Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.

RNA profiles are an informative phenotype of cellular and tissue states but can be costly to generate at massive scale. Here, we describe how gene expression levels can be efficiently acquired with random composite measurements-in which abundances are combined in a random weighted sum. We show (1) that the similarity between pairs of expression profiles can be approximated with very few composite measurements; (2) that by leveraging sparse, modular representations of gene expression, we can use random composite measurements to recover high-dimensional gene expression levels (with 100 times fewer measurements than genes); and (3) that it is possible to blindly recover gene expression from composite measurements, even without access to training data. Our results suggest new compressive modalities as a foundation for massive scaling in high-throughput measurements and new insights into the interpretation of high-dimensional data.

Connecting theory and experiment in cell and tissue mechanics.

Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.

A review on longitudinal data analysis with random forest.

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.

3D Traction Force Microscopy in Biological Gels: From Single Cells to Multicellular Spheroids.

Cell traction force plays a critical role in directing cellular functions, such as proliferation, migration, and differentiation. Current understanding of cell traction force is largely derived from 2D measurements where cells are plated on 2D substrates. However, 2D measurements do not recapitulate a vital aspect of living systems; that is, cells actively remodel their surrounding extracellular matrix (ECM), and the remodeled ECM, in return, can have a profound impact on cell phenotype and traction force generation. This reciprocal adaptivity of living systems is encoded in the material properties of biological gels. In this review, we summarize recent progress in measuring cell traction force for cells embedded within 3D biological gels, with an emphasis on cell-ECM cross talk. We also provide perspectives on tools and techniques that could be adapted to measure cell traction force in complex biochemical and biophysical environments.

Sensitivity-enhanced NMR 15N R1 and R1ρ relaxation experiments for the investigation of intrinsically disordered proteins at high magnetic fields.

NMR relaxation experiments provide residue-specific insights into the structural dynamics of proteins. Here, we present an optimized set of sensitivity-enhanced 15N R1 and R1ρ relaxation experiments applicable to fully protonated proteins. The NMR pulse sequences are conceptually similar to the set of TROSY-based sequences and their HSQC counterpart (Lakomek et al., J. Biomol. NMR 2012). Instead of the TROSY read-out scheme, a sensitivity-enhanced HSQC read-out scheme is used, with improved and easier optimized water suppression. The presented pulse sequences are applied on the cytoplasmic domain of the SNARE protein Synpatobrevin-2 (Syb-2), which is intrinsically disordered in its monomeric pre-fusion state. A two-fold increase in the obtained signal-to-noise ratio is observed for this intrinsically disordered protein, therefore offering a four-fold reduction of measurement time compared to the TROSY-detected version. The inter-scan recovery delay can be shortened to two seconds. Pulse sequences were tested at 600 MHz and 1200 MHz 1H Larmor frequency, thus applicable over a wide magnetic field range. A comparison between protonated and deuterated protein samples reveals high agreement, indicating that reliable 15N R1 and R1ρ rate constants can be extracted for fully protonated and deuterated samples. The presented pulse sequences will benefit not only for IDPs but also for an entire range of low and medium-sized proteins.

Systematic Investigation of Transcription Factor Activity in the Context of Chromatin Using Massively Parallel Binding and Expression Assays.

Precise gene expression patterns are established by transcription factor (TFs) binding to regulatory sequences. While these events occur in the context of chromatin, our understanding of how TF-nucleosome interplay affects gene expression is highly limited. Here, we present an assay for high-resolution measurements of both DNA occupancy and gene expression on large-scale libraries of systematically designed regulatory sequences. Our assay reveals occupancy patterns at the single-cell level. It provides an accurate quantification of the fraction of the population bound by a nucleosome and captures distinct, even adjacent, TF binding events. By applying this assay to over 1,500 promoter variants in yeast, we reveal pronounced differences in the dependency of TF activity on chromatin and classify TFs by their differential capacity to alter chromatin and promote expression. We further demonstrate how different regulatory sequences give rise to nucleosome-mediated TF collaborations that quantitatively account for the resulting expression.

