Fe-Fe Double-Atom Catalysts for Murine Coronavirus Disinfection: Nonradical Activation of Peroxides and Mechanisms of Virus Inactivation.

Peroxides find broad applications for disinfecting environmental pathogens particularly in the COVID-19 pandemic; however, the extensive use of chemical disinfectants can threaten human health and ecosystems. To achieve robust and sustainable disinfection with minimal adverse impacts, we developed Fe single-atom and Fe-Fe double-atom catalysts for activating peroxymonosulfate (PMS). The Fe-Fe double-atom catalyst supported on sulfur-doped graphitic carbon nitride outperformed other catalysts for oxidation, and it activated PMS likely through a nonradical route of catalyst-mediated electron transfer. This Fe-Fe double-atom catalyst enhanced PMS disinfection kinetics for inactivating murine coronaviruses (i.e., murine hepatitis virus strain A59 (MHV-A59)) by 2.17-4.60 times when compared to PMS treatment alone in diverse environmental media including simulated saliva and freshwater. The molecular-level mechanism of MHV-A59 inactivation was also elucidated. Fe-Fe double-atom catalysis promoted the damage of not only viral proteins and genomes but also internalization, a key step of virus lifecycle in host cells, for enhancing the potency of PMS disinfection. For the first time, our study advances double-atom catalysis for environmental pathogen control and provides fundamental insights of murine coronavirus disinfection. Our work paves a new avenue of leveraging advanced materials for improving disinfection, sanitation, and hygiene practices and protecting public health.

Variability of urban drainage area delineation and runoff calculation with topographic resolution and rainfall volume.

Designing green stormwater infrastructure (GSI) requires an accurate estimate of the contributing drainage area and a model for runoff generation. We examined some factors that add to the uncertainty associated with these two design steps in the urban environment. Delineated drainage areas at five GSI sites in southeastern Pennsylvania (PA) were compared for digital elevation model (DEM) resolutions (grid cell sizes) ranging from 8 to 300 cm. The findings point to an optimal DEM resolution range of 30-60 cm, with up to 100 cm resolution providing acceptable results for some sites. The delineated areas were validated with the observed flow and rainfall records at three sites by examining curve number (CN) values calculated for individual storms. The calculated CNs decreased with increasing rainfall volume, which supports a recommendation to consider a range of CNs in the GSI design process. The variation in calculated CNs was higher for the overestimated drainage areas derived from coarser DEM resolutions. We hypothesize that the observed continued decrease of CNs at high rainfall is the result of inlet bypass, a potentially significant factor in urban hydrology. The findings from this study provide insight into the variability in expected delineated drainage areas using standard methods in GSI design.

Unraveling the complexities of urban fluvial flood hydraulics through AI.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.

A Functional Human-on-a-Chip Autoimmune Disease Model of Myasthenia Gravis for Development of Therapeutics.

Myasthenia gravis (MG) is a chronic and progressive neuromuscular disease where autoantibodies target essential proteins such as the nicotinic acetylcholine receptor (nAChR) at the neuromuscular junction (NMJ) causing muscle fatigue and weakness. Autoantibodies directed against nAChRs are proposed to work by three main pathological mechanisms of receptor disruption: blocking, receptor internalization, and downregulation. Current in vivo models using experimental autoimmune animal models fail to recapitulate the disease pathology and are limited in clinical translatability due to disproportionate disease severity and high animal death rates. The development of a highly sensitive antibody assay that mimics human disease pathology is desirable for clinical advancement and therapeutic development. To address this lack of relevant models, an NMJ platform derived from human iPSC differentiated motoneurons and primary skeletal muscle was used to investigate the ability of an anti-nAChR antibody to induce clinically relevant MG pathology in the serum-free, spatially organized, functionally mature NMJ platform. Treatment of the NMJ model with the anti-nAChR antibody revealed decreasing NMJ stability as measured by the number of NMJs before and after the synchrony stimulation protocol. This decrease in NMJ stability was dose-dependent over a concentration range of 0.01-20 µg/mL. Immunocytochemical (ICC) analysis was used to distinguish between pathological mechanisms of antibody-mediated receptor disruption including blocking, receptor internalization and downregulation. Antibody treatment also activated the complement cascade as indicated by complement protein 3 deposition near the nAChRs. Additionally, complement cascade activation significantly altered other readouts of NMJ function including the NMJ fidelity parameter as measured by the number of muscle contractions missed in response to increasing motoneuron stimulation frequencies. This synchrony readout mimics the clinical phenotype of neurological blocking that results in failure of muscle contractions despite motoneuron stimulations. Taken together, these data indicate the establishment of a relevant disease model of MG that mimics reduction of functional nAChRs at the NMJ, decreased NMJ stability, complement activation and blocking of neuromuscular transmission. This system is the first functional human in vitro model of MG to be used to simulate three potential disease mechanisms as well as to establish a preclinical platform for evaluation of disease modifying treatments (etiology).

