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The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a be-spoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the average embedding representations of proteins learned by different PLMs, such as ESM2, Ankh, ProtT5-XL, ProstT5, xTrimoPGLM, SaProt with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3000 million parameters respectively have performance gains by 3-[Formula: see text] than all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3000 generated proteins and through a step of filtration processes including consensus of all open PLM-based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is the variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early-stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study, we obtained a new series of histologic slides from the adjacent recuts of the same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC cross-batch = 0.52 - 0.53 compared to AUC same-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Bathymetry critically influences the intrusion of warm Circumpolar Deep Water onto the continental shelf and under ice shelf cavities in Antarctica, thereby forcing ice melting, grounding line retreat, and sea level rise. We present a novel and comprehensive bathymetry of Antarctica that includes all ice shelf cavities and previously unmeasured continental shelf areas. The new bathymetry is based on a 3D inversion of a circumpolar compilation of gravity anomalies constrained by measurements from the International Bathymetric Chart of the Southern Ocean, BedMachine Antarctica, and discrete seafloor measurements from seismic and ocean robotic probes. Previously unknown troughs with thicker ice shelf cavities are revealed in many parts of Antarctica, especially East Antarctica. The greater depths of troughs on the continental shelf and ice shelf cavities imply that many glaciers are more vulnerable to ocean subsurface warming than previously thought, which may increase the projections of sea level rise from Antarctica., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The energy cascade, i.e. the transfer of kinetic energy from large-scale to small-scale flow motions, has been the cornerstone of turbulence theories and models since the 1940s. However, understanding the spatial organization of the energy transfer has remained elusive. In this work, we answer the question: What are the characteristic flow patterns surrounding regions of intense energy transfer? To that end, we utilize numerical data of isotropic turbulence to investigate the three-dimensional spatial structure of the energy cascade in the inertial range. Our findings indicate that forward energy-transfer events are predominantly confined in the high strain-rate region created between two distinct zones of elevated enstrophy. On average, these zones manifest in the form of two hairpin-like shapes with opposing orientations. The mean velocity field associated with the energy transfer exhibits a saddle point topology when observed in the frame of reference local to the event. The analysis also shows that the primary driving mechanism for the cascade involves strain-rate self-amplification, which is responsible for 85% of the energy transfer, whereas vortex stretching accounts for less than 15%., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Mechanical failure of medical implants, especially in orthopedic poses a significant burden to the patients and healthcare system. The majority of the implant failures are diagnosed at very late stages and are of mechanical causes. This makes the diagnosis and screening of implant failure very challenging. There have been several attempts for development of new implants and screening methods to address this issue; however, the majority of these methods focus on development of new implants or material and cannot satisfy the needs of the patients that have already been operated on. In this work we are introducing a novel screening method and investigate the feasibility of using low-intensity, low-frequency ultrasound acoustic waves for understanding of interfacial implant defects through computational simulation. In this method, we simultaneously apply and sense acoustic waves. COMSOL simulations proved the correlation between implant health condition, severity, and location of defects with measured acoustic signal. Moreover, we show that machine learning not only can detect and classify failure types, it can also assess the severity of the defects. We believe that this work can be used as a proof of concept to rationalize the development of non-invasive screening acoustic wearables for early detection of implant failure in patients with orthopedic implants., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

We present the discovery and characterization of a new multi-planetary system around the Sun-like star K2-360 (EPIC 201595106). K2-360 was first identified in K2 photometry as the host of an ultra-short-period (USP) planet candidate with a period of 0.88 d. We obtained follow-up transit photometry, confirming the star as the host of the signal. High precision radial velocity measurements from HARPS and HARPS-N confirm the transiting USP planet and reveal the existence of an outer (non-transiting) planet with an orbital period of ∼ 10 d. We measure a mass of 7.67 ± 0.75   M ⊕ and a radius of 1.57 ± 0.08   R ⊕ for the transiting planet, yielding a high mean density of 11 ± 2  g  cm - 3 , making it the densest well-characterized USP super-Earth discovered to date. We measure a minimum mass of 15.2 ± 1.8   M ⊕ for the outer planet, and explore a migration formation pathway via the von Zeipel-Kozai-Lidov (ZKL) mechanism and tidal dissipation., (© 2024. The Author(s).)

Non-invasive diagnostic modalities are integral to cardiovascular care; however, current systems primarily measure peripheral pressure, limiting the breadth of cardiovascular prognostication. We report a novel approach for extracting left side heart sounds using a brachial cuff device. The technique leverages brachial cuff device enhanced signal resolution to capture pressure fluctuations generated by cardiohemic system vibrations, the sound pressure waveform. We analyze left heart catheterization data alongside simultaneous brachial cuff device measurements to correlate sound pressure waveform features with left ventricle (LV) contractility. The extracted sound pressure waveform reveals two prominent oscillatory wave packets, termed WP1 and WP2, originating from cardiac structure vibrations associated with LV contractions and relaxation. We demonstrate that WP1 originates from LV contraction during systolic blood ejection through the aortic valve (AV) and is correlated with LV isovolumetric contraction, clinically measured by LV dPdt-max (Pearson-R = 0.65, p < 0.001). Additionally, we show that WP2 comes from LV elongation required for blood flow deceleration at the end of systole, causing AV closure, and is correlated with LV isovolumetric relaxation, measured by LV ndPdt-max (Pearson-R = 0.55, p < 0.001). These findings highlight the value of cuff sound pressure waveforms in providing insights about dynamic coupling of the LV-Aorta complex for non-invasive assessment of LV contractility., (© 2024. The Author(s).)

