Complex Relationship Research Articles

Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

Structural adhesives have seen a surge in applications for bonding Fibre reinforced polymers to strengthen structures. However, being polymeric materials, they can exhibit viscoelasticity, leading to creep, which can adversely affect bonded strengthening systems. This paper delves into the nonlinear shear creep behaviour of a typical structural adhesive using a machine learning approach. A deep neural networks (DNN) model was trained using a dataset of 7176 data points obtained from 25 sets of butt joint shear creep tests performed at various stress levels and temperatures. The DNN model successfully captures the complex relationship between temperature, stress, time, and shear creep with a performance value of R2 larger than 0.99. The creep compliance curves predicted by the model are in close agreement with the experimental results, highlighting the ability of the DNN model to effectively model the complex nonlinear shear creep behaviour, which is difficult to achieve with traditional physics-based viscoelastic models. The SHapley Additive exPlanations analysis further identified temperature as the most significant factor influencing the creep, followed by stress and time. The constructed DNN model and its corresponding analyses can provide a valuable reference for the structural adhesive to limit the negative effects of creep in practical applications.

Dental health in the shadows of global commitments: Addressing neglected priorities in lower middle-income countries.

I am writing to draw your attention to a serious concern that requires our attention around global health commitments: the frequently disregarded and undervalued dental reforms. In the realm of global health commitments, dental reforms are often ignored and underestimated. (1) Despite significant advancements in many other facets of health owing to these commitments, dental health remains marginalized within its sphere creating a glaring disparity, especially in lower middle-income countries where it’s a crucial concern.(2) Dental health has a significant impact on a person's quality of life in addition to preventing oral diseases. It has a significant impact on a person's quality of life, having an impact on their mental and physical well-being. Neglected oral health can affect speech, self-esteem, and psychosocial wellbeing as well as cause pain, infections, malnutrition, and systemic health problems.(3) The disruptive relationship between oral diseases and significant medical conditions like diabetes, cardiovascular disease, and adverse pregnancy outcomes emphasizes the complex relationship between dental health and overall health. Despite efforts to promote understanding, such as Universal Health Coverage (UHC) and the International Health Regulations (IHR), oral health is often disregarded in global discussion. Particularly in lower middle-income countries, oral health is neglected due to a lack of tailored interventions, which decreases its significance. A significant leap forward has been made with the recent World Health Organization (WHO) resolution that declares oral health a global priority and calls for its inclusion in national strategies. The WHO has acknowledged the need to address this problem immediately.(4) The challenge, however, is recognition into concrete, commitment-backed activities and specific interventions. The resolution has potential, but its effectiveness will be judged by the concrete measures taken to bridge the gaps. The Sustainable Development Goals (SDGs) offer a potential reference point for rectifying this gap. By integrating oral health in SDGs like Goal 3 on excellent health and well-being, a holistic approach may be taken. This integration could help bridge the gap between international commitments, raise the public's awareness of oral health, and foster tailored initiatives for dental reforms. ---Continue

Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

An Analysis of the Palestinian Foreign Trade During the Period 1995–2022: A Gravity Model Approach

This research investigates the determinants of Palestinian foreign trade by conducting a comprehensive analysis of various factors influencing both export and import volumes, using the gravity model as the analytical framework. The considered determinants include per capita GDP, Consumer Price Index (CPI), trade agreements, Israeli closure of commercial borders, and Geographical Distance. In terms of exports, the results demonstrate a statistically significant positive relationship with per capita GDP, affirming the hypothesis that heightened economic growth is associated with increased exports. On the import side, the Consumer Price Index (CPI) reveals a statistically significant positive association, showing that increased inflation is linked to a decline in imports, likely due to reduced purchasing power. Contrastingly, trade agreements show a complex relationship with both positive and negative potential impacts on both exports and imports. The complex nature of its impact necessitates further investigation. The Israeli closure of the commercial borders significantly hampers both import and export volumes, underscoring the challenges imposed by Israeli restrictions. Geographical Distance does not emerge as a significant factor in either exports or imports, challenging established economic theories regarding the impact of proximity on trade volumes. The research findings highlight the profound impact of Israeli occupation policies on the Palestinian economy, resulting in significant economic distortions and impeding economic development over several decades. Furthermore, the research shows the inability of the Palestinian Authority to implement independent economic policies due to the dominant role of Israel in decision-making processes. This inability, combined with the significant economic distortions caused by Israeli occupation policies, hinders further economic growth and development, as the Palestinian Authority cannot undertake crucial economic initiatives.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints.

