UMD General Research Works

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    Centering relationships in collegiate leadership curricula
    (Wiley, 2024-02-08) Holder, Courtney; Pursley, Dana
    The relational leadership model and the five practices of exemplary leadership are widely used models that both emphasize a relational approach to leadership and center the collegiate context as a transformative environment for practicing and developing leadership. This article highlights two different applications of these models and provides important considerations for designing relational leadership curricula and programs for college students.
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    Including Campus Forest Carbon Estimates Into Climate Mitigation Planning -- Year 2
    (2022-05-01) Albee, Maddy; Hoffman Delett, Camille; Panday, Frances Marie; Patterson, Amelia; James, Jarrett; Hurtt, George C.; Lamb, Rachel
    Summary of project led by student researchers in the UMD Department of Geographical Sciences to integrate high-resolution forest carbon estimates into the University of Maryland's Climate Action Plan and GHG Inventory. Covers year 2 progress of a three-year project funded by the UMD Sustainability Fund.
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    Supplementary materials for statistical and machine learning analyses demonstrate test-retest reliability assessment is misled by focusing on total duration of mobility tasks in Parkinson's disease
    (2023) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.
    Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into dynamic motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We evaluate the test-retest reliability of these tasks by assessing the performance of machine learning models based on quantitative sensor-derived measures, and statistical measures to examine total duration, subtask duration, and other quantitative measures across both trials. We show that the diagnostic accuracy of differentiating between PD and control participants decreases from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing relevant information. Although the total duration remains relatively consistent between trials, there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks overlooks the nuanced variations in movement captured by other quantitative measures.
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    An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches
    (MDPI, 2018-04-18) Fang, Li; Zhan, Xiwu; Hain, Christopher R.; Yin, Jifu; Liu, Jicheng; Schull, Mitchell A.
    Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.
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    How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area
    (MDPI, 2019-06-20) Nasri, Arefeh; Zhang, Lei
    Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.
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    Thermodynamic, Non-Extensive, or Turbulent Quasi-Equilibrium for the Space Plasma Environment
    (MDPI, 2019-08-22) Yoon, Peter H.
    The Boltzmann–Gibbs (BG) entropy has been used in a wide variety of problems for more than a century. It is well known that BG entropy is additive and extensive, but for certain systems such as those dictated by long-range interactions, it is speculated that the entropy must be non-additive and non-extensive. Tsallis entropy possesses these characteristics, and is parameterized by a variable q (𝑞=1 being the classic BG limit), but unless q is determined from microscopic dynamics, the model remains a phenomenological tool. To this day, very few examples have emerged in which q can be computed from first principles. This paper shows that the space plasma environment, which is governed by long-range collective electromagnetic interaction, represents a perfect example for which the q parameter can be computed from microphysics. By taking the electron velocity distribution function measured in the heliospheric environment into account, and considering them to be in a quasi-equilibrium state with electrostatic turbulence known as quasi-thermal noise, it is shown that the value corresponding to 𝑞=9/13=0.6923, or alternatively 𝑞=5/9=0.5556, may be deduced. This prediction is verified against observations made by spacecraft, and it is shown to be in excellent agreement. This paper constitutes an overview of recent developments regarding the non-equilibrium statistical mechanical approach to understanding the non-extensive nature of space plasma, although some recent new developments are also discussed.
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    Element Abundances of Solar Energetic Particles and the Photosphere, the Corona, and the Solar Wind
    (MDPI, 2019-11-20) Reames, Donald V.
    From a turbulent history, the study of the abundances of elements in solar energetic particles (SEPs) has grown into an extensive field that probes the solar corona and physical processes of SEP acceleration and transport. Underlying SEPs are the abundances of the solar corona, which differ from photospheric abundances as a function of the first ionization potentials (FIPs) of the elements. The FIP-dependence of SEPs also differs from that of the solar wind; each has a different magnetic environment, where low-FIP ions and high-FIP neutral atoms rise toward the corona. Two major sources generate SEPs: The small “impulsive” SEP events are associated with magnetic reconnection in solar jets that produce 1000-fold enhancements from H to Pb as a function of mass-to-charge ratio A/Q, and also 1000-fold enhancements in 3He/4He that are produced by resonant wave-particle interactions. In large “gradual” events, SEPs are accelerated at shock waves that are driven out from the Sun by wide, fast coronal mass ejections (CMEs). A/Q dependence of ion transport allows us to estimate Q and hence the source plasma temperature T. Weaker shock waves favor the reacceleration of suprathermal ions accumulated from earlier impulsive SEP events, along with protons from the ambient plasma. In strong shocks, the ambient plasma dominates. Ions from impulsive sources have T ≈ 3 MK; those from ambient coronal plasma have T = 1 – 2 MK. These FIP- and A/Q-dependences explore complex new interactions in the corona and in SEP sources.
