Emerging contaminants: A One Health perspective.

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Silicone cryogel skeletons enhance the survival and mechanical integrity of hydrogel-encapsulated cell therapies.

The transplantation of engineered cells that secrete therapeutic proteins presents a promising method for addressing a range of chronic diseases. However, hydrogels used to encase and protect non-autologous cells from immune rejection often suffer from poor mechanical properties, insufficient oxygenation, and fibrotic encapsulation. Here, we introduce a composite encapsulation system comprising an oxygen-permeable silicone cryogel skeleton, a hydrogel matrix, and a fibrosis-resistant polymer coating. Cryogel skeletons enhance the fracture toughness of conventional alginate hydrogels by 23-fold and oxygen diffusion by 2.8-fold, effectively mitigating both implant fracture and hypoxia of encapsulated cells. Composite implants containing xenogeneic cells engineered to secrete erythropoietin significantly outperform unsupported alginate implants in therapeutic delivery over 8 weeks in immunocompetent mice. By improving mechanical resiliency and sustaining denser cell populations, silicone cryogel skeletons enable more durable and miniaturized therapeutic implants.

Harnessing a Dielectric/Plasma Photonic Crystal as an Optical Microwave Filter: Role of Defect Layers and External Magnetic Fields.

We investigate the transmittance spectrum of a multichannel filter composed of dielectric (A) and plasma (P) materials in the microwave region within the transfer matrix formalism. Two configurations of the proposed filter are studied under the influence of an applied magnetic field: (1) a periodic structure containing (A/P)N unit cells surrounded by air and (2) the introduction of a second dielectric material (D) acting as a defect layer to produce an (AP)N/2/D/(AP)N/2 structure. Our findings reveal that in the periodic case, the number of resonant states of the transmittance increases with number N; however, the observed blue and red shifts depend on the intensity and orientation of the applied magnetic field. We present contour plots of the transmission coefficients that show the effect of the incident angle on the shifts of the photonic band gaps. Furthermore, we find that the introduction of a defect layer generates additional resonant states and merges the central resonant peak into a miniband of resonances. Moreover, we show that the number of resonant peaks and their locations can be modulated by increasing the unit cell number, N, as well as increasing the width of the inserted defect layer. Our proposed structures enable the design of novel photonic filters using magnetized plasma materials operating in the microwave region.

Triboelectric Energy Harvesting from Highly Conjugated Fused Aromatic Ladder Structure Under Extreme Environmental Conditions.

Practical application of triboelectric nanogenerators (TENGs) has been challenging, particularly, under harsh environmental conditions. This work proposes a novel 3D-fused aromatic ladder (FAL) structure as a tribo-positive material for TENGs, to address these challenges. The 3D-FAL offers a unique materials engineering platform for tailored properties, such as high specific surface area and porosity, good thermal and mechanical stability, and tunable electronic properties. The fabricated 3D-FAL-based TENG reaches a maximum peak power density of 451.2 µW cm-2 at 5 Hz frequency. More importantly, the 3D-FAL-based TENG maintains stable output performance under harsh operating environments, over wide temperature (-45-100 °C) and humidity ranges (8.3-96.7% RH), representing the development of novel FAL for sustainable energy generation under challenging environmental conditions. Furthermore, the 3D-FAL-based TENG proves to be a promising device for a speed monitoring system engaging reconstruction in virtual reality in a snowy environment.

Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances.

Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs).

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems.

We present a new velocity-gauge real-time, time-dependent density functional tight-binding (VG-rtTDDFTB) implementation in the open-source DFTB+ software package (https://dftbplus.org) for probing electronic excitations in large, condensed matter systems. Our VG-rtTDDFTB approach enables real-time electron dynamics simulations of large, periodic, condensed matter systems containing thousands of atoms with a favorable computational scaling as a function of system size. We provide computational details and benchmark calculations to demonstrate its accuracy and computational parallelizability on a variety of large material systems. As a representative example, we calculate laser-induced electron dynamics in a 512-atom amorphous silicon supercell to highlight the large periodic systems that can be examined with our implementation. Taken together, our VG-rtTDDFTB approach enables new electron dynamics simulations of complex systems that require large periodic supercells, such as crystal defects, complex surfaces, nanowires, and amorphous materials.

FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a convolutional neural network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, subinlet speed, and subinlet pressure. Compared to the bidirectional long- and short-term memory (BiLSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in the mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.