Fe-based single-atom catalysis for oxidizing contaminants of emerging concern by activating peroxides.

We prepared a single-atom Fe catalyst supported on an oxygen-doped, nitrogen-rich carbon support (SAFe-OCN) for degrading a broad spectrum of contaminants of emerging concern (CECs) by activating peroxides such as peroxymonosulfate (PMS). In the SAFe-OCN/PMS system, most selected CECs were amenable to degradation and high-valent Fe species were present for oxidation. Moreover, SAFe-OCN showed excellent performance for contaminant degradation in complex water matrices and high stability in oxidation. Specifically, SAFe-OCN, with a catalytic center of Fe coordinated with both nitrogen and oxygen (FeNxO4-x), showed 5.13-times increased phenol degradation kinetics upon activating PMS compared to the catalyst where Fe was only coordinated with nitrogen (FeN4). Molecular simulations suggested that FeNxO4-x, compared to FeN4, was an excellent multiple-electron donor and it could potential-readily form high-valent Fe species upon oxidation. In summary, the single-atom Fe catalyst enables efficient, robust, and sustainable water and wastewater treatment, and molecular simulations highlight that the electronic nature of Fe could play a key role in determining the activity of the single-atom catalyst.

A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning.

Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

Characterizing the Locus of a Peripheral Membrane Protein-Lipid Bilayer Interaction Underlying Protein Export Activity in E. coli.

Quantitative characterization of the strength of peripheral membrane protein-lipid bilayer interactions is fundamental in the understanding of many protein targeting pathways. SecA is a peripheral membrane protein that plays a central role in translocating precursor proteins across the inner membrane of E. coli. The membrane binding activity of the extreme N-terminus of SecA is critical for translocase function. Yet, the mechanical strength of the interaction and the kinetic pathways that this segment of SecA experiences when in proximity of an E. coli polar lipid bilayer has not been characterized. We directly measured the N-terminal SecA-lipid bilayer interaction using precision single molecule atomic force microscope (AFM)-based dynamic force spectroscopy. To provide conformational data inaccessible to AFM, we also performed all-atom molecular dynamics simulations and circular dichroism measurements. The N-terminal 10 amino acids of SecA have little secondary structure when bound to zwitterionic lipid head groups, but secondary structure, which rigidifies the lipid-bound protein segment, emerges when negatively charged lipids are present. Analysis of the single molecule protein-lipid dissociation data converged to a well-defined lipid-bound-state lifetime in the absence of force, τ0lipid = 0.9 s, which is well separated from and longer than the fundamental time scale of the secretion process, defined as the time required to translocate a single amino acid residue (∼50 ms). This value of τ0lipid is likely to represent a lower limit of the in vivo membrane-bound lifetime due to factors including the minimal system employed here.

A Human-Based Functional NMJ System for Personalized ALS Modeling and Drug Testing.

