The capabilities of the numerical simulation of technological processes are limited by the accuracy and efficiency of determining the properties of materials continuously changing under repeated ...heating and cooling. The parameters of structural transformations are the principal factors affecting the properties of alloyed steels. In this paper, we present a method for determining the parameters of relationships describing C-shaped curves in the experimental diagrams of isothermal decomposition of austenite. The proposed approach makes it possible to reconstruct the entire C-shaped curve using a relatively small fragment near the “nose” (based on three points). The joint processing of a series of curves provides determining the parameters of ferritic, pearlitic and bainitic transformation kinetics. However, one should take into account the distinctive features of the diffusion decomposition of austenite. For example, ferrite and pearlite are formed in overlapping temperature ranges and have similar mechanical properties, but their combining into a single ferrite-pearlitic structure complicates the construction of a mathematical model for the transformation. The bainitic transformation is a transient one between diffusion and diffusionless transformations. In a part of the transformation temperature range the limit of conversion level is a function of temperature (just as in the case of martensitic transformation). It has been shown that, for the case of ferrite-pearlitic transformation, the best results can be obtained with the use of Kolmogorov–Avrami equation, whereas for the case of bainitic transformation, the best results can be obtained with the use of Austin–Rickett equation modified to take into account an incomplete conversion level of the transformation.
To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of ...most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, cancer is a particular case owing to the vast size of tumour cell populations, chromosomal instability and its potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid to understand cancer progression and therapy failure. Indeed, such a framework could be applied to predict individual tumour behaviour and support treatment strategies.
Abstract The chromatin remodeler AT-Rich Interactive Domain 1A (ARID1A) is frequently mutated in ovarian clear cell carcinoma (OCCC) and endometriosis precursor lesions. Here, we show that knocking ...down ARID1A in an immortalized endometriosis cell line is sufficient to induce phenotypic changes indicative of neoplastic transformation as evidenced by higher efficiency of anchorage-independent growth, increased propensity to adhere to collagen, and greater capacity to invade basement membrane extract in vitro . ARID1A knockdown is associated with expression dysregulation of 99 target genes, and many of these expression changes are also observed in primary OCCC tissues. Further, pathway analysis indicates these genes fall within networks highly relevant to tumorigenesis including integrin and paxillin pathways. We demonstrate that the down-regulation of ARID1A does not markedly alter global chromatin accessibility or DNA methylation but unexpectedly, we find strong increases in the active H3K27ac mark in promoter regions and decreases of H3K27ac at potential enhancers. Taken together, these data provide evidence that ARID1A mutation can be an early stage event in the oncogenic transformation of endometriosis cells giving rise to OCCC.
Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode ...decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system is significantly rely on the extracted features. However, extracting signal characteristic is fairly time consuming and depends on specialized signal processing knowledge. Although some studies have developed highly accurate algorithms, the diagnostic results rely heavily on large data sets and unreliable human analysis. This study proposes an automatic feature learning neural network that utilizes raw vibration signals as inputs, and uses two convolutional neural networks with different kernel sizes to automatically extract different frequency signal characteristics from raw data. Then long short-term memory was used to identify the fault type according to learned features. The data is down-sampled before inputting into the network, greatly reducing the number of parameters. The experiment shows that the proposed method can not only achieve 98.46% average accuracy, exceeding some state-of-the-art intelligent algorithms based on prior knowledge and having better performance in noisy environments.
Rho GTPases are a family of small G proteins that regulate a wide array of cellular processes related to their key roles controlling the cytoskeleton. Cancer is a multistep disease caused by the ...accumulation of genetic mutations and epigenetic alterations, from the initial stages of cancer development when cells in normal tissues undergo transformation, to the acquisition of invasive and metastatic traits, responsible for a large number of cancer related deaths. In this review, we discuss the role of Rho GTPase signaling in cancer in every step of disease progression. Rho GTPases contribute to tumor initiation and progression, by regulating proliferation and apoptosis, but also metabolism, senescence, and cancer cell stemness. Rho GTPases play a major role in cell migration and in the metastatic process. They are also involved in interactions with the tumor microenvironment and regulate inflammation, contributing to cancer progression. After years of intensive research, we highlight the importance of relevant models in the Rho GTPase field, and we reflect on the therapeutic opportunities arising for cancer patients.
