Response Data Research Articles

Abstract B009: Characterization and CRISPR perturbation of cancer-associated fibroblast heterogeneity in vitro models

Abstract Cancer-associated fibroblasts (CAFs) are attractive therapeutic targets for cancer due to their abundance in the tumor microenvironment (TME) and extensive pro- and antitumorigenic functions. However, formulating CAF-targeting strategies remains challenging due to the complexity of CAF biology. CAFs specialize into phenotypically and functionally distinct subtypes that may respond differently to specific treatments. Thus, designing CAF-targeting therapies necessitates a thorough understanding of CAF heterogeneity and preclinical model systems that can reliably prototype CAF biology observed in tumors. However, CAF heterogeneity is still not well-understood for many solid tumors, including the stroma-rich pancreatic ductal adenocarcinoma (PDAC), and the translatability of stroma models has not been comprehensively evaluated. In this work, we developed in vitro cell lines of tumor-CAF cocultures using primary CAFs derived from PDAC and used single-cell transcriptomics and CRISPR perturbation to investigate CAF heterogeneity and subtype-specific responses. We found that coculturing primary CAFs with the BxPC3 PDAC cell line could capture the tumor-CAF crosstalks underlying CAF activation. We characterized eight subtypes, including myofibroblastic, inflammatory, extracellular matrix (ECM)-like, and interferon CAFs, which could be preserved with immortalization. We also delineated the trajectories of CAF subtype interconvertibility, highlighting inflammatory CAFs as the likely initial diseased state and ECM CAFs as a “reservoir” that seeded the later states. By correlating the in vitro subtypes with clinical data of immunotherapy responses, we established that the in vitro cell lines could model the immuno-modulatory and immunoresistant phenotypes of CAFs. Finally, using an immortalized tumor-CAF coculture line, we performed Perturb-seq single-cell perturbation to perturb several stroma genes of interest, including TGFBR1, SPATS2L, AEBP1, and PTGS1, and evaluated the subtype-specific effects of the perturbations. We found that perturbing TGFBR1 resulted in the expected eradication of myofibroblastic CAFs, while the other perturbations resulted in subtype-specific modulations of metabolic and extracellular matrix related pathways. Overall, our findings underscored that in vitro tumor-CAF coculture models could serve as a translatable system that effectively captured CAF biology and heterogeneity observed in tumors and highlighted some similarities between PDAC CAFs and previously characterized breast cancer CAFs. We also demonstrated that single-cell perturbation could be a useful approach for interrogating the distinct responses of different CAF subtypes to perturbations. Citation Format: Elysia Saputra, Shamsudheen Karuthedath Vellarikkal, Lixia Li, Hong Sun, Suchitra Natarajan, Federica Piccioni, Alex M. Tamburino, Xin Yu, Aleksandra K. Olow. Characterization and CRISPR perturbation of cancer-associated fibroblast heterogeneity in vitro models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B009.

Characterization of the temporal profile of the antinociceptive effects of an intravenous bolus of ketamine using the analgesia nociception index in no-anesthetized adult patients.

An effect-site target-controlled infusion (TCI) would allow a more precise titration of intravenous analgesics effect. The analgesia nociception index (ANI) continuously monitors the analgesia/nociception balance during general anesthesia. This study aims to derive a PKPD model of ketamine antinociceptive effect using the Domino PK parameter set and the ANI response data in awake patients without other drugs affecting the ANI response. Twenty awake adult patients were prospectively studied before general anesthesia. Patients received a single intravenous bolus of ketamine 0.1mg·kg- 1, and the subsequent ANI values were recorded. An effect compartment model incorporating the Domino PK parameter set was used to characterize the time lag between ketamine plasma concentrations and the ANI response. The model was parameterized with a single parameter Ke0. An Emax pharmacodynamic model was used to fit the ANI response data. Model parameters were estimated with NONMEM® 7.5. The minimum objective function value guided the model construction. After the ketamine administration, basal ANI values increased from 38.5 ± 4.95 to a maximum of 53.5 ± 4.95 with an observed time-to-peak effect of 1.83 ± 0.74min. Modeling analysis revealed hysteresis between predicted plasma concentrations from the Domino model and observed ANI data. Hysteresis was characterized, incorporating an estimated Keo of 0.238 (CI95% 0.20-0.28) min-1 to the described PK parameters set. The developed PKPD model, using Domino's PK parameters and the ANI response data, adequately characterized the temporal profile of ketamine's antinociceptive effect. The current estimated model parameters can be used to perform an effect-site TCI of ketamine for analgesic purposes.

