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Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of data-driven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://github.com/lidq92/MDTVSFA.

Genomic insertions, duplications and insertion/deletions (indels), which account for ~14% of human pathogenic mutations, cannot be accurately or efficiently corrected by current gene-editing methods, especially those that involve larger alterations (>100 base pairs (bp)). Here, we optimize prime editing (PE) tools for creating precise genomic deletions and direct the replacement of a genomic fragment ranging from ~1 kilobases (kb) to ~10 kb with a desired sequence (up to 60 bp) in the absence of an exogenous DNA template. By conjugating Cas9 nuclease to reverse transcriptase (PE-Cas9) and combining it with two PE guide RNAs (pegRNAs) targeting complementary DNA strands, we achieve precise and specific deletion and repair of target sequences via using this PE-Cas9-based deletion and repair (PEDAR) method. PEDAR outperformed other genome-editing methods in a reporter system and at endogenous loci, efficiently creating large and precise genomic alterations. In a mouse model of tyrosinemia, PEDAR removed a 1.38-kb pathogenic insertion within the Fah gene and precisely repaired the deletion junction to restore FAH expression in liver. Prime editing is expanded to deletions and replacements of genomic sequences of up to 10 kb.

As the global economic landscape evolves, the low technological content and persistent lack of international competitiveness in China’s manufacturing exports have become increasingly apparent, underscoring the urgent need for a transition from “quantity” to “quality” in the sector. Administrative approval reform, a key pillar of institutional innovation in the new era, plays a critical role in enhancing the technological complexity of manufacturing exports and strengthening international competitiveness. Using data from 2001 to 2013, this study investigates the impact of administrative approval reform on the technological complexity of manufacturing exports and explores its underlying mechanisms from both theoretical and empirical perspectives. Theoretical model analysis suggests that administrative approval reform effectively increases technological complexity by reducing the marginal and fixed costs associated with adjusting product complexity. Empirical findings provide robust evidence that administrative approval reform significantly enhances technological complexity, with results holding across various sensitivity tests. At the micro level, the reduction of institutional transaction costs emerges as a key channel through which the reform exerts its impact. Additionally, increasing investment in research and development, fixed assets, and technological innovation are identified as critical pathways influencing technological complexity. The reform’s effects are particularly pronounced for non-state-owned enterprises and firms located in coastal and port cities, as revealed by a heterogeneity analysis. Furthermore, a decomposition of city-level export technological complexity using the DOP method shows that improved inter-firm resource allocation—by shifting market share from firms with lower technological complexity to those with higher technological complexity—serves as the primary mechanism driving the observed improvements at the city level. This study contributes to the literature by providing empirical evidence on the role of administrative approval reform in fostering the technological upgrading of manufacturing exports, highlighting its differentiated impact across firm types and regions. The findings offer valuable insights for policymakers seeking to enhance the technological complexity and international competitiveness of manufacturing exports in China.

Chinese listed companies play a vital role in national economy, which has contributed more than half of the gross domestic product. But the environmental pollution existed in the production and operation of companies has been ignored, so an objective and comprehensive efficiency assessment of Chinese listed companies were urgent. This paper constructed an index system based on green perspective, employed a super-efficiency slacks-based measure DEA (SBM-DEA) model considering undesirable outputs to assess the sustainability efficiency of Chinese listed companies. The results indicated that (1) the green sustainability efficiency of listed companies needed to be greatly improved. The average efficiency score of samples was 0.461 over 3 years, so the improvement potential was about 53.9%, only 23 companies were relatively effective. (2) The sustainability efficiency of companies has shown a slow upward trend in volatility since 2017, the non-daily consumer goods sector was the most efficient, while utilities were relatively inefficient. (3) When efficient and inefficient companies were compared, the latter were found to have significant input surplus, especially in water consumption. (4) The analysis of sensitivity on inputs and outputs showed that attention should be paid to water consumption and greenhouse gas emissions. (5) Spearman non-parametric test verified that company size and debt-paying ability were the implicit factors affecting company sustainability efficiency. The results of performance evaluation can not only provide a potential reference for the operation and management of listed companies in China, but also have guiding significance for local governments to strengthen the supervision of companies. Graphical abstract

Background Medical practitioners use survival models to explore and understand the relationships between patients’ covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. Methods We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient’s covariates and treatment effectiveness in order to provide personalized treatment recommendations. Results We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient’s covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient’s features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it’s personalized treatment recommendations would increase the survival time of a set of patients. Conclusions The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient’s characteristics on their risk of failure.

