The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical. In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available. Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.

In the recent era of AI, instance segmentation has significantly advanced boundary and object detection especially in diverse fields (e.g., biological and environmental research). Despite its progress, edge detection amid adjacent objects (e.g., organism cells) still remains intractable. This is because homogeneous and heterogeneous objects are prone to being mingled in a single image. To cope with this challenge, we propose the weighted Mask R‐CNN designed to effectively separate overlapped objects in virtue of extra weights to adjacent boundaries. For numerical study, a range of experiments are performed with applications to simulated data and real data (e.g., Microcystis, one of the most common algae genera and cell membrane images). It is noticeable that the weighted Mask R‐CNN outperforms the standard Mask R‐CNN, given that the analytic experiments show on average 92.5% of precision and 96.4% of recall in algae data and 94.5% of precision and 98.6% of recall in cell membrane data. Consequently, we found that a majority of sample boundaries in real and simulated data are precisely segmented in the midst of object mixtures.

In this study, we propose an automated system for measuring the size of strawberries and predicting their weight using AI technology. The system combines computer vision techniques with LiDAR sensor data to accurately estimate the dimensions of strawberries and infer their weight. By integrating deep learning models, such as HRNet for keypoint detection, and leveraging the capabilities of LiDAR sensors, we minimize human intervention and achieve precise size measurement. The relative errors for the width and height of the strawberries are 3.71% and 5.42%, respectively, with the width exhibiting a lower error rate. The standard deviation for the width and height of the strawberries are 0.19% and 0.24%, this indicates that the individual strawberries had very low error rates in terms of their measurements for the width and height. Weight prediction was performed through regression analysis with width and height estimation. Experimental results demonstrate that our approach enables accurate weight prediction with a relative error of 10.3%. This automated technology holds great potential for strawberry harvesting and classification tasks, facilitating the automation of these processes.

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large‐margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an F1 score of 93.45%, outperforming the baseline models, which used RGB data alone.

Objective To compare and analyze the differences in the sociodemographic and clinical characteristics of suicide attempters who visited an emergency department (ED) before and during the coronavirus disease (COVID-19) pandemic.Methods This single center, retrospective study was conducted by reviewing the medical records of patients in the “self-injury/suicide” category of the National Emergency Department Information System who visited an ED between January 2019 and December 2020. We obtained information on baseline characteristics, suicide attempt, and disposition. Data were analyzed using the chi-squared test.Results A total of 456 patients were included. The number of patients visiting the ED for suicide attempts increased by 18.2% (from 209 to 247 cases) during the COVID-19 pandemic, and the ratio of suicide attempters to the total number of ED visits increased by 48.8% (from 0.43% to 0.64%, P<0.001). There were significant differences in methods of suicide attempt, endotracheal intubation, ED disposition, and the presence of mental illness. Drug overdose (42.1% vs. 53.4%) and gas inhalation (5.7% vs. 8.5%) increased, and hanging decreased (6.0% vs. 2.0%) during the pandemic. Endotracheal intubation (13.9% vs. 5.7%) and intensive care unit admission (29.7% vs. 14.6%) decreased. More patients with the history of mental illness visited during the pandemic (54.0% vs. 70.1%).Conclusion Since the COVID-19 pandemic began, suicide attempts have increased in this single ED although the lethality of those attempts is low.