Mitochondrial fatty acid β-oxidation is required for storage-lipid catabolism in a marine diatom
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Wind energy producers need deployable devices for wind turbines that prevent bat fatalities. Based on the speculation that bats approach turbines after visually mistaking them for trees, we tested a potential light-based deterrence method. It is likely that the affected bats see ultraviolet (UV) light at low intensities. Here, we present the results of a multi-month experiment to cast dim, flickering UV light across wind turbine surfaces at night. Our objectives were to refine and test a practical system for dimly UV-illuminating turbines while testing whether the experimental UV treatment influenced the activity of bats, birds, and insects. We mounted upward-facing UV light arrays on turbines and used thermal-imaging cameras to quantify the presence and activity of night-flying animals. The results demonstrated that the turbines can be lit to the highest reaches of the blades with “invisible” UV light, and the animal responses to such experimental treatment can be concurrently monitored. The UV treatment did not significantly change nighttime bat, insect, or bird activity at the wind turbine. Our findings show how observing flying animals with thermal cameras at night can help test emerging technologies intended to variably affect their behaviors around wind turbines.
Abstract In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe ( r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).
We present follow-up photometry and spectroscopy of ZTF J0328-1219, strengthening its status as a white dwarf exhibiting transiting planetary debris. Using TESS and Zwicky Transient Facility photometry, along with follow-up high-speed photometry from various observatories, we find evidence for two significant periods of variability at 9.937 and 11.2 hr. We interpret these as most likely the orbital periods of different debris clumps. Changes in the detailed dip structures within the light curves are observed on nightly, weekly, and monthly timescales, reminiscent of the dynamic behavior observed in the first white dwarf discovered to harbor a disintegrating asteroid, WD 1145+017. We fit previously published spectroscopy along with broadband photometry to obtain new atmospheric parameters for the white dwarf, with M * = 0.731 ± 0.023 M⊙, T eff = 7630 ± 140 K, and [Ca/He] = - 9.55 ± 0.12. With new high-resolution spectroscopy, we detect prominent and narrow Na D absorption features likely of circumstellar origin, with velocities 21.4 ± 1.0 km s -1 blueshifted relative to atmospheric lines. We attribute the periodically modulated photometric signal to dusty effluents from small orbiting bodies such as asteroids or comets, but we are unable to identify the most likely material that is being sublimated, or otherwise ejected, as the environmental temperatures range from roughly 400 to 700 K.
Photosynthetic organisms coordinate their metabolism and growth with diurnal light, which can range in intensity from limiting to excessive. Little is known about how light intensity impacts the diurnal program in Chlamydomonas reinhardtii, or how diurnal rhythms in gene expression and metabolism shape photoprotective responses at different times of day. To address these questions, we performed a systems analysis of synchronized Chlamydomonas populations acclimated to low, moderate, and high diurnal light. Transcriptomic and proteomic data revealed that the Chlamydomonas rhythmic gene expression program is resilient to limiting and excess light: genome-wide, waves of transcripts, and proteins peak at the same times in populations acclimated to stressful light intensities as in populations acclimated to moderate light. Yet, diurnal photoacclimation gives rise to hundreds of gene expression changes, even at night. Time course measurements of photosynthetic efficiency and pigments responsive to excess light showed that high light-acclimated cells partially overcome photodamage in the latter half of the day prior to cell division. Although gene expression and photodamage are dynamic over the diurnal cycle, Chlamydomonas populations acclimated to low and high diurnal light maintain altered photosystem abundance, thylakoid architecture, and non-photochemical quenching capacity through the night phase. This suggests that cells remember or anticipate the light intensities that they have typically encountered during the day. The integrated data constitute an excellent resource for understanding photoacclimation in eukaryotes under environmentally relevant conditions.
Options for tracking and reconstructing animal movement are increasingly accessible due to rapidly decreasing costs, smaller sizes, and a proliferation of analytical inference techniques (Rutz and Hays, 2009; Wikelski et al., 2007). A wide variety of options exists for determining movement patterns of animals—from high-resolution pinpoint ARGOS satellite tags to time-consuming and logistically challenging radio telemetry. One option, light-level geolocation, offers small, affordable devices with long battery lives known as global location sensors (GLS), making them ideal for gathering preliminary migratory data. Geolocation works by inferring patterns of animal movement from light-level transitions between day and night (Hill and Braun, 2001). The recent advances in tracking technologies allow biologists to zoom in on migratory behavior, delineating heretofore-unidentified intraspecific migratory behavior (Delmore et al., 2012). The ubiquity of migratory divides (populations within a species that exhibit different migratory patterns) remains largely unknown, but their existence can lead to favorable demographic metrics (e.g., genetic diversity) in conservation contexts (Møller et al., 2011) and necessitate the development of population-specific conservation plans (Delmore et al., 2012). Delimiting the populations within a species that migrate and the extent of their migrations relative to individuals that remain resident across a species’ range has important evolutionary, ecological, and conservation implications. Within this context, Los Alamos National Laboratory (LANL) biologists have leveraged the local Avian Nestbox Network (ANN; Fair and Myers, 2002) to evaluate the migratory behaviors of a common local bird species, the Western Bluebird (Sialia mexicana).