Ruxolitinib Cream Monotherapy Improved Symptoms and Quality of Life in Adults and Adolescents with Mild-to-Moderate Atopic Dermatitis: Patient-Reported Outcomes from Two Phase III Studies.

Atopic dermatitis (AD) is associated with itch, skin pain, sleep disturbances, and diminished quality of life (QoL). Ruxolitinib (Janus kinase [JAK] 1/JAK2 inhibitor) cream demonstrated efficacy and safety in adults and adolescents with mild-to-moderate AD in two phase III studies (TRuE-AD1/TRuE-AD2). In TRuE-AD1/TRuE-AD2, significant improvements in itch were observed as early as 12 h following application of ruxolitinib cream. The aim of this paper was to assess additional patient-reported outcomes (PROs) in the vehicle-controlled (VC) and long-term safety (LTS) periods of TRuE-AD1/TRuE-AD2. In the TRuE-AD studies, patients aged ≥12 years with AD were randomized 2:2:1 to apply twice-daily 1.5% ruxolitinib cream, 0.75% ruxolitinib cream, or vehicle cream continuously for 8weeks (VC period). During the LTS period, patients applied the same ruxolitinib cream strength, but on an as-needed basis; patients who initially applied vehicle were re-randomized to apply 0.75% or 1.5% ruxolitinib cream. Pooled data from both study periods were analyzed. PRO assessments included symptoms (itch [Patient-Oriented Eczema Measure, POEM], skin pain [numerical rating scale], and sleep [POEM and Patient-Reported Outcomes Measurement Information System]) and assessments of disease-specific QoL (Dermatology Life Quality Index [DLQI] and the children's version [CDLQI]). A total of 1208 and 1031 patients from the VC and LTS periods, respectively, were included in the analysis. Significant improvements in skin pain were observed within 12h among patients who applied ruxolitinib cream versus vehicle; improvements continued throughout the VC period. Improvements in patient-reported symptoms (including sleep) were observed within 2 weeks (first assessment) of ruxolitinib cream application. At Week 2, significant improvements in symptom burden and overall QoL were observed with ruxolitinib cream (0.75%/1.5%) versus vehicle in POEM (-8.9/-9.8 vs -2.2; both p<0.0001), DLQI (mean changes from baseline, -5.8/-6.1 vs -1.2; both p<0.0001), and CDLQI (-4.3/-5.3 vs -1.3; both p<0.0001). Further symptom burden and QoL improvements were reported during the VC period and were maintained through the end of the LTS period (Week 52). Consistent with the previously reported itch response data, ruxolitinib cream improved skin pain within 12 h of application. Ruxolitinib cream improved patient-reported AD symptom burden and overall QoL by Week 2. Improvements continued or were maintained for 52 weeks. (Graphical abstract and plain language summary available). ClinicalTrials.gov identifiers, NCT03745638 and NCT03745651 (both studies were registered on November 19, 2018).

Impact localization of CFRP laminate based on FBG sensing and GWO-BP neural network

Carbon fiber reinforced plastic (CFRP) structures are susceptible to external energy impact during service, resulting in invisible damage. Therefore, accurate localization of these impact sites is crucial for the maintenance and safety of CFRP structures. In this paper, the strain data of the impact response of the CFRP laminate is obtained by using the fiber grating (FBG) sensor, and the data are mapped to the impact location by BP neural network. However, the traditional BP neural network is susceptible to local minima and slow convergence speed. Therefore, Grey Wolf optimization algorithm (GWO) is introduced in this study to optimize the BP neural network, which improves the convergence speed and positioning accuracy of the model. The experimental results show that the GWO-BP neural network model has excellent performance in impact positioning, with the maximum positioning error of 17.01 mm and the average positioning error of 13.05 mm. Compared with other machine learning methods, it has higher accuracy and efficiency.