There is no consensus on the prevalence of sarcopenia or its impact on mortality in end‐stage renal disease patients undergoing dialysis. This review aimed to summarize the diagnostic criteria of sarcopenia and its prevalence and impact on the mortality of end‐stage renal disease patients undergoing dialysis. Embase, MEDLINE, PubMed, and Cochrane Library were searched from inception to 8 May 2021 to retrieve eligible studies that assessed muscle mass by commonly used instruments, such as dual‐energy X‐ray absorptiometry, bioelectrical impedance analysis, magnetic resonance imaging, and body composition monitor. Two assessment tools matched to study designs were employed to evaluate study quality. Pooled sarcopenia prevalence was calculated with 95% confidence interval (CI), and heterogeneity was estimated using the I2 test. Associations of sarcopenia with mortality were expressed as hazard ratio (HR) and 95% CI. The search identified 3272 studies, and 30 studies (6162 participants, mean age from 47.5 to 77.5 years) were analysed in this review. The risk of bias in the included studies was low to moderate. Twenty‐two studies defined sarcopenia based on low muscle mass (LMM) plus low muscle strength and/or low physical performance, while eight studies used LMM alone. Muscle mass was assessed by different instruments, and a wide range of cut‐off points were used to define LMM. Overall, sarcopenia prevalence was 28.5% (95% CI 22.9–34.1%) and varied from 25.9% (I2 = 94.9%, 95% CI 20.4–31.3%; combined criteria) to 34.6% (I2 = 98.1%, 95% CI 20.9–48.2%; LMM alone) (P = 0.247 between subgroups). The statistically significant differences were not found in the subgroups of diagnostic criteria (P > 0.05) and dialysis modality (P > 0.05). Additionally, the sarcopenia prevalence could not be affected by average age [regression coefficient 0.004 (95% CI: −0.005 to 0.012), P = 0.406] and dialysis duration [regression coefficient 0.002 (95% CI −0.002 to 0.005), P = 0.327] in the meta‐regression. The pooled analyses showed that combined criteria of sarcopenia were related to a higher mortality risk [HR 1.82 (I2 = 26.3%, 95% CI 1.38–2.39)], as was LMM [HR 1.61 (I2 = 26.0%, 95% CI 1.31–1.99)] and low muscle strength [HR 2.04 (I2 = 80.4%, 95% CI 1.19–3.5)]. Although there are substantial differences in diagnostic criteria, sarcopenia is highly prevalent in dialysis patients and is linked to increased mortality. The standardization of sarcopenia diagnostic criteria would be beneficial, and future longitudinal studies are needed to investigate the prevalence and prognostic value of sarcopenia in dialysis patients.

