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Methods and apparatus for rapid, culture free pathogen detection. The methods utilize optical spectroscopy techniques to identify and/or characterize pathogens in a sample via the detection of unique properties and/or analytes that are specific to particular pathogens.
Flavin-based electron bifurcation (FBEB) is an enzymatic mechanism that generates extremely high-energy electrons to drive unfavorable chemical reactions. It is utilized by the NADHdependent ferredoxin:NADP + -oxidoreductase (Nfn) enzyme in hyperthermophile Pyrococcus furiosus to bifurcate electrons from NADPH into the coupled low-potential (endergonic) and highpotential (exergonic) pathways. This process enables P. furiosus to live in harsh and uninviting environments. Despite its biological importance, the mechanisms used by Nfn to facilitate exceptional directional control over short-lived, high-energy electrons and to prevent undesired transfer, particularly along the low-potential pathway, are still not well understood. To elucidate how the protein environment contributes to electronic control in the lowpotential pathway, new techniques must be utilized to probe these unstable intermediates. In this study, we have adapted lowtemperature photoexcitation combined with electron paramagnetic resonance (EPR) to accumulate the short-lived intermediate and place it in the context of the other cofactors involved in the low-potential pathway of Nfn. We observed coincident growth of both the radical intermediate and its nearby [4Fe-4S] cluster over 4.5 h of illumination with NADPH at cryogenic temperatures. The photogenerated paramagnetic species were stable in LN 2 storage indefinitely and recombined when warmed to higher temperatures. The results provide insights into the electron transfer steps and cofactor interactions along the low potential pathway, facilitating a more robust mechanistic understanding of the high-energy events of electron bifurcation. Furthermore, through comparison of cryogenic and room temperature experiments, a potential gating step involving the movement of key residues important for the reversibility of electron flow along this pathway is suggested.
Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. Here, this Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.
Enveloped viruses, including human immunodeficiency virus (HIV) and SARS-CoV-2, target cells through membrane fusion process. The detailed understanding of the process is sought after for vaccine development but remains elusive due to current technique limitations for direct three-dimensional (3D) imaging of an individual virus during its viral entry. Recently, we developed a simple specimen preparation method for real-time imaging of metal dynamic liquid-vaper interface at nanometer resolution by transmission electron microscopy (TEM). Here, we extended this method to study biology sample through snapshot 3D structure of a single HIV (pseudo-typed with the envelope glycoprotein of vesicular stomatitis virus, VSV-G) at its intermediate stage of viral entry to HeLa cells in a liquid-phase environment. By individual-particle electron tomography (IPET), we found the viral surface release excess lipids with unbound viral spike proteins forming ~50-nm nanoparticles instead of merging cell membrane. Moreover, the spherical-shape shell formed by matrix proteins underneath the viral envelope does not disassemble into a cone shape right after fusion. Further, the snapshot 3D imaging of a single virus provides us a direct structure-based understanding of the viral entry mechanism, which can be used to examine other viruses to support the development of vaccines combatting the current ongoing pandemic.
Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 20 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.
Spatial and temporal heterogeneity of nutrient exchange within the rhizosphere is a topic of increasing interest, although challenging to study due to limits in existing analytical capabilities. Here, we developed and demonstrated a new approach applying laser ablation sample introduction to capillary absorption spectroscopy (LA-CAS) to characterize carbon isotopic distribution within plant tissues, rhizosphere, and soil. We exposed switchgrass plants to 13 CO 2 to allow tracing of 13 C-labelled photosynthates within plant biomass and into the associated soil. The LA-CAS methods we describe leverage continuous measurements of a sample stream derived from laser ablation line scans (10-25 µm in width) over a sample surface which enables the user to produce an isotope map with a) higher data density or b) over larger spatial areas versus previously existing techniques. This versatility of LA-CAS is assessed through testing of a range of laser parameters (spot size, scan rates) on various materials (soil, plant biomass/tissues, and rhizosphere). Here, we demonstrate the ability of LA-CAS to provide near instantaneous $δ$ 13 C measurements over isotopically distinct surfaces to enable high spatially resolved mapping of 13 C-labelled material within the rhizosphere. Applying LA-CAS analysis to plant biomass, we observed higher $δ$ 13 C values concentrated within phloem structures, consistent with localized photosynthate transport. When mapping across the rhizosphere, 13 C-enriched soil was typically present within 5-10µm of root boundaries with a steep spatial increase in $δ$ 13 C when the scan approached the middle of the root. As with all LA approaches, care is required to ensure accurate results as phenomena linked to ablation, combustion, and isotopic measurement can impart artifacts if not carefully controlled. Still, taken as a whole, our demonstrations highlight the increased sample throughput, improved data density, and enhanced $δ$ 13 C capability of LA-CAS versus other LA techniques and emphasize the role this method can play in future plant and rhizosphere related studies.
