Thermal transport in fullerene-based molecular junctions: molecular dynamics simulations.

We investigate phonon thermal transport of fullerene-based single-molecule junctions by employing classical molecular dynamics (MD) simulations. We compute the thermal conductances of C60fullerene monomers, dimers, and trimers utilizing three distinct MD methods. We observe the equilibration dynamics in one approach, and employ two other nonequilibrium steady state simulation methods. We discuss technical aspects of each simulation technique, and show that their predictions for the thermal conductance agree. Our simulations reveal that while the thermal conductance of fullerene monomer and dimer junctions remains similar, that of trimer junctions experiences a significant reduction. This study could assist in the design of high-performing thermoelectric junctions, where low thermal conductance is desired.

SARS-CoV-2 infection among physicians over time in Ontario, Canada: a population-based retrospective cohort study.

AIM: To assess this risk of SARS-CoV-2 infection among Ontario physicians by specialty and in comparison with non-physician controls during the COVID-19 pandemic. METHODS: In this retrospective cohort study, the primary outcome was incident SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR). Secondary outcomes were hospitalization, use of critical care, and mortality. RESULTS: From March 1, 2020 to December 31, 2022, 6172/30 617 (20%) active Ontario physicians tested positive for SARS-CoV-2. Infection was less likely if physicians were older (OR 0.78 [0.76-0.81] per 10 years), rural residents (OR 0.70 [0.59-0.83]), and lived in more marginalized neighborhoods (OR 0.74 [0.62-0.89]), but more likely if they were female (OR 1.14 [1.07-1.22]), worked in long-term care settings (OR 1.16 [1.02-1.32]), had higher patient volumes (OR 2.05 [1.82-2.30] for highest vs lowest), and were pediatricians (OR 1.25 [1.09-1.44]). Compared with community-matched controls (n=29 763), physicians had a higher risk of infection during the first two waves of the pandemic (OR 1.38 [1.20-1.59]) but by wave 3 the risk was no longer significantly different (OR 0.93 [0.83-1.05]). Physicians were less likely to be hospitalized within 14 days of their first positive PCR test than non-physicians (P<0.0001), but there was no difference in the use of critical care (P=0.48) or mortality (P=0.15). CONCLUSION: Physicians had higher rates of infection than community-matched controls during the first two waves of the pandemic in Ontario, but not from wave 3 onward. Physicians practicing in long-term care facilities and pediatricians were more likely to test positive for SARS-CoV-2 than other physicians.

Challenges in molecular dynamics simulations of heat exchange statistics.

We study heat exchange in temperature-biased metal-molecule-metal molecular junctions by employing the molecular dynamics simulator LAMMPS. Generating the nonequilibrium steady state with Langevin thermostats at the boundaries of the junction, we show that the average heat current across a gold-alkanedithiol-gold nanojunction behaves physically, with the thermal conductance value matching the literature. In contrast, the full probability distribution function for heat exchange, as generated by the simulator, violates the fundamental fluctuation symmetry for entropy production. We trace this failure back to the implementation of the thermostats and the expression used to calculate the heat exchange. To rectify this issue and produce the correct statistics, we introduce single-atom thermostats as an alternative to conventional many-atom thermostats. Once averaging heat exchange over the hot and cold thermostats, this approach successfully generates the correct probability distribution function, which we use to study the behavior of both the average heat current and its noise. We further examine the thermodynamic uncertainty relation in the molecular junction and show that it holds, albeit demonstrating nontrivial trends. Our study points to the need to carefully implement nonequilibrium molecular dynamics solvers in atomistic simulation software tools for future investigations of noise phenomena in thermal transport.

Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America.

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

COVID Vaccine Hesitancy and Long-Term Traffic Risks.

