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Automatic segmentation of early esophagus cancer (EEC) in gastrointestinal endoscopy (GIE) images is a critical and challenging task in clinical settings, which relies primarily on labor-intensive and time-consuming routines. EEC has often been diagnosed at the late stage since early signs of cancer are not obvious, resulting in low survival rates. This work proposes a deep learning approach based on the U-Net++ method to segment EEC in GIE images. A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center, West China Hospital of Sichuan University, China, have been utilized. The experimental result shows that our proposed method achieved promising results. Furthermore, the comparison has been made between the proposed and other U-Net-related methods using the same dataset. The mean and standard deviation (SD) of the dice similarity coefficient (DSC), intersection over union (IoU), precision (Pre), and recall (Rec) achieved by the proposed framework were DSC (%) ​= ​94.62 ​± ​0.02, IoU (%) ​= ​90.99 ​± ​0.04, Pre ​(%) = ​94.61 ​± ​0.04, and Rec (%) ​= ​95.00 ​± ​0.02, respectively, outperforming the others. The proposed method has the potential to be applied in EEC automatic diagnoses.

This paper presents the formulation of anti-mosquito soap from indigenous lye prepared from plantain peels ash. Neem oil is employed as the key repellent in this formulation. The study investigates the mosquito repelling power of the formulated soaps. The plantain peels ash was found to contain 9.72% of sodium and 76.93% of potassium, indicating its suitability for soap formulation purposes. Out of the 40 individuals supplied with the formulated soaps to measure the mosquito repelling power, 19 individuals (47.5%) reported 4 hours protection from mosquito sting, 8 individuals (20.0%) reported 3 hours protection from a mosquito bite. The results obtained from this study will inform local soap manufactures and the cosmetic industries to consider soap production with mosquito-repelling properties to lessen the possibility of people being bitten by mosquitoes which may result in malaria.

Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians’ burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models’ generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. Methods: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. Results: Compared with current related works, the proposed method validates better results (Dice similarity coefficient = 89.42 ± 3.92, Intersection over union = 80.04 ± 5.75, Precision = 91.72 ± 4.05, Recall = 90.11 ± 5.64, and Hausdorff distance = 23.28 ± 14.36) on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. Conclusion: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors.

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.

Infant attachment is a key predictor of later socioemotional functioning, but it is not clear how parental responsivity to infant expressive behavior is associated with attachment outcomes. A mid-range model of responsivity holds that both unresponsive and highly reactive parental behaviors lead to insecure and disorganized attachment. We examined the relationship between maternal (and infant) contingent responsivity and attachment in a high-risk sample. Participants were 625 infant-mother pairs from a longitudinal study of children with and without prenatal drug exposure and variable levels of associated social risks. Infant-mother pairs participated in the Face-to-Face/Still-Face paradigm (FFSF) at 4-months and in the Strange Situation Procedure (SSP) at 18-months. A model incorporating both linear and quadratic responsivity effects indicated that mothers who were either very high (reactive) or very low (unresponsive) in responsivity were more likely to have infants with disorganized attachment outcomes. While maternal responsivity was associated with attachment disorganization, no associations between maternal responsivity, and attachment security/insecurity were detected. Infant responsivity to mother was not associated with attachment outcomes. The findings suggest the importance of mid-range levels of maternal responsivity in the development of organized attachment among infants facing high levels of prenatal and social risk., (© 2021 John Wiley & Sons Ltd.)

Infant attachment is a critical indicator of healthy infant social-emotional functioning, which is typically measured using the gold-standard Strange Situation Procedure (SSP). However, expert-based attachment classifications from the SSP are time-intensive (with respect both to expert training and rating), and do not provide an objective, continuous record of infant behavior. To continuously quantify predictors of key attachment behaviors and dimensions, multimodal movement and audio data were collected during the SSP. Forty-nine 1-year-olds and their mothers participated in the SSP and were tracked in three-dimensional space using five synchronized Kinect sensors; LENA recordings were used to quantify crying duration. Theoretically-informed multimodal measures of attachment-related behavior (e.g., dyadic contact duration, infant velocity of approach toward the mother, and infant crying) were used to predict expert rating scales and dimensional summaries of attachment outcomes. Stepwise regressions identified sets of multimodal objective measures that were significant predictors of eight of nine of the expert ratings of infant attachment behaviors in the SSP's two reunions. These multimodal measures predicted approximately half of the variance in the summary approach/avoidance and resistance/disorganization attachment dimensions. Incorporating all objective measures as predictors regardless of significance levels, predicted individual ratings within an average of one point on the original Likert scales. The results indicate that relatively inexpensive Kinect and LENA sensors can be harnessed to quantify attachment behavior in a key assessment protocol, suggesting the promise of objective measurement to understanding infant-parent interaction., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Although difficulties with social relationships are key to autism spectrum disorder (ASD), no previous study has examined infant attachment security prior to ASD diagnosis. We prospectively assessed attachment security at 15 months in high-risk infants with later ASD (high-risk/ASD, n = 16), high-risk infants without later ASD (high-risk/no-ASD, n = 40), and low-risk infants without later ASD (low-risk/no-ASD, n = 39) using the Strange Situation Procedure. High-risk/ASD infants were disproportionately more likely to be classified as insecure (versus secure) and more likely to be classified as insecure-resistant (versus secure or avoidant) than high-risk/no-ASD and low-risk/no-ASD infants. High-risk infants with insecure-resistant attachments were over nine times more likely to receive an ASD diagnosis than high-risk infants with secure attachments. Insecure-resistant attachment in high-risk infants suggests a propensity toward negative affect with the parent in conditions of stress. Insecure-resistant attachment may prove useful as a potential early index of propensity toward ASD diagnosis in high-risk siblings, while insecure-resistant attachment in the context of emergent autism may contribute to difficulties experienced by children with ASD and their families., (© 2020 John Wiley & Sons Ltd.)

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children's interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children's location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children's velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.

Children from low SES backgrounds hear, on average, fewer words at home than those from high SES backgrounds. This word gap is associated with widening achievement differences in children's language abilities and school readiness. However relatively little is known about adult and child speech in childcare settings, in which approximately 30% of American children are enrolled. We examined the influence of teacher and peer language input on children's in-class language use and language development in an intervention classroom for low-SES, high-risk 2- to 3-year-olds. Over the course of a year, day-long recordings of the classroom were collected weekly with LENA recorders. Using LENA software algorithms, we found that language input from peers was positively related to children's in-class language use, both in-the-moment and over the course of each day, as were the number of conversational turns in which children and teachers engaged Both peer input and conversational turns with teachers were also positively related to children's language development rates, as indexed by increases in vocabulary size. Together these results indicate the importance of child-specific rates of classroom language input in the language development of high-risk, preschoolers., Competing Interests: The authors have declared that no competing interests exist.

A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching differential equation models, a novel modeling framework for representing abrupt changes in a system of differential equation models. Estimation was performed by combining the Kim filter (Kim and Nelson State-space models with regime switching: classical and Gibbs-sampling approaches with applications, MIT Press, Cambridge, 1999) and a numerical differential equation solver that can handle both ordinary and stochastic differential equations. The proposed approach was motivated by the need to represent discrete shifts in the movement dynamics of [Formula: see text] mother-infant dyads during the Strange Situation Procedure (SSP), a behavioral assessment where the infant is separated from and reunited with the mother twice. We illustrate the utility of a novel regime-switching differential equation model in representing children's tendency to exhibit shifts between the goal of staying close to their mothers and intermittent interest in moving away from their mothers to explore the room during the SSP. Results from empirical model fitting were supplemented with a Monte Carlo simulation study to evaluate the use of information criterion measures to diagnose sudden shifts in dynamics.

