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Worldwide secondary payload launch capabilities and future opportunities have been studied at the Jet Propulsion Laboratory (JPL). Launch vehicles have been identified as having near-term secondary payload opportunities for launching microspacecraft include: Pegasus, Taurus, LMLV-1, Delta II, Atlas II, Space Shuttle, Ariane 5, HIA, Molniya, Cosmos, and Proton.
Stratospheric measurements of H(sub 2)O and CH(sub 4) by the Atmospheric Trace Molecule Spectroscopy (ATMOS) Fourier transform spectrometer on the ATLAS-3 Shuttle flight in November 1994 have been used to investigate the altitude and geographic variability of H(sub 2)O and the quantity H(Eta?) = (H(sub 2)O + CH(sub 4)) in the tropics and at mid-latitudes (8-49in the northern hemisphere.
We have used recently released IRAS Sky Survey Atlas (ISSA) image data to investigate the surface brightness distribution of selected regions of infrared cirrus using power spectral analysis techniques.
A method for obtaining high time and spatial resolution convective cloud top data for the TTL Leonhard Pfister, Eric Jensen, Rei Ueyama, Eliot Atlas, and Maria Navarro Convective systems in the tropics have a maximum in the cloud top altitude distribution of about 13.5 km. However, there is a significant tail to this distribution -- a few percent reach the cold point tropopause (CPT) at 16.5 km, and there has been clear evidence of convective mass deposited as high as 19 km in the tropics. The region between 13.5 km and the cold point tropopause is transitional, between the free tropical troposphere where convective mixing dominates, and the stratosphere where slow upward ascent dominates. In this region (the Tropical Tropopause Layer), convective injection, slow ascent, and mixing from midlatitudes all have similar time scales. So, even though only a few percent of convective systems reach the CPT, convection is important. Space Based Lidar and cloud radar measurements have yielded information about long term average statistical distributions of cloud altitude as a function of location. However, we also need time-dependent cloud top altitude and cloud top potential temperature information, primarily to understand the water vapor and TTL cloud distributions. This is because the effect of convection depends on the local temperature, and on the subsequent temperature history. Time dependent cloud top information is also needed to understand short-lived tracers because cross-isentropic flow is time and space dependent. This paper presents a method of obtaining time and space dependent convective cloud top theta (and altitude) information using 3-hourly geostationary brightness temperature data, coupled with global 3 -hourly rainfall estimates and temperature analyses. We explore different mixing algorithms to obtain the most reasonable agreement with near-simultaneous observations by cloudsat and calipso. Observations of short-lived tracers from ATTREX, coupled with short-term trajectories are used to test the method's accuracy. An important caveat is the ambiguity of evaluating convective cloud top altitudes under from combined cloudsat and calipso measurements.
We have compared volume mixing ration profiles of N 2 O, CFC-11, CFC-12, CCI 4 , SF 6 , and HCI measured for the mid-latitude stratosphere by the ATMOS Fourier transform spectrometer during the ATLAS-3 Space Shuttle mission of NOvember 1994 with in situ measurements acquired aboard the NASA ER-2 aircraft during the same time period.
Season-to-season persistence of soil moisture drought varies across North America. Such inter-seasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool (December-April) and warm (May-July) season precipitation series and offers new insights on the intra-annual changes in drought for up to 2,000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and three-month long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the 20th century and are used to estimate relief and reversals for the most skillfully reconstructed past 500-years. Sub-continental to continental scale reversals of cool season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the Mid-Atlantic region and the US Southwest. Drought relief at the continental scale may arise in part from macroscale ocean-atmosphere processes, while the smaller scale regional reversals may reflect land-surface feedbacks and stochastic variability.
Across western North America (WNA), 20th-21st century anthropogenic warming has increased the prevalence and severity of concurrent drought and heat events, also termed hot droughts. However, the lack of independent spatial reconstructions of both soil moisture and temperature limits the potential to identify these events in the past and to place them in a long-term context. We develop the Western North American Temperature Atlas (WNATA), a data-independent 0.5° gridded reconstruction of summer maximum temperatures back to the 16th century. Our evaluation of the WNATA with existing hydroclimate reconstructions reveals an increasing association between maximum temperature and drought severity in recent decades, relative to the past five centuries. The synthesis of these paleo-reconstructions indicates that the amplification of the modern WNA megadrought by increased temperatures and the frequency and spatial extent of compound hot and dry conditions in the 21st century are likely unprecedented since at least the 16th century.
Groundwater has long been a critical source of potable water, supplying one-third of all global water withdrawals and providing strategic reserves in times of drought (Konikow, 2012; Famiglietti, 2014). The risk in relying on groundwater is that it is often pumped at rates far greater than it can be naturally replenished. With increasing population and a changing climate, the depletion of potable water supplies has become an ever-growing concern (Srinivasan et al., 2012). Recently, the concept of “resilience” has been applied to groundwater, leaving many questions to be explored in terms of what resilience should mean for aquifers (Foster and MacDonald, 2014; Grönwall and Oduro-Kwarteng, 2017). The term is commonly used to indicate resilience to impacts of climate change specifically, but here we consider resilience in the context of more general water insecurity. Relatedly, the concept of “sustainable yield” has emerged in an attempt to determine a metric that can ensure the long-term resilience of groundwater systems, but the definition is fraught with inherent uncertainties and its quantification poses challenges (Rudestam and Langridge, 2014; Grönwall and Oduro-Kwarteng, 2017). At the heart of these concepts is a focus on sustainable management of aquifers. A sustainable approach to managing groundwater resources usually involves balancing long-term abstraction with the long-term recharge taking into account the needs for environmental flows and implementing drought-preparedness measures (Robins et al. 2006; Foster and MacDonald, 2014). At a high level, a sustainable balance of groundwater abstraction versus aquifer recharge may be considered “Net Zero” (NZ) or “Net Positive” (NP) in cases where recharge is equal to or in excess of abstraction, respectively. However, NZ/NP water management programs that address these concerns have varied definitions of compliance, which include considerations for the application at hand (ILFI, 2020; USGBC, 2020). Locations with high risk of low or poor water supply may be particularly interested in pursuing NZ/NP water. The AQUEDUCT Water Risk Atlas (World Resources Institute, 2019) indicates long-term availability, providing risk levels for quantity and quality of water at specific locations across the globe, for existing and predicted future conditions. This paper follows the U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) NZ water definition adopted by the U.S. Army for use at their installations, including Army Reserve Centers (ARCs). This definition also relates to the U.S. Army’s current focus on water resilience, which includes short-term and long-term availability of water resources.
Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.
Efficient and accurate modeling of the coupled thermal-hydraulic-mechanical-chemical (THMC) processes in various rock formations is indispensable for designing energy geo-structures such as underground repositories for high-level nuclear wastes. Here, this work focuses on developing and verifying an implicit finite element solver for generic coupled THMC problems in geological settings. Starting from the mass, momentum, and energy balance laws, a specialized set of governing equations and a thermoporoelastic constitutive model is derived. This system is then solved by an implicit finite element (FE) scheme. Specifically, the residuals and the Jacobians are scripted in a user-defined element (UEL) subroutine which is then combined with the general-purpose FE software Abaqus Standard to solve initial-boundary value problems. Considering the complexity of the system, the UEL development follows a stepwise manner by first solving the coupled hydraulic-mechanical (HM) and thermal-hydraulic-mechanical (THM) equations before moving on to the full THMC problem. Each implementation step consists of at least one verification test by comparing computed results with closed-form analytical solutions to ensure that the various coupling effects are correctly realized. To demonstrate the robustness of the algorithm and to validate the UEL, a three-dimensional case study is performed with reference to the in-situ heating test of ATLAS at Belgium in 1980s. A hypothetical radionuclide leakage event is then simulated by activating the chemical-concentration degree of freedom and prescribing a constant high concentration at the heater's surface. The model predicts a limited contaminated regime after six years considering both diffusion and advection effects on species transport.
Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.
The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome.
We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.
A measurement of the production of a prompt J/ψ meson in association with a W ± boson with W± → μν and J/ψ → μ + μ - is presented for J/ψ transverse momenta in the range 8.5–150 GeV and rapidity |y J/ψ | < 2.1 using ATLAS data recorded in 2012 at the LHC. The data were taken at a proton-proton centre-of-mass energy of $\sqrt{s}$ = 8 TeV and correspond to an integrated luminosity of 20.3 fb -1 . The ratio of the prompt J/ψ plus W ± cross-section to the inclusive W ± cross-section is presented as a differential measurement as a function of J/ψ transverse momenta and compared with theoretical predictions using different double-parton-scattering cross-sections.
We present a complete model of a dark QCD sector with light dark pions, broadly motivated by hidden naturalness arguments. The dark quarks couple to the Standard Model via irrelevant Z- and Higgs-portal operators, which encode the low-energy effects of TeV-scale fermions interacting through Yukawa couplings with the Higgs field. The dark pions, depending on their CP properties, behave as either composite axion-like particles (ALPs) mixing with the Z or scalars mixing with the Higgs. The dark pion lifetimes fall naturally in the most interesting region for present and proposed searches for long-lived particles, at the LHC and beyond. This is demonstrated by studying in detail three benchmark scenarios for the symmetries and structure of the theory. Within a coherent framework, we analyze and compare the GeV-scale signatures of flavor-changing meson decays to dark pions, the weak-scale decays of Z and Higgs bosons to hidden hadrons, and the TeV-scale signals of the ultraviolet theory. New constraints are derived from B decays at CMS and from Z-initiated dark showers at LHCb, focusing on the displaced dimuon signature. We also emphasize the strong potential sensitivity of ATLAS and CMS to dark shower signals with large multiplicities and long lifetimes of the dark pions. As a key part of our phenomenological study, we perform a new data-driven calculation of the decays of a light ALP to exclusive hadronic Standard Model final states. The results are provided in a general form, applicable to any model with arbitrary flavor-diagonal couplings of the ALP to fermions.
We propose a new strategy to look for long-lived particles (LLP) at the LHC. The LLPs are produced at one experiment, but its decay products are detected by a detector at another experiment. We use a confining Hidden Valley scenario as a benchmark. Through showering and hadronization, the multiplicity of hidden mesons can be large, and their decay products, dimuon as chosen in this study, are typically too soft to pass triggers in traditional LHC searches. We find the best acceptance is achieved if we produce LLPs at collision points at the LHCb and ALICE experiments, and use the muon chamber of ATLAS for detection. This new search is cost-efficient since it does not require a new detector to be built. Meanwhile, it can provide coverage of interesting parameter space, which is complementary to other proposed LLP searches.
We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.
Bose-Einstein correlations of charged hadrons are measured over a broad multiplicity range, from a few particles up to about 250 reconstructed charged hadrons in proton-proton collisions at $ \sqrt{s} $ = 13 TeV. The results are based on data collected using the CMS detector at the LHC during runs with a special low-pileup configuration. Three analysis techniques with different degrees of dependence on simulations are used to remove the non-Bose-Einstein background from the correlation functions. All three methods give consistent results. The measured lengths of homogeneity are studied as functions of particle multiplicity as well as average pair transverse momentum and mass. The results are compared with data from both CMS and ATLAS at $ \sqrt{s} $ = 7 TeV, as well as with theoretical predictions.[graphic not available: see fulltext]