Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

Dimensional reduction in complex living systems: Where, why, and how.

The unprecedented prowess of measurement techniques provides a detailed, multi-scale look into the depths of living systems. Understanding these avalanches of high-dimensional data-by distilling underlying principles and mechanisms-necessitates dimensional reduction. We propose that living systems achieve exquisite dimensional reduction, originating from their capacity to learn, through evolution and phenotypic plasticity, the relevant aspects of a non-random, smooth physical reality. We explain how geometric insights by mathematicians allow one to identify these genuine hallmarks of life and distinguish them from universal properties of generic data sets. We illustrate these principles in a concrete example of protein evolution, suggesting a simple general recipe that can be applied to understand other biological systems.

Dimensional Reduction of Metal-Organic Frameworks for Enhanced Cryopreservation of Red Blood Cells.

To increase the red blood cell (RBC) cryopreservation efficiency by metal-organic frameworks (MOFs), a dimensional reduction approach has been proposed. Namely, 3D MOF nanoparticles are progressively reduced to 2D ultra-thin metal-organic layers (MOLs). We found that 2D MOLs are beneficial for enhanced interactions of the interfacial hydrogen-bonded water network and increased utilization of inner ordered structures, due to the higher surface-to-volume ratio. Specifically, a series of hafnium (Hf)-based 2D MOLs with different thicknesses (monolayer to stacked multilayers) and densities of hydrogen bonding sites have been synthesized. Both ice recrystallization inhibition activity (IRI) and RBCs cryopreservation assay confirm the pronounced better IRI activity and excellent cell recovery efficiency (up to ≈63 % at a very low concentration of 0.7 mg mL-1 ) of thin-layered Hf-MOLs compared to their 3D counterparts, thereby verifying the dimensional reduction strategy to improved cryoprotectant behaviors.

Analysis insights for three FRET pairs of chemically unlinked two-molecule FRET cytometry.

Förster resonance energy transfer (FRET) is the direct energy exchange between two-component fluorescent molecules. FRET methods utilize chemically linked molecules or unlinked fluorescent molecules such as fluoresscent protein-protein interactions. FRET is therefore a powerful indicator of molecular proximity, but standardized determination of FRET efficiency is challenged when investigating natural (chemically unlinked) interactions. In this paper, we have examined the interactions of tumor necrosis factor receptor-1 (TNFR1) molecules expressed as recombinant C-terminal fusion proteins of cyan, yellow, or red fluorescent protein (-CFP, -YFP, or -RFP) to evaluate two-molecule chemically unlinked FRET by flow cytometry. We demonstrate three independent FRET pairs of TNFR1 CFP→YFP (FRET-1), YFP→RFP (FRET-2) and CFP→RFP (FRET-3), by comparing TNFR1+TNFR1 with non-interacting TNFR1+CD27 proteins, on both LSR-II and Fortessa X-20 cytometers. We describe genuine FRET activities reflecting TNFR1 homotypic interactions. The FRET events can be visualized during sample acquisition via the use of "spiked" FRET donor cells, together with TNFR1+TNFR1 co-transfected cells, as FRET channel mean fluorescence intensity (MFI) overlays. FRET events can also be indicated by comparing concatenated files of cells expressing either FRET positive events (TNFR1+TNFR1) or FRET negative events (TNFR1+CD27) to generate single-cell scatter plots showing loss of FRET donor brightness. Robust determination of FRET efficiency is then confirmed at the single-cell level by applying matrix calculations based on the measurements of FRET, using donor, acceptor, and FRET fluorescent intensities (I), detector channel emission coefficient (S), fluorescent protein extinction coefficients (ε) and the α factor. In this TNFR1-based system the mean CFP→YFP FRET-1 efficiency is 0.43 (LSR-II) and 0.41 (Fortessa X-20), the mean YFP→RFP FRET-2 efficiency is 0.30 (LSR-II) and 0.29 (Fortessa X-20), and the mean CFP→RFP FRET-3 efficiency is 0.56 (LSR-II) and 0.54 (Fortessa X-20). This study also embraces multi-dimensional clustering using t-SNE, Fit-SNE, UMAP, Tri-Map and PaCMAP to further demonstrate FRET. These approaches establish a robust system for standardized detection of chemically unlinked TNFR1 homotypic interactions with three individual FRET pairs.