Scaling of Berry-curvature monopole dominated large linear positive magnetoresistance.

The linear positive magnetoresistance (LPMR) is a widely observed phenomenon in topological materials, which is promising for potential applications on topological spintronics. However, its mechanism remains ambiguous yet, and the effect is thus uncontrollable. Here, we report a quantitative scaling model that correlates the LPMR with the Berry curvature, based on a ferromagnetic Weyl semimetal CoS2 that bears the largest LPMR of over 500% at 2 K and 9 T, among known magnetic topological semimetals. In this system, masses of Weyl nodes existing near the Fermi level, revealed by theoretical calculations, serve as Berry-curvature monopoles and low-effective-mass carriers. Based on the Weyl picture, we propose a relation [Formula: see text], with B being the applied magnetic field and [Formula: see text] the average Berry curvature near the Fermi surface, and further introduce temperature factor to both MR/B slope (MR per unit field) and anomalous Hall conductivity, which establishes the connection between the model and experimental measurements. A clear picture of the linearly slowing down of carriers, i.e., the LPMR effect, is demonstrated under the cooperation of the k-space Berry curvature and real-space magnetic field. Our study not only provides experimental evidence of Berry curvature-induced LPMR but also promotes the common understanding and functional designing of the large Berry-curvature MR in topological Dirac/Weyl systems for magnetic sensing or information storage.

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials.

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental-computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS2 and MoSe2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Mapping functional regions of essential bacterial proteins with dominant-negative protein fragments.

Massively parallel measurements of dominant-negative inhibition by protein fragments have been used to map protein interaction sites and discover peptide inhibitors. However, the underlying principles governing fragment-based inhibition have thus far remained unclear. Here, we adapted a high-throughput inhibitory fragment assay for use in Escherichia coli, applying it to a set of 10 essential proteins. This approach yielded single amino acid resolution maps of inhibitory activity, with peaks localized to functionally important interaction sites, including oligomerization interfaces and folding contacts. Leveraging these data, we performed a systematic analysis to uncover principles of fragment-based inhibition. We determined a robust negative correlation between susceptibility to inhibition and cellular protein concentration, demonstrating that inhibitory fragments likely act primarily by titrating native protein interactions. We also characterized a series of trade-offs related to fragment length, showing that shorter peptides allow higher-resolution mapping but suffer from lower inhibitory activity. We employed an unsupervised statistical analysis to show that the inhibitory activities of protein fragments are largely driven not by generic properties such as charge, hydrophobicity, and secondary structure, but by the more specific characteristics of their bespoke macromolecular interactions. Overall, this work demonstrates fundamental characteristics of inhibitory protein fragment function and provides a foundation for understanding and controlling protein interactions in vivo.

Anomalous magnetic noise in an imperfectly flat landscape in the topological magnet Dy2Ti2O7.

Noise generated by motion of charge and spin provides a unique window into materials at the atomic scale. From temperature of resistors to electrons breaking into fractional quasiparticles, "listening" to the noise spectrum is a powerful way to decode underlying dynamics. Here, we use ultrasensitive superconducting quantum interference device (SQUIDs) to probe the puzzling noise in a frustrated magnet, the spin-ice compound Dy2Ti2O7 (DTO), revealing cooperative and memory effects. DTO is a topological magnet in three dimensions-characterized by emergent magnetostatics and telltale fractionalized magnetic monopole quasiparticles-whose real-time dynamical properties have been an enigma from the very beginning. We show that DTO exhibits highly anomalous noise spectra, differing significantly from the expected Brownian noise of monopole random walks, in three qualitatively different regimes: equilibrium spin ice, a "frozen" regime extending to ultralow temperatures, and a high-temperature "anomalous" paramagnet. We present several distinct mechanisms that give rise to varied colored noise spectra. In addition, we identify the structure of the local spin-flip dynamics as a crucial ingredient for any modeling. Thus, the dynamics of spin ice reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such, it points to intriguing cooperative and memory effects for a broad class of magnetic materials.