Ocean mesoscale eddies, with 20 - 300 km size, present in energetic regions of the global ocean, are known to impact local and remote atmospheric weather. The impact of eddies in the Mediterranean Sea on the local weather, however, remains largely unknown. Here, we study this impact during an extreme weather event observed over Israel on January 8 - 10 , 2020, resulting in heavy rains and floods. To do so, we designed a set of coupled and forced numerical simulations with a horizontal resolution of 5 km in both ocean and atmosphere. The coupled simulation successfully reproduces the main characteristics of the torrential rains observed during the event, whereas the forced simulations exhibit poorer performance. Our results emphasize the importance of mesoscale air-sea coupling in supplying moisture to the atmosphere, via mechanisms involving both sea surface temperature and surface currents associated with sea eddies. Extreme weather events are intensified by the Mediterranean Sea eddies, especially through atmospheric meso-cyclones., (© 2024. The Author(s).)

Determining Earth's structure is paramount to unravel its interior dynamics. Seismic tomography reveals positive wave speed anomalies throughout the mantle that spatially correlate with the expected locations of subducted slabs. This correlation has been widely applied in plate reconstructions and geodynamic modelling. However, global travel-time tomography typically incorporates only a limited number of easily identifiable body wave phases and is therefore strongly dependent on the source-receiver geometry. Here, we show how global full-waveform inversion is less sensitive to source-receiver geometry and reveals numerous previously undetected positive wave speed anomalies in the lower mantle. Many of these previously undetected anomalies are situated below major oceans and continental interiors, with no geologic record of subduction, such as beneath the western Pacific Ocean. Moreover, we find no statistically significant correlation positive anomalies as imaged using full-waveform inversion and past subduction. These findings suggest more diverse origins for these anomalies in Earth's lower mantle, unlocking full-waveform inversion as an indispensable tool for mantle exploration., (© 2024. The Author(s).)

Approximately 8100 km 2 of Alaska are leased to the oil and gas industry for exploration and extraction. According to industry estimates, subsurface expansion from these leases could cover up to 130.2 km 2 per pad. As industrial oil extraction activities increase across the thawing Alaskan permafrost, impacts on the permafrost environment will include rapid thaw, increased hydrological flux, and the release of climate warming greenhouse gases. Here, we use remote sensing and field observations to provide a first-order comparison of the direct impacts to the permafrost tundra from oil well pads, and the long-term consequences of a legacy oil pads on the warming North Slope of Alaska. We find that oil well pads on the permafrost accelerate permafrost degradation and persist despite remediation., (© 2024. The Author(s).)

Collective migration of eukaryotic cells is often guided by chemotaxis, and is critical in several biological processes, such as cancer metastasis, wound healing, and embryogenesis. Understanding collective chemotaxis has challenged experimental, theoretical and computational scientists because cells can sense very small chemoattractant gradients that are tightly controlled by cell-cell interactions and the regulation of the chemoattractant distribution by the cells. Computational models of collective cell migration that offer a high-fidelity resolution of the cell motion and chemoattractant dynamics in the extracellular space have been limited to a small number of cells. Here, we present Dynamic cluster field modeling (DCF), a novel computational method that enables simulations of collective chemotaxis of cellular systems with O ( 1000 ) cells and high-resolution transport dynamics of the chemoattractant in the time-evolving extracellular space. We illustrate the efficiency and predictive capabilities of our approach by comparing our numerical simulations with experiments in multiple scenarios that involve chemoattractant secretion and uptake by the migrating cells, cell migration in confined spaces, regulation of the attractant distribution by cell motion, and interactions of the chemoattractant with an enzyme. The proposed algorithm opens new opportunities to address outstanding problems that involve collective cell migration in the central nervous system, immune response and cancer metastasis., (© 2024. The Author(s).)

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips., (© 2024. The Author(s).)

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN's predictive power. We applied this method to analyze a DNN's capability in predicting brain metastasis from early-stage non-small-cell lung cancer biopsy slides. This study quantifies DNN's attention for brain metastasis prediction, targeting features at both the cellular scale and tissue scale in H&E-stained histological whole slide images. At the cellular scale, the predictive power of DNNs progressively increases with higher resolution and significantly decreases when the resolvable feature length exceeds 5 microns. Additionally, DNN uses more macro-scale features associated with tissue architecture and is optimized when assessing visual fields greater than 41 microns. Our study computes the length-scale requirements for optimal DNN learning on digital whole-slide microscopic images, holding the promise to guide future optical microscope designs in pathology applications and facilitating downstream deep learning analysis., (© 2024. The Author(s).)

Sodium serves as one of the primary cations in the central nervous system, playing a crucial role in maintaining normal brain function. In this study, we investigated alterations in sodium concentrations in the brain and/or cerebrospinal fluid across multiple models, including an aging model, a stroke model, a nitroglycerin (NTG)-induced rat migraine model, a familial hemiplegic migraine type 2 (FHM2) mouse model, and a transgenic mouse model of Alzheimer's disease (AD). Our results reveal that older rats exhibited higher sodium concentrations in cerebrospinal fluid (CSF), plasma, and various brain regions compared to their younger counterparts. Additionally, findings from the stroke model demonstrated a significant increase in sodium in the ischemic/reperfused region, accompanied by a decrease in potassium and an elevated sodium/potassium ratio. However, we did not detect significant changes in sodium in the NTG-induced rat migraine model or the FHM2 mouse model. Furthermore, AD transgenic mice showed no significant differences in sodium levels compared to wild-type mice in CSF, plasma, or the hippocampus. These results underscore the nuanced regulation of sodium homeostasis in various neurological conditions and aging, providing valuable insights into potential mechanisms underlying these alterations., (© 2024. The Author(s).)

