Abrupt climatic events recorded by the Ili loess during the last glaciation in Central Asia: Evidence from grain-size and minerals

The loess record of Central Asia provides an important archive of regional climate and environmental changes. In contrast to the widely investigated loess deposits in the Chinese Loess Plateau, Central Asian loess-paleosol sequences remain poorly understood. Here, we present an aeolian loess section in the southern Ili Basin. Based on granularity and mineralogical analyses, we reconstruct climatic changes during the last glaciation. The results indicated that most of the abrupt climatic events (such as Dansgaard-Oeschger events and Heinrich events) were imprinted in this loess section, although their amplitudes and ages showed some differences. Compared with the millennial oscillations recoded in loess and stalagmites in East Asia, the arid Central Asia responded more sensitively to the warming events than to the cooling events. The shifting trajectory of westerlies across Central Asia played an important role in dust deposition during the stadials. The North Atlantic climatic signals may have been transmitted from Central Asia to the East Asian monsoon regions via the westerlies

Demonstration of the first monolithically integrated self-rolled-up tube based vertical photonic coupler

We demonstrated the first monolithically integrated self-rolled-up SiN_x tube based vertical photonic coupler on top of a planar ridge waveguide. The coupling efficiency between the elements is >10 times higher than similar non-integrated device

Does green finance promote the social responsibility fulfilment of highly polluting enterprises? – empirical evidence from China

This study explores whether and how the development of green finance can facilitate the social responsibility of highly polluting enterprises. We conducted a quasi-natural experiment in 2017 in five Chinese provinces (districts), based on the establishment of green finance reform and innovation pilot zones. The research samples were China’s A-share heavy pollution-industry-listed companies from 2013 to 2020, and the difference in differences model was used to examine the relationship between green finance and social responsibility fulfilment of highly polluting firms. The mediating and moderating effects of financial constraints and media monitoring were also discussed. The findings indicate that the advancement of green finance significantly improves the level of social responsibility fulfilment of highly polluting firms, particularly in the area of environmental responsibility. Furthermore, strengthened financing constraints partially mediate the aforementioned relationship, and media monitoring positively moderates the facilitation effect of green finance development on highly polluting firms’ social responsibility fulfilment. Our study demonstrates that a higher degree of financing constraints is an important channel for establishing a green finance reform and innovation pilot zone to force enterprises to fulfilment their social responsibility, and provides theoretical support for governments and enterprises to better understand the policy effects

MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model

Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framework with Predictions Aggregation (MPCPA) based on denoising diffusion probabilistic model (DDPM) is proposed, in which conditional diffusion model training, DDPM data generation, a classifier, and strategy of prediction aggregation are included. Compared to federated learning, this framework necessitates fewer communications and leverages high-quality generated data to support robust privacy computing. Experimental validation across multiple datasets demonstrates that the proposed framework outperforms classic federated learning and approaches the performance of centralized learning with original data. Moreover, our approach demonstrates robust security, effectively addressing challenges such as image memorization and membership inference attacks. Our experiments underscore the efficacy of the proposed framework in the realm of privacy computing, with the code set to be released soon

Study on Growth and Change of Solid Particles with Water Flow in Oilfield Water-Injection Pipeline

The solid particles in oilfield water-injection pipelines with water flow will continuously grow and change, and the oversized solid particles may block the pores of the formation and reduce the oilfield recovery efficiency. Therefore, the study on the growth and change to solid particles during transportation has become a question of interest in oilfields. However, there is little research on this question currently. Therefore, on the basis of the liquid-solid two-phase flow model and the particle population balance model, a growth and change model of solid particles in long-distance water-injection pipelines flowing along water was established in this paper in consideration of the injected water temperature drop along the path, as well as the growth, coalescence, breakage and deposition of particles. Comparison of the field test results indicated that the average error of the particle size distribution fitting degree calculated by the model is 6.9%, and the average error of median diameter is 4.1%. This model was used for analyzing the impact of the flow rate, temperature and median diameter of the united station outlet in a block oilfield of Shengli Oilfield on the solid particle size of the wellheads, and the critical flow rate, temperature and median diameter of the united station outlet were predicted when the median diameter at the wellheads meets the injection requirement (< 2 μm). The establishment of this model cannot only be used for the study on the growth and change to solid particles in water-injection pipelines flowing along water, but also provides a technical reference for the study on the growth and change to low-concentration solid particles accompanying flow in long-distance liquid/gas phase pipelines