Empowered by the Experience: Playing as Female Characters in Video Games

Research on female video game characters often investigates negative outcomes of playing as stereotyped characters. Yet, video games increasingly offer people opportunities to play as capable, prominent female characters that may be meaningful and promote positive outcomes (e.g., feelings of empowerment). This manuscript shares the results of a mixed methods survey of individuals (&lt;em&gt;N&lt;/em&gt; = 751) recruited via online forums who recalled their experiences playing as female game characters. We analyzed qualitative response data using a traditional, top-down approach to identify themes. We focus our analysis and discussion on the results of a human-derived analysis, which indicated both positive and negative dimensions of experiences that participants found meaningful. Participants reported that taking on the role of a female character was often very important to them, but the reason for this significance varied across players. Often, these reasons involved their own gender identities and the context in which the gameplay occurred (e.g., online multiplayer). We consider findings as evidence of eudaimonic media effects.

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization. This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments. This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared. The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely. We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

Insights into Target Gas-Oxygen Interactions in Highly Sensitive Gas Sensors Using Data-Driven Methods.

In the gas-sensing mechanism of a metal-oxide-semiconductor (n-type) gas sensor, oxygen adsorption or desorption on the oxide surface leads to an increase or decrease in the resistance of the gas sensor system. Additionally, oxygen can be adsorbed again at the location where initially adsorbed oxygen reacted with the target gas. Thus, the adsorption-desorption equilibrium of the reducing gas on the oxide surface is a significant factor in determining the sensitivity and reaction rate. In particular, for ultralow-concentration gas measurements, the relative concentration of oxygen was very high. To design an ultrasensitive gas sensor, not only the reaction of the target gas but also the competing reaction between the target gas and oxygen must be considered. Although qualitative investigations of these complex relationships have been performed according to the gas concentration and flow rate, reliable quantitative results are limited. In this study, a quantitative approach was used to understand the correlation between oxygen and a target gas by applying data analysis methods. We investigated the behavior of oxygen and the target molecules depending on the gas concentration and flow rate using the parts per billion level of the acetone gas sensor. Initial response data according to various detection conditions were processed using principal component analysis and K-means clustering; as a result, four types of reaction behaviors were inferred for 15 types of reaction conditions. Furthermore, the response time, depending on the detection conditions, can be distinguished using the suggested categorization. Our investigation suggests a possibility beyond simple optimization through the data analysis of the gas-sensing results.

TMIC-40. TARGETING GLIOMA TUMOR MICROENVIRONMENT BY SPECIFIC NEUTRALIZATION OF EXTRACELLULAR NICOTINAMIDE PHOSPHORIBOSYLTRANSFERASE (ENAMPT) USING ALT-100, A NOVEL HUMANIZED MONOCLONAL ANTIBODY

