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Applications of Unmanned Aerial Vehicles (UAVs) in construction have been increased in recent years mainly for purposes of automatically collecting field data. In the market, there is a growing opportunity for using UAVs in material handling. On the other side, vertical transportation in construction sites is a crucial challenge that leads to a drop in productivity efficiency. This paper presents a proof of concept for automating the lifting procedure of lightweight instruments and materials in construction sites. To do so, a platform is developed that could automatically manage material/instrument handling requests via an autopilot drone. The aircraft position is tracked with an onboard GPS module which is not accurate in urban areas. So inaccurate GPS signals could deviate the drone from the predefined path. To tackle this problem, a machine vision algorithm is utilized to calibrate the real-time position of the aircraft. A self-supervised learning algorithm is integrated into the system to handling collision avoidance during the flight. Finally, the developed methods are tested with an UAV in a real world environment to demonstrate the potentials of hiring autopilot drones in construction.

Abstract Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers can be identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and is already studied using RNA-seq. In this study, we generate a large (325,025 cells and 49 patients) single cell multi-omic dataset and jointly quantify ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identify an association between one plasma cell subtype with myeloma progression that we call relapsed/refractory plasma cells (RRPCs). These cells are associated with chromosome 1q alterations, TP53 mutations, and higher expression of PHF19. We also identify downstream regulation of cell cycle inhibitors in these cells, possible regulation by the transcription factor (TF) PBX1 on chromosome 1q, and determine that PHF19 may be acting primarily through this subset of cells.

Abstract By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.

Abstract Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm and at ranges between 1 and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beamwidth. The measurement error can be reduced by normalising for beamwidth. However, the variation of measurements (i.e. the precision) was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beamwidth on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beamwidth that produces an estimated level of error that is acceptable for that study.

Abstract While chronological age is a strong predictor for health-related risk factors, it is an incomplete metric that fails to fully characterize the unique aging process of individuals with different genetic makeup, neurodevelopment, and environmental experiences. Recent advances in epigenomic array technologies have made it possible to generate DNA methylation-based biomarkers of biological aging, which may be useful in predicting a myriad of cognitive abilities and functional brain network organization across older individuals. It is currently unclear which cognitive domains are negatively correlated with epigenetic age above and beyond chronological age, and it is unknown if functional brain organization is an important mechanism for explaining these associations. In this study, individuals with accelerated epigenetic age (i.e. AgeAccelGrim) performed worse on tasks that spanned a wide variety of cognitive faculties including both fluid and crystallized intelligence (N = 103, average age = 68.98 years, 73 females, 30 males). Additionally, fMRI connectome-based predictive models suggested a mediating mechanism of functional connectivity on epigenetic age acceleration-cognition associations primarily in medial temporal lobe and limbic structures. This research highlights the important role of epigenetic aging processes on the development and maintenance of healthy cognitive capacities and function of the aging brain.

Objective: Hypothalamic signals potently stimulate energy expenditure by engaging peripheral mechanisms to restore energy homeostasis. Previous studies have identified several critical hypothalamic sites (e.g. preoptic area (POA) and ventromedial hypothalamic nucleus (VMN)) that could be part of an interconnected neurocircuit that controls tissue thermogenesis and essential for body weight control. However, the key neurocircuit that can stimulate energy expenditure has not yet been established. Methods: Here, we investigated the downstream mechanisms by which VMN neurons stimulate adipose tissue thermogenesis. We manipulated subsets of VMN neurons acutely as well as chronically and studied its effect on tissue thermogenesis and body weight control, using Sf1Cre and Adcyap1Cre mice and measured physiological parameters under both high-fat diet and standard chow diet conditions. To determine the node efferent to these VMN neurons, that is involved in modulating energy expenditure, we employed electrophysiology and optogenetics experiments combined with measurements using tissue-implantable temperature microchips. Results: Activation of the VMN neurons that express the steroidogenic factor 1 (Sf1; VMNSf1 neurons) reduced body weight, adiposity and increased energy expenditure in diet-induced obese mice. This function is likely mediated, at least in part, by the release of the pituitary adenylate cyclase-activating polypeptide (PACAP; encoded by the Adcyap1 gene) by the VMN neurons, since we previously demonstrated that PACAP, at the VMN, plays a key role in energy expenditure control. Thus, we then shifted focus to the subpopulation of VMNSf1 neurons that contain the neuropeptide PACAP (VMNPACAP neurons). Since the VMN neurons do not directly project to the peripheral tissues, we traced the location of the VMNPACAP neurons' efferents. We identified that VMNPACAP neurons project to and activate neurons in the caudal regions of the POA whereby these projections stimulate tissue thermogenesis in brown and beige adipose tissue. We demonstrated that selective activation of caudal POA projections from VMNPACAP neurons induces tissue thermogenesis, most potently in negative energy balance and activating these projections lead to some similar, but mostly unique, patterns of gene expression in brown and beige tissue. Finally, we demonstrated that the activation of the VMNPACAP neurons' efferents that lie at the caudal POA are necessary for inducing tissue thermogenesis in brown and beige adipose tissue. Conclusions: These data indicate that VMNPACAP connections with the caudal POA neurons impact adipose tissue function and are important for induction of tissue thermogenesis. Our data suggests that the VMNPACAP → caudal POA neurocircuit and its components are critical for controlling energy balance by activating energy expenditure and body weight control.