Introduction Deuterium and tritium, the two isotopes of hydrogen besides protium, are utilized in many industries. Deuterium is routinely used as a nuclear reactor moderator and molecular labeling agent, while tritium is used as an autoradiography label in medical imaging, and in an emerging market for self-powered lighting for watches, exit signs, firearm night-sights and future fusion fuel. Tritium is produced naturally at very low steady-state levels due to cosmic rays, natural nuclear decay reactions, as well as commercial nuclear fission reactors, and terrestrial nuclear testing conducted primarily in the mid-twentieth century. Tritium often accumulates in the heavy water used in nuclear fission reactors and sometimes appears in groundwater near nuclear reactors due to slight leakage from water sources in the reactor. Because of the health and environmental impacts from tritium leaking into the water sources, as well as the value of this precious material, it is desirable to remove and recover as much tritium as reasonably possible from contaminated water.
The detection of mid-infrared (MIR) light is technologically important for applications such as night vision, imaging, sensing, and thermal metrology. Traditional MIR photodetectors either require cryogenic cooling or have sophisticated device structures involving complex nanofabrication. Here, we conceive spectrally tunable MIR detection by using two-dimensional metal halide perovskites (2D-MHPs) as the critical building block. Leveraging the ultralow cross-plane thermal conductivity and strong temperature-dependent excitonic resonances of 2D-MHPs, we demonstrate ambient-temperature, all-optical detection of MIR light with sensitivity down to 1 nanowatt per square micrometer, using plastic substrates. Through the adoption of membrane-based structures and a photonic enhancement strategy unique to our all-optical detection modality, we further improved the sensitivity to sub–10 picowatt-per-square-micrometer levels. The detection covers the mid-wave infrared regime from 2 to 4.5 micrometers and extends to the long-wave infrared wavelength at 10.6 micrometers, with wavelength-independent sensitivity response. Our work opens a pathway to alternative types of solution-processable, long-wavelength thermal detectors for molecular sensing, environmental monitoring, and thermal imaging.
Vehicle collisions with deer (Odocoileus spp.) cause billions of dollars in damages and injure thousands of drivers every year in the United States, and few mitigation methods have proven effective. However, recent research suggests that vehicle lighting might influence white-tailed deer (Odocoileus virginianus; hereafter, deer) responses to oncoming vehicles. Most new vehicles are manufactured with light emitting diode (LED) headlights which differ in total radiance and wavelength of light emitted compared to the previous industry standard of tungstenhalogen (halogen) headlights. Also, frontal vehicle illumination through rear-facing lighting has shown promise in enhancing deer responses to vehicles, but its effectiveness has not been tested under various headlight conditions (headlight type or intensity). As such, it remains unclear how these aspects of vehicle lighting affect deer responses to an approaching vehicle. We used 23 captive, wild-type deer to investigate how variations in vehicle lighting affect deer responses to an approaching vehicle at night, when most collisions occur. We released deer into a 95 m long, 3 m wide chute and approached them from the opposite end with an electric golf cart outfitted with two versions of stock 2017–2020 Ford Fusion headlights (LED and halogen) and a 51 cm rear-facing lightbar to test how vehicle lighting affected deer avoidance behaviors in an imminent, head-on collision scenario. Each deer received eight lighting treatments consisting of unique combinations of headlight type (LED vs. halogen), light intensity (low vs. high beam), and rear-facing lighting (lightbar off vs. on). We measured deer alert and flight behavior using infrared videography. We found that the halogen, high beam, lightbar off treatment had the greatest probability of evoking an alert response. Furthermore, when the lightbar was off, high beams appeared to increase alert probability for halogen headlights. Also, we found evidence that high beam, halogen headlights tend to increase alert probability over high beam, LED headlights, when the lighbar was off. We found no effect of our lighting treatments on deer alert distance, flight probability, or flight initiation distance. Across all behavioral responses, the random effect deer ID explained 0.86–9.19 × more variation than our lighting treatments, reflecting large differences in responses among deer. Overall, we found that vehicle lighting can impact deer behavior during an imminent, head-on collision scenario, although lighting was ineffective at increasing favorable flight behaviors. Future research should investigate how vehicle lighting treatments affect free-ranging, wild deer in a variety of real-world scenarios and at longer approach distances.
