Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging.

A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

Correlation-informed ordered dictionary learning for imaging in complex media.

We propose a method for imaging in scattering media when large and diverse datasets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using the multidimensional scaling algorithm with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together, we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain connectivity needed in the second step. Through simulation experiments, we show that the proposed method is able to provide images in complex media whose resolution is that of a homogeneous medium.

Molecular dynamics in multidimensional space explains how mutations affect the association path of neomycin to a riboswitch.

Riboswitches are messenger RNA (mRNA) fragments binding specific small molecules to regulate gene expression. A synthetic N1 riboswitch, inserted into yeast mRNA controls the translation of a reporter gene in response to neomycin. However, its regulatory activity is sensitive to single-point RNA mutations, even those distant from the neomycin binding site. While the association paths of neomycin to N1 and its variants remain unknown, recent fluorescence kinetic experiments indicate a two-step process driven by conformational selection. This raises the question of which step is affected by mutations. To address this, we performed all-atom two-dimensional replica-exchange molecular dynamics simulations for N1 and U14C, U14C[Formula: see text], U15A, and A17G mutants, ensuring extensive conformational sampling of both RNA and neomycin. The obtained neomycin association and binding paths, along with multidimensional free-energy profiles, revealed a two-step binding mechanism, consisting of conformational selection and induced fit. Neomycin binds to a preformed N1 conformation upon identifying a stable upper stem and U-turn motif in the riboswitch hairpin. However, the positioning of neomycin in the binding site occurs at different RNA-neomycin distances for each mutant, which may explain their different regulatory activities. The subsequent induced fit arises from the interactions of the neomycin's N3 amino group with RNA, causing the G9 backbone to rearrange. In the A17G mutant, the critical C6-A17/G17 stacking forms at a closer RNA-neomycin distance compared to N1. These findings together with estimated binding free energies coincide with experiments and elucidate why the A17G mutation decreases and U15A enhances N1 activity in response to neomycin.

Multidimensional high-harmonic echo spectroscopy: Resolving coherent electron dynamics in the EUV regime.

We theoretically propose a multidimensional high-harmonic echo spectroscopy technique which utilizes strong optical fields to resolve coherent electron dynamics spanning an energy range of multiple electronvolts. Using our recently developed semi-perturbative approach, we can describe the coherent valence electron dynamics driven by a sequence of phase-matched and well-separated short few-cycle strong infrared laser pulses. The recombination of tunnel-ionized electrons by each pulse coherently populates the valence states of a molecule, which allows for a direct observation of its dynamics via the high harmonic echo signal. The broad bandwidth of the effective dipole between valence states originated from the strong-field excitation results in nontrivial ultra-delayed partial rephasing echo, which is not observed in standard two-dimensional optical spectroscopic techniques in a two-level molecular systems. We demonstrate the results of simulations for the anionic molecular system and show that the ultrafast valence electron dynamics can be well captured with femtosecond resolution.

Operando probing and adjusting of the complicated electrode process of multivalent metals at extreme temperature.

Nearly half of the elements in the periodic table are extracted, refined, or plated using electrodeposition in high-temperature melts. However, operando observations and tuning of the electrodeposition process during realistic electrolysis operations are extremely difficult due to severe reaction conditions and complicated electrolytic cell, which makes the improvement of the process very blind and inefficient. Here, we developed a multipurpose operando high-temperature electrochemical instrument that combines operando Raman microspectroscopy analysis, optical microscopy imaging, and a tunable magnetic field. Subsequently, the electrodeposition of Ti-which is a typical polyvalent metal and generally shows a very complex electrode process-was used to verify the stability of the instrument. The complex multistep cathodic process of Ti in the molten salt at 823 K was systematically analyzed by a multidimensional operando analysis strategy involving multiple experimental studies, theoretical calculations, etc. The regulatory effect and its corresponding scale-span mechanism of the magnetic field on the electrodeposition process of Ti were also elucidated, which would be inaccessible with existing experimental techniques and is significant for the real-time and rational optimization of the process. Overall, this work established a powerful and universal methodology for in-depth analysis of high-temperature electrochemistry.

