Reduced order models, such as Hollkamp and Gordon’s Implicit Condensation and Expansion (ICE) model, are a highly efficient alternative to full-order finite element models (FEM) of geometrically nonlinear structures. However, a reduced order model (ROM) is typically only valid for one FEM. It does not capture how each ROM coefficient changes due to variations in the FEM (e.g., design parameters or uncertainties), so if the FEM is updated then the ROM needs to be re-computed with a new set of static load–displacement data. This study presents a data-driven reduced order modeling approach that creates a single ROM that incorporates design variations in FEM. The proposed method applies Gaussian Process Regression (GPR) to the ICE approach, making each coefficient in an ICE ROM a regression model with respect to a collection of FEMs with varying material properties or geometric parameters. Once the GPR ROM has been identified, one can immediately produce an ICE ROM for a set of FEM parameters without a need to solve any static load–displacement cases on the full FEM. This dramatically enhances the computational efficiency and could be helpful when model uncertainty needs to be considered or when seeking to update a model to correlate with measurements. Additionally, the coefficients of a ROM can often change considerably if the scale on the load–displacement data changes, so it can be difficult to know whether the scaling that was chosen has really identified an accurate ROM. The proposed GPR ROM estimates the mean ROM coefficients for a range of load scaling as well as the uncertainty on each ROM coefficient with respect to the load level. This can be used to gauge the success of the ROM identification and to eliminate ROM coefficients that are unimportant and hence highly variable. The proposed GPR ROM approach is evaluated by applying it to flat and curved beam structures, revealing that the advantages outlined above can be realized with a relatively modest increase in cost relative to a traditional ICE ROM.
Built-up structures exhibit nonlinear dynamic behavior due to friction between interfaces that are fastened together. On the other hand, aircraft, spacecraft, and even automotive structures consist of thin panels to reduce their weight, which can exhibit geometric nonlinearity for displacements on the order of the thickness. As part of the Tribomechadynamics Research Challenge (TRC), this work seeks to predict these effects a priori, whereas most prior works have focused on tuning a model to experimental measurements. While methods are beginning to mature that can predict micro-slip nonlinearity of structures, and methods are well established for reduced-order modeling of geometrically nonlinear structures, these have not been combined previously. This paper presents a simulation approach used to predict the nonlinear response of a benchmark structure proposed in the TRC, which exhibits both geometric nonlinearity and micro-slip due to friction in the bolted connections. A two-dimensional model of the structure is created to enable a wide range of simulations to be performed with minimal computational cost, including some dynamic simulations where both friction and geometric nonlinearity are considered. The nonlinear modal behavior is predicted using quasi-static modal analysis (QSMA) and a recent extension called single-degree-of-freedom implicit condensation and expansion (SICE). Static load-displacement data is also used to define a non-parametric Iwan element that reproduces the modal behavior with high fidelity and yet with minimal computational cost. Additionally, limited simulations are performed on three-dimensional models, which are much more expensive but should be predictive, at least so long as Coulomb Friction is appropriate to model the interactions at the interfaces.
Background Previous research shows kinematic and kinetic coupling between the metatarsophalangeal (MTP) and midtarsal joints during gait. Studying the effects of MTP position as well as foot structure on this coupling may help determine to what extent foot coupling during dynamic and active movement is due to the windlass mechanism. This study’s purpose was to investigate the kinematic and kinetic foot coupling during controlled passive, active, and dynamic movements. Methods After arch height and flexibility were measured, participants performed four conditions: Seated Passive MTP Extension, Seated Active MTP Extension, Standing Passive MTP Extension, and Standing Active MTP Extension. Next, participants performed three heel raise conditions that manipulated the starting position of the MTP joint: Neutral, Toe Extension, and Toe Flexion. A multisegment foot model was created in Visual 3D and used to calculate ankle, midtarsal, and MTP joint kinematics and kinetics. Results Kinematic coupling (ratio of midtarsal to MTP angular displacement) was approximately six times greater in Neutral heel raises compared to Seated Passive MTP Extension, suggesting that the windlass only plays a small kinematic role in dynamic tasks. As the starting position of the MTP joint became increasingly extended during heel raises, the amount of negative work at the MTP joint and positive work at the midtarsal joint increased proportionally, while distal-to-hindfoot work remained unchanged. Correlations suggest that there is not a strong relationship between static arch height/flexibility and kinematic foot coupling. Conclusions Our results show that there is kinematic and kinetic coupling within the distal foot, but this coupling is attributed only in small measure to the windlass mechanism. Additional sources of coupling include foot muscles and elastic energy storage and return within ligaments and tendons. Furthermore, our results suggest that the plantar aponeurosis does not function as a rigid cable but likely has extensibility that affects the effectiveness of the windlass mechanism. Arch structure did not affect foot coupling, suggesting that static arch height or arch flexibility alone may not be adequate predictors of dynamic foot function.