Solvent-Mediated Modulation of the Au-S Bond in Dithiol Molecular Junctions.

Gold-dithiol molecular junctions have been studied both experimentally and theoretically. However, the nature of the gold-thiolate bond as it relates to the solvent has seldom been investigated. It is known that solvents can impact the electronic structure of single-molecule junctions, but the correlation between the solvent and dithiol-linked single-molecule junction conductance is not well understood. We study molecular junctions formed with thiol-terminated phenylenes from both 1-chloronaphthalene and 1-bromonaphthalene solutions. We find that the most probable conductance and the distribution of conductances are both affected by the solvent. First-principles calculations show that junction conductance depends on the binding configurations (adatom, atop, and bridge) of the thiolate on the Au surface, as has been shown previously. More importantly, we find that brominated solvents can restrict the binding of thiols to specific Au sites. This mechanism offers new insight into the effects of the solvent environment on covalent bonding in molecular junctions.

Single-Atom Rh1 Alloyed Co for Urea Electrosynthesis from CO2 and NO3.

Single-atom Rh1 alloyed Co (Rh1Co) is explored as an efficient catalyst for urea electrosynthesis via coelectrolysis of CO2 and NO3- (UECN). Theoretical calculations and in situ spectroscopic measurements unravel the synergetic effect of Co and Rh1 in promoting the UECN process, where the Rh1 site activates NO3- to form *NH2, while the Co site activates CO2 to form *CO. The formed *CO then desorbs from the Co site and transfers to the Rh1 site, followed by continuous C-N coupling with *NH2 formed on the Rh1 site to synthesize urea. Remarkably, Rh1Co assembled in a flow cell delivers the exceptional urea yield rate of 24.9 mmol h-1 g-1 and Faradaic efficiency of 51.1%, outperforming most previously reported UECN catalysts.

Lifshitz Transition in a Single-Atom-Thick GdxYb1-xPb3 Kagome Compound on Si(111).

Lanthanide (Ln) elements Gd and Yb alloyed with a Pb monolayer on the Si(111) substrate form LnPb3 compounds having the same crystal structure. They comprise a single-atom-thick Pb layer arranged in a slightly distorted kagome lattice with Ln atoms filling the hexagonal voids. They have similar electronic band structures except for the Fermi level position, which varies between the divalent Yb- and trivalent Gd-containing compounds by ∼0.47 eV. The ability to create a 2D solid solution with the unified continuous Pb layer and hexagonal voids randomly filled with either Gd or Yb atoms allows precise control of the Fermi level position. Small alteration of the Fermi level triggers drastic changes in the Fermi surface topology due to the Lifshitz transition, hence in the physical properties. In particular, the sheet resistance of the GdxYb1-xPb3/Si(111) system can be controllably varied over an order of magnitude range.

Low Vapor Pressure Solvents for Single-Molecule Junction Measurements.

Nonpolar solvents commonly used in scanning tunneling microscope-based break junction measurements exhibit hazards and relatively low boiling points (bp) that limit the scope of solution experiments at elevated temperatures. Here we show that low toxicity, ultrahigh bp solvents such as bis(2-ethylhexyl) adipate (bp = 417 °C) and squalane (457 °C) can be used to probe molecular junctions at ≥100 °C. With these, we extend solvent- and temperature-dependent conductance trends for junction components such as 4,4'-bipyridine and thiomethyl-terminated oligophenylenes and reveal the gold snapback distance is larger at 100 °C due to increased surface atom mobility. We further show the rate of surface transmetalation and homocoupling reactions using phenylboronic acids increases at 100 °C, while junctions comprising anticipated boroxine condensation products form only at room temperature in an anhydrous glovebox atmosphere. Overall, this work demonstrates the utility of low vapor pressure solvents for the comprehensive characterization of junction properties and chemical reactivity at the single-molecule limit.