Spaceborne radar remote sensing of the earth system is essential to study natural and man-made changes in the ecosystem, water and energy cycles, weather and air quality, sea level, and surface dynamics. A major challenge with current approaches is the lack of broad spectrum tunability due to narrow band microwave electronics, that limit systems to specific science variable retrievals. This results in a significant limitation in studying dynamic coupled earth system processes such as surface and subsurface hydrology from a single compact instrument, where co-located broad spectrum radar remote sensing is needed to sense multiple variables simultaneously or over a short duration. Rydberg atomic sensors are highly sensitive broad-spectrum quantum detectors that can be dynamically tuned to cover micro-to-millimeter waves with no requirement for RF band-specific electronics. Rydberg atomic sensors can use existing transmitted signals such as from navigation and communication satellites to enable remote sensing. We demonstrate remote sensing of soil moisture, an important earth system variable, via ground-based radar reflectometry with Rydberg atomic systems. To do this, we sensitize the atoms to XM satellite radio signals and use signal correlations to demonstrate use of these satellite signals for remote sensing of soil moisture., (© 2024. The Author(s).)

Unmanned underwater vehicles (UUVs) will see increased use in scientific research, military operations and maintenance of industrial infrastructure. Many of these applications require that the vehicle possess a long range while retaining precise maneuvering or station-keeping capabilities. Both current UUVs and biological swimmers, often considered the basis for the next generation of UUVs, face a trade-off between the two characteristics. Here, we introduce a novel hybrid propeller concept that enables thunniform-inspired vehicles, which imitate nature's most efficient swimmers, to also achieve high maneuverability. The propeller can produce enhanced three-dimensional kinematics of the caudal fin. An optimization procedure based on real-time experimental data is used to obtain the best kinematics to maneuver the vehicle, and a 4-step strategy is uncovered that results in a 49% increase in maneuverability with respect to conventional 2-D kinematics. The proposed mechanism is shown to be effective for a wide range of fin geometries and stiffness values., (© 2024. The Author(s).)

Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making., (© 2024. The Author(s).)

The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument onboard the Mars 2020 Perseverance rover detected so far some of the most intense fluorescence signals in association with sulfates analyzing abraded patches of rocks at Jezero crater, Mars. To assess the plausibility of an organic origin of these signals, it is key to understand if organics can survive exposure to ambient Martian UV after exposure by the Perseverance abrasion tool and prior to analysis by SHERLOC. In this work, we investigated the stability of organo-sulfate assemblages under Martian-like UV irradiation and we observed that the spectroscopic features of phthalic and mellitic acid embedded into hydrated magnesium sulfate do not change for UV exposures corresponding to at least 48 Martian sols and, thus, should still be detectable in fluorescence when the SHERLOC analysis takes place, thanks to the photoprotective properties of magnesium sulfate. In addition, different photoproduct bands diagnostic of the parent carboxylic acid molecules could be observed. The photoprotective behavior of hydrated magnesium sulfate corroborates the hypothesis that sulfates might have played a key role in the preservation of organics on Mars, and that the fluorescence signals detected by SHERLOC in association with sulfates could potentially arise from organic compounds., (© 2024. The Author(s).)

This study analyzes coupled atmosphere-ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959-2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest-northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño-Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic's Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability., (© 2024. The Author(s).)

Electrically detected magnetic resonance (EDMR) is a promising method to readout spins in miniaturized devices utilized as quantum magnetometers. However, the sensitivity has remained challenging. In this study, we present a tandem (de-)modulation technique based on a combination of magnetic field and radio frequency modulation. By enabling higher demodulation frequencies to avoid 1/f-noise, enhancing self-calibration capabilities, and eliminating background signals by 3 orders of magnitude, this technique represents a significant advancement in the field of EDMR-based sensors. This novel approach paves the way for EDMR being the ideal candidate for ultra-sensitive magnetometry at ambient conditions without any optical components, which brings it one step closer to a chip-based quantum sensor for future applications., (© 2024. The Author(s).)

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models., (© 2024. The Author(s).)

Members of the family Trichomeriaceae, belonging to the Chaetothyriales order and the Ascomycota phylum, are known for their capability to inhabit hostile environments characterized by extreme temperatures, oligotrophic conditions, drought, or presence of toxic compounds. The genus Knufia encompasses many polyextremophilic species. In this report, the genomic and morphological features of the strain FJI-L2-BK-P2 presented, which was isolated from the Mars 2020 mission spacecraft assembly facility located at the Jet Propulsion Laboratory in Pasadena, California. The identification is based on sequence alignment for marker genes, multi-locus sequence analysis, and whole genome sequence phylogeny. The morphological features were studied using a diverse range of microscopic techniques (bright field, phase contrast, differential interference contrast and scanning electron microscopy). The phylogenetic marker genes of the strain FJI-L2-BK-P2 exhibited highest similarities with type strain of Knufia obscura (CBS 148926 T ) that was isolated from the gas tank of a car in Italy. To validate the species identity, whole genomes of both strains (FJI-L2-BK-P2 and CBS 148926 T ) were sequenced, annotated, and strain FJI-L2-BK-P2 was confirmed as K. obscura. The morphological analysis and description of the genomic characteristics of K. obscura FJI-L2-BK-P2 may contribute to refining the taxonomy of Knufia species. Key morphological features are reported in this K. obscura strain, resembling microsclerotia and chlamydospore-like propagules. These features known to be characteristic features in black fungi which could potentially facilitate their adaptation to harsh environments., (© 2024. The Author(s).)