Inequality and COVID-19 in Sweden: Relative risks of nine bad life events, by four social gradients, in pandemic vs. prepandemic years.

The COVID-19 pandemic struck societies directly and indirectly, not just challenging population health but disrupting many aspects of life. Different effects of the spreading virus-and the measures to fight it-are reported and discussed in different scientific fora, with hard-to-compare methods and metrics from different traditions. While the pandemic struck some groups more than others, it is difficult to assess the comprehensive impact on social inequalities. This paper gauges social inequalities using individual-level administrative data for Sweden's entire population. We describe and analyze the relative risks for different social groups in four dimensions-gender, education, income, and world region of birth-to experience three types of COVID-19 incidence, as well as six additional negative life outcomes that reflect general health, access to medical care, and economic strain. During the pandemic, the overall population faced severe morbidity and mortality from COVID-19 and saw higher all-cause mortality, income losses and unemployment risks, as well as reduced access to medical care. These burdens fell more heavily on individuals with low income or education and on immigrants. Although these vulnerable groups experienced larger absolute risks of suffering the direct and indirect consequences of the pandemic, the relative risks in pandemic years (2020 and 2021) were conspicuously similar to those in prepandemic years (2016 to 2019).

The Geometry of Low- and High-Level Perceptual Spaces.

Low-level features are typically continuous (e.g., the gamut between two colors), but semantic information is often categorical (there is no corresponding gradient between dog and turtle) and hierarchical (animals live in land, water, or air). To determine the impact of these differences on cognitive representations, we characterized the geometry of perceptual spaces of five domains: a domain dominated by semantic information (animal names presented as words), a domain dominated by low-level features (colored textures), and three intermediate domains (animal images, lightly texturized animal images that were easy to recognize, and heavily texturized animal images that were difficult to recognize). Each domain had 37 stimuli derived from the same animal names. From 13 participants (9F), we gathered similarity judgments in each domain via an efficient psychophysical ranking paradigm. We then built geometric models of each domain for each participant, in which distances between stimuli accounted for participants' similarity judgments and intrinsic uncertainty. Remarkably, the five domains had similar global properties: each required 5-7 dimensions, and a modest amount of spherical curvature provided the best fit. However, the arrangement of the stimuli within these embeddings depended on the level of semantic information: dendrograms derived from semantic domains (word, image, and lightly texturized images) were more "tree-like" than those from feature-dominated domains (heavily texturized images and textures). Thus, the perceptual spaces of domains along this feature-dominated to semantic-dominated gradient shift to a tree-like organization when semantic information dominates, while retaining a similar global geometry.

A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Mass spectrometry of polymers: A tutorial review.

Ever since the inception of synthetic polymeric materials in the late 19th century, the number of studies on polymers as well as the complexity of their structures have only increased. The development and commercialization of new polymers with properties fine-tuned for specific technological, environmental, consumer, or biomedical applications requires powerful analytical techniques that permit the in-depth characterization of these materials. One such method with the ability to provide chemical composition and structure information with high sensitivity, selectivity, specificity, and speed is mass spectrometry (MS). This tutorial review presents and exemplifies the various MS techniques available for the elucidation of specific structural features in a synthetic polymer, including compositional complexity, primary structure, architecture, topology, and surface properties. Key to every MS analysis is sample conversion to gas-phase ions. This review describes the fundamentals of the most suitable ionization methods for synthetic materials and provides relevant sample preparation protocols. Most importantly, structural characterizations via one-step as well as hyphenated or multidimensional approaches are introduced and demonstrated with specific applications, including surface sensitive and imaging techniques. The aim of this tutorial review is to illustrate the capabilities of MS for the characterization of large, complex polymers and emphasize its potential as a powerful compositional and structural elucidation tool in polymer chemistry.

Single-Molecule Spectroscopy and Super-Resolution Mapping of Physicochemical Parameters in Living Cells.