Hexaazatriphenylene-Based Two-Dimensional Conductive Covalent Organic Framework with Anisotropic Charge Transfer.

The development of covalent organic frameworks (COFs) with efficient charge transport is of immense interest for applications in optoelectronic devices. To enhance COF charge transport properties, electroactive building blocks and dopants can be used to induce extended conduction channels. However, understanding their intricate interplay remains challenging. We designed and synthesized a tailor-made COF structure with electroactive hexaazatriphenylene (HAT) core units and planar dioxin (D) linkages, denoted as HD-COF. With the support of theoretical calculations, we found that the HAT units in the HD-COF induce strong, eclipsed π-π stacking. The unique stacking of HAT units and the weak in-plane conjugation of dioxin linkages leads to efficient anisotropic charge transport. We fabricated HD-COF films to minimize the grain boundary effect of bulk COFs, which resulted in enhanced conductivity. As a result, the HD-COF films showed an electrical conductivity as high as 1.25 S cm-1 after doping with tris(4-bromophenyl)ammoniumyl hexachloroantimonate.

Electron/Hole Mobilities of Periodic DNA and Nucleobase Structures from Large-Scale DFT Calculations.

Electron/hole transfer mechanisms in DNA and polynucleotide structures continue to garner considerable interest as emerging charge-transport systems and molecular electronics. To shed mechanistic insight into these electronic properties, we carried out large-scale density functional theory (DFT) calculations (up to 650 atoms) to systematically analyze the structural and electron/hole transport properties of fully periodic single- and double-stranded DNA. We examined the performance of various exchange-correlation functionals (LDA, BLYP, B3LYP, and B3LYP-D) and found that single-stranded thymine (T) and cytosine (C) are predominantly hole conductors, whereas single-stranded adenine (A) and guanine (G) are better electron conductors. For double-stranded DNA structures, the periodic A-T and G-C electronic band structures undergo a significant renormalization, which causes hole transport to only occur on the A and G nucleobases. Our calculations (1) provide new benchmarks for periodic nucleobase structures using dispersion-corrected hybrid functionals with large basis sets and (2) highlight the importance of dispersion effects for obtaining accurate geometries and electron/hole mobilities in these extended systems.

Degradation of Perfluorooctanoic Acid on Aluminum Oxide Surfaces: New Mechanisms from Ab Initio Molecular Dynamics Simulations.

Perfluorooctanoic acid (PFOA) is a part of a large group of anthropogenic, persistent, and bioaccumulative contaminants known as per- and polyfluoroalkyl substances (PFAS) that can be harmful to human health. In this work, we present the first ab initio molecular dynamics (AIMD) study of temperature-dependent degradation dynamics of PFOA on (100) and (110) surfaces of γ-Al2O3. Our results show that PFOA degradation does not occur on the pristine (100) surface, even when carried out at high temperatures. However, introducing an oxygen vacancy on the (100) surface facilitates an ultrafast (<100 fs) defluorination of C-F bonds in PFOA. We also examined degradation dynamics on the (110) surface and found that PFOA interacts strongly with Al(III) centers on the surface of γ-Al2O3, resulting in a stepwise breaking of C-F, C-C, and C-COO bonds. Most importantly, at the end of the degradation process, strong Al-F bonds are formed on the mineralized γ-Al2O3 surface, which prevents further dissociation of fluorine into the surrounding environment. Taken together, our AIMD simulations provide critical reaction mechanisms at a quantum level of detail and highlight the importance of temperature effects, defects, and surface facets for PFOA degradation on reactive surfaces, which have not been systematically explored or analyzed.

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems.

Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.

Implementation of real-time TDDFT for periodic systems in the open-source PySCF software package.

We present a new implementation of real-time time-dependent density functional theory (RT-TDDFT) for calculating excited-state dynamics of periodic systems in the open-source Python-based PySCF software package. Our implementation uses Gaussian basis functions in a velocity gauge formalism and can be applied to periodic surfaces, condensed-phase, and molecular systems. As representative benchmark applications, we present optical absorption calculations of various molecular and bulk systems and a real-time simulation of field-induced dynamics of a (ZnO)4 molecular cluster on a periodic graphene sheet. We present representative calculations on optical response of solids to infinitesimal external fields as well as real-time charge-transfer dynamics induced by strong pulsed laser fields. Due to the widespread use of the Python language, our RT-TDDFT implementation can be easily modified and provides a new capability in the PySCF code for real-time excited-state calculations of chemical and material systems.