Abstract BACKGROUND Intracellular nicotinamide phosphoribosyltransferase (iNAMPT is the rate-limiting enzyme of NAD salvage pathway and is preferentially used by glioma cells for regulating energy metabolism and homeostasis. When secreted, extracellular NAMPT (eNAMPT) exerts cytokine-like functions and is associated with poor prognosis in cancer patients. We have previously reported an essential role of iNAMPT for glioma cell proliferation and survival. Here, we examined whether eNAMPT influences glioma biology and tumor microenvironment by neutralizing eNAMPT using ALT-100, a novel humanized monoclonal antibody. METHODS Native levels of eNAMPT in blood samples of patients with primary/recurrent gliomas were determined using ELISA under an IRB approved protocol. Next, the effects of ALT-100 on glioma microenvironment were determined by treating viable human glioma tissue slices with ALT-100 and assessing downstream effects on RNA and protein using RNASeq, reverse phase protein array and spatial transcriptomics. Lastly, the in vivo effect of ALT-100-mediated eNAMPT neutralization on survival was determined in a mouse intracranial glioma model. RESULTS Measurable levels of eNAMPT were noted in all patient samples with a range of (3.7-71.2 ng/ml). Preliminary results showed a correlation of eNAMPT levels with grade of tumor. Human organotypic glioma slice cultures treated with ALT-100, showed a profound alteration of gene and protein expression particularly related to key energy metabolism and proliferation pathways and the PD-1/PD-L1 axis. Additionally, results of spatial profiling of ALT-100 treated human organotypic glioma slices will be presented. Survival and response data from ALT-100 treatment in ongoing in vivo experiment will also be reported. CONCLUSIONS This is the first report of ALT-100-mediated eNAMPT neutralization in gliomas and demonstrates profound effects on the tumor and its microenvironment. Given that ALT-100 is in phase 2 trials (NCT05938036) against ARDS, data from our studies can potentially accelerate translating ALT-100-mediated eNAMPT neutralization to treat gliomas.

DDDR-05. SPATIALLY RESOLVED DRUG SENSITIVITY PROFILING IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma is the most common malignant primary brain tumor in adults, characterized by poor overall survival and an urgent need for more effective treatment strategies. Macroscopic heterogeneity, as observed through MR imaging, divides the tumor into distinct regions: a tumor-cell dense contrast-enhancing tumor core and a non-contrast-enhancing infiltration zone that appears hyperintense on T2/FLAIR imaging. The infiltration zone contains both infiltrating tumor cells and non-malignant cells from the local brain microenvironment. Although genomic and transcriptomic differences between the tumor core and the infiltration zone have been reported, functional differences, such as differential drug sensitivities, remain largely unknown. METHODS To investigate this, we collected region-specific glioblastoma patient tissue samples from both the contrast-enhancing tumor core and the non-contrast-enhancing but 5-aminolevulinic acid (5-ALA) fluorescent infiltration zone during surgery. Using pharmacoscopy, a microscopy-based single-cell drug screening platform, we assessed drug responses across regions and patients. RESULTS To determine whether drug responses within a single tumor region were more similar compared to responses across different regions we collected multiple tissue samples from each tumor region in seven patients. We identified region-specific drug responses for temozolomide and lomustine, among other drugs, in some of the patients. However, drug sensitivities were not consistent within the same region across different patients. Thus, across patients, the region-specific drug responses were obscured by patient heterogeneity. To address intra- and inter-tumor drug response heterogeneity, we developed a complementarity score based on region-specific drug response data of 60 drugs across 23 paired samples. The complementarity score identified promising drug combinations that demonstrated strong anti-tumor activity across tumor regions and patients. CONCLUSIONS Monotherapy approaches have largely failed in glioblastoma, despite extensive molecular profiling efforts aimed at identifying tumor-specific vulnerabilities. Identifying drug combinations that synergistically target the locoregional heterogeneity of glioblastoma may open up new avenues of personalized therapy.

CTIM-29. TUMOR RESPONSES IN A RANDOMIZED PHASE IIB TRIAL OF A CMV VACCINE IMMUNOTHERAPEUTIC CANDIDATE (VBI-1901) IN RECURRENT GLIOBLASTOMAS