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality., (© 2024. The Author(s).)

Redox is a unique, programmable modality capable of bridging communication between biology and electronics. Previous studies have shown that the E. coli redox-responsive OxyRS regulon can be re-wired to accept electrochemically generated hydrogen peroxide (H 2 O 2 ) as an inducer of gene expression. Here we report that the redox-active phenolic plant signaling molecule acetosyringone (AS) can also induce gene expression from the OxyRS regulon. AS must be oxidized, however, as the reduced state present under normal conditions cannot induce gene expression. Thus, AS serves as a "pro-signaling molecule" that can be activated by its oxidation-in our case by application of oxidizing potential to an electrode. We show that the OxyRS regulon is not induced electrochemically if the imposed electrode potential is in the mid-physiological range. Electronically sliding the applied potential to either oxidative or reductive extremes induces this regulon but through different mechanisms: reduction of O 2 to form H 2 O 2 or oxidation of AS. Fundamentally, this work reinforces the emerging concept that redox signaling depends more on molecular activities than molecular structure. From an applications perspective, the creation of an electronically programmed "pro-signal" dramatically expands the toolbox for electronic control of biological responses in microbes, including in complex environments, cell-based materials, and biomanufacturing., (© 2024. The Author(s).)

This study focuses on the bubble dynamics and associated breakup of individual droplets of diesel and biodiesel under the influence of femtosecond laser pulses. The bubble dynamics were examined by suspending the droplets in the air through an acoustically levitated setup. The laser pulse energies ranged from 25 to 1050 µJ, and droplet diameters varied between 0.25 and 1.5 mm. High-speed shadowgraphy was employed to examine the influence of femtosecond laser intensity and multiple laser pulses on various spatial-temporal parameters. Four distinct sequences of regimes have been identified, depending on early and late times: bubble creation by individual laser pulses, coalescence, bubble rupture and expansion, and droplet fragmentation. At all laser intensities, early-time dynamics showed only bubble generation, while specifically at higher intensities, late-time dynamics revealed droplet breaking. The droplet breakup is further categorized into three mechanisms: steady sheet collapse, unstable sheet breakup, and catastrophic breakup, all following a well-known ligament and secondary breakup process. The study reveals that laser pulses with high repetition rates and moderate laser energy were the optimal choice for precise bubble control and cutting., (© 2024. The Author(s).)

The Mars Sample Return mission intends to retrieve a sealed collection of rocks, regolith, and atmosphere sampled from Jezero Crater, Mars, by the NASA Perseverance rover mission. For all life-related research, it is necessary to evaluate water availability in the samples and on Mars. Within the first Martian year, Perseverance has acquired an estimated total mass of 355 g of rocks and regolith, and 38 μmoles of Martian atmospheric gas. Using in-situ observations acquired by the Perseverance rover, we show that the present-day environmental conditions at Jezero allow for the hydration of sulfates, chlorides, and perchlorates and the occasional formation of frost as well as a diurnal atmospheric-surface water exchange of 0.5-10 g water per m 2 (assuming a well-mixed atmosphere). At night, when the temperature drops below 190 K, the surface water activity can exceed 0.5, the lowest limit for cell reproduction. During the day, when the temperature is above the cell replication limit of 245 K, water activity is less than 0.02. The environmental conditions at the surface of Jezero Crater, where these samples were acquired, are incompatible with the cell replication limits currently known on Earth., (© 2024. The Author(s).)

The ability to understand and manipulate numbers and quantities emerges during childhood, but the mechanism through which humans acquire and develop this ability is still poorly understood. We explore this question through a model, assuming that the learner is able to pick up and place small objects from, and to, locations of its choosing, and will spontaneously engage in such undirected manipulation. We further assume that the learner's visual system will monitor the changing arrangements of objects in the scene and will learn to predict the effects of each action by comparing perception with a supervisory signal from the motor system. We model perception using standard deep networks for feature extraction and classification. Our main finding is that, from learning the task of action prediction, an unexpected image representation emerges exhibiting regularities that foreshadow the perception and representation of numbers and quantity. These include distinct categories for zero and the first few natural numbers, a strict ordering of the numbers, and a one-dimensional signal that correlates with numerical quantity. As a result, our model acquires the ability to estimate numerosity, i.e. the number of objects in the scene, as well as subitization, i.e. the ability to recognize at a glance the exact number of objects in small scenes. Remarkably, subitization and numerosity estimation extrapolate to scenes containing many objects, far beyond the three objects used during training. We conclude that important aspects of a facility with numbers and quantities may be learned with supervision from a simple pre-training task. Our observations suggest that cross-modal learning is a powerful learning mechanism that may be harnessed in artificial intelligence., (© 2024. The Author(s).)