On May 8, 2017, Hanover residents voted overwhelmingly to transition to 100% renewable electricity by 2030 and heating, cooling and transportation by 2050. In so doing, we became the first municipality in the country to make this commitment by popular vote. To accomplish these goal town leaders developed a plan to address first transitioning municipal energy uses to renewable forms of energy, through on-site generation of the municipal electrical load, load reduction through energy efficiency improvements to town facilities and infrastructure. Significant progress has been accomplished in the generation of energy with 90%+ of the 2024 municipal load being offset by generation and through various energy efficiency projects. The utility owned street lighting was identified as a large energy consumer of 165 MWh annually (7% of the annual municipal load) due to old inefficient high-pressure sodium and mercury vapor streetlights with no ability to reduce wattage during periods with low lighting needs. The Hanover LED Streetlight Replacement Project replaced utility owned streetlights with town owned controllable (dimmable and trimmable) LED lights that can be adjusted by location and time of night. Coupled with ownership of the streetlights transfer to the town, the town’s share of the project pay back is 11 months and for the complete project including federal share the payback is approximately 3 years. With the completion of lighting upgrade, the annual streetlighting energy load has dropped by 60% to 67 MWh in the first year of operation (7/1/24 - 6/30/25). This amounts to an approximate $90,000 annual savings or a local 1% tax rate impact. Additionally, over 200,000 pounds of CO2 will be saved annually.
ABSTRACT A professional dark sky assessment normally requires the numerical modelling of light propagation from ground-based sources into the atmosphere. The spectral power distribution (SPD) of light escaping from an artificial source is one of the key parameters needed in predicting models to simulate the optical signatures of the night sky as a function of distance. SPD at zero distance is due to a non-trivial superposition of light beams, but does not necessarily coincide with the weighted SPD of individual lights. This is because most photons can be removed by obstacles or can interact with many surfaces before being directed into the atmosphere. We have developed an experimental and theoretical method for obtaining the initial SPD from ground-based spectroscopy of the night sky. The method is applicable to distant sources of light and is demonstrated in field measurements conducted on the light dome of Chicago with a new sky glow spectrometer. We show that the method requires analysing the relative impact of local light sources scattered around the measuring site on the radiance of the light dome. Our theoretical method along with ground-based spectroscopy represents a new approach to characterizing light-pollution sources which are otherwise difficult to study due to lack of information on either light-source inventory or a heterogeneous 3D structure of light-emitting and blocking environments.
Abstract In photosynthetic organisms, light acts as an environmental signal to control their development and physiology, as well as energy source to drive the conversion of CO 2 into carbohydrates used for growth or storage. The main storage carbohydrate in green algae is starch, which accumulates during the day and is broken down at night to meet cellular energy demands. The signaling role of light quality in the regulation of starch accumulation remains unexplored. Here, we identify PHOTOTROPIN-MEDIATED SIGNALING KINASE 1 (PMSK1) as a key regulator of starch metabolism inChlamydomonas reinhardtii. In its phosphorylated form (PMSK1-P), it activates GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE (GAP1), promoting starch biosynthesis. We show that blue light, perceived by PHOTOTROPIN, induces PMSK1 dephosphorylation that in turn represses GAP1 mRNA levels and reduces starch accumulation. These findings reveal a previously uncharacterized blue light-mediated signaling pathway that advances our understanding of photoreceptor-controlled carbon metabolism in microalgae.
The National September 11 Memorial & Museum completed a retrofit of the submergible LED lighting fixtures in its North and South Memorial Pools, located at the World Trade Center site in New York City. The two one-acre pools are illuminated nightly by custom LED fixtures set into their base, and the original lights — in place since 2011 — had begun reaching the end of their useful life, leaving sections of the pools dim or dark. Rather than fully replacing the fixtures, the project team worked with the original manufacturer, Acuity Brands, to retrofit and reuse existing components across all eight pool walls (34 fixtures per wall), reducing cost and waste. Fixtures were shipped in custom crates to Minnesota for retrofitting, then reinstalled onsite by Memorial & Museum staff and contractor ABM. The team addressed two key technical challenges during the project: early leakage in retrofitted fixtures, resolved by introducing vacuum-sealing and nitrogen-fill testing before shipment; and chord damage during transit, resolved with custom-designed shipping crates. All eight pool walls were successfully retrofitted and reinstalled by August 27, 2025, ahead of the 25th anniversary of the September 11, 2001 attacks. Sitewide electrical consumption in September 2025 reflected an approximate 7% reduction, contributing to the institution's broader net-zero and LEED Gold sustainability goals.
