Deep Equilibrium Multimodal Fusion

Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently. Most existing fusion approaches either learn a fixed fusion strategy during training and inference, or are only capable of fusing the information to a certain extent. Such solutions may fail to fully capture the dynamics of interactions across modalities especially when there are complex intra- and inter-modality correlations to be considered for informative multimodal fusion. In this paper, we propose a novel deep equilibrium (DEQ) method towards multimodal fusion via seeking a fixed point of the dynamic multimodal fusion process and modeling the feature correlations in an adaptive and recursive manner. This new way encodes the rich information within and across modalities thoroughly from low level to high level for efficacious downstream multimodal learning and is readily pluggable to various multimodal frameworks. Extensive experiments on BRCA, MM-IMDB, CMU-MOSI, SUN RGB-D, and VQA-v2 demonstrate the superiority of our DEQ fusion. More remarkably, DEQ fusion consistently achieves state-of-the-art performance on multiple multimodal benchmarks. The code will be released

Density Backbone Clustering Algorithm Based on Adaptive Threshold

The existing clustering algorithms are inaccurate to identify arbitrary clusters, sensitive to density changes within clusters, sensitive to outliers and difficult to determine the threshold. An adaptive threshold-constrained density cluster backbone clustering algorithm (DCBAT) is proposed to solve the problems. Firstly, the adaptive reachability density threshold is defined in combination with the skewness coefficient and points density mean. Under the constraint of the threshold, the core points with higher local densities and higher relative distances are grouped according to the reachability, and the initial clusters backbones are obtained. The non-core points are then assigned into the cluster which their nearest neighbors with higher density belong to. Finally, the adaptive density D-value threshold is proposed in combination with D-value mean and scale factor. According to the threshold, the initial cluster is separated at the point where the density varies sharply, and the final clusters are obtained. DCBAT fully considers the internal structure and distribution of the data when clustering, thereby improving the clustering performance. The performance of this algorithm is demonstrated compared with five excellent algorithms k-means,DBSCAN, OPTICS, CFDP and MulSim on eight datasets with various dimensions and types. DCBAT algorithm has the advantages of good recognition of arbitrary clusters, insensitivity to density changes within clusters, insensitivity to outliers and stable clustering result. Its overall performance is superior to comparison algorithms

Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

Annotation of candidate genes anchored by associated SNPs. (XLSX 34 kb

Exploring the prevalence and chest CT predictors of Long COVID in children: a comprehensive study from Shanghai and Linyi

IntroductionCOVID-19 constitutes a pandemic of significant detriment to human health. This study aimed to investigate the prevalence of Long COVID following SARS-CoV-2 infection, analyze the potential predictors of chest CT for the development of Long COVID in children.MethodsA cohort of children who visited the respiratory outpatient clinics at Shanghai Children's Medical Center or Linyi Maternal and Child Health Care Hospital from December 2022 to February 2023 and underwent chest CT scans within 1 week was followed up. Data on clinical characteristics, Long COVID symptoms, and chest CT manifestations were collected and analyzed. Multivariate logistic regression models and decision tree models were employed to identify factors associated with Long COVID.ResultsA total of 416 children were included in the study. Among 277 children who completed the follow-up, the prevalence of Long COVID was 23.1%. Chronic cough, fatigue, brain fog, and post-exertional malaise were the most commonly reported symptoms. In the decision tree model for Long COVID, the presence of increased vascular markings, the absence of normal CT findings, and younger age were identified as predictors associated with a higher likelihood of developing Long COVID in children. However, no significant correlation was found between chest CT abnormality and the occurrence of Long COVID.DiscussionLong COVID in children presents a complex challenge with a significant prevalence rate of 23.1%. Chest CT scans of children post-SARS-CoV-2 infection, identified as abnormal with increased vascular markings, indicate a higher risk of developing Long COVID

Smooth Non-negative Low-Rank Graph Representation for Clustering

The existing low-rank graph representation algorithms fail to capture the global representation structure of data accurately, and cannot make full use of the valid information of data to guide the construction of the representation graph, then the constructed representation graph does not have a connected structure suitable for clustering. A smooth non-negative low-rank graph representation method for clustering (SNLRR) is proposed to solve these problems. To more accurately capture the global representation structure of data, SNLRR uses a logarithmic determinant function that is more consistent with the rank characteristics of the matrix to replace the kernel norm to estimate the rank function smoothly, which can effectively reduce the impact of larger singular values of the matrix on the rank estimation, balance the contribution of all singular values to the rank estimation, enhance the accuracy of the rank estimation, so as to more accurately capture the global representation structure of the data. The distance regularization term is also introduced to adaptively assign the optimal nearest neighbor learning representation matrix for each data point to capture the local representation structure of data. Besides, SNLRR applies rank constraint on the Laplace matrix of representation matrix so that the learned representation graph has the same number of connected components as the real number of clusters, that is, the resulting representation graph has a interconnected structure suitable for clustering. Experimental results on seven datasets with high dimensions and complex distribution, using eight comparison algorithms, show that the clustering performance of SNLRR algorithm is better than that of the eight comparison algorithms, with an average increase of 0.2073 in accuracy and 0.1758 in NMI. Therefore, SNLRR is a graph representation clustering algorithm that can effectively handle data with high dimensions and complex distribution

A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing

Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.Published versio

ATF1 promotes the malignancy of lung adenocarcinoma cells by transcriptionally regulating ZNF143 expression

The clinical oncogenic functions and mechanisms of activating transcription factor 1 (ATF1) in the progression of lung adenocarcinoma have not been completely elucidated. In this study, by employing human lung adenocarcinoma tissues and cells, we detect the correlation of ATF1 expression with the clinicopathological features and prognosis of patients with lung adenocarcinoma and find that ATF1 promotes lung adenocarcinoma cell proliferation and migration by transcriptionally enhancing zinc finger protein 143 (ZNF143) expression. ATF1 and ZNF143 are strongly expressed in lung adenocarcinoma tissues compared with those in the adjacent normal tissues, and high ATF1 and ZNF143 expressions are related to poor disease-free survival of lung adenocarcinoma patients. ATF1 overexpression results in increased proliferation and migration of lung adenocarcinoma cells, whereas knockdown of ATF1 inhibits cell proliferation and migration. Furthermore, ATF1 transcriptionally regulates the expression of ZNF143, and ATF1 and ZNF143 expressions are positively correlated in lung adenocarcinoma tissues. ZNF143 knockdown blocks lung adenocarcinoma cell migration, which is mediated by ATF1 upregulation. Hence, this study provides a potential therapeutic candidate for the treatment of lung adenocarcinoma

Microfluidic contact-imaging cytometer system for flowing cell detection, recognition and counting.

<p>(A) Cell shadow image by contact imaging. (B) Captured video of flowing cells. (C) CMOS image sensor board schematic with external controls. (D) System board of the developed microfluidic cytometer.</p

Different contact imaging systems without optical lens.

<p>(A) Static contact imaging system. (B) Microfluidic contact imaging system with capillary flow. (C) The proposed microfluidic contact-imaging cytometer system with continuous flow: (C1) bonding process; (C2) overall system structure.</p