The p97-Ataxin 3 complex regulates homeostasis of the DNA damage response E3 ubiquitin ligase RNF8.

The E3 ubiquitin ligase RNF8 (RING finger protein 8) is a pivotal enzyme for DNA repair. However, RNF8 hyper-accumulation is tumour-promoting and positively correlates with genome instability, cancer cell invasion, metastasis and poor patient prognosis. Very little is known about the mechanisms regulating RNF8 homeostasis to preserve genome stability. Here, we identify the cellular machinery, composed of the p97/VCP ubiquitin-dependent unfoldase/segregase and the Ataxin 3 (ATX3) deubiquitinase, which together form a physical and functional complex with RNF8 to regulate its proteasome-dependent homeostasis under physiological conditions. Under genotoxic stress, when RNF8 is rapidly recruited to sites of DNA lesions, the p97-ATX3 machinery stimulates the extraction of RNF8 from chromatin to balance DNA repair pathway choice and promote cell survival after ionising radiation (IR). Inactivation of the p97-ATX3 complex affects the non-homologous end joining DNA repair pathway and hypersensitises human cancer cells to IR. We propose that the p97-ATX3 complex is the essential machinery for regulation of RNF8 homeostasis under both physiological and genotoxic conditions and that targeting ATX3 may be a promising strategy to radio-sensitise BRCA-deficient cancers.

Polydatin inhibited TNF-α-induced apoptosis of skeletal muscle cells through AKT-mediated p38 MAPK and NF-κB pathways.

Skeletal muscle atrophy severely impacts one's quality of life. The effects and mechanism of polydatin on skeletal muscle atrophy are unclear. This study investigated the effects and mechanism of polydatin on TNF-α-induced skeletal muscle cells. The skeletal muscle cell atrophy model was established by inducing C2C12 cells with TNF-α. Cell viability, IL-1ß levels and cell apoptosis were assessed. The mRNA and protein expression levels of apoptosis-related proteins were measured. Meanwhile, the binding of polydatin to AKT was analyzed by molecular docking. TNF-α reduced cell fusion and viability while up-regulated IL-1ß level and promoted cell apoptosis. TNF-α activated AKT, NF-κB, and p38 MAPK signaling pathways. Polydatin reversed these effects induced by TNF-α, with a low concentration being more effective. Polydatin was predicted to bind to GLY162, PHE161, GLU198, THR195 and GLU191 sites of AKT protein through van der Waals force and conventional hydrogen bonds. Overexpression of AKT led to increased phosphorylation levels of AKT, p38, and p65 proteins, as well as IL-1ß levels and cell apoptosis. Polydatin inhibited TNF-α-induced apoptosis of C2C12 cells by regulating NF-κB and p38 MAPK signaling pathways through AKT. This suggests that polydatin shows promise as a new drug for the treatment of skeletal muscle atrophy.

The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach.

The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated thoroughly via fitting epidemic models to the reported COVID-19 deaths. We downloaded the data for the 12 most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number ([Formula: see text]) and infection attack rate. We develop a simple epidemic model and fitted it to reported COVID-19 deaths in 12 African countries using iterated filtering and allowing a flexible transmission rate. We observe high heterogeneity in the case-fatality rate across the countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were most affected, exhibiting a relatively higher [Formula: see text] and infection attack rate. Thus, to effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things.

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one "representative" object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.

Distribution-Sensitive Unbalanced Data Oversampling Method for Medical Diagnosis.

Aiming at the problem of low accuracy of classification learning algorithm caused by serious imbalance of sample set in medical diagnostic application, this paper proposes a distribution-sensitive oversampling algorithm for imbalanced data. The algorithm accurately divides the minority samples into noise samples, unstable samples, boundary samples and stable samples according to the location of the minority samples. Different samples are processed differently to select the most suitable sample for the synthesis of new samples. In the case of sample synthesis, a distribution-sensitive sample synthesis method is adopted. Different sample synthesis methods are selected according to their different distance from the surrounding minority samples, so as to ensure that the newly synthesized samples have the same characteristics with the original minority samples. The real medical diagnostic data test shows that this algorithm improves the accuracy rate of classification learning algorithm compared with the existing sampling algorithms, especially for the accuracy rate and recall rate of minority classes.