The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model. In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities. In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels. This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

Quantum dipole interactions and transient hydrogen bond orientation order in cells, cellular membranes and myelin sheath: Implications for MRI signal relaxation, anisotropy, and T1 magnetic field dependence.

Despite significant impact on the study of human brain, MRI lacks a theory of signal formation that integrates quantum interactions involving proton dipoles (a primary MRI signal source) with brain intricate cellular environment. The purpose of the present study is developing such a theory. We introduce the Transient Hydrogen Bond (THB) model, where THB-mediated quantum dipole interactions between water and protons of hydrophilic heads of amphipathic biomolecules forming cells, cellular membranes and myelin sheath serve as a major source of MR signal relaxation. The THB theory predicts the existence of a hydrogen-bond-driven structural order of dipole-dipole connections within THBs as a primary factor for the anisotropy observed in MRI signal relaxation. We have also demonstrated that the conventional Lorentzian spectral density function decreases too fast at high frequencies to adequately capture the field dependence of brain MRI signal relaxation. To bridge this gap, we introduced a stretched spectral density function that surpasses the limitations of Lorentzian dispersion. In human brain, our findings reveal that at any time point only about 4% to 7% of water protons are engaged in quantum encounters within THBs. These ultra-short (2 to 3 ns), but frequent quantum spin exchanges lead to gradual recovery of magnetization toward thermodynamic equilibrium, that is, relaxation of MRI signal. By incorporating quantum proton interactions involved in brain imaging, the THB approach introduces new insights on the complex relationship between brain tissue cellular structure and MRI measurements, thus offering a promising new tool for better understanding of brain microstructure in health and disease.

Dietary Iron Is Necessary to Support Proliferative Regeneration after Intestinal Injury

BackgroundTissue repair and regeneration in the gastrointestinal system are crucial for maintaining homeostasis, with the process relying on intricate cellular interactions and affected by micro- and macro-nutrients. Iron, essential for various biological functions, plays a dual role in tissue healing by potentially causing oxidative damage and participating in anti-inflammatory mechanisms, underscoring its complex relationship with inflammation and tissue repair. ObjectiveThe study aimed to elucidate the role of low dietary iron in gastrointestinal tissue repair. MethodsWe utilized quantitative iron measurements to assess iron levels in inflamed regions of patients with ulcerative colitis and Crohn’s disease. In addition, 3 mouse models of gastrointestinal injury/repair (dextran sulfate sodium-induced colitis, radiation injury, and wound biopsy) were used to assess the effects of low dietary iron on tissue repair. ResultsWe found that levels of iron in inflamed regions of both patients with ulcerative colitis and Crohn’s disease are elevated. Similarly, during gastrointestinal repair, iron levels were found to be heightened, specifically in intestinal epithelial cells across the 3 injury/repair models. Mice on a low-iron diet showed compromised tissue repair with reduced proliferation. In standard diet, epithelial cells and the stem cell compartment maintain adequate iron stores. However, during a period of iron deficiency, epithelial cells exhaust their iron reserves, whereas the stem cell compartments maintain their iron pools. During injury, when the stem compartment is disrupted, low iron levels impair proliferation and compromise repair mechanisms. ConclusionsLow dietary iron impairs intestinal repair through compromising the ability of epithelial cells to aid in intestinal proliferation.

The Comparative Analysis of Forgetting and Retention Strategies in Gamification-Based Assessment and Formative-Based Assessment: Their Impact on Motivation for Learning

Background and Aim: The study investigates how two assessment methods, gamification-based and formative-based affect motivation for learning and memory retention strategies in the corporate training setting. The primary objective was to compare the effectiveness s these assessment approaches in learning motivation and in forgetting and retention. Materials and Methods: In this study, seventy-six employees working at a duty-free retail store in a Middle Eastern airport took part. The participants were divided into two groups. One group took a gamification-based assessment using Kahoot! application, while the other group took a formative-based assessment. Both groups were evaluated for recall and recognition after learning for one week. After two weeks, the content was reviewed, and recall and recognition tests were conducted using Kahoot! application for relearning. Finally, after the assessment, both groups were given a questionnaire about staff motivation in learning. Results: Gamification has been found to significantly enhance learners' motivation, resulting in improved scores in various areas, including interest-enjoyment, perceived competence, and effort importance. However, it was found that learners felt less tension and pressure. Compared to traditional formative assessments, gamified approaches lead to higher levels of confidence and perceived effort in learning. This method has improved forgetting and retention, demonstrated by higher recall, recognition, and relearning scores. Furthermore, the gamified approach creates a stimulating and challenging learning environment, as decreased tension and pressure indicated. Conclusion: The study's findings support the existing research, indicating that gamification significantly boosts motivation and improves memory retention in corporate training. These insights reveal the complex relationship between emotions and evaluations during the learning process and highlight the potential of gamified methods in corporate training. Further research should explore the underlying mechanisms and their compatibility with the existing literature on memory and learning processes.