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    A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems
    (MDPI, 2020-03-12) Lewis, Austin D.; Groth, Katrina M.
    Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.
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    Pilot Study to Detect Genes Involved in DNA Damage and Cancer in Humans: Potential Biomarkers of Exposure to E-Cigarette Aerosols
    (MDPI, 2021-03-22) Hamad, Samera H.; Brinkman, Marielle C.; Tsai, Yi-Hsuan; Mellouk, Namya; Cross, Kandice; Jaspers, Ilona; Clark, Pamela I.; Granville, Courtney A.
    There is a paucity of data on how gene expression enables identification of individuals who are at risk of exposure to carcinogens from e-cigarette (e-cig) vaping; and how human vaping behaviors modify these exposures. This pilot study aimed to identify genes regulated from acute exposure to e-cig using RT-qPCR. Three subjects (2M and 1F) made three visits to the lab (nTOT = 9 visits); buccal and blood samples were collected before and immediately after scripted vaping 20 puffs (nTOT = 18 samples); vaping topography data were collected in each session. Subjects used their own e-cig containing 50:50 propylene glycol (PG):vegetable glycerine (VG) +3–6 mg/mL nicotine. The tumor suppressor TP53 was significantly upregulated in buccal samples. TP53 expression was puff volume and flow rate dependent in both tissues. In blood, the significant downregulation of N-methylpurine DNA glycosylase (MPG), a base excision repair gene, was consistent across all subjects. In addition to DNA repair pathway, cell cycle and cancer pathways were the most enriched pathways in buccal and blood samples, respectively. This pilot study demonstrates that vaping 20 puffs significantly alters expression of TP53 in human tissues; vaping behavior is an important modifier of this response. A larger study is needed to confirm these relationships.
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    Assessing Hydrologic Cycle Dynamics Using High-Resolution Satellite Imagery
    (2023-10-16) Bachhu, Ankith; Petković, Veljko; Berbery, Ernesto Hugo; Petković, Veljko; Berbery, Ernesto Hugo
    This study presents an investigation of the hydrologic cycle over a two-decade span (2000 – 2020) using high-resolution satellite products, in-situ measurements, and modeled data. The scope of this work encompasses an examination of the accuracy of satellite-based estimates in calculating the water budget, both on a global scale and within the Mississippi River Basin. The global assessment considers land areas spanning latitudes 90°S to 90°N, while the Mississippi River Basin includes the Lower Mississippi, Arkansas-Red, Missouri, Ohio, and North Central sub-basins. We utilize the IMERG version-6 and PERSIANN precipitation datasets to quantify water inflow over these regions. Correspondingly, water outflow estimates incorporate the GLEAM product for evaporation, G-RUN and ERA5 datasets for runoff, and SMOPS and SMAP estimates for changes in soil moisture. The assessment of water budget changes assesses the difference between Inflow (Precipitation) and Outflow (Runoff, Evaporation, Δ Soil Moisture) components. Our findings reveal discernible discrepancies in the global water budget over an annual cycle, indicating the presence of water “leaks”. These leaks, warranting further investigation, may be attributed to factors such as snow, ice, and groundwater dynamics, which fall outside the scope of this study. On a smaller basin scale, the closure of the water budget is estimated to fall within the combined products’ uncertainty. This provides additional validation for the suspected factors contributing to the global scale “leak.” Analyzing the annual water cycle components, we find the inherent variability and uncertainty associated with satellite-derived products. The study advances comprehension of hydrologic processes and underscores the imperative for enhanced accuracy in satellite-based measurements. Notably, our findings accentuate the importance of a closed water budget as a defining criterion for the accuracy of these satellite-derived products.
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    A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera
    (MDPI, 2021-08-05) Usmani, Moiz; Brumfield, Kyle D.; Jamal, Yusuf; Huq, Anwar; Colwell, Rita R.; Jutla, Antarpreet
    Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal–oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction.
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    The Evolution of Research on Abundances of Solar Energetic Particles
    (MDPI, 2021-08-08) Reames, Donald V.