Abstract Cytomegalovirus (CMV) antigens have been reported in over 90% of GBMs. CD4+ and CD8+ T cells are most frequently directed against the gB and pp65 antigens, respectively, which are immunogenic targets in a CMV-based GBM immunotherapeutic. First-recurrent GBM patients were enrolled, with Karnofsky Performance Status of at least 70, to receive VBI-1901 (a gB/pp65 enveloped virus-like particle [eVLP]) adjuvanted with GM-CSF and given intradermally (NCT03382977). Patients were vaccinated with VBI-1901 every 4 weeks, with surveillance brain MRI scans every 6 weeks. Among 16 subjects receiving the highest dose of VBI-1901 in the Phase I/IIa portion of the study, the disease control rate was 44%, including 2 durable partial responses, which translated into a mOS of 12.9 months. Based on these results, the study was amended into a randomized study. Approximately 60 adult subjects (18 years or older) with first recurrence of WHO 2016 grade IV Glioblastoma, IDH-wildtype will be randomized at a 1:1 ratio to two open-label cohorts to receive: VBI-1901 or standard of care (SOC) treatment with lomustine or carmustine. Only subjects with a CD4/CD8 ratio ≥1 or a CD4 count of ≥ 400/uL at screening are eligible, as people with a preserved immune system are more likely to respond to immunization with VBI-1901 based on the initial phases of the study. As of May 15, 2024, 23 patients have been randomized across 6 sites. Among evaluable patients (n=13), disease control rates of 43% and 0% have been observed in the VBI-1901 and SOC arms, respectively, including 1 partial response in the VBI-1901 arm. Full enrollment (n=60) is anticipated by the end of the year across 11 sites, and updated tumor response data and select biomarkers associated previously with tumor responses will be presented.

QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis

Deep learning has dramatically advanced computer vision tasks, including person re-identification (re-ID), substantially improving matching individuals across diverse camera views. However, person re-ID systems remain vulnerable to adversarial attacks that introduce imperceptible perturbations, leading to misidentification and undermining system reliability. This paper addresses the challenge of robust person re-ID in the presence of adversarial examples by estimating attack intensity to enable effective detection and adaptive purification. The proposed approach leverages the observation that adversarial examples in retrieval tasks disrupt the relevance and internal consistency of retrieval results, degrading re-ID accuracy. This approach estimates the attack intensity and dynamically adjusts the purification strength by analyzing the query response data, addressing the limitations of fixed purification methods. This approach also preserves the performance of the model on clean data by avoiding unnecessary manipulation while improving the robustness of the system and its reliability in the presence of adversarial examples. The experimental results demonstrate that the proposed method effectively detects adversarial examples and estimates the attack intensity through query response analysis. This approach enhances purification performance when integrated with adversarial purification techniques in person re-ID systems.

Structural damage detection using a sensitivity-based model updating approach with autocorrelation function data

ABSTRACT This study presents a novel sensitivity equation for estimating structural parameters using autocorrelation function (ACF) data of dynamic responses. The proposed method reduces the need for extensive excitation frequencies, making structural parameter identification more practical and cost-effective, especially for large-scale structures. Furthermore, due to the quadratic relationship between ACF and the structural dynamic response, this approach exhibited greater sensitivity to damage than time-history-based methods. The method’s effectiveness was evaluated using simulated data from truss and offshore jacket platform models. Incomplete measurements were addressed by using partially measured structural characteristics without model reduction and data expansion processes. Measurement and modelling errors were simulated by adding uniformly distributed random error into the damaged structure data. The low values of the coefficient of variation indicated the method’s robustness against these errors. The accuracy of the method was confirmed through low root mean square error (RMSE) values and high variance accounted for (VAF) values.

An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose–response data

ABSTRACT Dose–response relationships are important in assessing the efficacy and potency of compounds, which can usually be characterized by a 4-parameter logistic (4-PL) model estimating EC50, slope factor, lower asymptote, and upper asymptote. EC50, the concentration of a compound that induces a response halfway between the baseline and maximum, is a key quantity to evaluate compound potency. For multi-donor dose–response data, it is often of interest to estimate the overall EC50 (i.e. the average EC50 of the population of donors) and its 95% confidence interval (CI). A few multi-donor EC50 estimation methods have been proposed in the literature. Jiang and Kopp-Schneider (2014) systematically compared the meta-analysis approach and the nonlinear mixed-effects approach and concluded that the meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments, while the nonlinear mixed-effects approach has the issue of convergence failures probably due to overparameterization. In this article, we propose a modification of the nonlinear mixed-effects approach by using the stochastic approximation expectation-maximization (SAEM) algorithm to estimate model parameters and using multiple starting points to search for globally optimal values, which can substantially alleviate the issue of convergence failures even for small number of donors (e.g. n = 3), and achieve a smaller absolute median bias and better coverage probability of 95% confidence interval than the meta-analysis approach when the number of donors is not too small (e.g. n ≥ 7).