Chemoresistance remains the uppermost disincentive for cancer treatment on account of many genetic and epigenetic alterations. Long non-coding RNAs (lncRNAs) are emerging players in promoting cancer initiation and progression. However, the regulation and function in chemoresistance are largely unknown. Herein, we identified ARHGAP5-AS1 as a lncRNA upregulated in chemoresistant gastric cancer cells and its knockdown reversed chemoresistance. Meanwhile, high ARHGAP5-AS1 expression was associated with poor prognosis of gastric cancer patients. Intriguingly, its abundance is affected by autophagy and SQSTM1 is responsible for transporting ARHGAP5-AS1 to autophagosomes. Inhibition of autophagy in chemoresistant cells, thus, resulted in the upregulation of ARHGAP5-AS1. In turn, it activated the transcription of ARHGAP5 in the nucleus by directly interacting with ARHGAP5 promoter. Interestingly, ARHGAP5-AS1 also stabilized ARHGAP5 mRNA in the cytoplasm by recruiting METTL3 to stimulate m 6 A modification of ARHGAP5 mRNA. As a result, ARHGAP5 was upregulated to promote chemoresistance and its upregulation was also associated with poor prognosis in gastric cancer. In summary, impaired autophagic degradation of lncRNA ARHGAP5-AS1 in chemoresistant cancer cells promoted chemoresistance. It can activate the transcription of ARHGAP5 in the nucleus and stimulate m 6 A modification of ARHGAP5 mRNA to stabilize ARHGAP5 mRNA in the cytoplasm by recruiting METTL3. Therefore, targeting ARHGAP5-AS1/ARHGAP5 axis might be a promising strategy to overcome chemoresistance in gastric cancer.

Silicon is getting much attention as the promising next-generation negative electrode materials for lithium-ion batteries with the advantages of abundance, high theoretical specific capacity and environmentally friendliness. In this work, a series of phosphorus (P)-doped silicon negative electrode materials (P-Si-34, P-Si-60 and P-Si-120) were obtained by a simple heat treatment method, which can maintain the original nanoparticle morphology. The P-Si-60 material shows excellent discharge specific capacity, rate performance and cycling performance. The discharge specific capacity after 50 cycles remains > 2000 mAh g−1 with a capacity retention rate of 74.3%. The excellent electrochemical properties of P-Si-60 material can be attributed to the phosphorus doping without destroying the original particle morphology and nanostructure and the higher intrinsic electric conductivity. It will bring new thoughts for the further application of silicon negative electrode materials.

CRISPR-Cas9-associated base editing is a promising tool to correct pathogenic single nucleotide mutations in research or therapeutic settings. Efficient base editing requires cellular exposure to levels of base editors that can be difficult to attain in hard-to-transfect cells or in vivo. Here we engineer a chemically modified mRNA-encoded adenine base editor that mediates robust editing at various cellular genomic sites together with moderately modified guide RNA, and show its therapeutic potential in correcting pathogenic single nucleotide mutations in cell and animal models of diseases. The optimized chemical modifications of adenine base editor mRNA and guide RNA expand the applicability of CRISPR-associated gene editing tools in vitro and in vivo. Cas9 base editors are promising tools for correcting pathogenic single nucleotide mutations. Here the authors chemically modify mRNA encoding the editor and the gRNA to enhance editing and broaden its application.

The rapid development of renewable energy enterprises has produced important benefits for contemporary efforts to address serious environmental pollution and depletion of fossil energy resources. However, the environmental pollution that exists in the production and operation of enterprises has been ignored, and so an objective evaluation of this issue is becoming urgent. This paper established an evaluation index system for green technology innovation efficiency and used dynamic data envelopment analysis (DEA) considering undesirable output to measure the green technology innovation efficiency of renewable energy enterprises, and the improvement potential of ineffective enterprises was put forward. The results show that: (1) the green technology innovation of renewable energy enterprises needs to be greatly improved. The average efficiency score of sample was 0.385 over four years, and only 16 enterprises were found to operate effectively; (2) when effective and inefficient DMUs were compared, the latter were found to have significant output shortfalls, especially in environmental tax, and were found to show an improvement potential of 55.71 percent; (3) the efficiency analysis of different types of renewable energy enterprises found that the green technology innovation efficiency score of nuclear energy enterprises was the highest, and rapidly rose; (4) the green technology innovation efficiency of renewable energy enterprises in the western region greatly exceeded the efficiency of the eastern and central regions. The efficiency evaluation results could not only provide a guidance for central and local governances to optimize the structure of renewable energy sector, but also potentially provide a reference for the operation and management of renewable energy enterprises in China.Graphic abstract