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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.

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.)