Notes

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n was correlated with damaged survival in ccRC and may play a crucial role in cancer-related inflammation via I-kappaB kinase/NF-kappaB and toll-like receptor signaling pathways.Information in living systems is part of a complex relationship between the internal organization and functionality of life. In a cell, both genetic-coding sequences and molecular-shape recognition are sources of biological information. For folded polymers, its spatial arrangement contains general references about conditions that shaped them, as imprints, defining the data for spatial (conformational) information. Considering the origin of life problem, prebiotic dynamics of matching and transfer of molecular shapes may emerge as a flow of information in prebiotic assemblages. The property of carrying information in molecular conformations is only displayed at this system organization level. Accordingly, spatial information is a resource for active system responses toward milieu disturbances. Propagation of resilient conformations could be the substrate for structural maintenance through dynamical molecular scaffolding. The above is a basis for adaptive behavior in potentially biogenic systems. Starting from non-structured populations of carrying-information polymers, in the present contribution, we advance toward an entire theoretical framework considering the active role of these polymers to support the emergence of adaptive response in systems that manage conformational information flow. We discuss this scenario as a previous step for the arising of sequential information carried out by genetic polymers.The Tibetan population has lived and successfully reproduced at high altitude for many generations. Studies have shown that Tibetans have various mechanisms for protection against high-altitude hypoxia, which are probably due, at least in part, to placental adaptation. However, comprehensive in silico analyses of placentas in Tibetans are lacking. We performed a microarray-based comparative transcriptome analysis of 10 Tibetan women from Yushu, Qinghai, CHN (∼3,780 m) and 10 European women living in Leadville, CO, United States (∼3,100 m) for less than three generations. Expression of HIF-1α, STAT3, EGFR, HSP5A, XBP1, and ATF6A mRNA was less in the Tibetan placentas as compared with European placentas. A total of 38 miRNAs were involved in regulating these genes. Differentially expressed genes were enriched for HIF1α signaling pathways, protein processing in the endoplasmic reticulum, PI3K-AKT signaling pathways, and MAPK signaling pathways. Based on the transcriptome profiles, the Tibetan population was distinct from the European population; placental tissues from the Tibetan population are lacking hypoxic responses, and "passivation" occurs in response to hypoxic stress. These results provide insights into the molecular signature of adaptation to high altitudes in these two populations.The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the in vivo binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV's impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding.Background Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient's quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through "The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts." Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employeated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusion Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.Heterogeneity among individual patients presents a fundamental challenge to effective treatment, since a treatment protocol working for a portion of the population often fails in others. We hypothesize that a computational pipeline integrating mathematical modeling and machine learning could be used to address this fundamental challenge and facilitate the optimization of individualized treatment protocols. We tested our hypothesis with the neuroendocrine systems controlled by the hypothalamic-pituitary-adrenal (HPA) axis. With a synergistic combination of mathematical modeling and machine learning (ML), this integrated computational pipeline could indeed efficiently reveal optimal treatment targets that significantly contribute to the effective treatment of heterogeneous individuals. What is more, the integrated pipeline also suggested quantitative information on how these key targets should be perturbed. Based on such ML revealed hints, mathematical modeling could be used to rationally design novel protocols and test their performances. We believe that this integrated computational pipeline, properly applied in combination with other computational, experimental and clinical research tools, can be used to design novel and improved treatment against a broad range of complex diseases.[This corrects the article DOI 10.3389/fpls.2021.695223.].[This corrects the article DOI 10.3389/fpls.2021.639639.].NAC transcriptional factors constitute a large family in rice and some of them have been demonstrated to play crucial roles in rice immunity. The present study investigated the function and mechanism of ONAC066 in rice immunity. ONAC066 shows transcription activator activity that depends on its C-terminal region in rice cells. ONAC066-OE plants exhibited enhanced resistance while ONAC066-Ri and onac066-1 plants showed attenuated resistance to Magnaporthe oryzae. A total of 81 genes were found to be up-regulated in ONAC066-OE plants, and 26 of them were predicted to be induced by M. ALK inhibitor oryzae. Four OsWRKY genes, including OsWRKY45 and OsWRKY62, were up-regulated in ONAC066-OE plants but down-regulated in ONAC066-Ri plants. ONAC066 bound to NAC core-binding site in OsWRKY62 promoter and activated OsWRKY62 expression, indicating that OsWRKY62 is a ONAC066 target. A set of cytochrome P450 genes were found to be co-expressed with ONAC066 and 5 of them were up-regulated in ONAC066-OE plants but down-regulated in ONAC066-Ri plants. ONAC066 bound to promoters of cytochrome P450 genes LOC_Os02g30110, LOC_Os06g37300, and LOC_Os02g36150 and activated their transcription, indicating that these three cytochrome P450 genes are ONAC066 targets. These results suggest that ONAC066, as a transcription activator, positively contributes to rice immunity through modulating the expression of OsWRKY62 and a set of cytochrome P450 genes to activate defense response.A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R 2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.
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