Remarkable decrease in lattice thermal conductivity of transition metals borides TiB2by dimensional reduction.

Two-dimensional transition metals borides TixBxhave excellent magnetic and electronic properties and great potential in metal-ion batteries and energy storage. The thermal management is important for the safety and stability in these applications. We investigated the lattice dynamical and thermal transport properties of bulk-TiB2and its two-dimensional (2D) counterparts based on density functional theory combined with solving phonon Boltzmann transport equation. The Poisson's ratio of bulk-TiB2is positive while it changes to negative for monolayer TiB2. We found that dimension reduction can cause the room-temperature in-plane lattice thermal conductivity decrease, which is opposite the trend of MoS2, MoSe2, WSe2and SnSe. Additionally, the room temperature thermal conductivity of mono-TiB2is only one sixth of that for bulk-TiB2. It is attributed to the higher Debye temperature and stronger bonding stiffness in bulk-TiB2. The bulk-TiB2has higher phonon group velocity and weaker anharmonic effect comparing with its 2D counterparts. On the other hand, the room temperature lattice thermal conductivity of mono-Ti2B2is two times higher than that of mono-TiB2, which is due to three-phonon selection rule caused by the horizontal mirror symmetry.

From quantum link models to D-theory: a resource efficient framework for the quantum simulation and computation of gauge theories.

Quantum link models provide an extension of Wilson's lattice gauge theory in which the link Hilbert space is finite-dimensional and corresponds to a representation of an embedding algebra. In contrast to Wilson's parallel transporters, quantum links are intrinsically quantum degrees of freedom. In D-theory, these discrete variables undergo dimensional reduction, thus giving rise to asymptotically free theories. In this way [Formula: see text] [Formula: see text] models emerge by dimensional reduction from [Formula: see text] [Formula: see text] quantum spin ladders, the [Formula: see text] confining [Formula: see text] gauge theory emerges from the Abelian Coulomb phase of a [Formula: see text] quantum link model, and [Formula: see text] QCD arises from a non-Abelian Coulomb phase of a [Formula: see text] [Formula: see text] quantum link model, with chiral quarks arising naturally as domain wall fermions. Thanks to their finite-dimensional Hilbert space and their economical mechanism of reaching the continuum limit by dimensional reduction, quantum link models provide a resource efficient framework for the quantum simulation and computation of gauge theories. This article is part of the theme issue 'Quantum technologies in particle physics'.

Higgs effect without lunch.

Reduction in effective space-time dimensionality can occur in field-theory models more general than the widely studied dimensional reductions based on technically consistent truncations. Situations where wave function factors depend non-trivially on coordinates transverse to the effective lower dimension can give rise to unusual patterns of gauge symmetry breaking. Leading-order gauge modes can be left massless, but naturally occurring Stueckelberg modes can couple importantly at quartic order and higher, thus generating a 'covert' pattern of gauge symmetry breaking. Such a situation is illustrated in a five-dimensional model of scalar electrodynamics in which one spatial dimension is taken to be an interval with Dirichlet/Robin boundary conditions on opposing ends. The Stueckelberg mode remains in the theory as a propagating scalar degree of freedom from a dimensionally reduced perspective, but it is not 'eaten' in a mass-generating mechanism. At leading order, it also makes no contribution to the conserved energy; for this reason, it may be called a (non-ghost) 'phantom'. This simple model illuminates a mechanism which also has been found in gravitational braneworld scenarios. This article is part of the theme issue 'The future of mathematical cosmology, Volume 2'.

SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach's effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

Investigation of Metastable Low Dimensional Halometallates.

The solvothermal synthesis, structure determination and optical characterization of five new metastable halometallate compounds, [1,10-phenH][Pb3.5I8] (1), [1,10-phenH2][Pb5I12]·(H2O) (2), [1,10-phen][Pb2I4] (3), [1,10-phen]2[Pb5Br10] (4) and [1,10-phenH][SbI4]·(H2O) (5), are reported. The materials exhibit rich structural diversity and exhibit structural dimensionalities that include 1D chains, 2D sheets and 3D frameworks. The optical spectra of these materials are consistent with bandgaps ranging from 2.70 to 3.44 eV. We show that the optical behavior depends on the structural dimensionality of the reported materials, which are potential candidates for semiconductor applications.