Free riders, who benefit from collective efforts to mitigate climate change but do not actively contribute, play a key role in shaping behavioral climate action. Using a sample of 2096 registered American voters, we explore the discrepancy between two groups of free riders: cynics, who recognize the significance of environmental issues but do not adopt sustainable behaviors, and doubters, who neither recognize the significance nor engage in such actions. Through statistical analyses, we show these two groups are different. Doubters are predominantly male, younger, with lower income and education, exhibit stronger conspiracy beliefs, lower altruism, and limited environmental knowledge, are more likely to have voted for Trump and lean towards conservative ideology. Cynics are younger, religious, higher in socioeconomic status, environmentally informed, liberal-leaning, and less likely to support Trump. Our research provides insights on who could be most effectively persuaded to make climate-sensitive lifestyle changes and provides recommendations to prompt involvement in individual sustainability behaviors. Our findings suggest that for doubters, incentivizing sustainability through positive incentives, such as financial rewards, may be particularly effective. Conversely, for cynics, we argue that engaging them in more community-driven and social influence initiatives could effectively translate their passive beliefs into active participation., (© 2024. The Author(s).)

Despite the general consensus that the warming over the high latitudes northern forests (HLNF) has led to enhanced photosynthetic activity and contributed to the greening trend, isolating the impact of temperature increase on photosynthesis and greenness has been difficult due to the concurring influence of the CO 2 fertilization effect. Here, using an ensemble of simulations from biogeochemical models that have contributed to the Trends in Net Land Atmosphere Carbon Exchange project (TRENDY), we identify an emergent relationship between the simulation of the climate-driven temporal changes in both gross primary productivity (GPP) and greenness (Leaf Area Index, LAI) and the model's spatial sensitivity of these quantities to growing-season (GS) temperature. Combined with spatially-resolved observations of LAI and GPP, we estimate that GS-LAI and GS-GPP increase by 17.0 ± 2.4% and 24.0 ± 3.0% per degree of warming, respectively. The observationally-derived sensitivities of LAI and GPP to temperature are about 40% and 71% higher, respectively, than the mean of the ensemble of simulations from TRENDY, primarily due to the model underestimation of the sensitivity of light use efficiency to temperature. We estimate that the regional mean GS-GPP increased 28.2 ± 5.1% between 1983-1986 and 2013-2016, much larger than the 5.8 ± 1.4% increase from the CO 2 fertilization effect implied by Wenzel et al. This suggests that warming, not CO 2 fertilization, is primarily responsible for the observed dramatic changes in the HLNF biosphere over the last century., (© 2024. The Author(s).)

Aluminum in its Al 3+ form is a metal that inhibits plant growth, especially in acidic soils (pH < 5.5). Rapid and accurate quantitative detection of Al 3+ in agricultural soils is critical for the timely implementation of remediation strategies. However, detecting metal ions requires time-consuming preparation of samples, using expensive instrumentation and non-portable spectroscopic techniques. As an alternative, electrochemical sensors offer a cost-effective and minimally invasive approach for in situ quantification of metal ions. Here, we developed and validated an electrochemical sensor based on bismuth-modified laser-induced graphene (LIG) electrodes for Al 3+ quantitative detection in a range relevant to agriculture (1-300 ppm). Our results show a linear Al 3+ detection range of 1.07-300 ppm with a variation coefficient of 5.3%, even in the presence of other metal ions (Pb 2+ , Cd 2+ , and Cu 2+ ). The sensor offers a limit of detection (LOD) of 0.34 ppm and a limit of quantification (LOQ) of 1.07 ppm. We compared its accuracy for soil samples with pH < 4.8 to within 89-98% of spectroscopic methods (ICP-OES) and potentiometric titration. This technology's portability, easy to use, and cost-effectiveness make it a promising candidate for in situ quantification and remediation of Al 3+ in agricultural soils and other complex matrices., (© 2024. The Author(s).)

The proposed chloride salt-bearing deposits on Mars have an enigmatic composition due to the absence of distinct spectral absorptions for the unique mineral at all wavelengths investigated. We report on analyses of remote visible-wavelength spectroscopic observations that exhibit properties indicative of the mineral halite (NaCl) when irradiated. Visible spectra of halite are generally featureless, but when irradiated by high-energy particles they develop readily-identifiable spectral alterations in the form of color centers. Consistent spectral characteristics observed in the reflectance data of the chloride salt-bearing deposits support the presence of radiation-formed color centers of halite on the surface of Mars. We observe a seasonal cycle of color center formation with higher irradiated halite values during winter months, with the colder temperatures interpreted as increasing the formation efficiency and stability. Irradiated halite identified on the surface of Mars suggests that the visible surface is being irradiated to the degree that defects are forming in alkali halide crystal structures., (© 2024. The Author(s).)

An animal's ability to sense odors declines during aging, and its olfactory drive is tuned by internal states such as satiety. However, whether internal states modulate an age-dependent decline in odor sensation is unknown. To address this issue, we utilized the nematode Caenorhabditis elegans and compared their chemotaxis abilities toward attractive odorants when aged under different dietary conditions. Feeding with the standard laboratory diet, Escherichia coli attenuated the chemotaxis ability toward diacetyl, isoamyl alcohol, and benzaldehyde when aged. On the other hand, feeding with either the lactic acid bacteria Lactobacillus reuteri or food deprivation selectively maintained the chemotaxis ability toward diacetyl. Our results suggest that ingestion of E. coli causes age-dependent chemotaxis decline. The changes in the chemotaxis behavior are attributed to the different expressions of diacetyl receptor odr-10, and the chemotaxis behavior of aged animals under food deprivation is shown to be dependent on daf-16. Our study demonstrates the molecular mechanism of how diet shapes the trajectory of age-dependent decline in chemosensory behaviors., (© 2024. The Author(s).)