Pd1Cu Single-Atom Alloys for High-Current-Density and Durable NO-to-NH3 Electroreduction.

Electrochemical reduction of NO to NH3 (NORR) offers a prospective method for efficient NH3 electrosynthesis. Herein, we first design single-atom Pd-alloyed Cu (Pd1Cu) as an efficient and robust NORR catalyst at industrial-level current densities (>0.2 A cm-2). Operando spectroscopic characterizations and theoretical computations unveil that Pd1 strongly electronically couples its adjacent two Cu atoms (Pd1Cu2) to enhance the NO activation while promoting the NO-to-NH3 protonation energetics and suppressing the competitive hydrogen evolution. Consequently, the flow cell assembled with Pd1Cu exhibits an unprecedented NH3 yield rate of 1341.3 µmol h-1 cm-2 and NH3-Faradaic efficiency of 85.5% at an industrial-level current density of 210.3 mA cm-2, together with an excellent long-term durability for 200 h of electrolysis, representing one of the highest NORR performances on record.

A New Methodology for the Oxygen Measurement in Lung Tissue of an Aged Ferret Model Proves Hypoxia During COVID-19.

Oxygen as a key element has a high impact on cellular processes. Infection with a pathogen such as SARS-CoV-2 and following inflammation may lead to hypoxic conditions in tissue that impact cellular responses. To develop optimized translational in vitro models for a better understanding of physiologic and pathophysiologic oxygen conditions, it is a prerequisite to determine oxygen levels generated in vivo. Our study objective was the establishment of an invasive method for oxygen measurements using a luminescence-based microsensor to determine the dissolved oxygen in the lung tissue of ferrets as animal models for SARS-CoV-2 research. In analogy to humans, aged ferrets are more likely to show clinical signs after SARS-CoV-2 infection compared to young animals. To investigate oxygen levels during a respiratory viral infection, we intratracheally infected nine aged (3-year-old) ferrets with SARS-CoV-2. The aged SARS-CoV-2 infected ferrets showed mild to moderate clinical signs associated with prolonged viral RNA shedding until 14 days post infection (dpi). SARS-CoV-2 infected ferrets showed histopathologic lung lesion scores that significantly negatively correlated with oxygen levels in lung tissue. At 4 dpi, oxygen levels in lung tissue were significantly lower (mean %O2 of 3.89 ≙ ≈ 27.78 mmHg) compared to the negative control group (mean %O2 of 8.65 ≙ ≈ 61.4 mmHg). In summary, we succeeded in determining the pathophysiologic oxygen conditions in the lung tissue of aged SARS-CoV-2-infected ferrets. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/). .

Molecular Intercalation Enables Phase Transition of MoSe2 for Durable Na-Ion Storage.

1T-MoSe2 is recognized as a promising anode material for sodium-ion batteries, thanks to its excellent electrical conductivity and large interlayer distance. However, its inherent thermodynamic instability often presents unparalleled challenges in phase control and stabilization. Here, a molecular intercalation strategy is developed to synthesize thermally stable 1T-rich MoSe2, covalently bonded to an intercalated carbon layer (1TR/2H-MoSe2@C). Density functional theory calculations uncover that the introduced ethylene glycol molecules not only serve as electron donors, inducing a reorganization of Mo 4d orbitals, but also as sacrificial guest materials that generate a conductive carbon layer. Furthermore, the C─Se/C─O─Mo bonds encourage strong interfacial electronic coupling, and the carbon layer prevents the restacking of MoSe2, regulating the maximum 1T phase to an impressive 80.3%. Consequently, the 1TR/2H-MoSe2@C exhibits an extraordinary rate capacity of 326 mAh g-1 at 5 A g-1 and maintains a long-term cycle stability up to 1500 cycles, with a capacity of 365 mAh g-1 at 2 A g-1. Additionally, the full cell delivers an appealing energy output of 194 Wh kg-1 at 208 W kg-1, with a capacity retention of 87.3% over 200 cycles. These findings contribute valuable insights toward the development of innovative transition metal dichalcogenides for next-generation energy storage technologies.