Protein–ligand interactions are crucial for a wide range of physiological processes. Many cellular functions result in these non-covalent “bonds” being mechanically strained, and this can be integral to proper cellular function. Broadly, two classes of force dependence have been observed—slip bonds, where the unbinding rate increases, and catch bonds, where the unbinding rate decreases. Despite much theoretical work, we cannot predict for which protein–ligand pairs, pulling coordinates, and forces a particular rate dependence will appear. Here, we assess the ability of MD simulations combined with enhanced sampling techniques to probe the force dependence of unbinding rates. We show that the infrequent metadynamics technique correctly produces both catch and slip bonding kinetics for model potentials. We then apply it to the well-studied case of a buckyball in a hydrophobic cavity, which appears to exhibit an ideal slip bond. Finally, we compute the force-dependent unbinding rate of biotin–streptavidin. Here, the complex nature of the unbinding process causes the infrequent metadynamics method to begin to break down due to the presence of unbinding intermediates, despite the use of a previously optimized sampling coordinate. Allowing for this limitation, a combination of kinetic and free energy computations predicts an overall slip bond for larger forces consistent with prior experimental results although there are substantial deviations at small forces that require further investigation. Furthermore, this work demonstrates the promise of predicting force-dependent unbinding rates using enhanced sampling MD techniques while also revealing the methodological barriers that must be overcome to tackle more complex targets in the future.
Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump–probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ( 14 CO 2 ) molecules in room-temperature CO 2 samples, with 1.4 parts-per-quadrillion (ppq, 10 − 15 ) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with 14 C/C up to ∼ 1.5 × natural abundance ( ∼ 1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO 2 isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for 14 CO 2 benefits a wide range of scientific fields that utilize 14 C for dating and isotope tracing, most notably atmospheric 14 CO 2 monitoring to track CO 2 emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.
Terrestrial nanoplastics (NPs) pose a serious threat to agricultural food production systems due to the potential harm of soil-born micro- and macroorganisms that promote soil fertility and ability of NPs to adsorb onto and penetrate into vegetables and other crops. Very little is known about the dispersion, fate and transport of NPs in soils. This is because of the challenges of analyzing terrestrial NPs by conventional microscopic techniques due to the low concentrations of NPs and absence of optical transparency in these systems. Herein, we investigate the potential utility of small-angle neutron scattering (SANS) and Ultra SANS (USANS) to probe the agglomeration behavior of NPs prepared from polybutyrate adipate terephthalate, a prominent biodegradable plastic used in agricultural mulching, in the presence of vermiculite, an artificial soil. SANS with the contrast matching technique was used to study the aggregation of NPs co-dispersed with vermiculite in aqueous media. We determined the contrast match point for vermiculite was 66% D 2 O / 33% H 2 O. At this condition, the signal for vermiculite was ~50–100%-fold lower that obtained using neat H 2 O or D 2 O as solvent. According to SANS and USANS, smaller-sized NPs (50 nm) remained dispersed in water and did not undergo size reduction or self-agglomeration, nor formed agglomerates with vermiculite. Larger-sized NPs (300–1000 nm) formed self-agglomerates and agglomerates with vermiculite, demonstrating their significant adhesion with soil. However, employment of convective transport (simulated by ex situ stirring of the slurries prior to SANS and USANS analyses) reduced the self-agglomeration, demonstrating weak NP-NP interactions. Convective transport also led to size reduction of the larger-sized NPs. Therefore, this study demonstrates the potential utility of SANS and USANS with contrast matching technique for investigating behavior of terrestrial NPs in complex soil systems.
