Neurons are highly susceptible to DNA damage accumulation due to their large energy requirements, elevated transcriptional activity, and long lifespan. While newer research has shown that DNA breaks ...and mutations may facilitate neuron diversity during development and neuronal function throughout life, a wealth of evidence indicates deficient DNA damage repair underlies many neurological disorders, especially age‐associated neurodegenerative diseases. Recently, efforts to clarify the molecular link between DNA damage and neurodegeneration have improved our understanding of how the genomic location of DNA damage and defunct repair proteins impact neuron health. Additionally, work establishing a role for senescence in the aging and diseased brain reveals DNA damage may play a central role in neuroinflammation associated with neurodegenerative disease.
Deficient DNA damage repair underlies neurological disorders and age‐associated neurodegenerative diseases. This review discusses our current understanding of how the genomic location of DNA damage and defunctional repair proteins impact neuron health.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•This damage detection method integrates an ANN model with system identification.•The ANN model is formed by modal properties and damage levels as inputs and outputs.•The effectiveness and robustness ...of the method are shown in the numerical example.•This method is experimentally verified by a seismically-excited structure.
Structural health monitoring is required to interpret damaged structures in terms of locations and severity, even remaining performance of the damaged members. Therefore, this study proposes a new artificial intelligence-based structural health monitoring strategy based on neural network modeling. A neural network model is developed in accordance with a numerical model which is derived from the identified modal properties under ambient vibrations. The stochastic subspace system identification is first implemented to derive the natural frequencies and mode shapes of a healthy structure. These natural frequencies and mode shapes are then employed to derive a simplified model of this structure, allowing changing stiffness terms to construct various damage patterns. A neural network model is trained and built by the modal properties of the structure with these damage patterns. After a critical event occurs (e.g., earthquakes), this neural network model can be employed to estimate the damage patterns in terms of stiffness reduction. In this study, a numerical example consisting of two damage scenarios is carried out. This example studies a seven-story building with a single and multiple damaged columns in order to evaluate performance of the proposed structural health monitoring strategy. Moreover, the proposed structural health monitoring strategy is also applied to an experimental test of a scaled twin-tower building with weak braces in some floors. Partially modal properties of the structure are obtained from the stochastic subspace system identification, while a simplified model is developed in accordance to the identified modal properties of the healthy building. Then, a neural network model is established based on this simplified model. After seismic events, this neural network model is employed to carry damage detection of this building in terms of damage locations and levels. As a result, the proposed artificial intelligence-based structural health monitoring strategy is quite effective to locate damage if the identified modal properties are relatively accurate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•A new time-varying damage index that preserves temporal information was proposed.•A convolutional neural network was developed to further extract temporal features.•Damage localization was realized ...using four transducers and limited training data.•Good transferability and robustness of the CNN were demonstrated.•Once constructed, the CNN can predict damage location in milliseconds.
Lamb wave-based SHM technology for damage detection and localization in plate-like structures has typically relied on post-processing of ultrasonic guided waves. Traditionally, the damage localization is realized using the time-of-flight (TOF) of damage-scattered waves. However, this method often requires the identification of a pure mode from the wave signal which is difficult in many cases. Damage index (DI) based methods offer another type of approaches that do not need such singal explanation. Since DI alone doesn’t contain temporal information, data fusion of signals from multiple actuator-sensor pairs must be performed for localization. As a result, a relatively dense actuator-sensor network is needed, and localization can only be realized within the region covered by the network. Realizing that temporal information contained in the wave signal is extremely important to damage localization, we propose a time-varying DI feature that preserves the temporal information to improve localization accuracy. In addition, we propose to use one-dimensional convolutional neural network (1-D CNN) to correlate the time-varying DI directly with the damage location. The equivariance property of CNN preserves the temporal information. The efficiency and feature extraction capability of the CNN help to build a neural network model with certain generalization capability, and thus the model trained on one plate can be applicable to a new plate. The performance of the proposed method was demonstrated in three cases: localization in the same plate with different damage locations, localization in a new plate with the same damage locations, and localization in a new plate but with different damage locations. Despite that only four transducers were used, and limited experimental data for training were available, good results have been obtained. Performance comparison with several other existing methods was also conducted.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Despite playing physiological roles in specific situations, DNA–RNA hybrids threat genome integrity. To investigate how cells do counteract spontaneous DNA–RNA hybrids, here we screen an siRNA ...library covering 240 human DNA damage response (DDR) genes and select siRNAs causing DNA–RNA hybrid accumulation and a significant increase in hybrid‐dependent DNA breakage. We identify post‐replicative repair and DNA damage checkpoint factors, including those of the ATM/CHK2 and ATR/CHK1 pathways. Thus, spontaneous DNA–RNA hybrids are likely a major source of replication stress, but they can also accumulate and menace genome integrity as a consequence of unrepaired DSBs and post‐replicative ssDNA gaps in normal cells. We show that DNA–RNA hybrid accumulation correlates with increased DNA damage and chromatin compaction marks. Our results suggest that different mechanisms can lead to DNA–RNA hybrids with distinct consequences for replication and DNA dynamics at each cell cycle stage and support the conclusion that DNA–RNA hybrids are a common source of spontaneous DNA damage that remains unsolved under a deficient DDR.