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR).

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l 0-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

Green function of correlated genes in a minimal mechanical model of protein evolution.

The function of proteins arises from cooperative interactions and rearrangements of their amino acids, which exhibit large-scale dynamical modes. Long-range correlations have also been revealed in protein sequences, and this has motivated the search for physical links between the observed genetic and dynamic cooperativity. We outline here a simplified theory of protein, which relates sequence correlations to physical interactions and to the emergence of mechanical function. Our protein is modeled as a strongly coupled amino acid network with interactions and motions that are captured by the mechanical propagator, the Green function. The propagator describes how the gene determines the connectivity of the amino acids and thereby, the transmission of forces. Mutations introduce localized perturbations to the propagator that scatter the force field. The emergence of function is manifested by a topological transition when a band of such perturbations divides the protein into subdomains. We find that epistasis-the interaction among mutations in the gene-is related to the nonlinearity of the Green function, which can be interpreted as a sum over multiple scattering paths. We apply this mechanical framework to simulations of protein evolution and observe long-range epistasis, which facilitates collective functional modes.

Multilayered 2D Cesium-Based Hybrid Perovskite with Strong Polarization Sensitivity: Dimensional Reduction of CsPbBr3.

3D perovskite CsPbBr3 has recently taken a blooming position for optoelectronic applications. However, due to the lack of natural anisotropy of optical attributes, it is a great challenge to fulfil polarization-sensitive photodetection. Here, for the first time, we exploited dimensionality reduction of CsPbBr3 to tailor a 2D-multilayered hybrid perovskite, (TRA)2 CsPb2 Br7 (1, in which TRA is (carboxy)cyclohexylmethylammonium), serving as a potential polarized-light detecting candidate. Its unique quantum-confined 2D structure results in intrinsic anisotropy of electrical conductivity, optical absorbance, and polarization-dependent responses. Particularly, it exhibits remarkable dichroism with the photocurrent ratio (Ipc /Ipa ) of ≈2.1, being much higher than that of the isotropic CsPbBr3 crystal and reported CH3 NH3 PbI3 nanowire (≈1.3), which reveals its great potentials for polarization-sensitive photodetection. Further, crystal-based detectors of 1 show fascinating responses to the polarized light, including high detectivity (>1010 Jones), fast responding time (≈300 µs), and sizeable on/off current ratios (>104 ). To our best knowledge, this is the first study on 2D Cs-based hybrid perovskite exhibiting strong polarization-sensitivity. The work highlights an effective pathway to explore new polarization sensitive candidates for hybrid perovskites and promotes their future electronic applications.

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

3D-to-2D Dimensional Reduction for Exploiting a Multilayered Perovskite Ferroelectric toward Polarized-Light Detection in the Solar-Blind Ultraviolet Region.

Polarized-light detection in solar-blind ultraviolet region is indispensable for optoelectronic applications, whereas new 2D candidates targeted at solar-blind UV range remain extremely scarce. 2D hybrid perovskite ferroelectrics that combine polarization and semiconducting properties are of increasing interest. Here, using the 3D-to-2D dimensional reduction of CH3 NH3 PbCl3 , we designed a multilayered hybrid perovskite ferroelectric, (CH3 CH2 NH3 )2 (CH3 NH3 )2 Pb3 Cl10 , which shows spontaneous polarization and a high Curie temperature (390 K) comparable with that of BaTiO3 (393 K). The wide band gap (ca. 3.35 eV) and anisotropic absorbance stemming from its intrinsic 2D motif, greatly favor its polarization-sensitive activity in UV region. The device displays excellent polarization-sensitive behavior under 266 nm, along with a large dichroic ratio (ca. 1.38) and high on/off current ratio (ca. 2.3×103 ).

Dimensional Reduction of Cs2 AgBiBr6 : A 2D Hybrid Double Perovskite with Strong Polarization Sensitivity.