BACKGROUND: COVID vaccine hesitancy identifies a discrepancy between personal decisions and public guidelines. We tested whether COVID vaccine hesitancy was associated with the long-term risks of a traffic crash. METHODS: We conducted a population-based longitudinal cohort analysis of adults by determining COVID vaccination status through linkages to electronic medical records. Traffic crashes requiring emergency medical care were defined by multicenter outcome ascertainment of all hospitals throughout the region over the subsequent year. RESULTS: We identified 11,598,549 total individuals, of whom 1,210,754 had not received a COVID vaccine. A total of 54,558 were subsequently injured in traffic crashes during the 1-year follow-up interval, equal to a risk of 4704 per million. Those who had not received a COVID vaccine had a 58% higher risk than those who had received a COVID vaccine (6983 vs 4438 per million, P < .001). The increased traffic risks among unvaccinated individuals included diverse subgroups, were accentuated for single-vehicle crashes, extended to fatal outcomes, exceeded the risks associated with sleep apnea, and persisted after adjustment for baseline characteristics. The increased risks were validated in analyses using Artificial Intelligence techniques and generally larger than the risks of other adverse events frequently ascribed to COVID vaccination. CONCLUSIONS: COVID vaccine hesitancy is associated with significant increased long-term risks of a traffic crash. A greater awareness of traffic risks might encourage patients to take protective actions for personal safety.

A global land cover training dataset from 1984 to 2020.

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

A Drosophila melanogaster model shows that fast growing Metarhizium species are the deadliest despite eliciting a strong immune response.

We used Drosophila melanogaster to investigate how differences between Metarhizium species in growth rate and mechanisms of pathogenesis influence the outcome of infection. We found that the most rapid germinators and growers in vitro and on fly cuticle were the fastest killers, suggesting that pre-penetration competence is key to Metarhizium success. Virulent strains also induced the largest immune response, which did not depend on profuse growth within hosts as virulent toxin-producing strains only proliferated post-mortem while slow-killing strains that were specialized to other insects grew profusely pre-mortem. Metarhizium strains have apparently evolved resistance to widely distributed defenses such as the defensin Toll product drosomycin, but they were inhibited by Bomanins only found in Drosophila spp. Disrupting a gene (Dif), that mediates Toll immunity has little impact on the lethality of most Metarhizium strains (an exception being the early diverged M. frigidum and another insect pathogen Beauveria bassiana). However, disrupting the sensor of fungal proteases (Persephone) allowed rapid proliferation of strains within hosts (with the exception of M. album), and flies succumbed rapidly. Persephone also mediates gender differences in immune responses that determine whether male or female flies die sooner. We conclude that some strain differences in growth within hosts depend on immune-mediated interactions but intrinsic differences in pathogenic mechanisms are more important. Thus, Drosophila varies greatly in tolerance to different Metarhizium strains, in part because some of them produce toxins. Our results further develop D. melanogaster as a tractable model system for understanding insect-Metarhizium interactions.

Renal Dysfunction After Heart Transplant.

BACKGROUND: Moderate or severe renal dysfunction (MSRD) is a frequent complication after heart transplantation. Strategies to delay progression and improve outcomes, such as renoprotective medications and timely referral to nephrology, remain important in providing care to heart transplant recipients with MSRD. RESEARCH QUESTION: What are chronic renal dysfunction's prevalence, risk factors, and optimal clinical management strategies in heart transplant recipients? DESIGN: This single-center, cross-sectional study examined patients who received a heart transplant from January 1, 2000, to June 30, 2011, and followed until December 31, 2011. Moderate or severe renal dysfunction was defined as a glomerular filtration rate of <60 mL/min/1.73 m2. RESULTS: The prevalence of MSRD among 195 heart transplant recipients was 60%. Variables associated with MSRD were female sex (odds ratio [OR]: 4.82; 95% CI, 1.72-13.54), greater age (OR: 1.10 per year; 95% CI, 1.06-1.14), time since transplant (OR: 1.0004 per year; 95% CI, 1.0001-1.0007), and mTOR inhibitor use (OR: 2.89; 95% CI, 1.24-6.71). Tacrolimus use (OR: 0.19; 95% CI, 0.05-0.71) and cyclosporine use (OR: 0.21; 95% CI, 0.05-0.80) were associated with patients without MSRD. Among patients with MSRD, 19.6% were referred to a nephrologist. Median eGFR remained stable at approximately 60 mL/min/1.73 m2 for 3 years after the study. CONCLUSIONS: Our results suggest that female sex, older age at transplant, and time since transplant are associated with MSRD in heart transplant recipients. Tacrolimus and cyclosporine use seemed renoprotective, but lower usage and increased mTOR inhibitor use may more likely indicate existing treatment patterns for patients with MSRD.

