Smooth Muscle Cell Reprogramming in Aortic Aneurysms

1. Download : Download high-res image (173KB)
2. Download : Download full-size image

     

Plant intelligence and attention

This article applies the phenomenological model of attention to plant monitoring of environmental stimuli and signal perception. Three complementary definitions of attention as selectivity, modulation and perdurance are explained with reference to plant signaling and behaviors, including foraging, ramet placement and abiotic stress communication. Elements of animal and human attentive attitudes are compared with plant attention at the levels of cognitive focus, context and margin. It is argued that the concept of attention holds the potential of becoming a cornerstone of plant intelligence studies.     

人工智能算法与生态环境模型耦合研究进展

人工智能算法在生态环境领域已有广泛应用,但在揭示自然科学现象规律时存在泛化能力不足、可解释性差等问题。为弥补这些不足,实现优势互补,将人工智能算法与具有物理机制的生态环境模型耦合研究已成为近些年快速发展的一种新型研究方法。本文从应用在生态环境领域的人工智能算法出发,概述了其分类和应用情况,重点梳理了人工智能算法与生态环境模型耦合研究的发展、现状及不足,提出了一个将人工智能与机理模型紧密耦合以重构机理过程的思路,分析了该网络部分参数的理论意义,提高可解释性和泛化能力的可行性,以及模拟机理过程运行的应用前景,并展望了人工智能算法与生态环境模型耦合研究的发展趋势。     

D-ORB: A Web Server to Extract Structural Features of Related But Unaligned RNA Sequences

Identifying the common structural elements of functionally related RNA sequences (family) is usually based on an alignment of the sequences, which is often subject to human bias and may not be accurate. The resulting covariance model (CM) provides probabilities for each base to covary with another, which allows to support evolutionarily the formation of double helical regions and possibly pseudoknots. The coexistence of alternative folds in RNA, resulting from its dynamic nature, may lead to the potential omission of motifs by CM. To overcome this limitation, we present D-ORB, a system of algorithms that identifies overrepresented motifs in the secondary conformational landscapes of a family when compared to those of unrelated sequences. The algorithms are bundled into an easy-to-use website allowing users to submit a family, and optionally provide unrelated sequences. D-ORB produces a non-pseudoknotted secondary structure based on the overrepresented motifs, a deep neural network classifier and two decision trees. When used to model an Rfam family, D-ORB fits overrepresented motifs in the corresponding Rfam structure; more than a hundred Rfam families have been modeled. The statistical approach behind D-ORB derives the structural composition of an RNA family, making it a valuable tool for analyzing and modeling it. Its easy-to-use interface and advanced algorithms make it an essential resource for researchers studying RNA structure. D-ORB is available at https://d-orb.major.iric.ca/.     

Unique corneal tomography features of allergic eye disease identified by OCT imaging and artificial intelligence

The purpose of this study was to assess unique corneal tomographic parameters of allergic eye disease (AED) using optical coherence tomography (OCT) and artificial intelligence (AI). A total of 57 eyes diagnosed with AED were included. The curvature and aberrations of the air‐epithelium (A‐E) and epithelium‐Bowman's layer (E‐B) interfaces were calculated. Random forest AI models were built combing this data with the parameters of healthy, forme fruste keratoconus (FFKC) and KC eyes. The AI models were cross‐validated with 3‐fold random sampling. Each model was limited to 10 trees. The AI model incorporating both A‐E and E‐B parameters provided the best classification of AED eyes (area under the curve = 0.958, sensitivity = 80.7%, specificity = 98.5%, precision = 88.2%). Further, the E‐B interface parameters provided the highest information gain in the AI model. A few AED eyes (n = 9) had tomography parameters similar to FFKC and KC eyes and may be at risk of progression to KC.     

Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective

The development of single-cell subclones, which can rapidly switch from dormant to dominant subclones, occur in the natural pathophysiology of multiple myeloma(MM) but is often "pressed" by the standard treatment of MM. These emerging subclones present a challenge, providing reservoirs for chemoresistant mutations. Technological advancement is required to track MM subclonal changes, as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease. Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells, which include immunohistochemistry, flow cytometry, or cytogenetics. Still, all of these technologies may be limited by the sensitivity for picking up rare events. In contrast, more incisive methods such as RNA sequencing, comparative genomic hybridization, or whole-genome sequencing are not yet commonly used in clinical practice. Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution, such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing, and their respective advantages and disadvantages. In addition, we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease, which has a broad prospect.     

GWAS‐based pathway analysis differentiates between fluid and crystallized intelligence

Cognitive abilities vary among people. About 40–50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) – the ability to reason in novel situations – and general crystallized intelligence (gC) – the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome‐wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene‐ and pathway‐based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long‐term depression (LTD) seemed to underlie gC. Thus, this study supports the gF–gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation.