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Predicting the invasion depth of superficial esophageal neoplasms (SENs) is crucial for an indication toward endoscopic treatment. We aimed to develop imaging biomarkers to predict the invasion depth of SENs using the intrapapillary capillary loop (IPCL) patterns. Texture and fractal analyses were performed on IPCL demarcations. Logistic regression models were built to distinguish narrow‐band images (NBIs) of T1b from T1a and high‐grade dysplasia (HGD). Classification performances of a base model composed of the age, sex, height, and body mass index of the patient, and a morphometric model comprising the texture and fractal metrics along with the base model parameters were compared. NBIs of 39 patients including T staging of 6 HGD, 22 T1a, and 11 T1b were analyzed. In the logistic regression by a multivariable generalized linear model classifying histological depths, only texture entropy was significantly and positively associated with the invasion depth. Lower texture entropy of IPCL demarcations was significantly associated with diagnoses of T1b (odds ratio: 0.144, 95% confidence interval: 0.019–0.687) in comparison to the stratum with higher texture entropy. The performance of the morphometric model represented by areas under the receiver operating curves (0.795) was better than that of the base model (0.562). We suggest morphometric analyses, particularly texture entropy, have the potential as image biomarkers to aid in the prediction of SEN invasion depth using IPCL patterns. [ABSTRACT FROM AUTHOR]

Naturally occurring protein switches have been repurposed for the development of biosensors and reporters for cellular and clinical applications.sup.1. However, the number of such switches is limited, and reengineering them is challenging. Here we show that a general class of protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which the binding of a peptide key triggers biological outputs of interest.sup.2. The designed sensors are modular molecular devices with a closed dark state and an open luminescent state; analyte binding drives the switch from the closed to the open state. Because the sensor is based on the thermodynamic coupling of analyte binding to sensor activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We create biosensors that can sensitively detect the anti-apoptosis protein BCL-2, the IgG1 Fc domain, the HER2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac troponin I and an anti-hepatitis B virus antibody with the high sensitivity required to detect these molecules clinically. Given the need for diagnostic tools to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).sup.3, we used the approach to design sensors for the SARS-CoV-2 spike protein and antibodies against the membrane and nucleocapsid proteins. The former, which incorporates a de novo designed spike receptor binding domain (RBD) binder.sup.4, has a limit of detection of 15 pM and a luminescence signal 50-fold higher than the background level. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes, and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions. A modular de novo designed biosensor platform consisting of a cage and key molecule is developed, and used to create sensors for seven distinct proteins including the spike protein from SARS-CoV-2 and anti-SARS antibodies., Author(s): Alfredo Quijano-Rubio [sup.1] [sup.2] , Hsien-Wei Yeh [sup.1] , Jooyoung Park [sup.1] [sup.6] , Hansol Lee [sup.3] , Robert A. Langan [sup.1] [sup.7] , Scott E. Boyken [sup.1] [sup.7] [...]

Background and Aim Methods Results Conclusions Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time‐consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability.A still image‐driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real‐world applicability, the model was validated using 113 10‐s colonoscopy video clips and 30 full colonoscopy videos to identify “adequate (BBPS 2–3)” or “inadequate (BBPS 0–1)” preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter‐rater variability was analyzed between the AI model and endoscopists.The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively.The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large‐scale screening programs. [ABSTRACT FROM AUTHOR]

This research examines the effects of gift prices on recipients' gratitude. Five studies show an inverted U-shaped relationship between gift price and recipients' gratitude. Recipients are more likely to appreciate gifts of monetary value that align with their expectations than inexpensive or expensive gifts whose values do not meet the recipients' expectations. Two parallel underlying mechanisms explain the inverted U-shaped relationship: when gift prices are lower than expected, recipients perceive givers as inconsiderate, and when gift prices are higher than expected, recipients feel indebted. Additionally, we examine two boundary conditions. Compared to North Americans, Asians are more likely to show an inverted U-shaped relationship. In addition, close friends, rather than distant friends, are more likely to show an inverted U-shaped relationship. The paper concludes with a discussion of contributions to the literature on gift-giving and practical implications. [ABSTRACT FROM AUTHOR]

Continuous monitoring of the prestressed members of a bridge under construction using the free cantilever method (FCM) is crucial for ensuring bridge safety. Temperature-sensitive sensors require special considerations as they may misinterpret the signal and tension. Moreover, the unnecessary and inappropriate use of features obtained from the sensor signal can deteriorate the efficiency of the signal and, therefore, tension analysis. This study proposes a tension estimation method using an embedded elastomagnetic (EM) sensor with a temperature-compensation technique. Changes in the signal due to the tension in the temporary steel rods were analyzed using a full-scale test, and the sensor data were acquired for 15 months via the field application. The temperature effect on the signal could be removed by subtracting the tension from the signal using the thermistor data, reducing the error by 91.99% when considering permeability. Additionally, linear regression (LR) and machine learning (ML) algorithms were adopted to predict the tension. Furthermore, the performances of both algorithms were compared using mean absolute error (MAE) and R2. For the prediction using each feature in magnetic hysteresis, LR surpassed ML and the permeability exhibited the highest prediction performance. Meanwhile, predictions using multiple features were attempted to investigate the applicability of ML. Two cases of prediction were performed using ML: on using all the features and the other using three features excluding coercivity, which showed poor relevance to tension. As a result, the performance of the tension prediction was improved significantly compared to the results obtained by LR. In summary, the obtained results have demonstrated that the utilization of selective features of data with temperature compensation techniques could enhance predictive power. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has devastated the ecosystem of tourism businesses. This study aimed to assess the effect of the pandemic on consumers' intentions to choose sharing economy products and services. A series of five experimental designs was conducted using online panel data. Consumers were found to be less likely to choose sharing economy products under pandemic conditions. During the pandemic, cleanliness is considered more important than location when people choose Airbnb accommodation. The results of this study contribute to a better understanding of the underlying psychological mechanisms that are driving consumers' decisions in the current environment of market uncertainty and offer evidence of the effects of the global health crisis on people's decisions to use sharing economy products in the tourism industry. [ABSTRACT FROM AUTHOR]