Targeting the ADPKD methylome using nanoparticle-mediated combination therapy.

DNA methylation aberrancies are found in autosomal dominant polycystic kidney disease (ADPKD), which suggests the methylome to be a promising therapeutic target. However, the impact of combining DNA methylation inhibitors (DNMTi) and ADPKD drugs in treating ADPKD and on disease-associated methylation patterns has not been fully explored. To test this, ADPKD drugs, metformin and tolvaptan (MT), were delivered in combination with DNMTi 5-aza-2'-deoxycytidine (Aza) to 2D or 3D cystic Pkd1 heterozygous renal epithelial cells (PKD1-Het cells) as free drugs or within nanoparticles to enable direct delivery for future in vivo applications. We found Aza synergizes with MT to reduce cell viability and cystic growth. Reduced representation bisulfite sequencing (RRBS) was performed across four groups: PBS, Free-Aza (Aza), Free-Aza+MT (F-MTAza), and Nanoparticle-Aza+MT (NP-MTAza). Global methylation patterns showed that while Aza alone induces a unimodal intermediate methylation landscape, Aza+MT recovers the bimodality reminiscent of somatic methylomes. Importantly, site-specific methylation changes associated with F-MTAza and NP-MTAza were largely conserved including hypomethylation at ADPKD-associated genes. Notably, we report hypomethylation of cancer-associated genes implicated in ADPKD pathogenesis as well as new target genes that may provide additional therapeutic effects. Overall, this study motivates future work to further elucidate the regulatory mechanisms of observed drug synergy and apply these combination therapies in vivo.

Genetic variation in disease resistance in Drosophila spp. is mitigated in Drosophila sechellia by specialization to a toxic host.

We found that Drosophila species vary in their susceptibility to the broad-spectrum entomopathogen, Metarhizium anisopliae (strain Ma549). Generalist species were generally more resistant than dietary specialists, with the cactophilic Drosophila buzzatii and Drosophila sechellia, a specialist of the Morinda citrifolia (Morinda) fruit, being most susceptible. Morinda fruit is reported to be toxic to most herbivores because it contains Octanoic Acid (OA). We confirmed that OA is toxic to Drosophila spp., other than D. sechellia, and we also found that OA is highly toxic to entomopathogenic fungi including Ma549 and Beauveria bassiana. Drosophila sechellia fed a diet containing OA, even at levels much less than found in Morinda fruit, had greatly reduced susceptibility to Ma549. This suggests that specializing to Morinda may have provided an enemy-free space, reducing adaptive prioritization on a strong immune response. Our results demonstrate that M. anisopliae and Drosophila species with divergent lifestyles provide a versatile model system for understanding the mechanisms of host-pathogen interactions at different scales and in environmental context.

Traumatic Brain Injury, Sleep, and Melatonin-Intrinsic Changes with Therapeutic Potential.