The current allocation of street space is based on expected vehicular peak-hour flows. Flexible and adaptive use of this space can respond to changing needs. To evaluate the acceptability of flexible street layouts, several urban environments were designed and implemented in virtual reality. Participants explored these designs in immersive virtual reality in a [Formula: see text] mixed factorial experiment, in which we analysed self-reported, behavioural and physiological responses from participants. Distinct communication strategies were varied between subjects. Participants' responses reveal a preference for familiar solutions. Unconventional street layouts are less preferred, perceived as unsafe and cause a measurably greater stress response. Furthermore, information provision focusing on comparisons lead participants to focus primarily on the drawbacks, instead of the advantages of novel scenarios. When being able to freely express thoughts and opinions, participants are focused more on the impact of space design on behaviour rather than the objective physical features themselves. Especially, this last finding suggests that it is vital to develop new street scenarios in an inclusive and democratic way: the success of innovating urban spaces depends on how well the vast diversity of citizens' needs is considered and met., (© 2024. The Author(s).)

Huntington's disease (HD) is a neurodegenerative disorder caused by a dominant gain-of-function mutation in the huntingtin gene, resulting in an elongated polyglutamine repeat in the mutant Huntingtin (mHtt) that mediates aberrant protein interactions. Previous studies implicated the ubiquitin-proteasome system in HD, suggesting that restoring cellular proteostasis might be a key element in suppressing pathology. We applied genetic interaction tests in a Drosophila model to ask whether modulating the levels of deubiquitinase enzymes affect HD pathology. By testing 32 deubiquitinase genes we found that overexpression of Yod1 ameliorated all analyzed phenotypes, including neurodegeneration, motor activity, viability, and longevity. Yod1 did not have a similar effect in amyloid beta overexpressing flies, suggesting that the observed effects might be specific to mHtt. Yod1 overexpression did not alter the number of mHtt aggregates but moderately increased the ratio of larger aggregates. Transcriptome analysis showed that Yod1 suppressed the transcriptional effects of mHtt and restored the expression of genes involved in neuronal plasticity, vesicular transport, antimicrobial defense, and protein synthesis, modifications, and clearance. Furthermore, Yod1 overexpression in HD flies leads to the upregulation of genes involved in transcriptional regulation and synaptic transmission, which might be part of a response mechanism to mHtt-induced stress., (© 2023. The Author(s).)

Lung cancer is the leading cause of cancer deaths in the United States and worldwide. While influenza illness is known to be particularly dangerous for frail and elderly patients, the relationship between influenza illness and outcomes in patients with cancer remains largely unknown. The Surveillance, Epidemiology, and End Results (SEER) database was queried to identify patients with non-small cell lung cancer (NSCLC) diagnosed between 2009 and 2015. Influenza-like illness (ILI) activity, provided by the Outpatient Influenza-like Illness Surveillance Network of the Center of Disease for Control and Prevention, was merged with the SEER dataset on the state-month level. Regional monthly mortality rates were compared during low versus high flu months in this ecological cohort study. 202,485 patients with NSCLC from 13 SEER-reporting states were included in the analysis. 53 of 1049 state-months (5.1%) had high flu activity. Monthly mortality rates during low and high flu months were 0.041 (95% CI 0.041-0.042) and 0.051 (95% CI 0.050-0.053), respectively (RR 1.24 [95% CI 1.21-1.27]). The association between ILI activity and mortality was observed at the individual state level and in all clinical and regional subgroups. Increased regional influenza activity is associated with higher mortality rates for patients with NSCLC. Vaccine-directed initiatives and increased awareness amongst providers will be necessary to address the growing but potentially preventable burden of influenza-related lung cancer deaths in the U.S., (© 2023. The Author(s).)

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections., (© 2023. The Author(s).)

With the advent of long-term human habitation in space and on the moon, understanding how the built environment microbiome of space habitats differs from Earth habitats, and how microbes survive, proliferate and spread in space conditions, is becoming more important. The microbial tracking mission series has been monitoring the microbiome of the International Space Station (ISS) for almost a decade. During this mission series, six unique strains of Gram-stain-positive bacteria, including two spore-forming and three non-spore-forming species, were isolated from the environmental surfaces of the ISS. The analysis of their 16S rRNA gene sequences revealed > 99% similarities with previously described bacterial species. To further explore their phylogenetic affiliation, whole genome sequencing was undertaken. For all strains, the gyrB gene exhibited < 93% similarity with closely related species, which proved effective in categorizing these ISS strains as novel species. Average nucleotide identity and digital DNA-DNA hybridization values, when compared to any known bacterial species, were < 94% and <50% respectively for all species described here. Traditional biochemical tests, fatty acid profiling, polar lipid, and cell wall composition analyses were performed to generate phenotypic characterization of these ISS strains. A study of the shotgun metagenomic reads from the ISS samples, from which the novel species were isolated, showed that only 0.1% of the total reads mapped to the novel species, supporting the idea that these novel species are rare in the ISS environments. In-depth annotation of the genomes unveiled a variety of genes linked to amino acid and derivative synthesis, carbohydrate metabolism, cofactors, vitamins, prosthetic groups, pigments, and protein metabolism. Further analysis of these ISS-isolated organisms revealed that, on average, they contain 46 genes associated with virulence, disease, and defense. The main predicted functions of these genes are: conferring resistance to antibiotics and toxic compounds, and enabling invasion and intracellular resistance. After conducting antiSMASH analysis, it was found that there are roughly 16 cluster types across the six strains, including β-lactone and type III polyketide synthase (T3PKS) clusters. Based on these multi-faceted taxonomic methods, it was concluded that these six ISS strains represent five novel species, which we propose to name as follows: Arthrobacter burdickii IIF3SC-B10 T (= NRRL B-65660 T = DSM 115933 T ), Leifsonia virtsii F6&#95;8S&#95;P&#95;1A T (= NRRL B-65661 T = DSM 115931 T ), Leifsonia williamsii F6&#95;8S&#95;P&#95;1B T (= NRRL B-65662 T = DSM 115932 T ), Paenibacillus vandeheii F6&#95;3S&#95;P&#95;1C T (= NRRL B-65663 T = DSM 115940 T ), and Sporosarcina highlanderae F6&#95;3S&#95;P&#95;2 T (= NRRL B-65664 T = DSM 115943 T ). Identifying and characterizing the genomes and phenotypes of novel microbes found in space habitats, like those explored in this study, is integral for expanding our genomic databases of space-relevant microbes. This approach offers the only reliable method to determine species composition, track microbial dispersion, and anticipate potential threats to human health from monitoring microbes on the surfaces and equipment within space habitats. By unraveling these microbial mysteries, we take a crucial step towards ensuring the safety and success of future space missions., (© 2023. The Author(s).)