Equipping Federated Graph Neural Networks with Structure-aware Group Fairness

Graph Neural Networks (GNNs) have been widely used for various types of graph data processing and analytical tasks in different domains. Training GNNs over centralized graph data can be infeasible due to privacy concerns and regulatory restrictions. Thus, federated learning (FL) becomes a trending solution to address this challenge in a distributed learning paradigm. However, as GNNs may inherit historical bias from training data and lead to discriminatory predictions, the bias of local models can be easily propagated to the global model in distributed settings. This poses a new challenge in mitigating bias in federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair Federated Graph Neural Network, that enhances group fairness of federated GNNs. As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN aims to mitigate both types of bias under federated settings. First, we provide theoretical insights on the connection between data bias in a training graph and statistical fairness metrics of the trained GNN models. Based on the theoretical analysis, we design $\text{F}^2$GNN which contains two key components: a fairness-aware local model update scheme that enhances group fairness of the local models on the client side, and a fairness-weighted global model update scheme that takes both data bias and fairness metrics of local models into consideration in the aggregation process. We evaluate $\text{F}^2$GNN empirically versus a number of baseline methods, and demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both fairness and model accuracy

A comparison of the low temperature transcriptomes of two tomato genotypes that differ in freezing tolerance: Solanum lycopersicum and Solanum habrochaites

BACKGROUND: Solanum lycopersicum and Solanum habrochaites are closely related plant species; however, their cold tolerance capacities are different. The wild species S. habrochaites is more cold tolerant than the cultivated species S. lycopersicum. RESULTS: The transcriptomes of S. lycopersicum and S. habrochaites leaf tissues under cold stress were studied using Illumina high-throughput RNA sequencing. The results showed that more than 200 million reads could be mapped to identify genes, microRNAs (miRNAs), and alternative splicing (AS) events to confirm the transcript abundance under cold stress. The results indicated that 21 % and 23 % of genes were differentially expressed in the cultivated and wild tomato species, respectively, and a series of changes in S. lycopersicum and S. habrochaites transcriptomes occur when plants are moved from warm to cold conditions. Moreover, the gene expression patterns for S. lycopersicum and S. habrochaites were dissimilar; however, there were some overlapping genes that were regulated by low temperature in both tomato species. An AS analysis identified 75,885 novel splice junctions among 172,910 total splice junctions, which suggested that the relative abundance of alternative intron isoforms in S. lycopersicum and S. habrochaites shifted significantly under cold stress. In addition, we identified 89 miRNA sequences that may regulate relevant target genes. Our data indicated that some miRNAs (e.g., miR159, miR319, and miR6022) play roles in the response to cold stress. CONCLUSIONS: Differences in gene expression, AS events, and miRNAs under cold stress may contribute to the observed differences in cold tolerance of these two tomato species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-015-0521-6) contains supplementary material, which is available to authorized users

Adverse renal outcomes following targeted therapies in renal cell carcinoma: a systematic review and meta-analysis

Introduction: To clarify the prevalence of adverse renal outcomes following targeted therapies in renal cell carcinoma (RCC).Methods: A systematic search was performed in MEDLINE, EMBASE, and Cochrane Central Library. Studies that had reported adverse renal outcomes following targeted therapies in RCC were eligible. Outcomes included adverse renal outcomes defined as either renal dysfunction as evidenced by elevated serum creatinine levels or the diagnosis of acute kidney injury, or proteinuria as indicated by abnormal urine findings. The risk of bias was assessed according to Cochrane handbook guidelines. Publication bias was assessed using Funnel plot analysis and Egger Test.Results: The occurrences of the examined outcomes, along with their corresponding 95% confidence intervals (CIs), were combined using a random-effects model. In all, 23 studies including 10 RCTs and 13 observational cohort studies were included. The pooled incidence of renal dysfunction and proteinuria following targeted therapies in RCC were 17% (95% CI: 12%–22%; I2 = 88.5%, p &lt; 0.01) and 29% (95% CI: 21%–38%; I2 = 93.2%, p &lt; 0.01), respectively. The pooled incidence of both types of adverse events varied substantially across different regimens. Occurrence is more often in polytherapy compared to monotherapy. The majority of adverse events were rated as CTCAE grades 1 or 2 events. Four studies were assessed as having low risk of bias.Conclusion: Adverse renal outcomes reflected by renal dysfunction and proteinuria following targeted therapies in RCC are not uncommon and are more often observed in polytherapy compared to monotherapy. The majority of the adverse events were of mild severity.Systematic Review Registration: Identifier CRD42023441979