Economic appraisal of prosthetic replacement of ocular surface ecosystem in Canada.

OBJECTIVE: To perform an economic appraisal of the Prosthetic Replacement of Ocular Surface Ecosystem (PROSE; BostonSight, Needham Heights, Mass.) lens in patients with a distorted corneal surface or ocular surface disease in Canada. DESIGN: Retrospective observational cohort study with cost, cost-utility, and benefit-cost analyses. PARTICIPANTS: Patients who received PROSE from the only PROSE clinic in Canada from 2018 to 2020. METHODS: Visual acuity (VA) outcomes of the participants were assessed. Benefits were defined as VA improvements that were converted into utilities and then quality-adjusted life years. Economic values were derived via government statements, clinic financial statements, and published literature. RESULTS: Average best-corrected VA (BCVA) improvement was -0.42 ± 0.41 logMAR (p = 2.68 × 10-13) or Snellen 20/53 for the overall cohort, -0.51 ± 0.48 (p = 5.42 × 10-8) or Snellen 20/65 for distorted corneal surface patients, and -0.31 ± 0.30 (p = 1.30 × 10-7) or Snellen 20/41 for ocular surface disease patients. This corresponded to discounted quality-adjusted life year gains of 0.51, 0.65, and 0.42, respectively, over an estimated 5-year PROSE device lifespan. Average cost to fit a patient with PROSE was USD$5 469.85 (CAD$7 087.28), of which USD$4 971.38 (CAD$6 441.42) was clinic cost and USD$498.47 (CAD$645.87) was patient cost. Cost-utility was USD$10 256.47 (CAD$13 289.31) for the overall cohort, USD$8 439.79 (CAD$10 935.44) for distorted corneal surface patients, and US$13 069.90 (CAD$16 934.67) for ocular surface disease patients. The benefit-cost ratio was 34.4 for all, 43.8 for distorted corneal surface patients, and 28.3 for ocular surface disease patients. CONCLUSIONS: Our economic appraisal demonstrated that PROSE treatment provides a significant, cost-effective benefit to Canadian patients with distorted corneal surfaces and ocular surface diseases. This indicates that PROSE clinics are an efficient investment.

Retinal nerve fiber layer in frontotemporal lobar degeneration and amyotrophic lateral sclerosis.

Purpose: Tauopathy and transactive response DNA binding protein 43 (TDP-43) proteinopathy are associated with neurodegenerative diseases. These proteinopathies are difficult to detect in vivo. This study examined if spectral-domain optical coherence tomography (SD-OCT) can differentiate in vivo the difference in peripapillary retinal nerve fibre layer (pRNFL) thickness and macular retinal thickness between participants with presumed tauopathy (progressive supranuclear palsy) and those with presumed TDP-43 proteinopathy (amyotrophic lateral sclerosis and semantic variant primary progressive aphasia). Study design: Prospective, multi-centre, observational study. Materials and methods: pRNFL and macular SD-OCT images were acquired in both eyes of each participant using Heidelberg Spectralis SD-OCT. Global and pRNFL thickness in 6 sectors were analyzed, as well as macular thickness in a central 1 mm diameter zone and 4 surrounding sectors. Linear mixed model methods adjusting for baseline differences between groups were used to compare the two groups with respect to pRNFL and macular thickness. Results: A significant difference was found in mean pRNFL thickness between groups, with the TDP-43 group (n = 28 eyes) having a significantly thinner pRNFL in the temporal sector than the tauopathy group (n = 9 eyes; mean difference = 15.46 µm, SE = 6.98, p = 0.046), which was not significant after adjusting for multiple comparisons. No other significant differences were found between groups for pRNFL or macular thickness. Conclusion: The finding that the temporal pRNFL in the TDP-43 group was on average 15.46 µm thinner could potentially have clinical significance. Future work with larger sample sizes, longitudinal studies, and at the level of retinal sublayers will help to determine the utility of SD-OCT to differentiate between these two proteinopathies.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics.

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

Prosthetic Replacement of the Ocular Surface Ecosystem for Limbal Stem Cell Deficiency: A Case Series.