In this article, we investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of nonstationary noise, which is an obstacle to achieving accurate observable estimates. Leveraging Bayesian methods, we design a strategy to enhance PEC stability and accuracy. Our experiments using a five-qubit implementation of the Bernstein–Vazirani algorithm and conducted on the ibm_kolkata device reveal a 42% improvement in accuracy and a 60% enhancement in stability compared to nonadaptive PEC. These results underscore the importance of adaptive estimation processes to effectively address nonstationary noise, vital for advancing PEC utility.

Social media has become an integral part of everyday life for many individuals, serving as a platform to express opinions, share memories and lifestyles, follow news, and adapt to social trends and norms. The wealth of user information and analytics on these platforms has facilitated the development and sale of tailored products and services, benefiting advertisers and researchers seeking survey participants. Social media advertising has demonstrated its effectiveness in reaching hard-to-reach populations. However, transport researchers have yet to capitalise on this potential fully. This paper presents our experience using social media to recruit participants for two smartphone travel surveys conducted in Australia. We demonstrate that social media recruitment and smartphone-based travel surveys are highly effective, adaptable, and can be rapidly deployed in response to research opportunities, such as during the early phase of the COVID-19 pandemic when traditional methods may be less suitable. This approach also holds great potential for travel surveys targeting the general population. This paper shares several lessons from this experiment, including our administrative approach and detailed technical instructions to utilise open-source software tools for conducting smartphone travel surveys like ours. This approach significantly reduces study costs compared to most commercial solutions.

Abstract The complexation of trivalent lanthanides and minor actinides (Am3+, Cm3+, and Cf3+) by the acyclic aminopolycarboxylate chelators 6,6′-((ethane-1,2-diylbis–((carboxymethyl)azanediyl))bis–(methylene))dipicolinic acid (H4octapa) and 6,6'-((((4-(1-(2-(2-(2-hydroxyethoxy)ethoxy)ethyl)-1H-1,2,3-triazol-4-yl)pyridine-2,6-diyl)bis–(methylene))bis–((carboxymethyl)azanediyl))bis–(methylene)) dipicolinic acid (H4pypa-peg) were studied using potentiometry, spectroscopy, competitive complexation liquid–liquid extraction, and ab initio molecular dynamics simulations. Two studied reagents are strong multidentate chelators, well-suited for applications seeking radiometal coordination for in-vivo delivery and f-element isolation. The previously reported H4octapa forms a compact coordination packet, while H4pypa-peg is less sterically constrained due to the presence of central pyridine ring. The solubility of H4octapa is limited in a non-complexing high ionic strength perchlorate media. However, the introduction of a polyethylene glycol group in H4pypa-peg increased the solubility without influencing its ability to complex the lanthanides and minor actinides in solution.

Abstract Epigenetic variation in plant populations is an important factor in determining phenotype and adaptation to the environment. However, while advances have been made in the molecular and computational methods to analyze the methylation status of a given sample of DNA, tools to profile and compare the methylomes of multiple individual plants or groups of plants at high resolution and low cost are lacking. Here, we describe a computational approach and R package (sounDMR) that leverages the benefits of long read nanopore sequencing to enable robust identification of differential methylation from complex experimental designs, as well as assess the variability within treatment groups and identify individual plants of interest. We demonstrate the utility of this approach by profiling a population of Arabidopsis thaliana exposed to a demethylating agent and identify genomic regions of high epigenetic variability between individuals. Given the low cost of nanopore sequencing devices and the ease of sample preparation, these results show that high resolution epigenetic profiling of plant populations can be made more broadly accessible in plant breeding and biotechnology.