Loss of the neuromuscular junction (NMJ) is an early and critical hallmark in all forms of ALS. The study design was to develop a functional NMJ disease model by integrating motoneurons (MNs) differentiated from multiple ALS-patients' induced pluripotent stem cells (iPSCs) and primary human muscle into a chambered system. NMJ functionality was tested by recording myotube contractions while stimulating MNs by field electrodes and a set of clinically relevant parameters were defined to characterize the NMJ function. Three ALS lines were analyzed, 2 with SOD1 mutations and 1 with a FUS mutation. The ALS-MNs reproduced pathological phenotypes, including increased axonal varicosities, reduced axonal branching and elongation and increased excitability. These MNs formed functional NMJs with wild type muscle, but with significant deficits in NMJ quantity, fidelity and fatigue index. Furthermore, treatment with the Deana protocol was found to correct the NMJ deficits in all the ALS mutant lines tested. Quantitative analysis also revealed the variations inherent in each mutant lines. This functional NMJ system provides a platform for the study of both fALS and sALS and has the capability of being adapted into subtype-specific or patient-specific models for ALS etiological investigation and patient stratification for drug testing.

A Kernel Theory of Modern Data Augmentation.

Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.

Penetration into membrane of amino-terminal region of SecA when associated with SecYEG in active complexes.

The general secretory (Sec) system of Escherichia coli translocates both periplasmic and outer membrane proteins through the cytoplasmic membrane. The pathway through the membrane is provided by a highly conserved translocon, which in E. coli comprises two heterotrimeric integral membrane complexes, SecY, SecE, and SecG (SecYEG), and SecD, SecF, and YajC (SecDF/YajC). SecA is an associated ATPase that is essential to the function of the Sec system. SecA plays two roles, it targets precursors to the translocon with the help of SecB and it provides energy via hydrolysis of ATP. SecA exists both free in the cytoplasm and integrally membrane associated. Here we describe details of association of the amino-terminal region of SecA with membrane. We use site-directed spin labelling and electron paramagnetic resonance spectroscopy to show that when SecA is co-assembled into lipids with SecYEG to yield highly active translocons, the N-terminal region of SecA penetrates the membrane and lies at the interface between the polar and the hydrophobic regions, parallel to the plane of the membrane at a depth of approximately 5 Å. When SecA is bound to SecYEG, preassembled into proteoliposomes, or nonspecifically bound to lipids in the absence of SecYEG, the N-terminal region penetrates more deeply (8 Å). Implications of partitioning of the SecA N-terminal region into lipids on the complex between SecB carrying a precursor and SecA are discussed.

Single-Molecule Peptide-Lipid Affinity Assay Reveals Interplay between Solution Structure and Partitioning.

Interactions between short protein segments and phospholipid bilayers dictate fundamental aspects of cellular activity and have important applications in biotechnology. Yet, the lack of a suitable methodology for directly probing these interactions has hindered the mechanistic understanding. We developed a precision atomic force microscopy-based single-molecule force spectroscopy assay and probed partitioning into lipid bilayers by measuring the mechanical force experienced by a peptide. Protein segments were constructed from the peripheral membrane protein SecA, a key ATPase in bacterial secretion. We focused on the first 10 amino-terminal residues of SecA (SecA2-11) that are lipophilic. In addition to the core SecA2-11 sequence, constructs with nearly identical chemical composition but with differing geometry were used: two copies of SecA2-11 linked in series and two copies SecA2-11 linked in parallel. Lipid bilayer partitioning interactions of peptides with differing structures were distinguished. To model the energetic landscape, a theory of diffusive barrier crossing was extended to incorporate a superposition of potential barriers with variable weights. Analysis revealed two dissociation pathways for the core SecA2-11 sequence with well-separated intrinsic dissociation rates. Molecular dynamics simulations showed that the three peptides had significant conformational differences in solution that correlated well with the measured variations in the propensity to partition into the bilayer. The methodology is generalizable and can be applied to other peptide and lipid species.

Variable Mortality From the 1918-1919 Influenza Pandemic During Military Training.

During the 1918-1919 pandemic, influenza mortality widely varied across populations and locations. Records of U.S. military members in mobilization camps (n = 40), military academies, and officer training schools were examined to document differences in influenza experiences during the fall 1918. During the fall-winter 1918-1919, mortality percentages were higher among soldiers in U.S. Army mobilization camps (0.34-4.3%) than among officer trainees (0-1.0%). Susceptibility to infection and clinical expressions of 1918 pandemic influenza varied largely based on host epidemiological characteristics rather than the inherent virulence of the virus.