Prior research in behavioral economics has examined the effects of nudging and the diverse aspects of choice on individuals' decisions and behaviors. Based on this premise, the current research offers a novel and timely view by examining how communication messages in public service advertisements (PSAs) can alter the perception of threat under uncertain situations such as the SARS-CoV-2 coronavirus pandemic. This article investigates the role of additional relative statistical information on the perception of threat and stockpiling intention. First, we examine whether there is a reduction in the perceived threat of the coronavirus if information about the potential severity of an alternative threat (car accidents) is activated, when compared to offering only statistics on the disease caused by SARS-CoV-2, which is known as COVID-19. Furthermore, we established the mediating role of a perceived threat in consumers' decisions and behavior in times of severe crisis. This suggests that organizations and policymakers can influence individuals by increasing or decreasing their perceived level of threat depending on the desired outcomes (e.g., respecting authorities' recommendations or avoiding stockpiling). This research offers a deeper understanding of how consumers can be "nudged" toward desired behavior in the context of public health and safety. [ABSTRACT FROM AUTHOR]

The sunk‐cost fallacy is a well‐documented cognitive bias in the decision‐making literature. Although the emerging literature on childhood socioeconomic status suggests that early‐life environments shape individuals' decision strategies and have a long‐lasting impact on their decisions, little is known about the impact of childhood socioeconomic status on the sunk‐cost fallacy. Using two different scenarios and an actual choice task, we provide converging evidence that individuals who grew up in resource‐scarce environments (those with lower childhood socioeconomic status) are reluctant to abandon inferior choices merely because they have already invested substantial resources in them, resulting in the sunk‐cost fallacy. This fallacy occurs because individuals with lower childhood socioeconomic status tend to perceive the loss of their prior investments as more wasteful than those with higher childhood socioeconomic status. [ABSTRACT FROM AUTHOR]

This article investigates the role of diverse nudging communication strategies on perceived threat and stockpiling intention. Across three studies, the authors examined the various effects of "nudging" on consumer behavior. Study 1 demonstrates that a commonly used picture has a stronger impact on perceived threat than a less frequently exposed picture regardless of its accuracy. Study 2 shows that the perceived threat of COVID-19, in terms of severe health consequences, is lower when using an indirect (vs. direct) explanation of the virus, as well as when reducing the amount of information about the virus. Study 3 investigates the impact of salient negative information and childhood socioeconomic status (SES). Findings reveal that negative information about deaths associated with the virus increases the level of perceived threat and stockpiling intention, especially among people of low childhood SES. [ABSTRACT FROM AUTHOR]

This study sought to assess the prevalence of intimate partner violence (IPV) victimization and to examine the sex-stratified associations between IPV and suicidal ideation in South Korea. This study used nationally representative longitudinal survey data from 2011 to 2020 of the Korean Welfare Panel Study of 9732 married adults at baseline. Participants were annually asked about IPV experiences of verbal abuse, physical threat, or physical assault over the past year in 2011–2019. Suicidal ideation in the past year was assessed in the following year 2012–2020 of IPV experiences. Using multilevel logistic regression models, we examined the associations between IPV and suicidal ideation among married adults. The prevalence of verbal abuse, physical threat, and physical assault was respectively 19.8%, 3.0%, and 1.4% in females (n = 5056); and 18.8%, 2.7%, and 1.0% in males (n = 4676). After adjusting for potential confounders (e.g., lifetime suicidal ideation and random effects of individuals and households) in the same year when participants had IPV experiences, all types of IPV experiences were statistically associated with suicidal ideation in the following year among both females and males, except for physical threat among males. We found a statistically significant association between physical threat and suicidal ideation among females (adjusted odds ratio: 2.22, 95% confidence interval: 1.62, 3.04), whereas no association was observed among males. Our findings suggest that public health interventions related to suicidal behaviors that can be attributable to experiencing IPV are necessary among both females and males in South Korea. [ABSTRACT FROM AUTHOR]

Recently, we developed and reported an easy and cost-effective process called nitridation-induced self-forming Al composites (NISFAC) for manufacturing aluminum matrix composites from Al powder. In this process, AlN is formed by an in situ reaction between Al powder and nitrogen gas, and Al/AlN composites are self-sintered at temperatures below the melting point of aluminum because of the thermal energy released by the nitridation reaction of the Al powder. Because AlN is formed by an in situ reaction in the Al matrix, it is not only chemically stable, but also beneficial in terms of its wettability with the matrix. In this study, we prepared Al/AlN composites using the NISFAC process by increasing the amount of ex-situ added AlN reinforcement. The coefficient of thermal expansion (CTE) was effectively reduced by increasing the volume fraction of AlN, which was significantly lower than the theoretically expected value; a CTE of ∼10.8 × 10−6/°C could be achieved by adding only 20 vol.% of AlN. This demonstrates that the in situ formation of AlN during the NISFAC process may help reduce the CTE of Al/AlN composites. [ABSTRACT FROM AUTHOR]

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification. [ABSTRACT FROM AUTHOR]