Abstract High-fat diet (HFD) is associated with Alzheimer’s disease (AD) and type 2 diabetes risk, which share features such as insulin resistance and amylin deposition. We examined gene expression associated with astrocytes and microglia since dysfunction of these cell types is implicated in AD pathogenesis. We hypothesize gene expression changes in disease-associated astrocytes (DAA), disease-associated microglia and human Alzheimer’s microglia exist in diabetic and obese individuals before AD development. By analyzing bulk RNA-sequencing (RNA-seq) data generated from brains of mice fed HFD and humans with AD, 11 overlapping AD-associated differentially expressed genes were identified, including Kcnj2, C4b and Ddr1, which are upregulated in response to both HFD and AD. Analysis of single cell RNA-seq (scRNA-seq) data indicated C4b is astrocyte specific. Spatial transcriptomics (ST) revealed C4b colocalizes with Gfad, a known astrocyte marker, and the colocalization of C4b expressing cells with Gad2 expressing cells, i.e., GABAergic neurons, in mouse brain. There also exists a positive correlation between C4b and Gad2 expression in ST indicating a potential interaction between DAA and GABAergic neurons. These findings provide novel links between the pathogenesis of obesity, diabetes and AD and identify C4b as a potential early marker for AD in obese or diabetic individuals.

Continued large-scale public investment in declining ecosystems depends on demonstrations of “success”. While the public conception of “success” often focuses on restoration to a pre-disturbance condition, the scientific community is more likely to measure success in terms of improved ecosystem health. Using a combination of literature review, workshops and expert solicitation we propose a generalized framework to improve ecosystem health in highly altered river basins by reducing ecosystem stressors, enhancing ecosystem processes and increasing ecosystem resilience. We illustrate the use of this framework in the Mississippi-Atchafalaya River Basin (MARB) of the central United States (U.S.), by (i) identifying key stressors related to human activities, and (ii) creating a conceptual ecosystem model relating those stressors to effects on ecosystem structure and processes. As a result of our analysis, we identify a set of landscape-level indicators of ecosystem health, emphasizing leading indicators of stressor removal (e.g., reduced anthropogenic nutrient inputs), increased ecosystem function (e.g., increased water storage in the landscape) and increased resilience (e.g., changes in the percentage of perennial vegetative cover). We suggest that by including these indicators, along with lagging indicators such as direct measurements of water quality, stakeholders will be better able to assess the effectiveness of management actions. For example, if both leading and lagging indicators show improvement over time, then management actions are on track to attain desired ecosystem condition. If, however, leading indicators are not improving or even declining, then fundamental challenges to ecosystem health remain to be addressed and failure to address these will ultimately lead to declines in lagging indicators such as water quality. Although our model and indicators are specific to the MARB, we believe that the generalized framework and the process of model and indicator development will be valuable in an array of altered river basins.

The consistently low activity level of the old solar analog 51 Peg not only facilitated the discovery of the first hot Jupiter, but also led to the suggestion that the star could be experiencing a magnetic grand minimum. However, the 50 yr time series showing minimal chromospheric variability could also be associated with the onset of weakened magnetic braking (WMB), where sufficiently slow rotation disrupts cycling activity and the production of large-scale magnetic fields by the stellar dynamo, thereby shrinking the Alfvén radius and inhibiting the efficient loss of angular momentum to magnetized stellar winds. In this Letter, we evaluate the magnetic evolutionary state of 51 Peg by estimating its wind braking torque. We use new spectropolarimetric measurements from the Large Binocular Telescope to reconstruct the large-scale magnetic morphology, we reanalyze archival X-ray measurements to estimate the mass-loss rate, and we detect solar-like oscillations in photometry from the Transiting Exoplanet Survey Satellite, yielding precise stellar properties from asteroseismology. Our estimate of the wind braking torque for 51 Peg clearly places it in the WMB regime, driven by changes in the mass-loss rate and the magnetic field strength and morphology that substantially exceed theoretical expectations. Although our revised stellar properties have minimal consequences for the characterization of the exoplanet, they have interesting implications for the current space weather environment of the system.