By dimensional reduction of the 3D motif of Cs2 AgBiBr6 , a lead-free 2D hybrid double perovskite, (i-PA)2 CsAgBiBr7 (1, i-PA=isopentylammonium), was successfully designed. It adopts a quantum-confined bilayered structure with alternating organic and inorganic sheets. Strikingly, the unique 2D architecture endows it highly anisotropic nature of physical properties, including electric conductivity and optical absorption (the ratio αb /αc =1.9 at 405 nm). Such anisotropy attributes result in the strong polarization-sensitive responses with large dichroic ratios up to 1.35, being comparable to some 2D inorganic materials. This is the first study on the hybrid double perovskites with strong polarization sensitivity. A crystal device of 1 also exhibits rapid response speed (ca. 200 µs) and excellent stabilities. The family of 2D hybrid double perovskites are promising optoelectronic candidates, and this work paves a new pathway for exploring new green polarization-sensitive materials.

Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.

Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vec-based metrics and are only partially correlated (rho = 0.5-0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title + abstracts are all publicly available from http://arrowsmith.psych.uic.edu/arrowsmith_uic/word_similarity_metrics.html for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics.

An Effective Approach for Reliability-Based Sensitivity Analysis with the Principle of Maximum Entropy and Fractional Moments.

The reliability-based sensitivity analysis requires to recursively evaluate a multivariate structural model for many failure probability levels. This is in general a computationally intensive task due to irregular integrations used to define the structural failure probability. In this regard, the performance function is first approximated by using the multiplicative dimensional reduction method in this paper, and an approximation for the reliability-based sensitivity index is derived based on the principle of maximum entropy and the fractional moment. Three examples in the literature are presented to examine the performance of this entropy-based approach against the brute-force Monte-Carlo simulation method. Results have shown that the multiplicative dimensional reduction based entropy approach is rather efficient and able to provide reliability estimation results for the reliability-based sensitivity analysis of a multivariate structural model.

Single sample scoring of molecular phenotypes.

BACKGROUND: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. CONCLUSIONS: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data).

BACKGROUND: Despite the successful mapping of genes involved in the determinism of numerous traits, a large part of the genetic variation remains unexplained. A possible explanation is that the simple models used in many studies might not properly fit the actual underlying situations. Consequently, various methods have attempted to deal with the simultaneous mapping of genomic regions, assuming that these regions might interact, leading to a complex determinism for various traits. Despite some successes, no gold standard methodology has emerged. Actually, combining several interaction mapping methods might be a better strategy, leading to positive results over a larger set of situations. Our work is a step in that direction. RESULTS: We first have demonstrated why aggregating results from several distinct methods might increase the statistical power while controlling the type I error. We have illustrated the approach using 6 existing methods (namely: MDR, Boost, BHIT, KNN-MDR, MegaSNPHunter and AntEpiSeeker) on simulated and real data sets. We have used a very simple aggregation strategy: a majority vote across the best loci combinations identified by the individual methods. In order to assess the performances of our aggregation approach in problems where most individual methods tend to fail, we have simulated difficult situations where no marginal effects of individual genes exist and where genetic heterogeneity is present. we have also demonstrated the use of the strategy on real data, using a WTCCC dataset on rheumatoid arthritis. Since we have been using simplistic assumptions to infer the expected power of the aggregation method, the actual power we estimated from our simulations has turned out to be a bit smaller than theoretically expected. Results nevertheless have shown that grouping the results of several methods is advantageous in terms of power, accuracy and type I error control. Furthermore, as more methods should become available in the future, using a grouping strategy will become more advantageous since adding more methods seems to improve the performances of the aggregated method. CONCLUSIONS: The aggregation of methods as a tool to detect genetic interactions is a potentially useful addition to the arsenal used in complex traits analyses.

[Hg4 Te8 (Te2 )4 ]8- : A Heavy Metal Porphyrinoid Embedded in a Lamellar Structure.

The use of ionic liquids (Cn C1 Im)[BF4 ] with long alkyl chains (n=10, 12) in the ionothermal treatment of Na2 [HgTe2 ] led to lamellar crystal structures with molecular macrocyclic anions [Hg8 Te16 ]8- (1), the heaviest known topological relative of porphyrin. [Hg8 Te16 ]8- differs from porphyrin by the absence of an electronic π-system, which prevents a "global" aromaticity. Quantum chemical studies reveal instead small ring currents in the pyrrole-type five-membered rings that indicate weak local (σ) aromaticity. As a result of their lamellar nature, the compounds are promising candidates for the formation of sheets containing chalcogenidometalate anions.