Bacteria often reside in sessile communities called biofilms, where they adhere to a variety of surfaces and exist as aggregates in a viscous polymeric matrix. Biofilms are resistant to antimicrobial treatments, and are a major contributor to the persistence and chronicity of many bacterial infections. Herein, we determined that the CpxA-CpxR two-component system influenced the ability of enteropathogenic Yersinia pseudotuberculosis to develop biofilms. Mutant bacteria that accumulated the active CpxR~P isoform failed to form biofilms on plastic or on the surface of the Caenorhabditis elegans nematode. A failure to form biofilms on the worm surface prompted their survival when grown on the lawns of Y. pseudotuberculosis . Exopolysaccharide production by the hms loci is the major driver of biofilms formed by Yersinia . We used a number of molecular genetic approaches to demonstrate that active CpxR~P binds directly to the promoter regulatory elements of the hms loci to activate the repressors of hms expression and to repress the activators of hms expression. Consequently, active Cpx-signalling culminated in a loss of exopolysaccharide production. Hence, the development of Y. pseudotuberculosis biofilms on multiple surfaces is controlled by the Cpx-signalling, and at least in part this occurs through repressive effects on the Hms-dependent exopolysaccharide production.
Background The etiology of hamstring strain injury (HSI) in American football is multi-factorial and understanding these risk factors is paramount to developing predictive models and guiding prevention and rehabilitation strategies. Many player-games are lost due to the lack of a clear understanding of risk factors and the absence of effective methods to minimize re-injury. This paper describes the protocol that will be followed to develop the HAMstring InjuRy (HAMIR) index risk prediction models for HSI and re-injury based on morphological, architectural, biomechanical and clinical factors in National Collegiate Athletic Association Division I collegiate football players. Methods A 3-year, prospective study will be conducted involving collegiate football student-athletes at four institutions. Enrolled participants will complete preseason assessments of eccentric hamstring strength, on-field sprinting biomechanics and muscle–tendon volumes using magnetic-resonance imaging (MRI). Athletic trainers will monitor injuries and exposure for the duration of the study. Participants who sustain an HSI will undergo a clinical assessment at the time of injury along with MRI examinations. Following completion of structured rehabilitation and return to unrestricted sport participation, clinical assessments, MRI examinations and sprinting biomechanics will be repeated. Injury recurrence will be monitored through a 6-month follow-up period. HAMIR index prediction models for index HSI injury and re-injury will be constructed. Discussion The most appropriate strategies for reducing risk of HSI are likely multi-factorial and depend on risk factors unique to each athlete. This study will be the largest-of-its-kind (1200 player-years) to gather detailed information on index and recurrent HSI, and will be the first study to simultaneously investigate the effect of morphological, biomechanical and clinical variables on risk of HSI in collegiate football athletes. The quantitative HAMIR index will be formulated to identify an athlete’s propensity for HSI, and more importantly, identify targets for injury mitigation, thereby reducing the global burden of HSI in high-level American football players. Trial Registration The trial is prospectively registered on ClinicalTrials.gov (NCT05343052; April 22, 2022).
Common research practices in neuroimaging studies using functional magnetic resonance imaging may produce outcomes that are difficult to replicate. Results that cannot be replicated have contributed to a replication crisis in psychology, neuroscience, and other disciplines over the years. Here we replicate two previous papers in which the authors present two analysis paths for a dataset in which participants underwent fMRI while performing a recognition memory test for old and new words. Both studies found activation in the medial temporal lobe including the hippocampus, with the first demonstrating a distinction in activation corresponding to true and perceived oldness of stimuli and the second demonstrating that activation reflects the subjective experience of the participant. We replicated the behavioral and MRI acquisition parameters reported in the two target articles (Daselaar et al., 2006; Daselaar et al., 2006) with N = 53 participants. We focused fMRI analyses on regions of interest reported in the target articles examining fMRI activation for differences corresponding with true and perceived oldness and those associated with the subjective memory experiences of recollection, familiarity, and novelty. Comparisons between true and perceived oldness revealed main effects not only for true, but also perceived oldness along with a significant interaction. We replicate the findings of recollection and familiarity signals in the hippocampus and medial temporal lobe cortex, respectively, but failed to replicate a novelty signal in the anterior medial temporal lobe. These results remained when we analyzed only correct trials, indicating that the effects were not due to selectively averaging correct and incorrect trials. Taken together, our findings demonstrate that activation in the hippocampus corresponds to the subjective experience associated with correct recognition memory retrieval.