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST.
The Problem: Sleep disturbance is common among nursing center residents and places them at risk for poor health outcomes. Lighting that changes in color and intensity over the course of the day and night may improve sleep and other outcomes, like dementia behaviors, but has has not been rigorously evaluated under real-world conditions. The Resolution: This single-facility study establishes that facility-level tuned lighting is feasible to implement and evaluate in a nursing center. Researchers randomly-assigned three corridors to one of two lighting conditions, then the other. Residents experienced half as many sleep disturbances while exposed to tuned vs. static lighting. There was no effect on behaviors. Tips for Success: Tuned lighting is a low-risk intervention that could be employed in new construction or when retrofitting fixtures into existing buildings. Key Words: Nursing home, long-term care, lighting, sleep, behaviors
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all SNe discovered. Thus, photometric classification is crucial, but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory's Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multiband light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we augment the simulated training set to be representative of the photometric redshift distribution per SNe class, the cadence of observations, and the flux uncertainty distribution of the test set. Then we build a classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits, yielding a similar performance to the winning PLAsTiCC entry. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is important, with light curves of 150 days yielding the highest performance. Cadence also has an important impact on SNe classification; events with median inter-night gap <3.5 days yield higher classification performance. Interestingly, we find that large gaps (>10 days) in light-curve observations do not impact performance if sufficient observations are available on either side, due to the effectiveness of the Gaussian process interpolation. This analysis is the first exploration of the impact of observing strategy on photometric SN classification with LSST.
The proper timing of flowering, which is key to maximize reproductive success and yield, relies in many plant species on the coordination between environmental cues and endogenous developmental programs. The perception of changes in day length is one of the most reliable cues of seasonal change, and this involves the interplay between the sensing of light signals and the circadian clock. Here, we describe a Brachypodium distachyon mutant allele of the evening complex protein EARLY FLOWERING 3 (ELF3). We show that the elf3 mutant flowers more rapidly than wild type plants in short days as well as under longer photoperiods but, in very long (20 h) days, flowering is equally rapid in elf3 and wild type. Furthermore, flowering in the elf3 mutant is still sensitive to vernalization, but not to ambient temperature changes. Molecular analyses revealed that the expression of a short-day marker gene is suppressed in elf3 grown in short days, and the expression patterns of clock genes and flowering time regulators are altered. We also explored the mechanisms of photoperiodic perception in temperate grasses by exposing B. distachyon plants grown under a 12 h photoperiod to a daily night break consisting of a mixture of red and far-red light. We showed that 2 h breaks are sufficient to accelerate flowering in B. distachyon under non-inductive photoperiods and that this acceleration of flowering is mediated by red light. Finally, we discuss advances and perspectives for research on the perception of photoperiod in temperate grasses.
The composition and transformations of biomass burning aerosols (BBA) have been measured onboard the NOAA Twin Otter research aircraft during the Fire Influence on Regional to Global Environments and Air Quality field study. Here, we analyze real-time aerosol mass spectrometry measurements across three flights during the afternoon, late afternoon, and night of August 28, 2019, for one midsized wildfire. Analysis of several metrics showed that the aerosol composition and optical properties varied depending on the burning conditions at the fire zone and the time of day the BBA was emitted, with substantial variations in the available sunlight. The total aerosol mass loadings were dominated by organic components with a much smaller contribution from inorganic species. A gradual buildup of organic material was observed during the afternoon as the plume aged, indicating the condensation of photochemically formed low-volatility oxidized organic compounds. Highly hygroscopic ammonium nitrate was the main inorganic component, suggesting potential water content in BBA particles and the likelihood of their aqueous-phase reactivity. Depletions of particle-phase NO 3 – and Cl – relative to carbon monoxide were observed in the late afternoon and nighttime plumes, respectively, aligning with known gas-particle partitioning thermodynamics and the heterogeneous chemistry of dissolved nitrate and chloride. The wavelength-dependent light absorption by aerosol species was higher for the plume sampled at night and showed no significant changes with plume age, despite observed trends in composition and mass downwind. These differences in particle composition and optical properties demonstrate that the processes involved in BBA aging are not uniform for the same wildfire over the course of the day and depend highly on when the BBA was emitted, as well as the burning phase at the emissions source.