By superlocalizing the positions of millions of single molecules over many camera frames, a class of super-resolution fluorescence microscopy methods known as single-molecule localization microscopy (SMLM) has revolutionized how we understand subcellular structures over the past decade. In this review, we highlight emerging studies that transcend the outstanding structural (shape) information offered by SMLM to extract and map physicochemical parameters in living mammalian cells at single-molecule and super-resolution levels. By encoding/decoding high-dimensional information-such as emission and excitation spectra, motion, polarization, fluorescence lifetime, and beyond-for every molecule, and mass accumulating these measurements for millions of molecules, such multidimensional and multifunctional super-resolution approaches open new windows into intracellular architectures and dynamics, as well as their underlying biophysical rules, far beyond the diffraction limit.

Fast-Charging, Binder-Free Lithium Battery Cathodes Enabled via Multidimensional Conductive Networks.

To meet the growing demands in both energy and power densities of lithium ion batteries, electrode structures must be capable of facile electron and ion transport while minimizing the content of electrochemically inactive components. Herein, binder-free LiFePO4 (LFP) cathodes are fabricated with a multidimensional conductive architecture that allows for fast-charging capability, reaching a specific capacity of 94 mAh g-1 at 4 C. Such multidimensional networks consist of active material particles wrapped by 1D single-walled carbon nanotubes (CNTs) and bound together using 2D MXene (Ti3C2Tx) nanosheets. The CNTs form a porous coating layer and improve local electron transport across the LFP surface, while the Ti3C2Tx nanosheets provide simultaneously high electrode integrity and conductive pathways through the bulk of the electrode. This work highlights the ability of multidimensional conductive fillers to realize simultaneously superior electrochemical and mechanical properties, providing useful insights into future fast-charging electrode designs for scalable electrochemical systems.

Overlapping representations of observed actions and action-related features.

The lateral occipitotemporal cortex (LOTC) has been shown to capture the representational structure of a smaller range of actions. In the current study, we carried out an fMRI experiment in which we presented human participants with images depicting 100 different actions and used representational similarity analysis (RSA) to determine which brain regions capture the semantic action space established using judgments of action similarity. Moreover, to determine the contribution of a wide range of action-related features to the neural representation of the semantic action space we constructed an action feature model on the basis of ratings of 44 different features. We found that the semantic action space model and the action feature model are best captured by overlapping activation patterns in bilateral LOTC and ventral occipitotemporal cortex (VOTC). An RSA on eight dimensions resulting from principal component analysis carried out on the action feature model revealed partly overlapping representations within bilateral LOTC, VOTC, and the parietal lobe. Our results suggest spatially overlapping representations of the semantic action space of a wide range of actions and the corresponding action-related features. Together, our results add to our understanding of the kind of representations along the LOTC that support action understanding.

Assessing chromatin relocalization in 3D using the patient rule induction method.

Three-dimensional (3D) genome architecture is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Inferring 3D chromatin configurations has been advanced by the emergence of chromatin conformation capture assays, notably Hi-C, and attendant 3D reconstruction algorithms. These have enhanced understanding of chromatin spatial organization and afforded numerous downstream biological insights. Until recently, comparisons of 3D reconstructions between conditions and/or cell types were limited to prescribed structural features. However, multiMDS, a pioneering approach developed by Rieber and Mahony (2019). that performs joint reconstruction and alignment, enables quantification of all locus-specific differences between paired Hi-C data sets. By subsequently mapping these differences to the linear (1D) genome the identification of relocalization regions is facilitated through the use of peak calling in conjunction with continuous wavelet transformation. Here, we seek to refine this approach by performing the search for significant relocalization regions in terms of the 3D structures themselves, thereby retaining the benefits of 3D reconstruction and avoiding limitations associated with the 1D perspective. The search for (extreme) relocalization regions is conducted using the patient rule induction method (PRIM). Considerations surrounding orienting structures with respect to compartmental and principal component axes are discussed, as are approaches to inference and reconstruction accuracy assessment. The illustration makes recourse to comparisons between four different cell types.

Advancements in Functional Nanomaterials Inspired by Viral Particles.