OBJECTIVES/GOALS: The 'field effect' is a concept in pathology that pre-malignant tissue changes forecast health. Spatial transcriptomics could detect these changes earlier than histopathology, suggesting new early cancer screening methods. Knowing how normal tissue damage relates to cancer’s origin and progression may improve long-term outcomes. METHODS/STUDY POPULATION: We trained DEGAS, our machine learning framework, with prostate cancer data, combining both general cancer patterns and in-depth genetic information from individual tumors. The Tumor Cancer Genome Atlas (TCGA) shows how gene patterns in tumors relate to patient outcomes, emphasizing the differences between tumors from different patients (intertumor). On the other hand, spatial transcriptomics (ST) shows the genetic variety within a single tumor (intratumor) but has limited samples, making it hard to know which genetic differences are important for treatment. DEGAS bridges these areas by finding tissue sections that resemble those in TCGA profiles and are key indicators of patient survival. DEGAS serves as a valuable tool for generating clinically-important hypotheses. RESULTS/ANTICIPATED RESULTS: DEGAS identified benign-appearing glands in a normal prostate as being highly associated with poor progression-free survival. These glands have transcriptional signatures similar to high-grade prostate cancer. We confirmed this finding in a separate prostate cancer ST dataset. By integrating single cell (SC) data we demonstrated that cells annotated as cancerous in the SC data map to regions of benign glands in the ST dataset. We pinpoint several genes, chiefly Microseminoprotein-β (MSMB, PSP94), where reduced expression is highly correlated with poor progression-free survival. Cell type specific differential expression analysis further revealed that loss of MSMB expression associated with poor outcomes occurs specifically in luminal epithelia, the putative progenitor of prostate cancer. DISCUSSION/SIGNIFICANCE: DEGAS reveals that normal-appearing tissue can be highly-associated with tumor progression and underscores the importance of the 'field effect' in cancer research. Traditional analysis may miss such nuance, hiding key transitional cell states. Validating gene markers could boost early cancer detection and understanding of metastasis.

Abstract The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.

Summary: The E3 ubiquitin ligase Ube3a is biallelically expressed in neural progenitors and glial cells, suggesting that UBE3A gain-of-function mutations might cause neurodevelopmental disorders irrespective of parent of origin. Here, we engineered a mouse line that harbors an autism-linked UBE3AT485A (T503A in mouse) gain-of-function mutation and evaluated phenotypes in animals that inherited the mutant allele paternally, maternally, or from both parents. We find that paternally and maternally expressed UBE3AT503A results in elevated UBE3A activity in neural progenitors and glial cells. Expression of UBE3AT503A from the maternal allele, but not the paternal one, leads to a persistent elevation of UBE3A activity in neurons. Mutant mice display behavioral phenotypes that differ by parent of origin. Expression of UBE3AT503A, irrespective of its parent of origin, promotes transient embryonic expansion of Zcchc12 lineage interneurons. Phenotypes of Ube3aT503A mice are distinct from Angelman syndrome model mice. Our study has clinical implications for a growing number of disease-linked UBE3A gain-of-function mutations.

According to the recent world bank report, around 80% of a life-cycle cost of a road is devoted to maintenance which includes monitoring and repair processes. To more effectively keep road serviceability, knowing the current status of road health is crucial. Typically, the monitoring processes are handled by human-intensive procedures. So, automating this task could lead to saving time and cost and also, improve efficiency. Although, this task has been optimized by Ground Vehicles further. Yet it lacks the disadvantages of human-intensive procedure. As they are still semi-manual, creates traffic issues, and not being eco-friendly or cost efficient. In recent years, UAVs have been successfully utilized to handle a wide range of labor-based tasks including road assessments. This paper presents a drone-based solution to automate road monitoring and segmentation as well as addressing the practical challenges of using drones for this purpose. To do so, a platform is developed that controls a drone through a road monitoring flight using computer vision-based techniques. The platform, rather than sending maneuvering commands to the drone during a flight starting from takeoff to landing, firstly could detect road boundaries by finding vanishing points, and secondly, could identify the dash lines and the center of the road. Finally, the captured road is segmented and labeled with the temporal and geographical information supplied by the Inertial Measurement Unit (IMU) of the drone. It has been tried to optimize the platform in order to handle all the processes in real-time while the UAV is following the road during a flight. To evaluate the proposed idea, the developed platform is tested in urban areas. The achieved results demonstrate how effectively could detect and segment a road in different environments using an off-the-shelf UAV. This platform could improve the automation of the data gathering process required in road maintenance.

Asteroseismology of dwarf stars cooler than the Sun is very challenging owing to the low amplitudes and rapid timescales of oscillations. Here we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ( ${\nu }_{\max }\sim 4300$ μ Hz) in the nearby K dwarf σ Draconis using extreme-precision Doppler velocity observations from the Keck Planet Finder and 20 s cadence photometry from NASA’s Transiting Exoplanet Survey Satellite. The star is the coolest dwarf star to date with both velocity and luminosity observations of solar-like oscillations, having amplitudes of 5.9 ± 0.8 cm s ^−1 and 0.8 ± 0.2 ppm, respectively. These measured values are in excellent agreement with established luminosity−velocity amplitude relations for oscillations and provide further evidence that mode amplitudes for stars with T _eff < 5500 K diminish in scale following an ( L / M ) ^1.5 relation. By modeling the star’s oscillation frequencies from photometric data, we measure an asteroseismic age of 4.5 ± 0.9 (ran) ± 1.2 (sys) Gyr. The observations demonstrate the capability of next-generation spectrographs and precise space-based photometry to extend observational asteroseismology to nearby cool dwarfs, which are benchmarks for stellar astrophysics and prime targets for directly imaging planets using future space-based telescopes.