Investigation of epidemic spreading process on multiplex networks by incorporating fatal properties.

Numerous efforts have been devoted to investigating the network activities and dynamics of isolated networks. Nevertheless, in practice, most complex networks might be interconnected with each other (due to the existence of common components) and exhibit layered properties while the connections on different layers represent various relationships. These types of networks are characterized as multiplex networks. A two-layered multiplex network model (usually composed of a virtual layer sustaining unaware-aware-unaware (UAU) dynamics and a physical one supporting susceptible-infected-recovered-dead (SIRD) process) is presented to investigate the spreading property of fatal epidemics in this manuscript. Due to the incorporation of the virtual layer, the recovered and dead individuals seem to play different roles in affecting the epidemic spreading process. In details, the corresponding nodes on the virtual layer for the recovered individuals are capable of transmitting information to other individuals, while the corresponding nodes for the dead individuals (which are to be eliminated) on the virtual layer should be removed as well. With the coupled UAU-SIRD model, the relationships between the focused variables and parameters of the epidemic are studied thoroughly. As indicated by the results, the range of affected individuals will be reduced by a large amount with the incorporation of virtual layers. Furthermore, the effects of recovery time on the epidemic spreading process are also investigated aiming to consider various physical conditions. Theoretical analyses are also derived for scenarios with and without required time periods for recovery which validates the reducing effects of incorporating virtual layers on the epidemic spreading process.

Low Use of Oral Anticoagulant Prescribing for Secondary Stroke Prevention: Results From the Ontario Stroke Registry.

BACKGROUND: Oral anticoagulation reduces the risk of stroke in atrial fibrillation but is often underused. OBJECTIVES: To identify factors associated with oral anticoagulant prescribing and adherence after stroke or transient ischemic attack (TIA). RESEARCH DESIGN: Retrospective cohort study using linked Ontario Stroke Registry and prescription claims data. SUBJECTS: Consecutive patients with atrial fibrillation and ischemic stroke/TIA admitted to 11 stroke centers in Ontario, Canada between 2003 and 2011. MEASURES: We used modified Poisson regression models to determine predictors of anticoagulant prescribing and multiple logistic regression to determine predictors of 1-year adherence. RESULTS: Of the 5781 patients in the study cohort, 4235 (73%) were prescribed oral anticoagulants at discharge. Older patients were less likely to receive anticoagulation [adjusted relative risk (aRR) for each additional year=0.997; 95% confidence interval (CI), 0.995-0.998], as were those with TIA compared with ischemic stroke (aRR=0.904; 95% CI, 0.865-0.945), prior gastrointestinal bleed (aRR=0.778; 95% CI, 0.693-0.873), dementia (aRR=0.912; 95% CI, 0.856-0.973), and those from a long-term care facility (aRR=0.810; 95% CI, 0.737-0.891). After limiting the sample to those without obvious contraindications to anticoagulation, age, dementia, and long-term care residence continued to be associated with lower prescription of oral anticoagulants. One-year adherence to therapy was similar across most patient groups. CONCLUSIONS: Age, dementia, and long-term care residence are predictors of lower oral anticoagulant use for secondary stroke prevention and represent key target areas for quality improvement initiatives.

Academic year-end changeover and stroke outcomes.