Coupled ionic and electronic transport underpins processes as diverse as electrochemical energy conversion, biological signaling, and soft adaptive electronics. Yet, how chemical environments such as pH modulate this coupling at the molecular scale remains poorly understood. Here, we show that the protonation state of carboxylated polythiophenes provides precise chemical control over ion dynamics, doping efficiency, solvent uptake, and mechanical response. Using a suite of multimodal operando techniques, supported by simulations, we reveal that pH dictates the balance of cation/anion uptake during electrochemical doping. Mapping across pH uncovers a quasi-nonswelling regime (≈pH 3–3.5) where charge compensation proceeds with minimal volumetric change yet pronounced stiffening. These findings establish molecular acidity as a general strategy to program ionic preference and mechanical stability, offering design principles for pH-responsive mixed conductors and soft electronic materials that couple ionic, electronic, and mechanical functionality.
The continuing increase in the brilliance of synchrotron radiation beamlines allows for many new and exciting experiments that were impossible before the present generation of synchrotron radiation sources came on line. However, the exposure to such intense beams also tests the limits of what samples can endure. Whilst the effects of radiation induced damage in a static experiment often can easily be recognized by changes in the diffraction or spectroscopy curves, the influence of radiation on chemical or physical processes, where one expects curves to change, is less often recognized and can be misinterpreted as a 'real' result instead of as a 'radiation influenced result'. This is especially a concern in time-resolved materials science experiments using techniques as powder diffraction, small angle scattering and x-ray absorption spectroscopy. In this work, the effects of radiation (5–50 keV) on some time-resolved processes in different types of materials and in different physical states are discussed. We show that such effects are not limited to soft matter and biology but rather can be found across the whole spectrum of materials research, over a large range of radiation doses and is not limited to very high brilliance beamlines.
Natural transformation and homologous recombination in a soil bacterium, Acinetobacter baylyi ADP1, occur with exceptionally high efficiency. These genetic features can be harnessed to address a wide variety of fundamental and applied scientific topics. Recent advances in synthetic biology and laboratory evolution have led to renewed appreciation for the use of A. baylyi as a model organism. To complement several review articles that highlight new tool sets, this chapter focuses on simple protocols and examples of transformation assays that facilitate genetic analysis and engineering. Whole genome sequencing often reveals extensive genetic variation among closely related isolates that can confound the association of genotypic and phenotypic changes. In A. baylyi, such associations can be deciphered in unique ways by directly transforming cells with linear DNA fragments. The resulting allelic replacement, which occurs at high frequency, rapidly generates desired mutants via targeted chromosomal editing. Diverse screening and selection methods can be used to test hypotheses and streamline experimental strategies to reveal the significance of specific DNA sequences. Moreover, large procedural variations are well tolerated, and techniques can be readily adapted for new purposes. Furthermore, one goal of highlighting natural transformation methodology in A. baylyi is to expand the community of researchers using this versatile bacterial host.
Agriculture is under pressure to provide food for a growing population and the feedstock required to drive the bioeconomy. Methods to breed and genetically modify plants are inadequate to keep pace. When engineering crops, traits are painstakingly introduced into plants one-at-a-time, combine unpredictably, and are continuously expressed. Synthetic biology is changing these paradigms with new genome construction tools, computer aided design (CAD), and artificial intelligence (AI). “Smart plants” contain circuits that respond to environmental change, alter morphology, or respond to threats. Further, the plant and associated microbes (fungi, bacteria, archaea) are now being viewed by genetic engineers as a holistic system. Historically, plant health has been enhanced by many natural and laboratory-evolved soil microbes marketed to enhance growth, provide nutrients, or confer pest/stress resistance. Synthetic biology has expanded the number of species that can be engineered, increased the complexity of engineered functions, controlled environmental release, and assembled stable consortia. New CAD tools will manage genetic engineering projects spanning multiple plant genomes (nucleus, chloroplast, mitochondrion) and the thousands of genomes of associated bacteria/fungi. Here, this review covers advanced genetic engineering techniques to drive the next agricultural revolution, as well as push plant engineering into new realms for manufacturing, infrastructure, sensing, and remediation.
Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions but present conceptual and computational challenges to both theory and simulations. We review recent developments in an approach addressing these challenges–local molecular field (LMF) theory. LMF theory exploits an exact and physically suggestive separation of intermolecular Coulomb interactions into strong short-range and uniformly slowly varying long-range components. This allows us to accurately determine the averaged effects of the long-range components on the short-range structure using effective single particle fields and analytical corrections, greatly reducing the need for complex lattice summation techniques used in most standard approaches. Furthermore, the simplest use of these ideas in aqueous solutions leads to the short solvent (SS) model, where both solvent–solvent and solute–solvent Coulomb interactions have only short-range components. Here we use the SS model to give a simple description of pairing of nucleobases and biologically relevant ions in water.
Thiol-norbornene photoclick hydrogels are highly efficient in tissue engineering applications due to their fast gelation, cytocompatibility, and tunability. In this work, we utilized the advantageous features of polyethylene glycol (PEG)-thiol-ene resins to enable fabrication of complex and heterogeneous tissue scaffolds using 3D bioprinting and in-air drop encapsulation techniques. We demonstrated that photoclickable PEG-thiol-ene resins could be tuned by varying the ratio of PEG-dithiol to PEG norbornene to generate a wide range of mechanical stiffness (0.5–12 kPa) and swelling ratios. Importantly, all formulations maintained a constant, rapid gelation time (<0.5 s). We used this resin in biological projection microstereolithography (BioPµSL) to print complex structures with geometric fidelity and demonstrated biocompatibility by printing cell-laden microgrids. Moreover, the rapid gelling kinetics of this resin permitted high-throughput fabrication of tunable, cell-laden microgels in air using a biological in-air drop encapsulation apparatus (BioIDEA). We demonstrated that these microgels could support cell viability and be assembled into a gradient structure. This PEG-thiol-ene resin, along with BioPµSL and BioIDEA technology, will allow rapid fabrication of complex and heterogeneous tissues that mimic native tissues with cellular and mechanical gradients. The engineered tissue scaffolds with a controlled microscale porosity could be utilized in applications including gradient tissue engineering, biosensing, and in vitro tissue models.
Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.
While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. Here we report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of “unknown unknown” trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.
Interfacial water exhibits rich and complex behaviour, playing an important part in chemistry, biology, geology and engineering. However, there is still much debate on the fundamental properties of water at hydrophobic interfaces, such as orientational ordering, the concentration of hydronium and hydroxide, improper hydrogen bonds and the presence of large electric fields. This controversy arises from the challenges in measuring interfacial systems, even with the most advanced experimental techniques and theoretical approaches available. Here we report on an in-solution, interface-selective Raman spectroscopy method using multivariate curve resolution to probe hexadecane-in-water emulsions, aided by a monomer-field theoretical model for Raman spectroscopy. Our results indicate that oil-water emulsion interfaces can exhibit reduced tetrahedral order and weaker hydrogen bonding, along with a substantial population of free hydroxyl groups that experience about 95 cm -1 redshift in their stretching mode compared with planar oil-water interfaces. Given the known electrostatic zeta potential characteristic of oil droplets, we propose the existence of a strong electric field (about 50-90 MV cm -1 ) emanating from the oil phase. This field is inferred indirectly but supported by control experiments and theoretical estimates. These observations are either absent or opposite in the molecular hydrophobic interface formed by small solutes or at planar oil-water interfaces. Instead, water structural disorder and enhanced electric fields emerge as unique features of the mesoscale interface in oil-water emulsions, potentially contributing to the accelerated chemical reactivity observed at hydrophobic-water interfaces.