Ab initio methods for predicting NMR parameters in the solid state are an essential tool for assigning experimental spectra and play an increasingly important role in structural characterizations. Recently, a molecular correction (MC) technique has been developed which combines the strengths of plane-wave methods (GIPAW) with single molecule calculations employing Gaussian basis sets. The GIPAW + MC method relies on a periodic calculation performed at a lower level of theory to model the crystalline environment. The GIPAW result is then corrected using a single molecule calculation performed at a higher level of theory. The success of the GIPAW + MC method in predicting a range of NMR parameters is a result of the highly local character of the tensors underlying the NMR observable. However, in applications involving strong intermolecular interactions we find that expanding the region treated at the higher level of theory more accurately captures local many-body contributions to the N15 NMR chemical shielding (CS) tensor. We propose alternative corrections to GIPAW which capture interactions between adjacent molecules at a higher level of theory using either fragment or cluster-based calculations. Benchmark calculations performed on N15 and C13 data sets show that these advanced GIPAW-corrected calculations improve the accuracy of chemical shielding tensor predictions relative to existing methods. Specifically, cluster-based N15 corrections show a 24% and 17% reduction in RMS error relative to GIPAW and GIPAW + MC calculations, respectively. Comparing the benchmark data sets using multiple computational models demonstrates that N15 CS tensor calculations are significantly more sensitive to intermolecular interactions relative to C13. However, fragment and cluster-based corrections that include direct hydrogen bond partners are sufficient for optimizing the accuracy of GIPAW-corrected methods. Finally, GIPAW-corrected methods are applied to the particularly challenging NMR spectral assignment of guanosine dihydrate which contains two guanosine molecules in the asymmetric unit.
Background Emerging research on positive and adverse childhood experiences (PCEs and ACEs) indicates that both may be important to adult health, but little is understood about the pathways through which childhood experiences affect adult health. Objective The aims of this study were to 1) examine how shame may mediate the relationship between childhood experiences and health, and 2) whether PCEs moderated the relationship between ACEs, shame, and adult health. Participants and setting The sample consisted of 206 low-income adults ages 18–55 who were living in a community in the Intermountain West. Methods Participants were recruited at a local food bank and community center where various services for low-income residents were offered. Each participant completed a 15–20-minute survey. The data were analyzed using a structural equation modeling (SEM) framework. Results Shame mediated the relationship between both ACEs and PCEs with depression in the expected direction. Among participants with low-to-moderate PCEs, ACEs were directly associated with shame and tobacco usage. Among participants with high PCEs, ACEs were not associated with shame, depression, nor stress, and the relationship between ACEs and tobacco usage was attenuated. Conclusion Shame may be an important pathway through which childhood experiences affect adult health. Additionally, promoting high levels of PCEs may mitigate the negative effects of early adversity on adult health.
Peak fitting of x-ray photoelectron spectroscopy (XPS) data is the primary method for identifying and quantifying the chemical states of the atoms near the surface of a sample. Peak fitting is typically based on the minimization of a figure-of-merit, such as the residual standard deviation (RSD). Here, we show that optimal XPS peak fitting is obtained when the peak shape (the synthetic mathematical function that represents the chemical states of the material) best matches the physics and chemistry of the underlying data. However, because this ideal peak shape is often unknown, constraints on the components of a fit are usually necessary to obtain good fits to data. These constraints may include fixing the relative full width at half maxima (peak widths), area ratios, and/or the relative positions of fit components. As shown in multiple examples, while unconstrained, less-than-optimal peak shapes may produce lower RSDs, they often lead to incorrect results. Thus, the “suboptimal” results (somewhat higher RSDs) that are obtained when constraints are applied to less-than-perfect peak shapes are often preferable because they prevent a fit from yielding unphysical or unchemical results. XPS peak fitting is best performed when all the information available about a sample is used, including its expected chemical and physical composition, information from other XPS narrow and survey scans from the same material, and information from other analytical techniques.