The solar-type subgiant β Hyi has long been studied as an old analog of the Sun. Although the rotation period has never been measured directly, it was estimated to be near 27 days. As a Southern Hemisphere target, it was not monitored by long-term stellar activity surveys, but archival International Ultraviolet Explorer data revealed a 12 yr activity cycle. Previous ground-based asteroseismology suggested that the star is slightly more massive and substantially larger and older than the Sun, so the similarity of both the rotation rate and the activity cycle period to solar values is perplexing. We use two months of precise time-series photometry from the Transiting Exoplanet Survey Satellite to detect solar-like oscillations in β Hyi and determine the fundamental stellar properties from asteroseismic modeling. We also obtain a direct measurement of the rotation period, which was previously estimated from an ultraviolet activity–rotation relation. We then use rotational evolution modeling to predict the rotation period expected from either standard spin-down or weakened magnetic braking (WMB). We conclude that the rotation period of β Hyi is consistent with WMB and that changes in stellar structure on the subgiant branch can reinvigorate the large-scale dynamo and briefly sustain magnetic activity cycles. Our results support the existence of a “born-again” dynamo in evolved subgiants—previously suggested to explain the cycle in 94 Aqr Aa—which can best be understood within the WMB scenario.

We present the atmospheric characterization of the substellar companion HD 19467 B as part of the pioneering James Webb Space Telescope Guaranteed Time Observer program to obtain moderate resolution spectra ( R ∼ 2700, 3–5 μ m) of a high-contrast companion with the NIRSpec integral field unit (IFU). HD 19467 B is an old, ∼9 Gyr, companion to a solar-type star with multiple measured dynamical masses. The spectra show detections of CO, CO _2 , CH _4 , and H _2 O. We forward model the spectra using Markov Chain Monte Carlo methods and atmospheric model grids to constrain the effective temperature and surface gravity. We then use NEWERA-PHOENIX grids to constrain nonequilibrium chemistry parameterized by K _zz and explore molecular abundance ratios of the detected molecules. We find an effective temperature of 1103 K, with a probable range from 1000 to 1200 K, a surface gravity of 4.50 dex, with a range of 4.14–5.00, and deep vertical mixing, log _10 ( K _zz ), of 5.03, with a range of 5.00–5.44. All molecular mixing ratios are approximately solar, leading to a C/O ∼ 0.55, which is expected from a T5.5 brown dwarf. Finally, we calculate an updated dynamical mass of HD 19467 B using newly derived NIRCam astrometry, which we find to be ${71.6}_{-4.6}^{+5.3}{M}_{\mathrm{Jup}}$ , in agreement with the mass range we derive from evolutionary models, which we find to be 63–75 M _Jup . These observations demonstrate the excellent capabilities of the NIRSpec IFU to achieve detailed spectral characterization of substellar companions at high contrast close to bright host stars, in this case at a separation of ∼1.″6 with a contrast of 10 ^−4 in the 3–5 μ m range.

Stellar spin down is a critical yet poorly understood component of stellar evolution. In particular, results from the Kepler Mission imply that mature age, solar-type stars have inefficient magnetic braking, resulting in a stalled spin-down rate. However, a large number of precise asteroseismic ages are needed for mature (≥3 Gyr) stars in order to probe the regime where traditional and stalled spin-down models differ. In this paper, we present a new asteroseismic benchmark star for gyrochronology discovered using reprocessed Kepler short cadence data. KIC 11029516 (Papayu) is a bright ( Kp = 9.6 mag) solar-type star with a well-measured rotation period (21.1 ± 0.8 days) from spot modulation using 4 yr of Kepler long-cadence data. We combine asteroseismology and spectroscopy to obtain T _eff = 5888 ± 100 K, [Fe/H] = 0.30 ± 0.06 dex, M = 1.24 ± 0.05 M _⊙ , R = 1.34 ± 0.02 R _⊙ , and age of 4.0 ± 0.4 Gyr, making Papayu one of the most similar stars to the Sun in terms of temperature and radius with an asteroseismic age and a rotation period measured from spot modulation. We find that Papayu sits at the transition of where traditional and weakened spin-down models diverge. A comparison with stars of similar zero-age main-sequence temperatures supports previous findings that weakened spin-down models are required to explain the ages and rotation periods of old solar-type stars.