Environmental regulation can facilitate the economy's green transformation through two channels: green technology innovation and industrial structure upgrading. However, the roles of economic ...development levels play on the effects of environmental regulation are usually ignored. In view of this, we estimate the heterogeneous impacts of environmental regulation on green technology innovation and industrial structure in 105 Chinese environmental monitoring cities through the partially linear functional-coefficient panel models. The new methodological framework can allow the variable, environmental regulation, to enter the model with coefficients being functions of economic development levels. Our results show that when the economic development levels are low, environmental regulation will restrain the development of green technology innovation but have insignificant impacts on the upgrading of industrial structure. With the growth of economic development levels, environmental regulation will show relatively weak impacts on green technology innovation and industrial structure. And when the economic development levels tend to be high, environmental regulation will significantly promote green technology innovation and industrial structure upgrading. Furthermore, we identify the mixed impacts of environmental regulation in different Chinese cities and put forward relevant policy recommendations for green transformation in China.
•We study the impacts of ER on green technology innovation and industrial structure.•The partially linear functional-coefficient panel models are used to do analysis.•The impacts of ER are mixed in regions with different economic development levels.
The identification of succinate dehydrogenase (SDH), fumarate hydratase (FH) and isocitrate dehydrogenase (IDH) mutations in human cancers has rekindled the idea that altered cellular metabolism can ...transform cells. Inactivating SDH and FH mutations cause the accumulation of succinate and fumarate, respectively, which can inhibit 2-oxoglutarate (2-OG)-dependent enzymes, including the EGLN prolyl 4-hydroxylases that mark the hypoxia inducible factor (HIF) transcription factor for polyubiquitylation and proteasomal degradation. Inappropriate HIF activation is suspected of contributing to the pathogenesis of SDH-defective and FH-defective tumours but can suppress tumour growth in some other contexts. IDH1 and IDH2, which catalyse the interconversion of isocitrate and 2-OG, are frequently mutated in human brain tumours and leukaemias. The resulting mutants have the neomorphic ability to convert 2-OG to the (R)-enantiomer of 2-hydroxyglutarate ((R)-2HG). Here we show that (R)-2HG, but not (S)-2HG, stimulates EGLN activity, leading to diminished HIF levels, which enhances the proliferation and soft agar growth of human astrocytes. These findings define an enantiomer-specific mechanism by which the (R)-2HG that accumulates in IDH mutant brain tumours promotes transformation and provide a justification for exploring EGLN inhibition as a potential treatment strategy.
The rapid growth of digital technologies and the extraordinary amount of data that devices and applications collect each day are increasingly driving companies to radically transform the business ...architecture through which they create and appropriate value. However, companies may fail to extract value from digital transformation due to the disconnection between strategy formulation and strategy implementation. Through the analysis of three case studies of firms that digitally transformed their business—namely ABB, CNH Industrial, and Vodafone—this article presents a framework than can help companies implement their digital transformation strategy and thereby renovate their business model.
Tcf-1 (encoded by Tcf7) not only plays critical roles in promoting T cell development and differentiation but also has been identified as a tumor suppressor involved in preventing T cell malignancy. ...However, the comprehensive mechanisms of Tcf-1 involved in T cell transformation remain poorly understood. In this study, Tcf7
mice were crossed with Vav-cre, Lck-cre, or Cd4-cre mice to delete Tcf-1 conditionally at the beginning of the HSC, DN2-DN3, or DP stage, respectively. The defective T cell development phenotypes became gradually less severe as the deletion stage became more advanced in distinct mouse models. Interestingly, consistent with Tcf7
mice, Tcf7
Vav-cre mice developed aggressive T cell lymphoma within 45 weeks, but no tumors were generated in Tcf7
Lck-cre or Tcf7
Cd4-cre mice. Single-cell RNA-seq (ScRNA-seq) indicated that ablation of Tcf-1 at distinct phases can subdivide DN1 cells into three clusters (C1, C2, and C3) and DN2-DN3 cells into three clusters (C4, C5, and C6). Moreover, Tcf-1 deficiency redirects bifurcation among divergent cell fates, and clusters C1 and C4 exhibit high potential for leukemic transformation. Mechanistically, we found that Tcf-1 directly binds and mediates chromatin accessibility for both typical T cell regulators and proto-oncogenes, including Myb, Mycn, Runx1, and Lyl1 in the DN1 phase and Lef1, Id2, Dtx1, Fyn, Bcl11b, and Zfp36l2 in the DN2-DN3 phase. The aberrant expression of these genes due to Tcf-1 deficiency in very early T cells contributes to subsequent tumorigenesis. Thus, we demonstrated that Tcf-1 plays stage-specific roles in regulating early thymocyte development and transformation, providing new insights and evidence for clinical trials on T-ALL leukemia.
Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic ...rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.
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