Decompositions of Information Criteria in Joint Model Under Probit Link for Response

ABSTRACTThe item response and response time are naturally encountered in variety of computerized testing, which are jointly analysed to achieve efficient model fitting. Several novel Bayesian assessment criteria, which quantify the enhancement in the fit of one dataset given the other, are proposed by utilizing decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo‐marginal likelihood (LPML) under a logistic link. However, the calculation of these criteria under a probit link, a commonly employed alternative, remains unexplored. Moreover, decomposing DIC and LPML under probit link presents greater challenges than in the logistic model due to the intricate additional dimension integral. This paper addresses this gap by demonstrating the application of Bayesian model assessment criteria with a normal ogive model in the context of item response data within a joint modelling framework. Additionally, we also illustrate how to implement Bayesian sampling via a powerful R package ‘nimble’. Extensive simulation studies are conducted to evaluate the empirical performance of those criteria. Further, a detailed analysis of the Programme for International Student Assessment (PISA) data is carried out to illustrate the methodology.

Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

SmoothRide: A Versatile Solution to Combat Cybersickness in Elevation-Altering Environments.

Cybersickness continues to bar many individuals from taking full advantage of virtual reality (VR) technology. Previous work has established that navigating virtual terrain with elevation changes poses a significant risk in this regard. In this paper, we investigate the effectiveness of three cybersickness reduction strategies on users performing a navigation task across virtual elevation-altering terrain. These strategies include static field of view (FOV) reduction, a flat surface approach that disables terrain collision and maintains constant elevation for users, and SmoothRide, a novel technique designed to dampen a user's perception of vertical motion as they travel. To assess the impact of these strategies, we conducted a within-subjects study involving 61 participants. Each strategy was compared against a control condition, where users navigated across terrain without any cybersickness reduction measures in place. Cybersickness data were collected using the Fast Motion Sickness Scale (FMS) and Simulator Sickness Questionnaire (SSQ), along with galvanic skin response (GSR) data. We measured user presence using the IGroup Presence questionnaire (IPQ) and a Single-Item Presence Scale (SIP). Our findings reveal that users experienced significantly lower levels of cybersickness using SmoothRide or FOV reduction. Presence scores reported on the IPQ were statistically similar between SmoothRide and the control condition. Conversely, terrain flattening had adverse effects on user presence scores, and we could not identify a significant effect on cybersickness compared to the control. We demonstrate that SmoothRide is an effective, lightweight, configurable, and easy-to-integrate tool for reducing cybersickness in simulations featuring elevation-altering terrain.

Context-Aware Adaptive Encryption: Integrating Sensitive Data Detection and Network intrusion detection for Dynamic Data Security and Encryption

In today's digital landscape, ensuring the security of sensitive data and protecting against network intrusions are critical challenges. This research project develops and evaluates a novel context-aware adaptive encryption system that integrates sensitive data detection, network intrusion detection, and dynamic encryption techniques to enhance data security. The proposed system employs deep learning models to identify sensitive information and machine learning algorithms to monitor network activity for potential intrusions. Upon detecting sensitive data or a security threat, the system automatically applies encryption with adjustable strength based on the context, increasing protection in high-risk situations. This approach minimizes unnecessary overhead in low-risk scenarios while maintaining robust security measures. Through simulations using real-world data, the system's effectiveness in accurately detecting sensitive information and network intrusions, as well as its capability to adapt encryption dynamically, is evaluated. The results demonstrate the potential of combining machine learning with adaptive security measures to create a responsive and efficient data protection system.