The JWST NIRSpec integral field unit (IFU) presents a unique opportunity to observe directly imaged exoplanets from 3 to 5 μ m at moderate spectral resolution ( R ∼ 2700) and thereby better constrain the composition, disequilibrium chemistry, and cloud properties of their atmospheres. In this work, we present the first NIRSpec IFU high-contrast observations of a substellar companion that requires starlight suppression techniques. We develop specific data-reduction strategies to study faint companions around bright stars and assess the performance of NIRSpec at high contrast. First, we demonstrate an approach to forward model the companion signal and the starlight directly in the detector images, which mitigates the effects of NIRSpec’s spatial undersampling. We demonstrate a sensitivity to planets that are 3 × 10 ^−6 fainter than their stars at 1″, or 3 × 10 ^−5 at 0.″3. Then, we implement a reference star point-spread function subtraction and a spectral extraction that does not require spatially and spectrally regularly sampled spectral cubes. This allows us to extract a moderate resolution ( R ∼ 2,700) spectrum of the faint T dwarf companion HD 19467 B from 2.9 to 5.2 μ m with a signal-to-noise ratio of ∼10 per resolution element. Across this wavelength range, HD 19467 B has a flux ratio varying between 10 ^−5 and 10 ^−4 and a separation relative to its star of 1.″6. A companion paper by Hoch et al. more deeply analyzes the atmospheric properties of this companion based on the extracted spectrum. Using the methods developed here, NIRSpec’s sensitivity may enable direct detection and spectral characterization of relatively old (∼1 Gyr), cool (∼250 K), and closely separated (∼3–5 au) exoplanets that are less massive than Jupiter.

The 1RXS J034231.8+121622 system consists of an M dwarf primary and a directly imaged low-mass stellar companion. We use high-resolution spectroscopic data from Keck/KPIC to estimate the objects' atmospheric parameters and radial velocities (RVs). Using PHOENIX stellar models, we find that the primary has a temperature of 3460 ± 50 K and a metallicity of 0.16 ± 0.04, while the secondary has a temperature of 2510 ± 50 K and a metallicity of ${0.13}_{-0.11}^{+0.12}$ . Recent work suggests this system is associated with the Hyades, giving it an older age than previous estimates. Both metallicities agree with current Hyades [Fe/H] measurements (0.11–0.21). Using stellar evolutionary models, we obtain significantly higher masses for the objects, 0.30 ± 0.15 M _⊙ and 0.08 ± 0.01 M _⊙ (84 ± 11 M _Jup ), respectively. Using the RVs and a new astrometry point from Keck/NIRC2, we find that the system is likely an edge-on, moderately eccentric ( ${0.41}_{-0.08}^{+0.27}$ ) configuration. We also estimate the C/O ratio of both objects using custom grid models, obtaining 0.42 ± 0.10 (primary) and 0.55 ± 0.10 (companion). From these results, we confirm that this system most likely went through a binary star formation process in the Hyades. The significant changes in this system's parameters since its discovery highlight the importance of high-resolution spectroscopy for both orbital and atmospheric characterization of directly imaged companions.

In the late stage of terrestrial planet formation, planets are predicted to undergo pairwise collisions known as giant impacts. Here, we present a high-resolution database of giant impacts for differentiated colliding bodies of iron–silicate composition, with target masses ranging from 1 × 10 ^−4 M _⊕ up to super-Earths (5 M _⊕ ). We vary the impactor-to-target mass ratio, core–mantle (iron–silicate) fraction, impact velocity, and impact angle. Strength in the form of friction is included in all simulations. We find that, due to strength, the collisions with bodies smaller than about 2 ×10 ^−3 M _⊕ can result in irregular shapes, compound-core structures, and captured binaries. We observe that the characteristic escaping velocity of smaller remnants (debris) is approximately half of the impact velocity, significantly faster than currently assumed in N -body simulations of planet formation. Incorporating these results in N -body planet formation studies would provide more realistic debris–debris and debris–planet interactions.

Despite a growing sample of precisely measured stellar rotation periods and ages, the strength of magnetic braking and the degree of departure from standard (Skumanich-like) spin-down have remained persistent questions, particularly for stars more evolved than the Sun. Rotation periods can be measured for stars older than the Sun by leveraging asteroseismology, enabling models to be tested against a larger sample of old field stars. Because asteroseismic measurements of rotation do not depend on starspot modulation, they avoid potential biases introduced by the need for a stellar dynamo to drive starspot production. Using a neural network trained on a grid of stellar evolution models and a hierarchical model-fitting approach, we constrain the onset of weakened magnetic braking (WMB). We find that a sample of stars with asteroseismically measured rotation periods and ages is consistent with models that depart from standard spin-down prior to reaching the evolutionary stage of the Sun. We test our approach using neural networks trained on model grids produced by separate stellar evolution codes with differing physical assumptions and find that the choices of grid physics can influence the inferred properties of the braking law. We identify the normalized critical Rossby number Ro _crit /Ro _⊙ = 0.91 ± 0.03 as the threshold for the departure from standard rotational evolution. This suggests that WMB poses challenges to gyrochronology for roughly half of the main-sequence lifetime of Sun-like stars.