Multiple myeloma is a hematological malignancy affecting the plasma cells. It is the second most common hematologic cancer in adults. Over 90% of patients develop local osteolytic lesions and skeletal-related events at some point during the progression of the disease. Bone lesions can induce severe pain and immobility and can also increase the risk of fractures and osteomyelitis. Skeletal complications are associated with poor clinical outcomes, affecting quality of life and mortality. Current standards of care for myeloma, e.g., autologous stem-cell transplantation (ASCT) and chemotherapy, do not lessen the risk of adverse events in bone. Once bone lesions are present, bone-targeted interventions are limited, with bone antiresorptive drugs being a mainstay of treatment. This review highlights the growing literature surrounding osteolytic lesions and bone infections associated with multiple myeloma and assesses current and emerging treatments. Emerging evidence from clinical trials suggests that denosumab can reduce skeletal-related events, and the potential application of bortezomib/1D11 can reduce bone destruction and pathological fractures in MM patients. Once established, bone lesions are prone to develop osteomyelitis – especially in immunocompromised individuals. Antibiotics and surgical interventions have been used to manage bone infections in most reported cases. As the bone infection risk associated with MM bone lesions become more evident, there is scope to improve patient management by mitigating this risk with prophylactic antimicrobial therapy.
Chemometrics/informatics, and data analysis in general, are increasingly important in x-ray photoelectron spectroscopy (XPS) because of the large amount of information (spectra/data) that is often collected in degradation, depth profiling, operando, and imaging studies. In this guide, we present chemometrics/informatics analyses of XPS data using a summary statistic (pattern recognition entropy), principal component analysis, multivariate curve resolution (MCR), and cluster analysis. These analyses were performed on C 1s, O 1s, and concatenated (combined) C 1s and O 1s narrow scans obtained by repeatedly analyzing samples of cellulose and tartaric acid, which led to their degradation. We discuss the following steps, principles, and methods in these analyses: gathering/using all of the information about samples, performing an initial evaluation of the raw data, including plotting it, knowing which chemometrics/informatics analyses to choose, data preprocessing, knowing where to start the chemometrics/informatics analysis, including the initial identification of outliers and unexpected features in data sets, returning to the original data after an informatics analysis to confirm findings, determining the number of abstract factors to keep in a model, MCR, including peak fitting MCR factors, more complicated MCR factors, and the presence of intermediates revealed through MCR, and cluster analysis. Some of the findings of this work are as follows. The various chemometrics/informatics methods showed a break/abrupt change in the cellulose data set (and in some cases an outlier). For the first time, MCR components were peak fit. Peak fitting of MCR components revealed the presence of intermediates in the decomposition of tartaric acid. Cluster analysis grouped the data in the order in which they were collected, leading to a series of average spectra that represent the changes in the spectra. This paper is a companion to a guide that focuses on the more theoretical aspects of the themes touched on here.
Self-compassion is emerging as a method to support teachers dealing with the stress of teaching. In this qualitative study, the authors investigate the ways in which self-compassion already exists in the teaching context and in what ways self-compassion intersects with emotion regulation. Teachers shared critical incidents of unsatisfactory outcomes in their teaching. Through a priori coding and I-poems, the authors found self-compassion present before the resolution, after the resolution and even used as the resolution to conflict in those unsatisfactory critical incidents, indicating the usefulness of self-compassion as a method of emotion regulation. The earlier the use of self-compassion, the more quickly emotional recovery occurred. Implications for teacher education are the need for explicit teaching, practicing, and encouragement of self-compassion as an important element in pre-service and in-service teacher development and resilience in the profession.
Chemometrics/informatics, and data analysis in general, are increasingly important topics in X-ray photoelectron spectroscopy (XPS) because of the large amount of information (data/spectra) that are often collected in degradation, depth profiling, operando, and imaging studies. In this guide, we discuss vital, theoretical aspects and considerations for chemometrics/informatics analyses of X-ray photoelectron spectroscopy (XPS) data with a focus on exploratory data analysis (EDA) tools that can be used to probe XPS data sets. These tools include a summary statistic (pattern recognition entropy, PRE), principal component analysis (PCA), multivariate curve resolution (MCR), and cluster analysis. The use of these tools is explained through the following steps: A. Gather/use all the available information about one’s samples, B. Examine (plot) the raw data, C. Developing a general strategy for the chemometrics/informatics analysis, D. Preprocess the data, E. Where to start a chemometrics/informatics analysis, including identifying outliers or unexpected features in data sets, F. Determine the number of abstract factors to keep in a model, G. Return to the original data after a chemometrics/informatics analysis to confirm findings, H. Perform multivariate curve resolution (MCR), I. Peak fit the MCR factors, J. Identify intermediates in MCR analyses, K. Perform cluster analysis, and L. How to start doing chemometrics/informatics in one’s work. This guide has a companion paper that illustrates these steps/principles by applying them to two fairly large XPS data sets. In these papers, special emphasis is placed on MCR. Indeed, in this paper and its companion, we believe that, for the first time, it is suggested and shown that (i) MCR components/factors can be peak fit as though they were XPS narrow scans, and (ii) MCR can reveal intermediates in the degradation of a material. The other chemometrics/informatics methods are also useful in demonstrating the presence of outliers, a break (irregularity) in one of the data sets, and the general trajectory/evolution of the data sets. Cluster analysis generated a series of average spectra that describe the evolution of one of the data sets.