BACKGROUND: Lower quality of care and poorer outcomes are suspected when new trainees (eg, residents) start in July in teaching hospitals, the so-called "the July effect." We evaluated outcomes and processes of care among patients with an acute ischemic stroke (AIS) admitted in July versus other 11 months of the year. METHODS: We evaluated AIS patients admitted to 11 tertiary stroke centers in Ontario, Canada between July 1, 2003, and March 31, 2008, identified from the Registry of the Canadian Stroke Network. The main outcomes were death at 30 days and poor functional outcome defined as death at 30 days or a modified Rankin Scale 3-5 at discharge. RESULTS: Of 10,319 eligible AIS patients, 882 (8.5%) were admitted in July and 9437 during the remaining months. There was no difference in baseline characteristics or stroke severity between the 2 groups. Patients admitted in July were less likely to receive thrombolysis (12% vs. 16%; odds ratio (OR), .72; 95% confidence interval (CI), .59-.89), dysphagia screening (64% vs. 68%; OR, .86; 95% CI, .74-.99), and stroke unit care (62% vs. 68%; OR, .78; 95% CI, .68-.90). July admission was not associated with either of higher death at 30 days (adjusted OR, .88; 95% CI, .74-1.03) or poor functional outcome (adjusted OR, .92; 95% CI, .74-1.14). Results remained consistent in the sensitivity analysis by including both July and August as part of the "July effect." CONCLUSIONS: AIS patients admitted to tertiary stroke centers during July had similar outcomes despite slightly less frequent thrombolysis and stroke unit care.

CT Reconstruction using Nonlinear Diffusion Posterior Sampling with Detector Blur Modeling.

There has been a great deal of work seeking to improve image quality in CT reconstruction through deep-learning-based denoising; however, there are many applications where it is spatial resolution that limits application and diagnostics. In this work, we week to improve spatial resolution in CT reconstructions through a combination of deep learning and physical modeling of detector blur. To achieve this goal, we leverage diffusion models as deep image priors to help regularize a joint deblurring and reconstruction problem. Specifically, we adopt Diffusion Posterior Sampling (DPS) as a way to combine a deep prior with a likelihood-based forward model for the measurements. The model we adopt is nonlinear since detector blur is applied after the nonlinear attenuation given by the Beer-Lambert lab. We trained a score estimator for a CT score-based prior, and then apply Bayes rule to combine this prior with a measurement likelihood score for CT reconstruction with detector blur. We demonstrate the approach in simulated data, and compare image outputs with traditional filtered-backprojection (FBP) and model-based iterative reconstruction (MBIR) across a range of exposures. We find a particular advantage of the DPS approach for low exposure data and report on major differences in the errors between DPS and classical reconstruction methods.

Diffusion Posterior Sampling for Nonlinear CT Reconstruction.

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model. However, current methods only rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model.

Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model, allowing for one-time training for different applications. However, baseline DPS can struggle with large variability, hallucinations, and slow reconstruction. This work introduces a number of strategies designed to enhance the stability and efficiency of DPS CT reconstruction. Specifically, jumpstart sampling allows one to skip many reverse time steps, significantly reducing the reconstruction time as well as the sampling variability. Additionally, the likelihood update is modified to simplify the Jacobian computation and improve data consistency more efficiently. Finally, a hyperparameter sweep is conducted to investigate the effects of parameter tuning and to optimize the overall reconstruction performance. Simulation studies demonstrated that the proposed DPS technique achieves up to 46.72% PSNR and 51.50% SSIM enhancement in a low-mAs setting, and an over 31.43% variability reduction in a sparse-view setting. Moreover, reconstruction time is sped up from >23.5 s/slice to <1.5 s/slice. In a physical data study, the proposed DPS exhibits robustness on an anthropomorphic phantom reconstruction which does not strictly follow the prior distribution. Quantitative analysis demonstrates that the proposed DPS can accommodate various dose levels and number of views. With 10% dose, only a 5.60% and 4.84% reduction of PSNR and SSIM was observed for the proposed approach. Both simulation and phantom studies demonstrate that the proposed method can significantly improve reconstruction accuracy and reduce computational costs, greatly enhancing the practicality of DPS CT reconstruction.