Brain-behavior associations in fMRI studies are typically restricted to a single level of analysis: either a circumscribed brain region-of-interest (ROI) or a larger network of brain regions. However, this common practice may not always account for the interdependencies among ROIs of the same network or potentially unique information at the ROI-level, respectively. To account for both sources of information, we combined measurement and structural components of structural equation modeling (SEM) approaches to empirically derive networks from ROI activity, and to assess the association of both individual ROIs and their respective whole-brain activation networks with task performance using three large task-fMRI datasets and two separate brain parcellation schemes. The results for working memory and relational tasks revealed that well-known ROI-performance associations are either non-significant or reversed when accounting for the ROI's common association with its corresponding network, and that the network as a whole is instead robustly associated with task performance. The results for the arithmetic task revealed that in certain cases, an ROI can be robustly associated with task performance, even when accounting for its associated network. The SEM framework described in this study provides researchers additional flexibility in testing brain-behavior relationships, as well as a principled way to combine ROI- and network-levels of analysis., (Copyright © 2017 Elsevier Inc. All rights reserved.)

According to dominant theories of affect, humans innately and universally express a set of emotions using specific configurations of prototypical facial activity. Accordingly, thousands of studies have tested emotion recognition using sets of highly intense and stereotypical facial expressions, yet their incidence in real life is virtually unknown. In fact, a commonplace experience is that emotions are expressed in subtle and nonprototypical forms. Such facial expressions are at the focus of the current study. In Experiment 1, we present the development and validation of a novel stimulus set consisting of dynamic and subtle emotional facial displays conveyed without constraining expressers to using prototypical configurations. Although these subtle expressions were more challenging to recognize than prototypical dynamic expressions, they were still well recognized by human raters, and perhaps most importantly, they were rated as more ecological and naturalistic than the prototypical expressions. In Experiment 2, we examined the characteristics of subtle versus prototypical expressions by subjecting them to a software classifier, which used prototypical basic emotion criteria. Although the software was highly successful at classifying prototypical expressions, it performed very poorly at classifying the subtle expressions. Further validation was obtained from human expert face coders: Subtle stimuli did not contain many of the key facial movements present in prototypical expressions. Together, these findings suggest that emotions may be successfully conveyed to human viewers using subtle nonprototypical expressions. Although classic prototypical facial expressions are well recognized, they appear less naturalistic and may not capture the richness of everyday emotional communication. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Electrodermal activity was examined as a measure of physiological arousal within a naturalistic play context in 2-year-old toddlers ( N = 27) with and without autism spectrum disorder. Toddlers with autism spectrum disorder were found to have greater increases in skin conductance level than their typical peers in response to administered play activities. In the autism spectrum disorder group, a positive relationship was observed between restrictive and repetitive behaviors and skin conductance level increases in response to mechanical toys, whereas the opposite pattern was observed for passive toys. This preliminary study is the first to examine electrodermal activity levels in toddlers with autism spectrum disorder during play-based, naturalistic settings, and it highlights the potential for electrodermal activity as a measure of individual variability within autism spectrum disorder and early development.

Four conditioned approach experiments with rats assessed for effects of number of acquisition trials on extinction of conditioned responding, when number of acquisition sessions and total acquisition time were held constant. In Experiment 1, 32 trials per acquisition session led to more extinction responding than did 1 or 2 trials per session but less than did 4 trials per session. In Experiment 2, 2 trials per acquisition session led to more spontaneous recovery than did 32 trials per session. These latter findings are reminiscent of the overtraining extinction effect (OEE). Experiment 3 attempted to reduce the OEE with a preconditioning phase of partial reinforcement. Experiment 4 attempted to reduce the beneficial within-subject effects of increasing the number of acquisition trials on extinction observed by Gottlieb and Rescorla (2010) by extinguishing stimuli in different sessions. Overall, results suggest a procedural asymmetry: between-subject, increasing the number of trials between any pair of trials does not lead to greater persistence of responding during extinction; within-subject, it does. Results are discussed from an associative perspective, with a focus on explanations involving either frustration or comparator mechanisms, and from an information processing perspective, with a focus on Rate Estimation Theory., (Copyright © 2012. Published by Elsevier B.V.)