Abstract The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale with problem size and complexity for realistic hardware implementations. Here, we quantify scaling of the expected resource requirements by synthesizing optimized circuits for hardware architectures with varying levels of connectivity. Assuming noisy gate operations, we estimate the number of measurements needed to sample the output of the idealized QAOA circuit with high probability. We show the number of measurements, and hence total time to solution, grows exponentially in problem size and problem graph degree as well as depth of the QAOA ansatz, gate infidelities, and inverse hardware graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.

Abstract The increasing number of positron-emission tomography (PET) tracers being developed to aid drug development and create new diagnostics has led to an increased need for radiosynthesis development and optimization. Current radiosynthesis instruments are designed to produce large-scale clinical batches and are often limited to performing a single synthesis before they must be decontaminated by waiting for radionuclide decay, followed by thorough cleaning or disposal of synthesizer components. Though with some radiosynthesizers it is possible to perform a few sequential radiosyntheses in a day, none allow for parallel radiosyntheses. Throughput of one or a few experiments per day is not well suited for rapid optimization experiments. To combat these limitations, we leverage the advantages of droplet-radiochemistry to create a new platform for high-throughput experimentation in radiochemistry. This system contains an array of 4 heaters, each used to heat a set of 16 reactions on a small chip, enabling 64 parallel reactions for the rapid optimization of conditions in any stage of a multi-step radiosynthesis process. As examples, we study the syntheses of several 18F-labeled radiopharmaceuticals ([18F]Flumazenil, [18F]PBR06, [18F]Fallypride, and [18F]FEPPA), performing > 800 experiments to explore the influence of parameters including base type, base amount, precursor amount, solvent, reaction temperature, and reaction time. The experiments were carried out within only 15 experiment days, and the small volume (~ 10 μL compared to the ~ 1 mL scale of conventional instruments) consumed ~ 100 × less precursor per datapoint. This new method paves the way for more comprehensive optimization studies in radiochemistry and substantially shortening PET tracer development timelines.

Abstract The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing Tr(ρ2). The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows.

Abstract We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information “impressions,” which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer’s disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19 high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Abstract Background To help clinicians provide timely treatment and delay disease progression, it is crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early and late MCI stages before they progress to Alzheimer’s disease (AD). In the process of diagnosing MCI and AD in living patients, brain scans are collected using neuroimaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain scans measure the volume and molecular activity within the brain resulting in a very promising avenue to diagnose patients early in a minimally invasive manner. Methods We have developed an optimal transport based transfer learning model to discriminate between early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome the overfitting problem and improve model stability and prediction accuracy. Results With the transfer learning methods that we have developed, we outperform the current state of the art MCI stage classification frameworks and show that it is crucial to leverage Alzheimer’s disease and normal control subjects to accurately predict early and late stage cognitive impairment. Conclusions Our method is the current state of the art based on benchmark comparisons. This method is a necessary technological stepping stone to widespread clinical usage of MRI-based early detection of AD.

During the first half of their main-sequence lifetimes, stars rapidly lose angular momentum to their magnetized winds, a process known as magnetic braking. Recent observations suggest a substantial decrease in the magnetic braking efficiency when stars reach a critical value of the Rossby number, the stellar rotation period normalized by the convective overturn timescale. Cooler stars have deeper convection zones with longer overturn times, reaching this critical Rossby number at slower rotation rates. The nature and timing of the transition to weakened magnetic braking have previously been constrained by several solar analogs and two slightly hotter stars. In this Letter, we derive the first direct constraints from stars cooler than the Sun. We present new spectropolarimetry of the old G8 dwarf τ Cet from the Large Binocular Telescope, and we reanalyze a published Zeeman Doppler image of the younger G8 star 61 UMa, yielding the large-scale magnetic field strengths and morphologies. We estimate mass-loss rates using archival X-ray observations and inferences from Ly α measurements, and we adopt other stellar properties from asteroseismology and spectral energy distribution fitting. The resulting calculations of the wind braking torque demonstrate that the rate of angular momentum loss drops by a factor of 300 between the ages of these two stars (1.4–9 Gyr), well above theoretical expectations. We summarize the available data to help constrain the value of the critical Rossby number, and we identify a new signature of the long-period detection edge in recent measurements from the Kepler mission.