In singulo probing of viral RNA dynamics by multichromophore fluorescence dequenching.

Current understanding of virus life-cycle states and transitions between them is mainly built on knowledge of the protein shell structure encapsulating the genome. Little is known about the genome fate during viral transitions. Here, changes in the fluorescence rate from multilabeled transcript viral RNAs encapsulated in Brome mosaic virus capsids were examined as a function of the RNA state. A simple kinetic model relating chain compactness to single-molecule fluorescence emission suggests that in a dense multichromophore system the rate of energy transfer should scale with distance more gradually than the rate of the Förster energy transfer between two chromophores, which varies sharply as the reciprocal of distance to the sixth power. As a proof-of-principle experiment, we have compared predictions from a numerical model for confined diffusive motion with the fluorescence emission from virus-encapsulated and free single RNA molecules decorated with multiple cyanine dyes and encapsulated inside microscopic emulsion droplets. We found that the effective quantum yield per labeled particle depends on the expansion state, in agreement with theoretical predictions. Since fluorescence single particle tracking is now a well-established methodology for the study of virus life cycle, the findings reported here may pave the way toward reducing the existing gap between in vitro and cellular in singulo studies of the fates of viral RNA.

Extracytoplasmic stress responses induced by antimicrobial cationic polyethylenimines.

The ability of an antimicrobial, cationic polyethylenimine (PEI+) to induce the three known extracytoplasmic stress responses of Escherichia coli was quantified. Exposure of E. coli to PEI+ in solution revealed specific, concentration-dependent induction of the Cpx extracytoplasmic cellular stress response, ~2.0-2.5-fold at 320 µg/mL after 1.5 h without significant induction of the σ(E) or Bae stress responses. In comparison, exposure of E. coli to a non-antimicrobial polymer, poly(ethylene oxide) (PEO), resulted in no induction of the three stress responses. The antimicrobial small molecule vanillin, a known membrane pore-forming compound, was observed to cause specific, concentration-dependent induction of the σ(E) stress response, ~6-fold at 640 µg/mL after 1.5 h, without significant induction of the Cpx or Bae stress responses. The different stress response induction profiles of PEI+ and vanillin suggest that although both are antimicrobial compounds, they interact with the bacterial membrane and extracytoplasmic area by unique mechanisms. EPR studies of liposomes containing spin-labeled lipids exposed to PEI+, vanillin, and PEO reveal that PEI+ and PEO increased membrane stability, whereas vanillin was found to have no effect.

Frequency and management of breakthrough bleeding with continuous use of the transvaginal contraceptive ring: a randomized controlled trial.

OBJECTIVE: To assess bleeding patterns with continuous use of the transvaginal contraceptive ring. METHODS: We did a prospective analysis of daily menstrual flow during a 21/7 cycle followed by 6 months of continuous use and institution of a randomized protocol to manage breakthrough bleeding/spotting. Seventy-four women completed the baseline 21/7 phase and were randomized equally into two groups during the continuous phase. Group 1 was instructed to replace the ring monthly on the same calendar day with no ring-free days. Group 2 was instructed to use the same process, but if breakthrough bleeding/spotting occurred for 5 days or more, they were to remove the ring for 4 days, store it, and then reinsert that ring. RESULTS: Sixty-five women completed the continuous phase with reduced average flow scores in the continuous phase compared with the 21/7 phase (P<.02). Most patients had no to minimal bleeding during continuous use, with group 2 experiencing a statistically greater percentage of days without breakthrough bleeding or spotting (95%) compared with group 1 (89%) (P=.016). Instituting a 4-day hormone-free interval was more (P<.001) effective in resolving breakthrough bleeding/spotting than continuing ring use. CONCLUSION: A reduction in bleeding occurred during continuous use with replacement of the transvaginal ring compared with baseline 21/7 use. Continuous vaginal ring use resulted in an acceptable bleeding profile in most patients, reduction in flow, reduction in pelvic pain, and a high continuation rate.