Synopsis
DNA‐RNA hybrids are a source of spontaneous DNA damage. DNA damage checkpoint (ATR/CHK1, ATM/CHK2 pathways) and post‐replicative repair factors (UBE2B, RAD18) safeguard against DNA‐RNA hybrids at the different stages of the cell cycle.
DNA‐RNA hybrids are a natural source of spontaneous DNA damage.
The post‐replicative repair and DNA damage checkpoint pathways prevent DNA‐RNA hybrid accumulation by promoting repair of the hybrid‐driven DNA damage.
DNA‐RNA hybrids form spontaneously by different mechanisms at each cell cycle stage with distinct impact on DNA replication and the DNA damage response.
DNA‐RNA hybrids are a source of spontaneous DNA damage. DNA damage checkpoint (ATR/CHK1, ATM/CHK2 pathways) and post‐replicative repair factors (UBE2B, RAD18) safeguard against DNA‐RNA hybrids at the different stages of the cell cycle.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Radiometals possess an exceptional breadth of decay properties and have been applied to medicine with great success for several decades. The majority of current clinical use involves diagnostic ...procedures, which use either positron-emission tomography (PET) or single-photon imaging to detect anatomic abnormalities that are difficult to visualize using conventional imaging techniques (e.g., MRI and X-ray). The potential of therapeutic radiometals has more recently been realized and relies on ionizing radiation to induce irreversible DNA damage, resulting in cell death. In both cases, radiopharmaceutical development has been largely geared toward the field of oncology; thus, selective tumor targeting is often essential for efficacious drug use. To this end, the rational design of four-component radiopharmaceuticals has become popularized. This Review introduces fundamental concepts of drug design and applications, with particular emphasis on bifunctional chelators (BFCs), which ensure secure consolidation of the radiometal and targeting vector and are integral for optimal drug performance. Also presented are detailed accounts of production, chelation chemistry, and biological use of selected main group and rare earth radiometals.
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IJS, KILJ, NUK, PNG, UL, UM
Damage characterization of laminated composites has been thoroughly studied the last decades where researchers developed several damage models, and in combination with experimental evidence, ...contributed to better understanding of the structural behavior of these structures. Experimental techniques played an essential role on this progress and among the techniques that were utilized, acoustic emission (AE) was extensively used due to its advantages for in-situ damage monitoring with high sensitivity and its capability to inspect continuously a relatively large area. This paper presents a comprehensive review on the use of AE for damage characterization in laminated composites. The review is divided into two sections; the first section discusses the literature for damage diagnostics and it is presented in three subsections: damage initiation detection, damage type identification and damage localization, while the second section is devoted to damage prognostics and it focuses on the remaining useful life (RUL) and residual strength prediction of composite structures using AE data. In every section, efforts have been made to analyze the most relevant literature, discuss in a critical manner the results and conclusions, and identify possibilities for future work.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Different microstructure of SLM superalloy leads to poor ductility and low performance.•Both monotonic and cyclic damage evolution is developed and related to porosity and inclusions in the ...material.•The developed damage model predicts fatigue damage of the superalloys well.
In the present work the superalloy Inconel 718 manufactured by selective laser melting (SLM) was investigated with focus on micro-structural damage evolution under both monotonic and cyclic loading conditions. Material testing and characterization reveal significantly accelerated damage evolution of the SLM material, in comparing with the forged Inconel 718. An accumulated plastic strain based damage model was proposed to predict the damage evolution in the alloys under both monotonic and cyclic loading. The damage mechanism related to detrimental precipitate particles and porous defects in the material was discussed and considered in the model, which can predict both monotonic and cyclic material degradation processes with agreement.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Impact resistance and damage tolerance are of great significance in the design of composite structures. This study researched the damage and failure mechanism of carbon fiber reinforced ...poly‐ether‐ether‐ketone (CF/PEEK) composite laminates under the low‐velocity impact (LVI) and compression after impact (CAI) loading conditions. The test included four impact energy levels (15, 30, 45, and 60 J) and compared the effect of two different stacking sequences (0°/90°8S and 0°/45°/90°/−45°4S) on performance. The results shown that the peak impact force of the two different stacking sequences increased from 7.8 kN/8.3 kN–11.4 kN/13.7 kN, and the CAI strength decreased from 370.5 MPa/419.3 MPa to 212.8 MPa/232.5 MPa, respectively. Nondestructive testing of low‐velocity impact specimens by ultrasonic C‐Scan was employed to investigate structural damage. Digital image correlation (DIC) was employed to perform full‐field displacement measurements for the CAI experiment. The cross‐section of typical specimen was observed using a scanning electron microscope (SEM) to determine the failure mode of the specimen. In addition, a 3D damage model based on continuum damage mechanics was established, with the consideration of the interlaminar delamination damage and intralaminar damage. Compared with the experimental results, the errors of the numerical simulation of the peak impact force, impact energy absorption, and CAI strength are 3.8%–14.8%, 3.7%–6.9%, and 2.2%–6.7%, respectively, which verifies the validity and rationality of the model. Furthermore, the numerical model and interpolation function were used to predict the ultimate residual strength.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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