Abstract A composable design scheme is presented for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. The proposed object‐oriented approach is based on constructing an expressive set of common data structures and methods that enables programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of the scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows synthesis of new hybrid algorithms and workflows via the extension, specialisation, and dynamic customisation of the abstract core classes defined by the proposed design. The design scheme is implemented using the hardware‐agnostic programming language QCOR into the QuaSiMo library. To validate the implementation, the authors test and show its utility on commercial quantum processors from IBM and Rigetti, running some prototypical quantum simulations.

Current Food and Drug Administration (FDA)-approved CD19-specific chimeric antigen receptor (CAR) T-cell therapies for B-cell malignancies are constitutively active and while efficacious, can cause morbidity and mortality. Their toxicities might be reduced if CAR T-cell activity was regulatable rather than constitutive. To test this, we compared the efficacies and morbidities of constitutively active (conventional) and regulatable (switchable) CAR (sCAR) T-cells specific for human CD19 (huCD19) in an immune-competent huCD19+ transgenic mouse model.Conventional CAR (CAR19) and sCAR T-cells were generated by retrovirally transducing C57BL/6 (B6) congenic T-cells with constructs encoding antibody-derived single chain Fv (sFv) fragments specific for huCD19 or a peptide neoepitope (PNE), respectively. Transduced T-cells were adoptively transferred into huCD19 transgenic hemizygous (huCD19Tg/0) B6 mice; healthy B-cells in these mice expressed huCD19Tg. Prior to transfer, recipients were treated with a lymphodepleting dose of cyclophosphamide to enhance T-cell engraftment. In tumor therapy experiments, CAR19 or sCAR T-cells were adoptively transferred into huCD19Tg/0 mice bearing a syngeneic B-cell lymphoma engineered to express huCD19. To regulate sCAR T cell function, a switch protein was generated that contained the sCAR-specific PNE genetically fused to an anti-huCD19 Fab fragment. Recipients of sCAR T-cells were injected with the switch to link sCAR effector with huCD19+ target cells. Mice were monitored for survival, tumor burden (where appropriate), morbidity (as measured by weight loss and clinical scores), and peripheral blood lymphocyte frequency.CAR19 and sCAR T-cells functioned comparably regarding in vivo expansion and B-cell depletion. However, sCAR T-cells were better tolerated as evidenced by the recipients’ enhanced survival, reduced weight loss, and improved clinical scores. Discontinuing switch administration allowed healthy B-cell frequencies to return to pretreatment levels.In our mouse model, sCAR T-cells killed huCD19+ healthy and malignant B-cells and were better tolerated than CAR19 cells. Our data suggest sCAR might be clinically superior to the current FDA-approved therapies for B-cell lymphomas due to the reduced acute and chronic morbidities and mortality, lower incidence and severity of side effects, and B-cell reconstitution on cessation of switch administration.

Abstract The road to computing on quantum devices has been accelerated by the promises that come from using Shor’s algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as $$O(n^{5}d)$$ O ( n 5 d ) , where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method’s performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.

Anthropologists develop long-term engagements with communities, animals, and the ecosystems they all share. This approach can provide important context that is necessary for One Health research, which may otherwise overlook the perspectives and lived experiences of community members. This paper presents two case studies that illustrate the importance of leveraging long-term, holistic, engagements with communities in moving the One Health concept forward. The first illustrates the complexity of understanding the health of people and animals within the context of environmental change in South India. The second provides insights into how the conservation of endangered species requires considering the entanglements of people, domestic animals, and the landscapes they share with wildlife in Madagascar. We demonstrate the value of integrating anthropological perspectives within interdisciplinary One Health research and interventions to better understand the complexity of systems.

Abstract Offshore decommissioning activities are expected to increase as oil and gas subsea infrastructure becomes obsolete. Decisions on decommissioning alternatives will benefit from quantifying and understanding the marine communities associated with these structures. As a case study, fish assemblages associated with an inshore network of subsea pipelines located on the North West shelf of Western Australia were compared to those in surrounding natural reef and soft sediment habitats using remotely operated vehicles fitted with a stereo-video system (stereo-ROVs). The number of species, the abundance, biomass, feeding guild composition and the economic value of fishes were compared among habitats. The community composition of fish associated with pipelines was distinct from those associated with natural habitats, and was characterised by a greater abundance and/or biomass of fish from higher trophic levels (e.g. piscivores, generalist carnivores and invertivores), including many species considered to be of value to commercial and recreational fishers. Biomass of fish on pipelines was, on average, 20 times greater than soft sediments, and was similar to natural reefs. However, the biomass of species considered important to fisheries recorded on the pipelines was, on average 3.5 times greater than reef and 44.5 times greater than soft sediment habitats. This study demonstrates that fish assemblages on the pipeline infrastructure exhibit high ecological and socioeconomic values.

Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.

Abstract Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.