Dynamics of benthic microeukaryotic communities in a mangrove wetland invaded by Spartina alterniflora: Effects of vegetation, seasonality, and sediment depth

Benthic microeukaryotes are crucial mediators of biogeochemical cycles in coastal wetland ecosystems, yet their spatial and temporal variability remains poorly understood. This study delineates the diversity patterns of benthic microeukaryotes in a Spartina alterniflora-invaded mangrove ecosystem in Fujian, China. Using high-throughput sequencing of 18S rRNA gene transcripts, we identified the influences of vegetation, seasonality, and sediment depth on microeukaryotic communities. We discovered that vegetation cover significantly affects community composition, primarily driven by nutrient concentrations and pH. The community structure of microeukaryotes varied seasonally and vertically, correlating with changes in sediment temperature, pH, salinity, and fucoxanthin concentration. Notably, invasive Spartina alterniflora habitats showed enhanced heterotrophic interactions, suggesting that invasive species can reshape benthic microeukaryotic co-occurrence patterns. Seasonal co-occurrence patterns revealed dominant Bacillariophyta assemblages exhibited distinct network modules enriched in the cold (spring) and warm (summer and fall) seasons, respectively, which indicated potential ecological niche differentiation. Our findings reveal the complex relationships between environmental factors and benthic microeukaryotic diversity, offering insights into microbial responses to natural and invasive vegetation influences.

Using statistical models to depict the response of multi-timescale drought to forest cover change across climate zones

Abstract. The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modeling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored, owing to the complex relationship between drought conditions and forests. In this study, we use historical forest and climate datasets to explore the relationship between forest cover fraction and drought from 1992–2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of forest cover change, precipitation and temperature on droughts across different timescales and climate zones. Our findings reveal that precipitation is the dominant factor (among the three factors) leading to drought in the equatorial, temperate and snow regions, while temperature controls drought in the arid region. The impact of forest cover changes on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in the arid region, while temperature modulates the impact of forest cover changes on both short- and long-term drought in the arid region as well as only on long-term drought in the temperate region. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation.

Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes.

This study examines the relationship between physiological complexity, as measured by Approximate Entropy (ApEn) and Sample Entropy (SampEn), and fitness levels in female athletes. Our focus is on their association with maximal oxygen consumption (VO2,max). Our findings reveal a complex relationship between entropy metrics and fitness levels, indicating that higher fitness typically, though not invariably, correlates with greater entropy in physiological time series data; however, this is not consistent for all individuals. For Heart Rate (HR), entropy measures suggest stable patterns across fitness categories, while pulse oximetry (SpO2) data shows greater variability. For instance, the medium fitness group displayed an ApEn(HR) = 0.57±0.13 with a coefficient of variation (CV) of 22.17 and ApEn(SpO2) = 0.96±0.49 with a CV of 46.08%, compared to the excellent fitness group with ApEn(HR) = 0.60±0.09 with a CV of 15.19% and ApEn(SpO2) =0.85±0.42 with a CV of 49.46%, suggesting broader physiological responses among more fit individuals. The larger standard deviations and CVs for SpO2 entropy may indicate the body's proficient oxygen utilization at higher levels of physical demand. Our findings advocate for combining entropy metrics with wearable sensor technology for improved biomedical analysis and personalized healthcare.

Modulation of the Gut Microbiota by Nutrition and Its Relationship to Epigenetics.