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Purpose: Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. Approach: We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. Results: The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. Conclusion: This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model.

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We develop the algorithm that performs this reconstruction, including an ordered-subsets variant for accelerated processing and demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

Exploring the mechanism of LncRNA CASC15 affecting hepatocellular carcinoma through miRNA.

This study aimed to determine the potential mechanisms through which long noncoding (Lnc) RNA cancer susceptibility candidate 15 (CASC15) affects hepatocellular carcinoma (HCC). We retrieved HCC RNA-seq and clinical information from the UCSC Xena database. The differential expression (DE) of CASC15 was detected. Overall survival was analyzed using Kaplan-Meier (K-M) curves. Molecular function and signaling pathways affected by CASC15 were determined using Gene Set Enrichment Analysis. Associations between CASC15 and the HCC microenvironment were investigated using immuno-infiltration assays. A differential CASC15-miRNA-mRNA network and HCC-specific CASC15-miRNA-mRNA ceRNA network were constructed. The overexpression of CASC15 in HCC tissues was associated with histological grade, clinical stage, pathological T stage, poor survival, more complex immune cell components, and 12 immune checkpoints. We identified 27 DE miRNAs and 270 DE mRNAs in the differential CASC15-miRNA-mRNA network, and 10 key genes that were enriched in 12 cancer-related signaling pathways. Extraction of the HCC-specific CASC15-miRNA-mRNA network revealed that IGF1R, MET, and KRAS were associated with HCC progression and occurrence. Our bioinformatic findings confirmed that CASC15 is a promising prognostic biomarker for HCC, and elevated levels in HCC are associated with the tumor microenvironment. We also constructed a disease-specific CASC15-miRNA-mRNA regulatory ceRNA network that provides a new perspective for the precise indexing of patients with elevated levels of CASC15.

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis.

BACKGROUND: Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition. METHODS: Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature. RESULTS: The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase. SIGNIFICANCE: The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.

Identification of molecular subtypes and prognostic signatures based on transient receptor potential channel-related genes to predict the prognostic risk of hepatocellular carcinoma: A review.

Abnormal transient receptor potential (TRP) channel function interferes with intracellular calcium-based signaling and causes malignant phenotypes. However, the effects of TRP channel-related genes on hepatocellular carcinoma (HCC) remain unclear. This study aimed to identify HCC molecular subtypes and prognostic signatures based on TRP channel-related genes to predict prognostic risks. Unsupervised hierarchical clustering was applied to identify HCC molecular subtypes using the expression data of TRP channel-related genes. This was followed by a comparison of the clinical and immune microenvironment characteristics between the resulting subtypes. After screening for differentially expressed genes among subtypes, prognostic signatures were identified to construct risk score-based prognostic and nomogram models and predict HCC survival. Finally, tumor drug sensitivities were predicted and compared between the risk groups. Sixteen TRP channel-related genes that were differentially expressed between HCC and non-tumorous tissues were used to identify 2 subtypes. Cluster 1 had higher TRP scores, better survival status, and lower levels of clinical malignancy. Immune-related analyses also revealed higher infiltration of M1 macrophages and higher immune and stromal scores in Cluster 1 than in Cluster 2. After screening differentially expressed genes between subtypes, 6 prognostic signatures were identified to construct prognostic and nomogram models. The potential of these models to assess the prognostic risk of HCC was further validated. Furthermore, Cluster 1 was more distributed in the low-risk group, with higher drug sensitivities. Two HCC subtypes were identified, of which Cluster 1 was associated with a favorable prognosis. Prognostic signatures related to TRP channel genes and molecular subtypes can be used to predict HCC risk.

Prognostic significance of LRRC1 in hepatocellular carcinoma and construction of relevant prognostic model.