Stochastic Nonlinear Model Updating in Structural Dynamics Using a Novel Likelihood Function within the Bayesian-MCMC Framework

The study presents a novel approach for stochastic nonlinear model updating in structural dynamics, employing a Bayesian framework integrated with Markov Chain Monte Carlo (MCMC) sampling for parameter estimation by using an approximated likelihood function. The proposed methodology is applied to both numerical and experimental cases. The paper commences by introducing Bayesian inference and its constituents: the likelihood function, prior distribution, and posterior distribution. The resonant decay method is employed to extract backbone curves, which capture the non-linear behaviour of the system. A mathematical model based on a single degree of freedom (SDOF) system is formulated, and backbone curves are obtained from time response data. Subsequently, MCMC sampling is employed to estimate the parameters using both numerical and experimental data. The obtained results demonstrate the convergence of the Markov chain, present parameter trace plots, and provide estimates of posterior distributions of updated parameters along with their uncertainties. Experimental validation is performed on a cantilever beam system equipped with permanent magnets and electromagnets. The proposed methodology demonstrates promising results in estimating parameters of stochastic non-linear dynamical systems. Through the use of the proposed likelihood functions using backbone curves, the probability distributions of both linear and non-linear parameters are simultaneously identified. Based on this view, the necessity to segregate stochastic linear and non-linear model updating is eliminated.

Perceived Risk of Elective Total Hip Arthroplasty: A Brazilian Hip Society Survey.

As total hip arthroplasty (THA) has become more common and widely available, candidates now often present with multiple risk factors that increase the risk of complications, directly impacting the cost of surgery and provider compensation. The present study was designed to better understand the impact of these factors and the perceptions of hip surgeons in Brazil. All 880 full members of the Brazilian Hip Society (SBQ) were invited to complete an online questionnaire on preoperative risk factors, adapted from the previous American Association of Hip and Knee Surgeons (AAHKS) survey. Statistical analyses of survey response data were performed. A total of 398 responses were received (45.2% of SBQ members). Most (81.7%) work in high-complexity hospitals; 46% have more than 15 years of experience in THA; and 30.2% perform more than 100 THAs/year (47% perform 40 to 100/year). Overall, 56.5% restrict THA eligibility based on modifiable risk factors: 76.6% view malnutrition and 65.8% view poorly controlled diabetes as factors precluding THA; most (89.2%) recommend waiting 6 to 24 months after bariatric surgery. Although 59.5% of respondents turn down candidates who abuse alcohol, 80.9% are willing to operate on smokers and 75.6%, on opioid users. Advanced age was not a relevant factor for 87.2, and 71.1% perceive risk as equal in men and women. Among respondents who restrict THA eligibility, 78% do so based on personal experience or literature, while 32.6% follow the opinion of the patient's primary clinician. 90.4% believe risk-based compensation would expand access to THA. Compared to their peers in Latin America, Asia, Europe, and the United States, SBQ members have a largely similar perception of restrictions to performing THA, but are more willing to operate on smokers and more likely to defer THA in post-bariatric patients. Nearly all favor adjusting surgeon compensation to take patient risk factors into account.

A Study on the Effect of the Physical Servicescape of Nail Shop on the Customer’s Internal Responses: The Moderating Effect of Service Encounter Quality

This study analyzed the effect of the physical servicescape factor on the customer’s internal response, and analyzed the moderating effect of service encounter quality in this influence relationship. In order to achieve the purpose of the study, variables for each research analysis were derived by reviewing previous studies related to physical servicescape, internal response, and service encounter quality. Subsequently, after establishing a research model and collecting response data of a total of 238 people through a questionnaire method, the hypotheses of this study were verified using the statistical processing program SPSS 26.0. The main research results according to the above research purpose and method are summarized as follows. First, as a result of analyzing the effect of the quality evaluation level of physical servicescape factors perceived by nail shop consumers on their internal response level, the atmosphere factor had the strongest positive effect on the internal response level to the nail shop service. Second, as a result of analyzing the moderating effect of service encounter quality in the relationship between the quality evaluation level of the components of the nail shop’s physical servicescape perceived by nail shop consumers on the level of internal response to their nail shop service, the service encounter quality alone had a positive effect on internal response, but no moderating effect was observed. Through the above research results, this study presented implications for the internal and external dimensions for improving the quality of nail shop service.