    Sixty years of study of energetic particle abundances have made a major contribution to our understanding of the physics of solar energetic particles (SEPs) or solar cosmic rays. An early surprise was the observation in small SEP events of huge enhancements in the isotope 3He from resonant wave–particle interactions, and the subsequent observation of accompanying enhancements of heavy ions, later found to increase 1000-fold as a steep power of the mass-to-charge ratio A/Q, right across the elements from H to Pb. These “impulsive” SEP events have been related to magnetic reconnection on open field lines in solar jets; similar processes occur on closed loops in flares, but those SEPs are trapped and dissipate their energy in heat and light. After early controversy, it was established that particles in the large “gradual” SEP events are accelerated at shock waves driven by wide, fast coronal mass ejections (CMEs) that expand broadly. On average, gradual SEP events give us a measure of element abundances in the solar corona, which differ from those in the photosphere as a classic function of the first ionization potential (FIP) of the elements, distinguishing ions and neutrals. Departures from the average in gradual SEPs are also power laws in A/Q, and fits of this dependence can determine Q values and thus estimate source plasma temperatures. Complications arise when shock waves reaccelerate residual ions from the impulsive events, but excess protons and the extent of abundance variations help to resolve these processes. Yet, specific questions about SEP abundances remain.
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    Environmental Injustice and Industrial Chicken Farming in Maryland
    (MDPI, 2021-10-20) Hall, Jonathan; Galarraga, Joseph; Berman, Isabelle; Edwards, Camryn; Khanjar, Niya; Kavi, Lucy; Murray, Rianna; Burwell-Naney, Kristen; Jiang, Chengsheng; Wilson, Sacoby
    Maryland’s growing chicken industry, including concentrated animal feeding operations (CAFOs) and meat processing plants, raises a number of concerns regarding public health and environmental justice. Using hot spot analysis, we analyzed the totality of Maryland’s CAFOs and meat processing plants and those restricted to the Eastern Shore to assess whether communities of color and/or low socioeconomic status communities disproportionately hosted these types of facilities at the census tract level. We used zero-inflated regression modeling to determine the strength of the associations between environmental justice variables and the location of CAFOs and meatpacking facilities at the State level and on the Eastern Shore. Hot spot analyses demonstrated that CAFO hot spots on the Eastern Shore were located in counties with some of the lowest wealth in the State, including the lowest ranking county—Somerset. Zero-inflated regression models demonstrated that increases in median household income across the state were associated with a 0.04-unit reduction in CAFOs. For every unit increase in the percentage of people of color (POC), there was a 0.02-unit increase in meat processing facilities across the state. The distribution of CAFOs and meat processing plants across Maryland may contribute to poor health outcomes in areas affected by such production, and contribute to health disparities and health inequity.
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    Limits to Perception by Quantum Monitoring with Finite Efficiency
    (MDPI, 2021-11-17) García-Pintos, Luis Pedro; del Campo, Adolfo
    We formulate limits to perception under continuous quantum measurements by comparing the quantum states assigned by agents that have partial access to measurement outcomes. To this end, we provide bounds on the trace distance and the relative entropy between the assigned state and the actual state of the system. These bounds are expressed solely in terms of the purity and von Neumann entropy of the state assigned by the agent, and are shown to characterize how an agent’s perception of the system is altered by access to additional information. We apply our results to Gaussian states and to the dynamics of a system embedded in an environment illustrated on a quantum Ising chain.
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    From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
    (MDPI, 2021-12-21) Cao, Chun-Jun
    In this note, I review a recent approach to quantum gravity that “gravitizes” quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insights from tensor network toy models in AdS/CFT, I propose that approximate quantum error correction codes, when re-adapted into the aforementioned framework, also have promise in emerging gravity in near-flat geometries.
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    Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery
    (MDPI, 2022-02-02) Khemani, Varun; Azarian, Michael H.; Pecht, Michael G.
    Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.