The intestinal microbiota is a community of microorganisms inhabiting the human intestines, potentially influencing both physiological and pathophysiological processes in the human body. Existing evidence suggests that nutrients can influence the modulation of the gut microbiota. However, there is still limited evidence regarding the effects of vitamin and mineral supplementation on the human gut microbiota through epigenetic modification. It is plausible that maintaining an adequate dietary intake of vitamin D, iron, fibre, zinc and magnesium may have a beneficial effect on alleviating inflammation in the body, reducing oxidative stress, and improving the condition of the intestinal microbiota through various epigenetic mechanisms. Moreover, epigenetics involves alterations in the phenotype of a cell without changing its fundamental DNA sequence. It appears that the modulation of the microbiota by various nutrients may lead to epigenetic regulation. The correlations between microbiota and epigenetics are potentially interdependent. Therefore, the primary objective of this review is to identify the complex relationships between diet, gut microbiota, and epigenetic regulation. These interactions could play a crucial role in systemic health.

Hungry herbivores and thirsty plants: Browsing wildlife shape savanna tree transpiration independently of water use strategies

Abstract Plant transpiration is a key component of the hydrological cycle that moves water from the soil through plants to the atmosphere and is largely determined by environmental conditions. The challenges of highly variable climatic conditions and water scarcity in the semi‐arid savannas of southern Africa drive trees to exhibit different water use strategies. An additional challenge for savanna trees arises from the browsing of large mammals, and its potential impact on transpiration has largely been overlooked. To assess water use strategies and the impact of browsing on transpiration in three common savanna woody species (Colophospermum mopane, Catophractes alexandri, Senegalia mellifera), we conducted an extensive field experiment in a wildlife reserve in Namibia. We established a browsing gradient ranging from 0% to 100% leaf removal to disentangle complex relationships, and we measured sap flow representing whole‐tree transpiration, stomatal conductance as a proxy for leaf level transpiration, and environmental factors including soil moisture, solar radiation, air temperature and air humidity. We discovered a unimodal relationship between browsing intensity and sap flow, consistent across all species, and peaking at low to moderate browsing levels equivalent to approximately 30% leaf removal. This universal pattern is remarkable since we found water use strategies to differ among species: Colophospermum mopane and C. alexandri exhibit a water‐saving behaviour to reduce water loss under unfavourable conditions while S. mellifera demonstrates water‐spending characteristics. The deviation from a negative linear effect of browsing does not meet the functional expectation when browsing is translated into a simple leaf area reduction. Instead, it can be attributed to stomatal adjustments that partially compensate for transpiratory surface loss. We propose that this conceptual mechanism can be generalised to other woody species and various ecosystems. In addition, common transpiration models relying solely on leaf area indices and abiotic factors can be improved by including a non‐linear relationship to reproduce the effect of browsing accurately. Our findings highlight the role of herbivores in shaping the connection between the Earth's spheres and improve our understanding of ecohydrological and vegetation dynamics in Southern African savannas. Read the free Plain Language Summary for this article on the Journal blog.

THE RELATIONSHIP BETWEEN INTERNET USAGE PREVALENCE, FAMILY DYNAMICS AND ADOLESCENT RESILIENCE: A COMPREHENSIVE REVIEW

In the modern digital era, characterized by pervasive internet usage, understanding the complex relationship between online prevalence, family dynamics, and adolescent resilience is an essential area of study. Through the analysis of these variables, this review study seeks to comprehend how internet usage among adolescents affects their behaviour, mental health, family dynamics, parental involvement, and communication patterns. The research is done to shed light on the viable tactics for developing resilient behaviours in adolescents and enhancing family settings in the face of pervasive internet usage. The recent studys findings and limitations have been carefully considered and closely scrutinized. The reason for the research gap has also been closely examined. A variety of secondary sources, such as research journals, and other websites, were utilized to create the literature for this study. All of the research has been carefully reviewed and summarized. The selected articles were all up to date, from reliable publications, and relevant to the planned research. Paper type: Review paper KEYWORDS: Internet Usage, Family Dynamics, Adolescent Resilience, Technology Impact, Digital Culture, Parental Influence, Social Media Effects, Psychological Development.

Multi-objective optimization for composition design of civil materials based on data-driven method

The performance of civil materials depends on their compositions. The material design is an important, challenging, and time-consuming work during the construction due to the complex relationship among different compositions and the conflict between properties and economic cost. This study developed a data-driven multi-objective optimization design framework to determine the reasonable material compositions based on laboratory experiment, reduced order model (ROM), and multi-objective optimization (MOO). The ROM was used to capture the complex relationship between the material compositions and the corresponding material performance. The MOO was utilized to determine the pareto optimal solution of compositions in the global space. Taking examples of cement grouts and modified asphalt binders, the develop framework was demonstrated and verified. The developed framework provides an excellent tool for composition design of civil materials.

