Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile.

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Enhancing Composite Toughness Through Hierarchical Interphase Formation.

High strength and ductility are highly desired in fiber-reinforced composites, yet achieving both simultaneously remains elusive. A hierarchical architecture is developed utilizing high aspect ratio chemically transformable thermoplastic nanofibers that form covalent bonding with the matrix to toughen the fiber-matrix interphase. The nanoscale fibers are electrospun on the micrometer-scale reinforcing carbon fiber, creating a physically intertwined, randomly oriented scaffold. Unlike conventional covalent bonding of matrix molecules with reinforcing fibers, here, the nanofiber scaffold is utilized - interacting non-covalently with core fiber but bridging covalently with polymer matrix - to create a high volume fraction of immobilized matrix or interphase around core reinforcing elements. This mechanism enables efficient fiber-matrix stress transfer and enhances composite toughness. Molecular dynamics simulation reveals enhancement of the fiber-matrix adhesion facilitated by nanofiber-aided hierarchical bonding with the matrix. The elastic modulus contours of interphase regions obtained from atomic force microscopy clearly indicate the formation of stiffer interphase. These nanoengineered composites exhibit a ≈60% and ≈100% improved in-plane shear strength and toughness, respectively. This approach opens a new avenue for manufacturing toughened high-performance composites.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

A modular metagenomics analysis system for integrated multi-step data exploration.

Motivation: Computational analysis of large-scale metagenomics sequencing datasets has proved to be both incredibly valuable for extracting isolate-level taxonomic and functional insights from complex microbial communities. However, thanks to an ever-expanding ecosystem of metagenomics-specific algorithms and file formats, designing studies, implementing seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. Furthermore, there is little inter-communication between output data of different analytic purposes, such as short-read classification and metagenome assembled genomes (MAG) reconstruction. One-click pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues. Results: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations, The module system is a bioinformatics-optimzied scaffold designed to be rapidly iterated upon by the research community at large. Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP.

Reactivity of the Polyamide Membrane Monomer with Free Chlorine: Role of Bromide.

Aromatic polyamide-based membranes are widely used for reverse osmosis (RO) and nanofiltration (NF) treatment but degrade when exposed to free chlorine (HOCl/OCl-). The reaction mechanisms with free chlorine were previously explored, but less is known about the role of bromide (Br-) in these processes. Br- may impact these reactions by reacting with HOCl to form HOBr, which then triggers other brominating agents (Br2O, Br2, BrOCl, and BrCl) to form. This study examined the reactivities of these brominating agents with a polyamide monomer model compound, benzanilide (BA), and a modified version of it, N-CH3-BA. The results indicated that all these brominating agents only attacked the aromatic ring adjacent to the amide N, rather than the amide N, different from the previously examined chlorinating agents (HOCl, OCl-, and Cl2) that attacked both sites. Orton rearrangement was not observed. Species-specific rate constants (ki, M-1 s-1) between BA and HOBr, Br2O, Br2, BrOCl, and BrCl were determined to be (5.3 ± 1.2) × 10-2, (1.2 ± 0.4) × 101, (3.7 ± 0.2) × 102, (2.2 ± 0.6) × 104, and (6.6 ± 0.9) × 104 M-1 s-1, respectively, such that kBrCl > kBrOCl > kBr2 > kBr2O > kHOBr. N-CH3-BA exhibited lower reactivity than BA. Model predictions of BA loss during chlorination with varied Br- and/or Cl- concentrations were established. These findings will ultimately enable membrane degradation and performance loss following chlorination in mixed halide solutions to be better predicted during pilot- and full-scale NF and RO treatment.

Historically unprecedented Northern Gulf of Mexico hurricane activity from 650 to 1250 CE.

Hurricane Michael (2018) was the first Category 5 storm on record to make landfall on the Florida panhandle since at least 1851 CE (Common Era), and it resulted in the loss of 59 lives and $25 billion in damages across the southeastern U.S. This event placed a spotlight on recent intense (exceeding Category 4 or 5 on the Saffir-Simpson Hurricane Wind Scale) hurricane landfalls, prompting questions about the natural range in variability of hurricane activity that the instrumental record is too short to address. Of particular interest is determining whether the frequency of recent intense hurricane landfalls in the northern Gulf of Mexico (GOM) is within or outside the natural range of intense hurricane activity prior to 1851 CE. In this study, we identify intense hurricane landfalls in northwest Florida during the past 2000 years based on coarse anomaly event detection from two coastal lacustrine sediment archives. We identified a historically unprecedented period of heightened storm activity common to four Florida panhandle localities from 650 to 1250 CE and a shift to a relatively quiescent storm climate in the GOM spanning the past six centuries. Our study provides long-term context for events like Hurricane Michael and suggests that the observational period 1851 CE to present may underrepresent the natural range in landfalling hurricane activity.

Reactivity of the Polyamide Membrane Monomer with Free Chlorine: Reaction Kinetics, Mechanisms, and the Role of Chloride.