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    Evaluation of SNPP and NOAA-20 VIIRS Datasets Using RadCalNet and Landsat 8/OLI Data
    (MDPI, 2022-08-12) Jing, Xin; Uprety, Sirish; Liu, Tung-Chang; Zhang, Bin; Shao, Xi
    In this study, we used RVUS data from RadCalNet as a benchmark to verify the radiometric accuracy and stability of operational and reprocessed SNPP/VIIRS data and the accuracy of NOAA-20/VIIRS data, as well as to assess the efficiency of the SNPP/VIIRS reprocessing algorithm. In addition, to remove the uncertainty of the RVUS site itself, we used Landsat 8/OLI as another benchmark with which to validate the accuracy and stability of VIIRS data through the RUVS site. The radiometric biases of the operational and reprocessed SNPP VIIRS bands were within ±4% and ±2%, respectively, as compared with the RUVS site and OLI, except for the M10 and M11 bands. In particular, the biases of the M5 and M7 bands were reduced by ~2% in this study. NOAA-20 VIIRS, on the other hand, was consistently lower than SNPP by ~2 to ~4% for all the bands. For the equivalent bands, the drift differences between operational and reprocessed SNPP/VIIRS and OLI were no larger than 0.24%/year and 0.1%/year, respectively. The reprocessing algorithm of SNPP VIIRS efficiently improved the radiometric accuracy and stability of the SNPP/VIIRS dataset to meet its specifications.
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    Efficient Identification of Jiles–Atherton Model Parameters Using Space-Filling Designs and Genetic Algorithms
    (MDPI, 2022-08-18) Khemani, Varun; Azarian, Michael H.; Pecht, Michael G.
    The Jiles–Atherton model is widespread in the hysteresis description of ferromagnetic, ferroelectric, magneto strictive, and piezoelectric materials. However, the determination of model parameters is not straightforward because the model involves numerical integration and the solving of ordinary differential equations, both of which are error prone. As a result, stochastic optimization techniques have been used to explore the vast ranges of these parameters in an effort to identify the parameter values that minimize the error differential between experimental and modelled hysteresis curves. Because of the time-consuming nature of these optimization techniques, this paper explores the design space of the parameters using a space-filling design. This design provides a narrower range of parameters to look at with optimization algorithms, thereby reducing the time required to identify the optimal Jiles–Atherton model parameters. This procedure can also be carried out without using expensive hysteresis measurement devices, provided the desired transformer’s secondary voltage is known.
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    A Calibrated Lunar Microwave Radiative Transfer Model Based on Satellite Observations
    (MDPI, 2022-11-01) Yang, Hu; Burgdorf, Martin
    As a potential external calibration reference for spaceborne microwave sounding instruments, accurate and reliable information of lunar disk-averaged radiance at millimeter band are important and fundamental. Based on study for 2-D lunar scans of the Advanced Technology Microwave Sounder (ATMS) on board the NOAA-20 satellite, the lunar radiance spectrum from 23 to 183 GHz at full moon phase has been reported in our previous work. In this study, the performance of a lunar microwave radiative transfer model (RTM) developed by Keihm was investigated (cited as Keihm model in this paper) . By taking the ATMS observations as the reference truth, the surface emissivity in the lunar RTM can be calibrated. The calibrated RTM model was then evaluated by independent satellite observation data sets from AMSU (Advanced Microwave Sounding Unit) and MHS (Microwave Humidity Sounder) instruments on several NOAA satellites. Results show that with the calibrated model, significant improvement can be made to reduce the uncertainties in the lunar microwave RTM simulations at millimeter wavelengths.
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    Noncovalent PEGylation of protein and peptide therapeutics
    (Wiley, 2023-05-03) Andrianov, Alexander K.
    Clinical applications of protein therapeutics—an advanced generation of drugs characterized by high biological specificity—are rapidly expanding. However, their development is often impeded by unfavorable pharmacokinetic profiles and largely relies on the use of drug delivery systems to prolong their in vivo half-life and suppress undesirable immunogenicity. Although a commercially established PEGylation technology based on protein conjugation with poly(ethylene glycol) (PEG)—protective steric shield resolves some of the challenges, the search for alternatives continues. Noncovalent PEGylation, which mainly relies on multivalent (cooperative) interactions and high affinity (host–guest) complexes formed between protein and PEG offers a number of potential advantages. Among them are dynamic or reversible protection of the protein with minimal loss of biological activity, drastically lower manufacturing costs, “mix-and-match” formulations approaches, and expanded scope of PEGylation targets. While a great number of innovative chemical approaches have been proposed in recent years, the ability to effectively control the stability of noncovalently assembled protein–PEG complexes under physiological conditions presents a serious challenge for the commercial development of the technology. In an attempt to identify critical factors affecting pharmacological behavior of noncovalently linked complexes, this Review follows a hierarchical analysis of various experimental techniques and resulting supramolecular architectures. The importance of in vivo administration routes, degradation patterns of PEGylating agents, and a multitude of potential exchange reactions with constituents of physiological compartments are highlighted.