Traumatic brain injury (TBI) is one of the most prevalent causes of morbidity in the United States and is associated with numerous chronic sequelae long after the point of injury. One of the most common long-term complaints in patients with TBI is sleep dysfunction. It is reported that alterations in melatonin follow TBI and may be linked with various sleep and circadian disorders directly (via cellular signaling) or indirectly (via free radicals and inflammatory signaling). Work over the past two decades has contributed to our understanding of the role of melatonin as a sleep regulator and neuroprotective anti-inflammatory agent. Although there is increasing interest in the treatment of insomnia following TBI, a lack of standardization and rigor in melatonin research has left behind a trail of non-generalizable data and ambiguous treatment recommendations. This narrative review describes the underlying biochemical properties of melatonin as they are relevant to TBI. We also discuss potential benefits and a path forward regarding the therapeutic management of TBI with melatonin treatment, including its role as a neuroprotectant, a somnogen, and a modulator of the circadian rhythm.

The magnitude and pace of photosynthetic recovery after wildfire in California ecosystems.

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Point-of-Care Amenable Detection of Mycoplasma genitalium and Its Antibiotic Resistance Mutations.

Mycoplasma genitalium (MG) is an emerging sexually transmitted bacterium. Due to its fastidious and slow-growing nature, MG is difficult to detect through culture-based diagnostics. Like Neisseria gonorrheae, another bacterial pathogen linked to sexually transmitted infections (STIs), MG has developed resistance to macrolide and fluoroquinolone antibiotics used to treat STIs. The ability to detect MG and identify genomic mutations associated with antibiotic resistance simultaneously can enable antibiotic stewardship and mitigate the spread of antibiotic-resistant MG. Toward this end, we first developed a multiplexed probe-based PCR-melt assay that detects MG and the presence of macrolide resistance mutations in the 23S rRNA gene and fluoroquinolone resistance mutations in the parC gene. Each target was identified via its unique combination of fluorescence label and melting temperature. This approach allowed differentiation between the different types of mutations at the genes of interest. Following initial assay optimization, the assay was integrated into a droplet magnetofluidic cartridge used in a portable platform to integrate automated sample extraction, PCR amplification, and detection. Lastly, we demonstrated that the integrated assay and droplet magnetofluidic platform could detect MG and antibiotic resistance-associated mutations in clinical isolates spiked into urine samples in 40 min.

LncRNA XIST regulates breast cancer stem cells by activating proinflammatory IL-6/STAT3 signaling.

Aberrant expression of XIST, a long noncoding RNA (lncRNA) initiating X chromosome inactivation (XCI) in early embryogenesis, is a common feature of breast cancer (BC). However, the roles of post-XCI XIST in breast carcinogenesis remain elusive. Here we identify XIST as a key regulator of breast cancer stem cells (CSCs), which exhibit aldehyde dehydrogenase positive (ALDH+) epithelial- (E) and CD24loCD44hi mesenchymal-like (M) phenotypes. XIST is variably expressed across the spectrum of BC subtypes, and doxycycline (DOX)-inducible knockdown (KD) of XIST markedly inhibits spheroid/colony forming capacity, tumor growth and tumor-initiating potential. This phenotype is attributed to impaired E-CSC in luminal and E- and M-CSC activities in triple-negative (TN) BC. Gene expression profiling unveils that XIST KD most significantly affects cytokine-cytokine receptor interactions, leading to markedly suppressed expression of proinflammatory cytokines IL-6 and IL-8 in ALDH- bulk BC cells. Exogenous IL-6, but not IL-8, rescues the reduced sphere-forming capacity and proportion of ALDH+ E-CSCs in luminal and TN BC upon XIST KD. XIST functions as a nuclear sponge for microRNA let-7a-2-3p to activate IL-6 production from ALDH- bulk BC cells, which acts in a paracrine fashion on ALDH+ E-CSCs that display elevated cell surface IL-6 receptor (IL6R) expression. This promotes CSC self-renewal via STAT3 activation and expression of key CSC factors including c-MYC, KLF4 and SOX9. Together, this study supports a novel role of XIST by derepressing let-7 controlled paracrine IL-6 proinflammatory signaling to promote CSC self-renewal.

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.