Aromatic polyamide thin-film composite membranes are widely used in reverse osmosis (RO) and nanofiltration (NF) due to their high water permeability and selectivity. However, these membranes undergo biofouling and can degrade and eventually fail during free chlorine exposure. To better understand this effect, the reactivity of the polyamide monomer (benzanilide (BA)) with free chlorine was tested under varying pH and chloride (Cl-) conditions. The kinetic results indicated that the current existing mechanisms, especially the Orton rearrangement, were invalid. Revised reaction pathways were proposed where BA chlorination was driven by two independent pathways involving the anilide ring and amide nitrogen moieties. The ability for one moiety to be chosen over the other was highly dependent on the pH, Cl- concentration, and the resulting chlorinating agents (e.g., Cl2, HOCl, OCl-, and Cl2O) generated. Species-specific rate constants for BA with Cl2, OCl-, and HOCl equaled (7.6 ± 0.19) × 101, (1.7 ± 1.5) × 101, (2.1 ± 0.71) × 10-2 M-1 s-1, respectively. A similar value for Cl2O could not be accurately estimated under the tested conditions. The behavior of these chlorinating agents differed for each reactive site such that OCl- > HOCl for N-chlorination and Cl2 > HOCl > OCl- for anilide ring chlorination. Experiments with modified monomers indicated that substituent placement largely affected which reactive site was kinetically favorable. Overall, such findings provide a predictive model of how the polyamide monomer degrades during chlorine exposure and guidance on how chlorine-resistant polyamide membranes should be designed.

The intertropical convergence zone modulates intense hurricane strikes on the western North Atlantic margin.

Most Atlantic hurricanes form in the Main Development Region between 9°N to 20°N along the northern edge of the Intertropical Convergence Zone (ITCZ). Previous research has suggested that meridional shifts in the ITCZ position on geologic timescales can modulate hurricane activity, but continuous and long-term storm records are needed from multiple sites to assess this hypothesis. Here we present a 3000 year record of intense hurricane strikes in the northern Bahamas (Abaco Island) based on overwash deposits in a coastal sinkhole, which indicates that the ITCZ has likely helped modulate intense hurricane strikes on the western North Atlantic margin on millennial to centennial-scales. The new reconstruction closely matches a previous reconstruction from Puerto Rico, and documents a period of elevated intense hurricane activity on the western North Atlantic margin from 2500 to 1000 years ago when paleo precipitation proxies suggest that the ITCZ occupied a more northern position. Considering that anthropogenic warming is predicted to be focused in the northern hemisphere in the coming century, these results provide a prehistoric analog that an attendant northern ITCZ shift in the future may again return the western North Atlantic margin to an active hurricane interval.

Effects of a catheter-associated urinary tract infection prevention campaign on infection rate, catheter utilization, and health care workers' perspective at a community safety net hospital.

Preventing catheter-associated urinary tract infections is in the forefront of health care quality. However, nurse and physician engagement is a common barrier in infection prevention efforts. After implementation of a multidisciplinary catheter-associated urinary tract infection (CAUTI) prevention campaign, we studied the impact of our campaign and showed its association with reducing the CAUTI rate and catheter utilization and the positive effect on health care workers' engagement and perspectives. CAUTI prevention campaigns can lead to lower infection rates and change health care workers' perspective.

Hydrothermal Synthesis and Processing of Barium Titanate Nanoparticles Embedded in Polymer Films.

Barium titanate nanoparticles embedded in flexible polymer films were synthesized using hydrothermal processing methods. The resulting films were characterized with respect to material composition, size distribution of nanoparticles, and spatial location of particles within the polymer film. Synthesis conditions were varied based on the mechanical properties of the polymer films, ratio of polymer to barium titanate precursors, and length of aging time between initial formulations of the solution to final processing of nanoparticles. Block copolymers of poly(styrene-co-maleic anhydride) (SMAh) were used to spatially separate titanium precursors based on specific chemical interactions with the maleic anhydride moiety. However, the glassy nature of this copolymer restricted mobility of the titanium precursors during hydrothermal processing. The addition of rubbery butadiene moieties, through mixing of the SMAh with poly(styrene-butadiene-styrene) (SBS) copolymer, increased the nanoparticle dispersion as a result of greater diffusivity of the titanium precursor via higher mobility of the polymer matrix. Additionally, an aminosilane was used as a means to retard cross-linking in polymer-metalorganic solutions, as the titanium precursor molecules were shown to react and form networks prior to hydrothermal processing. By adding small amounts of competing aminosilane, excessive cross-linking was prevented without significantly impacting the quality and composition of the final barium titanate nanoparticles. X-ray diffraction and X-ray photoelectron spectroscopy were used to verify nanoparticle compositions. Particle sizes within the polymer films were measured to be 108 ± 5 nm, 100 ± 6 nm, and 60 ± 5 nm under different synthetic conditions using electron microscopy. Flexibility of the films was assessed through measurement of the glass transition temperature using dynamic mechanical analysis. Dielectric permittivity was measured using an impedance analyzer.

Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis.

The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.