicators such as accuracy, precision, recall, etc. to evaluate the performance of the model. At last, some simulation experiments are conducted on real-world data collected from famous international universities. The case study analyzes health literacy difference between China of developed countries. Some prediction results can be obtained from the case study. The proposed approach can be proved effective from the discussion of prediction results.

High Throughput Multidimensional Kinetic Screening in Continuous Flow Reactors.

An automated high throughput multidimensional reaction screening platform based on an inline Fourier-transform infrared spectroscopy is presented. By combining flow chemistry, machine automation and inline analysis, the platform is able to screen reactions in multidimensions (residence time, monomer concentration, degree of polymerization, reaction temperature and monomer conversion) rapidly and efficiently way. Kinetic data libraries associated with high data precision (absolute error <4 %), high reproducibility and high data density are built with ease from the platform. To test the method, we screened the reversible addition-fragmentation chain transfer polymerization of methyl acrylate in unmatched detail, and the ring opening metathesis polymerization of methyl-5-norbornene-2-carboxylate. The method we introduce is a key step in providing "big data" for data driven research in the future, and already at present allows for precise prediction of reaction outcomes within the high-dimensional chemical parameter space that is screened.

Caregiver Quality of Life During Pediatric Influenza-Like Illness: A Cross-Sectional Study During the COVID-19 Pandemic.

The objective of this study was to quantify the quality of life (QoL) of caregivers with children with influenza-like illnesses (ILI) and to identify factors associated with worse QoL. This was a cross-sectional cohort study of caregivers in a pediatric emergency department with previously healthy young children with ILI. The primary outcome was caregiver QoL. Additional measures included health literacy, social support, and caregiver health status. Two hundred and eighty-one caregivers completed the study. And 41% reported overall QoL was worse during their child's illness. The median QoL score was 3.8 [3.1, 4.6] in a 7-point scale. Illness duration was associated with worse overall QoL score (0.128 worse for each additional day of illness). The median emotions domain score was 2.5 [1.5, 4.0], the worst of any domain. Caregivers who perceived worse illness severity had lower emotions domain scores (2.61 vs 6.00, P = .0269). Caregivers with adequate literacy had lower mean QoL scores (3.08 vs 4.44, P < .0001). Childhood illnesses worsen caregiver QoL. Factors associated with worse QoL were perception of illness severity and duration. Addressing caregiver QoL could mitigate the impact of childhood acute illnesses on caregiver wellbeing.

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.

Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting.

Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs.

BACKGROUND: The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited. RESULTS: In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein-protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite-lipid, 62 trait-metabolite, and 89 trait-lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene-trait or gene-metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)-GmSEI-GmDGAT1a-triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)-GmPHS-D-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)-GmbZIP123-GmHD-ZIPIII-10-miR166s-oil content. CONCLUSIONS: This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

New method for reducing viscosity and shear stress in hydrogel 3D printing via multidimension vibration.

Microextrusion 3D bioprinting is a comparatively easy method to fabricate structures in tissue engineering. But high viscosity and wall shear stress in the tube and nozzle often lead to low cell survival rate of printed tissue. To reduce the viscosity and shear stress of materials in biological 3D printing, a multidimension microvibration assisted hydrogel 3D printing method was proposed. The compliant mechanism driven by piezoceramic was applied to 3D printing of hydrogels. The shear stress and viscosity of hydrogels could be effectively reduced by multidimension microvibration. Simulation analysis of the extrusion device was carried out to study the influence of vibration parameters on viscosity and shear stress, and optimized multidimension vibration forms and vibration parameters were selected for experiments. The experiment results show that multidimension microvibration can effectively reduce the viscosity of hydrogels and improve printing resolution and print speed.

Learning spatial structures of proteins improves protein-protein interaction prediction.

Spatial structures of proteins are closely related to protein functions. Integrating protein structures improves the performance of protein-protein interaction (PPI) prediction. However, the limited quantity of known protein structures restricts the application of structure-based prediction methods. Utilizing the predicted protein structure information is a promising method to improve the performance of sequence-based prediction methods. We propose a novel end-to-end framework, TAGPPI, to predict PPIs using protein sequence alone. TAGPPI extracts multi-dimensional features by employing 1D convolution operation on protein sequences and graph learning method on contact maps constructed from AlphaFold. A contact map contains abundant spatial structure information, which is difficult to obtain from 1D sequence data directly. We further demonstrate that the spatial information learned from contact maps improves the ability of TAGPPI in PPI prediction tasks. We compare the performance of TAGPPI with those of nine state-of-the-art sequence-based methods, and TAGPPI outperforms such methods in all metrics. To the best of our knowledge, this is the first method to use the predicted protein topology structure graph for sequence-based PPI prediction. More importantly, our proposed architecture could be extended to other prediction tasks related to proteins.

Linguistic Validation of the Japanese Version of the Multidimensional Dyspnea Profile and Relation to Physical Activity in Patients with COPD.

PURPOSE: This study aimed to develop a linguistically validated Japanese translation of the multidimensional dyspnea profile (MDP) and assess whether worsening of dyspnea's sensory and affective domains during exercise had detrimental effects on physical activity in stable outpatients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: The Japanese version of the MDP was prepared in collaboration with Mapi Research Trust (Lyon, France) after the approval of the developer. Physical activity was assessed using a 3-axis accelerometer. Dyspnea upon exertion was investigated using a 3-minute step test. RESULTS: The Japanese version of the MDP was obtained and validated linguistically. Air-hunger was significantly associated with total calories from walking (r = - 0.47, p < 0.05), while anxiety and depression were significantly correlated with both the amount and intensity of physical activity (r = - 0.49, p < 0.05, and r = - 0.46, p < 0.05, respectively). CONCLUSION: The Japanese version of the MDP was suggested to reflect both pulmonary functions, ventilatory response during exercise, and intensity and amount of physical activity in patients with COPD.

A lightweight multi-dimension dynamic convolutional network for real-time semantic segmentation.

Semantic segmentation can address the perceived needs of autonomous driving and micro-robots and is one of the challenging tasks in computer vision. From the application point of view, the difficulty faced by semantic segmentation is how to satisfy inference speed, network parameters, and segmentation accuracy at the same time. This paper proposes a lightweight multi-dimensional dynamic convolutional network (LMDCNet) for real-time semantic segmentation to address this problem. At the core of our architecture is Multidimensional Dynamic Convolution (MDy-Conv), which uses an attention mechanism and factorial convolution to remain efficient while maintaining remarkable accuracy. Specifically, LMDCNet belongs to an asymmetric network architecture. Therefore, we design an encoder module containing MDy-Conv convolution: MS-DAB. The success of this module is attributed to the use of MDy-Conv convolution, which increases the utilization of local and contextual information of features. Furthermore, we design a decoder module containing a feature pyramid and attention: SC-FP, which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and CamVid datasets, LMDCNet achieves accuracies of 73.8 mIoU and 69.6 mIoU at 71.2 FPS and 92.4 FPS, respectively, without pre-training or post-processing. Our designed LMDCNet is trained and inferred only on one 1080Ti GPU. Our experiments show that LMDCNet achieves a good balance between segmentation accuracy and network parameters with only 1.05 M.