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model.

BACKGROUND: Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth. METHODS: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. RESULTS: Of 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, -6.82 to 35.1) and zone 5 (13.4%; IQR, -7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). CONCLUSIONS: We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

Multicentric clinical evaluation of a computed tomography-based fully automated deep neural network for aortic maximum diameter and volumetric measurements.

OBJECTIVE: This study aims to evaluate a fully automatic deep learning-based method (augmented radiology for vascular aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements. METHODS: A clinical validation dataset was constructed from preoperative and postoperative aortic computed tomography angiography (CTA) scans for assessing these functions. The dataset totaled 350 computed tomography angiography scans from 216 patients treated at two different hospitals. ARVA's ability to segment the aorta into seven morphologically based aortic segments and measure maximum outer-to-outer wall transverse diameters and compute volumes for each was compared with the measurements of six experts (ground truth) and thirteen clinicians. RESULTS: Ground truth (experts') measurements of diameters and volumes were manually performed for all aortic segments. The median absolute diameter difference between ground truth and ARVA was 1.6 mm (95% confidence interval [CI], 1.5-1.7; and 1.6 mm [95% CI, 1.6-1.7]) between ground truth and clinicians. ARVA produced measurements within the clinical acceptable range with a proportion of 85.5% (95% CI, 83.5-86.3) compared with the clinicians' 86.0% (95% CI, 83.9-86.0). The median volume similarity error ranged from 0.93 to 0.95 in the main trunk and achieved 0.88 in the iliac arteries. CONCLUSIONS: This study demonstrates the reliability of a fully automated artificial intelligence-driven solution capable of quick aortic segmentation and analysis of both diameter and volume for each segment.

Aerodynamic effects cause higher forest evapotranspiration and water yield reductions after wildfires in tall forests.

Wildfires are increasing in frequency, intensity, and extent globally due to climate change and they can alter forest composition, structure, and function. The destruction and subsequent regrowth of young vegetation can modify the ecosystem evapotranspiration and downstream water availability. However, the response of forest recovery on hydrology is not well known with even the sign of evapotranspiration and water yield changes following forest fires being uncertain across the globe. Here, we quantify the effects of forest regrowth after catastrophic wildfires on evapotranspiration and runoff in the world's tallest angiosperm forest (Eucalyptus regnans) in Australia. We combine eddy covariance measurements including pre- and post-fire periods, mechanistic ecohydrological modeling and then extend the analysis spatially to multiple fires in eucalypt-dominated forests in south-eastern Australia by utilizing remote sensing. We find a fast recovery of evapotranspiration which reaches and exceeds pre-fire values within 2 years after the bushfire, a result confirmed by eddy covariance data, remote sensing, and modeling. Such a fast evapotranspiration recovery is likely generalizable to tall eucalypt forests in south-eastern Australia as shown by remote sensing. Once climate variability is discounted, ecohydrological modeling shows evapotranspiration rates from the recovering forest which reach peak values of +20% evapotranspiration 3 years post-fire. As a result, modeled runoff decreases substantially. Contrary to previous research, we find that the increase in modeled evapotranspiration is largely caused by the aerodynamic effects of a much shorter forest height leading to higher surface temperature, higher humidity gradients and therefore increased transpiration. However, increases in evapotranspiration as well as decreases in runoff caused by the young forest are constrained by energy and water limitations. Our result of an increase in evapotranspiration due to aerodynamic warming in a shorter forest after wildfires could occur in many parts of the world experiencing forest disturbances.