OBJECTIVES: To assess outcomes of limbal stem cell deficiency (LSCD) in patients treated with Prosthetic Replacement of the Ocular Surface Ecosystem (PROSE). METHODS: Retrospective case series. Patients with LSCD who received PROSE treatment were included. Data including best-corrected visual acuity (BCVA) and LSCD staging before and after PROSE dispensing were collected to characterize each case. RESULTS: Five eyes of four patients were included. All patients were female, with an age range of 21 to 80 years. Each patient received a PROSE device with diameters ranging from 16 to 18.5 mm. Follow-up ranged from 11 to 29 months. Tolerated wear times ranged from 3.5 to 10 hr daily. Four eyes showed improved BCVA and unchanged LSCD staging as per the global consensus after PROSE treatment. Three of these eyes had stage 3 and one had stage 1C LSCD at diagnosis. The fifth eye had worse BCVA and recurrence of stage 3 LSCD post-living-related conjunctival limbal allograft transplant despite PROSE treatment. CONCLUSIONS: Prosthetic Replacement of the Ocular Surface Ecosystem may be a viable treatment for LSCD, including severe cases, because it can provide symptom relief and improve vision. Its customizability, as demonstrated in this study, is beneficial for troubleshooting issues with fitting. Future studies are needed to further assess PROSE as treatment for LSCD.

Prosthetic Replacement of the Ocular Surface Ecosystem for Terrien Marginal Degeneration: A Case Series.

OBJECTIVES: To assess outcomes of the Prosthetic Replacement of the Ocular Surface Ecosystem (PROSE) treatment in patients with advanced Terrien marginal degeneration (TMD). METHODS: This is a retrospective case series of patients with advanced TMD who were assessed and fit with customized PROSE lenses. Data were collected on PROSE fitting details including visual acuity (VA) before and after PROSE, slit-lamp findings, and corneal tomography scans. RESULTS: Six eyes in four patients were included. All patients attempted at least one other contact lens (CL) modality before PROSE. Some patients had corneal comorbidities such as pseudopterygium and pseudobleb that contributed to intolerance to previous lenses and warranted extra considerations in the fitting process. With PROSE, VA improved in all six eyes. Patients with structural corneal comorbidities achieved improved vision, comfort, and lens tolerance with PROSE. Two eyes had noncorneal ocular comorbidities that limited PROSE efficacy. Another eye discontinued PROSE wear because of limbal stem-cell disease progression necessitating a limbal stem-cell transplant. CONCLUSIONS: PROSE treatment can be an effective option to improve vision and comfort for patients with advanced TMD who are intolerant to first-line therapeutic CL modalities, even in the presence of other corneal comorbidities.

Electrocatalytic water oxidation performance in an extended porous organic framework with a covalent alliance of distinct Ru sites.

The rational synthesis of durable, earth-abundant efficient electrocatalysts for the oxygen evolution reaction (OER) from water is one of the most important routes for storing renewable energy and minimizing fossil fuel combustion. The prime hurdles for effectively utilizing commercial RuO2 as (OER) electrocatalysts are its very low stability, catalyst deactivation, and high cost. In this work, we explored a Ru-integrated porous organic polymer (Ru@Bpy-POP) by a facile one-pot Friedel-Crafts alkylation strategy between redox-active (Ru(demob)3Cl2) and a carbazole unit, which is composed of unique features including an extended framework unit, isolated active sites, and tunable electrode kinetics. Ru@Bpy-POP can serve as a bridge between a Metal-Organic Framework (MOF) and POP-based catalytic systems with a balanced combination of covalent bonds (structural stability) and open metal sites (single site catalysis). Ru@Bpy-POP, deposited on a three-dimensional nickel foam electrode support, exhibits a promising electrocatalytic OER activity with an ultra-low ruthenium loading compared to a benchmark RuO2 catalyst, providing an overpotential of about 270 mV to reach 10 mA cm-2 in an alkaline medium. Moreover, a high current density of 248 mA cm-2 was achieved for the Ru@Bpy-POP catalyst at only 1.6 V (vs. RHE), which is much higher than 91 mA cm-2 for commercial RuO2. The robust, albeit highly conjugated, POP framework not only triggered facile electro-kinetics but also suppressed aggregation and metallic corrosion during electrolysis. In particular, the benefits of covalent integration of distinct Ru sites into the framework can modulate intermediate adsorption and charge density, which contributes to its exceptional OER activity. All of the critical steps involved in OER are complemented by Density Functional Theory (DFT) calculations, which suggest that electrocatalytic water oxidation proceeds from a closed-shell configuration to open-shell electronic configurations with high-spin states. These open-shell configurations are more stable than their closed-shell counterparts by 1 eV, improving the overall catalytic activity.