This study aimed to elucidate the prognostic value of the leucine rich repeat containing 1 (LRRC1) gene in hepatocellular carcinoma (HCC) and to determine the effects of high and low LRRC1 expression on mutation and immune cell infiltration. We downloaded HCC mRNA-seq expression and clinical data from University of California Santa Cruz Xena. The expression of LRRC1 was compared between HCC tumor and normal samples. Tumor samples were divided according to high and low LRRC1 expression. Differentially expressed genes between the 2 groups were identified, and function, mutation, and immune cell infiltration were analyzed. Genes associated with immune cells were identified using weighted gene co-expression network analysis, and transcription factors of these genes were predicted. Moreover, a prognostic model was developed and its performance was evaluated. The expression of LRRC1 was upregulated in HCC tissues, and this indicated a poor prognosis for patients with HCC. Differentially expressed genes between high and low LRRC1 expression were significantly enriched in pathways associated with cancer, amino acid metabolism, carbohydrate metabolism, and the immune system. We identified 15 differentially infiltrated immune cells between tumors with high and low LRRC1 expression and 14 of them correlated with LRRC1 gene expression. Weighted gene co-expression network analysis identified 83 immune cell-related genes, 27 of which had prognostic value. Cyclic AMP-response element binding protein regulated annexin A5, matrix metallopeptidase 9, and LRRC1 in the transcription factor regulatory network. Finally, a prognostic model composed of 7 genes were generated, which could accurately predict the prognosis of HCC patients. The LRRC1 gene might serve as a potential immune-associated prognostic biomarker for HCC.

New Insights for the Design of Bionic Robots: Adaptive Motion Adjustment Strategies During Feline Landings.

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy.

A Hierarchical Error Correction Strategy for Text DNA Storage.

DNA storage has been a thriving interdisciplinary research area because of its high density, low maintenance cost, and long durability for information storage. However, the complexity of errors in DNA sequences including substitutions, insertions and deletions hinders its application for massive data storage. Motivated by the divide-and-conquer algorithm, we propose a hierarchical error correction strategy for text DNA storage. The basic idea is to design robust codes for common characters which have one-base error correction ability including insertion and/or deletion. The errors are gradually corrected by the codes in DNA reads, multiple alignment of character lines, and finally word spelling. On one hand, the proposed encoding method provides a systematic way to design storage friendly codes, such as 50% GC content, no more than 2-base homopolymers, and robustness against secondary structures. On the other hand, the proposed error correction method not only corrects single insertion or deletion, but also deals with multiple insertions or deletions. Simulation results demonstrate that the proposed method can correct more than 98% errors when error rate is less than or equal to 0.05. Thus, it is more powerful and adaptable to the complicated DNA storage applications.

The ubiquitin-dependent ATPase p97 removes cytotoxic trapped PARP1 from chromatin.

Poly (ADP-ribose) polymerase (PARP) inhibitors elicit antitumour activity in homologous recombination-defective cancers by trapping PARP1 in a chromatin-bound state. How cells process trapped PARP1 remains unclear. Using wild-type and a trapping-deficient PARP1 mutant combined with rapid immunoprecipitation mass spectrometry of endogenous proteins and Apex2 proximity labelling, we delineated mass spectrometry-based interactomes of trapped and non-trapped PARP1. These analyses identified an interaction between trapped PARP1 and the ubiquitin-regulated p97 ATPase/segregase. We found that following trapping, PARP1 is SUMOylated by PIAS4 and subsequently ubiquitylated by the SUMO-targeted E3 ubiquitin ligase RNF4, events that promote recruitment of p97 and removal of trapped PARP1 from chromatin. Small-molecule p97-complex inhibitors, including a metabolite of the clinically used drug disulfiram (CuET), prolonged PARP1 trapping and enhanced PARP inhibitor-induced cytotoxicity in homologous recombination-defective tumour cells and patient-derived tumour organoids. Together, these results suggest that p97 ATPase plays a key role in the processing of trapped PARP1 and the response of tumour cells to PARP inhibitors.