Virus-like particles (VLPs) are nanostructures composed of one or more structural proteins, exhibiting stable and symmetrical structures. Their precise compositions and dimensions provide versatile opportunities for modifications, enhancing their functionality. Consequently, VLP-based nanomaterials have gained widespread adoption across diverse domains. This review focuses on three key aspects: the mechanisms of viral capsid protein self-assembly into VLPs, design methods for constructing multifunctional VLPs, and strategies for synthesizing multidimensional nanomaterials using VLPs. It provides a comprehensive overview of the advancements in virus-inspired functional nanomaterials, encompassing VLP assembly, functionalization, and the synthesis of multidimensional nanomaterials. Additionally, this review explores future directions, opportunities, and challenges in the field of VLP-based nanomaterials, aiming to shed light on potential advancements and prospects in this exciting area of research.

Dimension Evolution of Self-Assembled Organic Microcrystal for Laser and Polarization-Rotation Function.

Multidimensional integrated micro/nanostructures are vitally important for the implementation of versatile photonic functionalities, whereas current material structures still suffer undesired surface defects and contaminations in either multistep micro/nanofabrications or extreme synthetic conditions. Herein, the dimension evolution of organic self-assembled structures 2D microrings and 3D microhelixes for multidimensional photonic devices is realized via a protic/aprotic solvent-directed molecular assembly method based on a multiaxial confined-assisted growth mechanism. The 2D microrings with consummate circle boundaries and molecular-smooth surfaces function as high-quality whispering-gallery-mode microcavities for dual-wavelength energy-influence-dependent switchable lasing. Moreover, the 3D microhelixes with smooth surfaces and natural twistable characteristics act as active photon-transport materials and polarization rotators. These results will broaden the horizon of constructing multidimensional microstructures for integrated photonic circuits.

Multidimensional Engineering Induced Interfacial Polarization by in-Situ Confined Growth of MoS2 Nanosheets for Enhanced Microwave Absorption.

Interface design has enormous potential for the enhancement of interfacial polarization and microwave absorption properties. However, the construction of interfaces is always limited in components of a single dimension. Developing systematic strategies to customize multidimensional interfaces and fully utilize advantages of low-dimensional materials remains challenging. Two-dimensional transition metal dichalcogenides (TMDCs) have garnered significant attention owing to their distinctive electrical conductivity and exceptional interfacial effects. In this study, a series of hollow TMDCs@C fibers are synthesized via sacrificial template of CdS and confined growth of TMDCs embedded in the fibers. The complex permittivity of the hollow TMDCs@C fibers can be adjusted by tuning the content of CdS templates. Importantly, the multidimensional interfaces of the fibers contribute to elevating the microwave absorption performance. Among the hollow TMDCs@C fibers, the minimum reflection loss (RLmin) of the hollow MoS2@C fibers can reach -52.0 dB at the thickness of 2.5 mm, with a broad effective absorption bandwidth of 4.56 GHz at 2.0 mm. This work establishes an alternative approach for constructing multidimensional coupling interfaces and optimizing TMDCs as microwave absorption materials.

Atomic Imaging of Multi-Dimensional Ruddlesden-Popper Interfaces in Lead-Halide Perovskites.

Ruddlesden-Popper (RP) interface with defined stacking structure will fundamentally influence the optoelectronic performances of lead-halide perovskite (LHP) materials and devices. However, it remains challenging to observe the atomic local structures in LHPs, especially for multi-dimensional RP interface hidden inside the nanocrystal. In this work, the advantages of two imaging modes in scanning transmission electron microscopy (STEM), including high-angle annular dark field (HAADF) and integrated differential phase contrast (iDPC) STEM, are successfully combined to study the bulk and local structures of inorganic and organic/inorganic hybrid LHP nanocrystals. Then, the multi-dimensional RP interfaces in these LHPs are atomically resolved with clear gap and blurred transition region, respectively. In particular, the complex interface by the RP stacking in 3D directions can be analyzed in 2D projected image. Finally, the phase transition, ion missing, and electronic structures related to this interface are investigated. These results provide real-space evidence for observing and analyzing atomic multi-dimensional RP interfaces, which may help to better understand the structure-property relation of LHPs, especially their complex local structures.

Addressing noise in co-expression network construction.

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The 'one-size-fits-all' approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.

AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction.

The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.