TDP2 promotes repair of topoisomerase I-mediated DNA damage in the absence of TDP1

The abortive activity of topoisomerases can result in clastogenic and/or lethal DNA damage in which the topoisomerase is covalently linked to the 3'- or 5'-terminus of a DNA strand break. This type of DNA damage is implicated in chromosome translocations and neurological disease and underlies the clinical efficacy of an important class of anticancer topoisomerase 'poisons'. Tyrosyl DNA phosphodiesterase-1 protects cells from abortive topoisomerase I (Top1) activity by hydrolyzing the 3'-phosphotyrosyl bond that links Top1 to a DNA strand break and is currently the only known human enzyme that displays this activity in cells. Recently, we identified a second tyrosyl DNA phosphodiesterase (TDP2; aka TTRAP/EAPII) that possesses weak 3'-tyrosyl DNA phosphodiesterase (3'-TDP) activity, in vitro. Herein, we have examined whether TDP2 contributes to the repair of Top1-mediated DNA breaks by deleting Tdp1 and Tdp2 separately and together in murine and avian cells. We show that while deletion of Tdp1 in wild-type DT40 cells and mouse embryonic fibroblasts decreases DNA strand break repair rates and cellular survival in response to Top1-induced DNA damage, deletion of Tdp2 does not. However, deletion of both Tdp1 and Tdp2 reduces rates of DNA strand break repair and cell survival below that observed in Tdp1(-)(/)(-) cells, suggesting that Tdp2 contributes to cellular 3'-TDP activity in the absence of Tdp1. Consistent with this idea, over-expression of human TDP2 in Tdp1(-)(/)(-)/Tdp2(-)(/)(-)(/)(-) DT40 cells increases DNA strand break repair rates and cell survival above that observed in Tdp1(-)(/)(-) DT40 cells, suggesting that Tdp2 over-expression can partially complement the defect imposed by loss of Tdp1. Finally, mice lacking both Tdp1 and Tdp2 exhibit greater sensitivity to Top1 poisons than do mice lacking Tdp1 alone, further suggesting that Tdp2 contributes to the repair of Top1-mediated DNA damage in the absence of Tdp1. In contrast, we failed to detect a contribution for Tdp1 to repair Top2-mediated damage. Together, our data suggest that Tdp1 and Tdp2 fulfil overlapping roles following Top1-induced DNA damage, but not following Top2-induced DNA damage, in vivo

Hierarchically coupled ultradian oscillators generating robust circadian rhythms

Ensembles of mutually coupled ultradian cellular oscillators have been proposed by a number of authors to explain the generation of circadian rhythms in mammals. Most mathematical models using many coupled oscillators predict that the output period should vary as the square root of the number of participating units, thus being inconsistent with the well-established experimental result that ablation of substantial parts of the suprachiasmatic nuclei (SCN), the main circadian pacemaker in mammals, does not eliminate the overt circadian functions, which show no changes in the phases or periods of the rhythms. From these observations, we have developed a theoretical model that exhibits the robustness of the circadian clock to changes in the number of cells in the SCN, and that is readily adaptable to include the successful features of other known models of circadian regulation, such as the phase response curves and light resetting of the phase

A study on decision-making of food supply chain based on big data

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value from big data

Condition monitoring approach for permanent magnet synchronous motor drives based on the INFORM method

This paper proposes a monitoring scheme based on saliency tracking to assess the health condition of PMSM drives operating under non stationary conditions. The evaluated scheme is based on the INFORM methodology, which is associated to the accurate sensorless control of PM drives without zero speed limitation. The result is a monitoring scheme that is able to detect faults that would be very difficult to evaluate under nonstationary conditions. A relevant aspect of the proposed scheme is that it remains valid for full speed range, and can be used for standstill operation. Additionally, the approach is insensitive to the inverter nonlinearities which enhance the detection capabilities further respect to similar topologies. In this work the proposed approach is evaluated numerically and experimentally in the presence of incipient winding faults and inter-turn short circuits in a PM conventional drive. The obtained results show quick response and excellent detection capabilities not only in the detection of faults, but to determine their magnitude which is vital to avoid further degradation

A coordinated DNA damage response promotes adult quiescent neural stem cell activation

Stem and differentiated cells frequently differ in their response to DNA damage, which can determine tissue sensitivity. By exploiting insight into the spatial arrangement of subdomains within the adult neural subventricular zone (SVZ) in vivo, we show distinct responses to ionising radiation (IR) between neural stem and progenitor cells. Further, we reveal different DNA damage responses between neonatal and adult neural stem cells (NSCs). Neural progenitors (transit amplifying cells and neuroblasts) but not NSCs (quiescent and activated) undergo apoptosis after 2 Gy IR. This response is cell type- rather than proliferationdependent and does not appear to be driven by distinctions in DNA damage induction or repair capacity. Moreover, exposure to 2 Gy IR promotes proliferation arrest and differentiation in the adult SVZ. These 3 responses are ataxia telangiectasia mutated (ATM)- dependent and promote quiescent NSC (qNSC) activation, which does not occur in the subdomains that lack progenitors. Neuroblasts arising post-IR derive from activated qNSCs rather than irradiated progenitors, minimising damage compounded by replication or mitosis. We propose that rather than conferring sensitive cell death, apoptosis is a form of rapid cell death that serves to remove damaged progenitors and promote qNSC activation. Significantly, analysis of the neonatal (P5) SVZ reveals that although progenitors remain sensitive to apoptosis, they fail to efficiently arrest proliferation. Consequently, their repopulation occurs rapidly from irradiated progenitors rather than via qNSC activation

Comparative Analyses by Sequencing of Transcriptomes during Skeletal Muscle Development between Pig Breeds Differing in Muscle Growth Rate and Fatness

Understanding the dynamics of muscle transcriptome during development and between breeds differing in muscle growth is necessary to uncover the complex mechanism underlying muscle development. Herein, we present the first transcriptome-wide longissimus dorsi muscle development research concerning Lantang (LT, obese) and Landrace (LR, lean) pig breeds during 10 time-points from 35 days-post-coitus (dpc) to 180 days-post-natum (dpn) using Solexa/Illumina's Genome Analyzer. The data demonstrated that myogenesis was almost completed before 77 dpc, but the muscle phenotypes were still changed from 77 dpc to 28 dpn. Comparative analysis of the two breeds suggested that myogenesis started earlier but progressed more slowly in LT than in LR, the stages ranging from 49 dpc to 77 dpc are critical for formation of different muscle phenotypes. 595 differentially expressed myogenesis genes were identified, and their roles in myogenesis were discussed. Furthermore, GSK3B, IKBKB, ACVR1, ITGA and STMN1 might contribute to later myogenesis and more muscle fibers in LR than LT. Some myogenesis inhibitors (ID1, ID2, CABIN1, MSTN, SMAD4, CTNNA1, NOTCH2, GPC3 and HMOX1) were higher expressed in LT than in LR, which might contribute to more slow muscle differentiation in LT than in LR. We also identified several genes which might contribute to intramuscular adipose differentiation. Most important, we further proposed a novel model in which MyoD and MEF2A controls the balance between intramuscular adipogenesis and myogenesis by regulating CEBP family; Myf5 and MEF2C are essential during the whole myogenesis process while MEF2D affects muscle growth and maturation. The MRFs and MEF2 families are also critical for the phenotypic differences between the two pig breeds. Overall, this study contributes to elucidating the mechanism underlying muscle development, which could provide valuable information for pig meat quality improvement

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science