This investigation documents that the Rugged Terrain Unit, the Stimson formation, and the Greenheugh sandstone were deposited in a 1200 m-deep lake that formed after the emergence of Mt. Sharp in Gale crater, Mars, nearly 4 billion years ago. In fact, the Curiosity rover traversed on a surface that once was the bottom of this lake and systematically examined the strata that were deposited in its deepest waters on the crater floor to layers that formed along its shoreline on Mt. Sharp. This provided a rare opportunity to document the evolution of one aqueous episode from its inception to its desiccation and to determine the warming mechanism that caused it. Deep water lacustrine siltstones directly overlie conglomerates that were deposited by mega floods on the crater floor. This indicates that the inception phase of the lake was sudden and took place when flood waters poured into the crater. The lake expanded quickly and its shoreline moved up the slope of Mt. Sharp during the lake-level rise phase and deposited a layer of sandstone with large cross beds under the influence of powerful storm waves. The lake-level highstand phase was dominated by strong bottom currents that transported sediments downhill and deposited one of the most distinctive sedimentological features in Gale crater: a layer of sandstone with a 3 km-long field of meter-high subaqueous antidunes (the Washboard) on Mt. Sharp. Bottom current continued downhill and deposited sandstone and siltstone on the foothills of Mt. Sharp and on the crater floor, respectively. The lake-level fall phase caused major erosion of lacustrine strata that resulted in their patchy distribution on Mt. Sharp. Eroded sediments were then transported to deep waters by gravity flows and were re-deposited as conglomerate and sandstone in subaqueous channels and in debris flow fans. The desiccation phase took place in calm waters of the lake. The aqueous episode we investigated was vigorous but short-lived. Its characteristics as determined by our sedimentological study matches those predicted by an asteroid impact. This suggests that the heat generated by an impact transformed Mars into a warm, wet, and turbulent planet. It resulted in planet-wide torrential rain, giant floods on land, powerful storms in the atmosphere, and strong waves in lakes. The absence of age dates prevents the determination of how long the lake existed. Speculative rates of lake-level change suggest that the lake could have lasted for a period ranging from 16 to 240 Ky., (© 2023. The Author(s).)

Land degradation and soil erosion are becoming increasingly problematic in Africa's rapidly developing urban areas, particularly in Major Port Cities. Uncontrolled expansion and human pressures are hindering planning, adaptation, and conservation efforts. To understand the extent of these issues, this study combined morphometric analysis, soil loss calculation, field monitoring, and remote sensing and GIS tools to assess soil erosion in the Metropolis of Tangier (Morocco) located at the confluence of the Mediterranean Sea and the Atlantic Ocean at the Strait of Gibraltar. The study relied on data from 13 rain gauge stations, official reports, and remote sensing acquisitions, as well as field observations. Results showed an average soil erosion rate of 24.2 t/ha/year, equivalent to an annual soil loss of 588,051 t/year. This high rate was largely due to areas with a high erosion risk (99.8%), covering only 8.3% of the territory, which were characterized by recently burned topsoil, fallow land, and steep slopes. These areas included both uncontrolled neighbourhoods and areas for planned urban and industrial expansion, posing a threat to the landscape's sustainability and socio-economic prospects. The morphometric analysis revealed its high vulnerability to erosion and degradation, with the highest soil loss rates observed in the eastern and western regions. The study also found that flash floods caused by hydroclimatic hazards can lead to significant damage to infrastructure and equipment, particularly in western sub-basins and mountainous regions. In conclusion, the use of remote sensing and GIS technologies provided valuable insights into the physical characteristics and vulnerability of the Tangier Metropolis to land degradation and soil erosion. These findings emphasize the need for effective land management practices and conservation measures to mitigate the impacts of land degradation and soil erosion in the face of climate change. This information is crucial for decision-makers and stakeholders to develop strategies to address these pressing issues., (© 2023. Springer Nature Limited.)

