Efficient dealkalization of red mud and recovery of valuable metals by a sulfur-oxidizing bacterium

Red mud (RM) is a highly alkaline polymetallic waste generated via the Bayer process during alumina production. It contains metals that are critical for a sustainable development of modern society. Due to a shortage of global resources of many metals, efficient large-scale processing of RM has been receiving increasing attention from both researchers and industry. This study investigated the solubilization of metals from RM, together with RM dealkalization, via sulfur (S(0)) oxidation catalyzed by the moderately thermophilic bacterium Sulfobacillus thermosulfidooxidans. Optimization of the bioleaching process was conducted in shake flasks and 5-L bioreactors, with varying S(0):RM mass ratios and aeration rates. The ICP analysis was used to monitor the concentrations of dissolved elements from RM, and solid residues were analyzed for surface morphology, phase composition, and Na distribution using the SEM, XRD, and STXM techniques, respectively. The results show that highest metal recoveries (89% of Al, 84% of Ce, and 91% of Y) were achieved with the S(0):RM mass ratio of 2:1 and aeration rate of 1 L/min. Additionally, effective dealkalization of RM was achieved under the above conditions, based on the high rates (>95%) of Na, K, and Ca dissolution. This study proves the feasibility of using bacterially catalyzed S(0) oxidation to simultaneously dealkalize RM and efficiently extract valuable metals from the amassing industrial waste

Federated Class-Incremental Learning with Prompting

As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients. However, most existing works assume that the client's data are fixed. In real-world scenarios, such an assumption is most likely not true as data may be continuously generated and new classes may also appear. To this end, we focus on the practical and challenging federated class-incremental learning (FCIL) problem. For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid). In this paper, we propose a novel method called Federated Class-Incremental Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT does not use a rehearsal-based buffer to keep exemplars of old data. We choose to use prompts to ease the catastrophic forgetting of the old classes. Specifically, we encode the task-relevant and task-irrelevant knowledge into prompts, preserving the old and new knowledge of the local clients and solving the problem of catastrophic forgetting. We first sort the task information in the prompt pool in the local clients to align the task information on different clients before global aggregation. It ensures that the same task's knowledge are fully integrated, solving the problem of non-iid caused by the lack of classes among different clients in the same incremental task. Experiments on CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves significant accuracy improvements over the state-of-the-art methods

Recurrent chromosome reshuffling and the evolution of neo-sex chromosomes in parrots

The karyotype of most birds has remained considerably stable during more than 100 million years’ evolution, except for some groups, such as parrots. The evolutionary processes and underlying genetic mechanism of chromosomal rearrangements in parrots, however, are poorly understood. Here, using chromosome-level assemblies of four parrot genomes, we uncover frequent chromosome fusions and fissions, with most of them occurring independently among lineages. The increased activities of chromosomal rearrangements in parrots are likely associated with parrot-specific loss of two genes, ALC1 and PARP3, that have known functions in the repair of double-strand breaks and maintenance of genome stability. We further find that the fusion of the ZW sex chromosomes and chromosome 11 has created a pair of neo-sex chromosomes in the ancestor of parrots, and the chromosome 25 has been further added to the sex chromosomes in monk parakeet. Together, the combination of our genomic and cytogenetic analyses characterizes the complex evolutionary history of chromosomal rearrangements and sex chromosomes in parrots

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

A Study on Hydrochemical Characteristics and Evolution Processes of Groundwater in the Coastal Area of the Dagujia River Basin, China

Groundwater resource is vital for industrial, drinking and irrigation purposes in the Dagujia river basin, China. The objective of this work was to comprehensively assess the hydrochemical characteristics and evolution processes of the Quaternary aquifer (QA) and the bedrock aquifer (BA) of the basin using statistical methods and hydrochemical plots. In total, 56 groundwater samples were collected from the QA (34 samples) and BA (22 samples). In addition, statistical methods combined with the geographic information system were used to identify the hydrochemical parameters of groundwater, as well as its spatial distribution in the Dagujia river basin. The Piper diagram showed that Ca-Na-HCO3 was the dominant groundwater facies type, while nine QA samples collected near the coastal line showed the Na-Cl facies type. On the other hand, the Gibbs diagram showed that most samples fell in the rock dominance zone. The principal component analysis results showed that the water–rock interaction and anthropogenic activities are the controlling factors, which is consistent with the results obtained using other methods. The results of this study indicated that rock weathering controls the hydrochemical characteristics of groundwater, while anthropogenic contamination and sea water intrusion are becoming increasingly serious issues for both QA and BA in the Dagujia river basin. Therefore, both Quaternary and bedrock aquifers require more attention

Effect of Graphene Oxide on Anti-aging Property of Nitrile Butadiene Rubber

The blends with higher damping performance was prepared based on nitrile butadiene rubber(NBR) with addition of graphene oxide(GO) and modified graphene oxide(MGO) prepared by improved Hammer method. Meanwhile, the damping property and the anti-aging property of the blends were investigated by DMA, AFM, SEM and so forth. The results show that after the addition of the GO and MGO, the tangent of loss angle(tanδ) increases and also the anti-aging property is improved. When adding less amount of GO in the matrix, the anti-aging property is better; when adding MGO in the matrix, the amount of addition is not obviously related with the anti-aging property of the blends. The dispersion of GO and MGO has positive correlation with its anti-aging property. By microscopic analysis, the main reason for the decrease of anti-aging property of the blends is the agglomeration of the GO. The interface effect formed by the addition of MGO and GO is the main reason for its high damping property and anti-aging property

Fast Scalable Supervised Hashing

10.1145/3209978.3210035ACM SIGIR Conference on Information Retrieval 2018735-74

Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review

The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review’s insights into the pandemic response. Finally, we summarize the study’s limitations and highlight promising future research directions in DCT