While grain growth is traditionally viewed as a purely thermally driven process, nanocrystalline metals can undergo grain growth under mechanical loads, even at room temperature. We performed a detailed atomistic study of the heterogeneous nature of mechanically accelerated grain growth in a polycrystalline Pt nanowire. Using molecular dynamics simulations, we compared the grain-growth behavior of individual grains during tensile and shear cyclic loading, for three different equivalent strain levels, and at two temperatures. Pure thermal grain growth with no mechanical loading provided a baseline reference case. On average, grains that were already susceptible to thermal grain growth were stimulated to grow faster with mechanical loading, as expected. However, when analyzed on a grain-by-grain basis, the results were far more complex: grains that grew fastest under one stimuli were less accelerated under other stimuli. Even when the magnitude of loading changed, the relative growth of individual grains was distorted. We interpret this complexity from the perspective of superimposed growth mechanisms.
We provide the first solar system wide compendium of speleogenic processes and products. An examination of 15 solar system bodies revealed that six cave‐forming processes occur beyond Earth including volcanic (cryo and magmatic), fracturing (tectonic and impact melt), dissolution, sublimation, suffusion, and landslides. Although no caves (i.e., confirmed entrances with associated linear passages) have been confirmed, 3,545 SAPs (subsurface access points) have been identified on 11 planetary bodies and the potential for speleogenic processes (and thus SAPs) was observed on an additional four planetary bodies. The bulk of our knowledge on extraterrestrial SAPs is based on global databases for the Moon and Mars, which are bodies for which high‐resolution imagery and other data are available. To further characterize most of the features beyond the Moon and Mars, acquisition (preferably global coverage) and subsequent analysis of high‐resolution imagery will be required. The next few decades hold considerable promise for further identifying and characterizing caves across the solar system.
Imposter Phenomenon (IP) was coined in 1978 by Clance and Imes and has been an important construct in explaining individuals’ experiences of believing that achievements are a result of luck or misperceptions of others rather than personal competence. The Clance Imposter Phenomenon Scale (CIPS), developed in 1985, is a prominent operationalization of this construct. Although this scale has been widely used since its inception, its factor structure has been inconsistent across studies and no tests of measurement invariance are documented in the literature. Using a large university sample ( n = 830), the current study was designed to: (a) examine the factor structure of the CIPS using cross validation, (b) examine measurement invariance across gender, and (c) examine differences in the CIPS factor by demographics. We found that a single factor structure for the CIPS was the best fitting model. We also found that the measure achieved invariance across gender after removing item 18 (and in the absence of items 1, 2, and 19, removed for poor fit). Finally, we found that being single, a woman, and having experienced lower socioeconomic status (SES) were all associated with higher IP. We discuss these findings in the context of a university setting and suggest avenues for future research.
Purpose Non-operative management of rectal cancer is a feasible and appealing treatment option for patients who develop a complete response after neoadjuvant therapy. However, identifying patients who are complete responders is often a challenge. This review aims to present and discuss current evidence and recommendations regarding the assessment of treatment response in rectal cancer. Methods A review of the current literature on rectal cancer restaging was performed. Studies included in this review explored the optimal interval between the end of neoadjuvant therapy and restaging, as well as modalities of assessment and their diagnostic performance. Results The current standard for restaging rectal cancer is a multimodal assessment with the digital rectal examination, endoscopy, and T2-weighted MRI with diffusion-weighted imaging. Other diagnostic procedures under investigation are PET/MRI, radiomics, confocal laser endomicroscopy, artificial intelligence-assisted endoscopy, cell-free DNA, and prediction models incorporating one or more of the above-mentioned exams. Conclusion Non-operative management of rectal cancer requires a multidisciplinary approach. Understanding of the robustness and limitations of each exam is critical to inform patient selection for that treatment strategy.
CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47–0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.