In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement "internal" parallel insertion that does not rely on adding successive layers to "external" edges for growth. Approaches like this can produce enhanced exponential growth behavior that is less limited by volume and external surface interference, for an early step toward efficiently building two and three dimensional shapes in logarithmic time. We experimentally demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism and results in the exponential growth of a population of polymers per unit time. In the supplementary material, we note that an "extension" beyond conventional Turing machine theory is needed to theoretically analyze exponential growth itself in programmable physical systems. Sequential physical Turing Machines that run a roughly constant number of Turing steps per unit time cannot achieve an exponential growth of structure per time. In contrast, the "active" self-assembly model in this paper, computationally equivalent to a Push-Down Automaton, is exponentially fast when implemented in molecules, but is taxonomically less powerful than a Turing machine. In this sense, a physical Push-Down Automaton can be more powerful than a sequential physical Turing Machine, even though the Turing Machine can compute any computable function. A need for an "extended" computational/physical theory arises, described in the supplementary material section S1., (© 2023. The Author(s).)

Energy has been propelling the development of human civilization for millennia. Humanity presently stands at Type 0.7276 on the Kardashev Scale, which was proposed to quantify the relationship between energy consumption and the development of civilizations. However, current predictions of human civilization remain underdeveloped and energy consumption models are oversimplified. In order to improve the precision of the prediction, we use machine learning models random forest and autoregressive integrated moving average to simulate and predict energy consumption on a global scale and the position of humanity on the Kardashev Scale through 2060. The result suggests that global energy consumption is expected to reach ~ 887 EJ in 2060, and humanity will become a Type 0.7449 civilization. Additionally, the potential energy segmentation changes before 2060 and the influence of the advent of nuclear fusion are discussed. We conclude that if energy strategies and technologies remain in the present course, it may take human civilization millennia to become a Type 1 civilization. The machine learning tool we develop significantly improves the previous projection of the Kardashev Scale, which is critical in the context of civilization development., (© 2023. The Author(s).)

The ability to finely control the surface plasmon polariton (SPP) modes of plasmonic waveguides unveils many potential applications in nanophotonics. This work presents a comprehensive theoretical framework for predicting the propagation characteristics of SPP modes at a Schottky junction exposed to a dressing electromagnetic field. Applying the general linear response theory towards a periodically driven many-body quantum system, we obtain an explicit expression for the dielectric function of the dressed metal. Our study demonstrates that the dressing field can be used to alter and fine-tune the electron damping factor. By doing so, the SPP propagation length could be controlled and enhanced by appropriately selecting the intensity, frequency and polarization type of the external dressing field. Consequently, the developed theory reveals an unexplored mechanism for enhancing the SPP propagation length without altering other SPP characteristics. The proposed improvements are compatible with existing SPP-based waveguiding technologies and could lead to breakthroughs in the design and fabrication of state-of-the-art nanoscale integrated circuits and devices in the near future., (© 2023. The Author(s).)

Modern communication and navigation systems are increasingly relying on atomic clocks. As timing precision requirements increase, demands for lower SWaP (size, weight, and power) clocks rise. However, it has been challenging to break through the general trade-off trend between the clock stability performance and SWaP. Here we demonstrate micro mercury trapped ion clock (M2TIC) prototypes integrated with novel micro-fabricated technologies to simultaneously achieve high performance and low SWaP. The M2TIC prototypes could reach the [Formula: see text]-stability level in 1 day with a SWaP of 1.1 L, 1.2 kg, and under 6 W of power. This stability level is comparable to the widely used rack-mount Microchip 5071A cesium frequency standard. These standalone prototypes survived regular commercial shipping across the North American continent to a government laboratory, where their performance was independently tested. The M2TIC sets a new reference point for SWaP and performance and opens opportunities for high-performance clocks in terrestrial and space applications., (© 2023. The Author(s).)

Terrestrial open water evaporation is difficult to measure both in situ and remotely yet is critical for understanding changes in reservoirs, lakes, and inland seas from human management and climatically altered hydrological cycling. Multiple satellite missions and data systems (e.g., ECOSTRESS, OpenET) now operationally produce evapotranspiration (ET), but the open water evaporation data produced over millions of water bodies are algorithmically produced differently than the main ET data and are often overlooked in evaluation. Here, we evaluated the open water evaporation algorithm, AquaSEBS, used by ECOSTRESS and OpenET against 19 in situ open water evaporation sites from around the world using MODIS and Landsat data, making this one of the largest open water evaporation validations to date. Overall, our remotely sensed open water evaporation retrieval captured some variability and magnitude in the in situ data when controlling for high wind events (instantaneous: r 2  = 0.71; bias = 13% of mean; RMSE = 38% of mean). Much of the instantaneous uncertainty was due to high wind events (u > mean daily 7.5 m·s -1 ) when the open water evaporation process shifts from radiatively-controlled to atmospherically-controlled; not accounting for high wind events decreases instantaneous accuracy significantly (r 2  = 0.47; bias = 36% of mean; RMSE = 62% of mean). However, this sensitivity minimizes with temporal integration (e.g., daily RMSE = 1.2-1.5 mm·day -1 ). To benchmark AquaSEBS, we ran a suite of 11 machine learning models, but found that they did not significantly improve on the process-based formulation of AquaSEBS suggesting that the remaining error is from a combination of the in situ evaporation measurements, forcing data, and/or scaling mismatch; the machine learning models were able to predict error well in and of itself (r 2  = 0.74). Our results provide confidence in the remotely sensed open water evaporation data, though not without uncertainty, and a foundation by which current and future missions may build such operational data., (© 2023. The Author(s).)