Enhancement of Coffee Quality Attributes by Combining Processing Methods and Varieties

The intricate framework of attributes that define coffee quality, from varietals to processing methods, presents a comprehensive array of sensory experiences that influence consumption patterns. This research delves into the complex relationship between the characteristics of coffee beans, specifically varietal distinctions, applied processing methodologies, and the resulting sensory attributes, across both Arabica and Robusta species. Utilizing comprehensive linear mixed model analyses, this study examines the sensory intricacies, with a notable emphasis on flavor, aroma, and acidity, and their correlation with different countries of origin. Drawing from a diverse dataset that encompasses various global regions, our findings underscore the pivotal role of regional nuances in shaping the sensory evaluation of coffee. While Arabica beans exhibited certain distinct sensory attributes anchored to specific processing methods and regions, Robusta beans presented variations that were more nuanced. The results align with the existing literature, emphasizing the integral role of regional influences in coffee evaluations. This study reveals that specific Arabica varieties, such as Bourbon and Pacamara, enhance flavor when processed using the ‘Natural/Dry’ method. Meanwhile, certain Robusta beans processed with one of the methods showcased improved flavor scores. These insights provide the coffee industry with targeted strategies, reshaping cultivation and processing to meet discerning consumer preferences.

Nourishing the gut: the impact of diet on host-gut microbiota interaction.

Understanding the spectrum of drivers that influence the gut microbiome (GM) remains a crucial field of investigation. Among these factors, diet has received particular attention, as it could explain up to 20% of the variability in GM composition between individuals. This review focuses on the complex relationships between different dietary patterns and GM in humans, based on recent findings. Current evidence underscores the multifaceted impact of diet on GM richness, diversity, and overall composition. Key contributing factors encompass dietary habits, nutritional interventions, food quality and variety, macronutrient distribution, timing of feeding, and selective exclusion of certain foods. The intricate interplay between diet and GM is of fundamental importance in shaping the interaction between the host and the environment. Further understanding the causal impact of diet on GM has promising potential for the advancement of strategies to promote health and mitigate cardio-metabolic disease risks through dietary interventions. http://links.lww.com/COCN/A21.

The impact of environmental regulatory instruments on agribusiness technology innovation-A study of configuration effects based on fsQCA.

This paper investigates the complex causal relationships between various types of environmental regulatory instruments (ERI) and agri-firms' technological innovation employing fuzzy set qualitative comparative analysis (fsQCA). The study finds a well-designed set of ERI can promote technological innovation in agribusiness; control-command ERI cannot promote technological innovation in agribusiness solely, market-incentivized ERI is indispensable in promoting firms' innovation performance, implicit ERI plays an important role in promoting firms' innovation and voluntary ERI does not play a significant role in promoting firms' technological innovation. The government should coordinate among various types of ERI and improve the design of ERI to achieve a win-win situation for both economic and environmental performance in the agriculture sector.

Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity

Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.

Influence of Flight Altitude and Surface Characteristics on UAS-LiDAR Ground Height Estimate Accuracy in Juncus roemerianus Scheele-Dominated Marshes

Management and monitoring of vulnerable coastal marshes rely on accurate ground height estimates. However, marsh surface characteristics such as vegetation and water presence complicate aerial remote sensing of the ground. Towards developing an improved understanding and techniques for these remote sensing efforts, this study established relationships among data collection flight altitude, surface characteristics, and ground height estimate accuracy within Juncus roemerianus Scheele-dominated marshes. Uncrewed Aerial System (UAS) Light Detection and Ranging (LiDAR) sampling was conducted at five altitudes for five marsh sites and one local control site. Survey-grade topographic measurements and marsh surface characteristics were recorded at each site for comparison. Root Mean Square Error (RMSE) and linear mixed-effects modeling were used to quantify relationships among vertical error, altitude, and surface characteristics. For low (24–72 m) and high (96–120 m) altitudes Above Ground Level (AGL), the RMSE values were 49 cm and 17 cm, respectively. Despite this appreciable improvement in accuracy with increasing flight altitude, point density values of these datasets limit applications. Linear mixed-effects modeling further emphasized the complex relationships between sensor footprint size, surface characteristics, and ground height estimates. These findings have direct implications for elevation modeling and monitoring efforts of frequently inundated, coastal marshes.