Spatial and Source Apportionment Analysis of PAH and Metal Contaminants in the Illinois River’s Peoria Pool Sediments

In this study, sediments from the Peoria Pool of the Illinois River were analyzed for the presence and concentrations of polycyclic aromatic hydrocarbons (PAHs) and metals. The PAH source apportionment analysis utilized 225 PAH profiles from 10 PAH sources collected through a literature search. The analysis suggested that anthropogenic sources contributed the PAHs while the majority of metals originated from the Earth’s crust. Higher PAH concentrations were generally found in sediment cores taken close to the main river channel or the mouths of backwater lakes where they join the river, suggesting an association with main river flows. Concentrations of PAHs in the backwater areas varied considerably. The source apportionment analysis suggested that coal dust; coal and wood combustion soot; asphalt and coal-tar sealcoat; and traffic emissions are the main sources of PAHs in Illinois River sediments. The analysis also indicated coal dust or an aggregated PAH input from several sources contributed approximately 47 ± 7% of total PAHs in the Peoria Pool sediments; coal combustion (soot) contributed 28 ± 4%; traffic emissions (soot) contributed 15 ± 3%; and wood combustion contributed 5%. The combined gas phase of coal and wood combustion and traffic exhaust accounted for another 5% of the total PAHs. The analysis indicated that the majority of metals (60%) in the Illinois River sediments were derived from crustal material weathering. Beyond that, industrial emissions contributed slightly more than 20% of the total metals, and traffic emissions accounted for the remaining 20%.Illinois Sustainable Technology Center (ISTC) ; Grant No. SR2.Ope

Experiments of the efficacy of tree ring blue intensity as a climate proxy in central and western China

To investigate the potential value of blue intensity as a robust climate proxy in central and western China, 4 species from 5 sites were assessed. As well as latewood inverted BI (LWBinv), we also examined earlywood BI (EWB). To explore the sensitivity of using different extraction parameter settings using CooRecorder, seven percentile variant settings for EWB and LWBinv were used; F50:50 to F95:05. The RW, EWB, and LWBinv were detrended using an age-dependent spline. Correlation analysis was applied between the tree ring parameter chronologies and monthly/seasonal variables of mean temperature, precipitation, and scPDSI. Linear regression was also used to further highlight the potential of developing climate reconstructions using these species. Only subtle differences were found between the different percentile extraction variants. However, the analysis suggested that F80:20 or F85:15 variants marginally provided better performance. As has been shown for many other northern hemisphere studies, inverse latewood intensity expresses a strong positive relationship with growing season temperatures (the two southern sites explaining almost 60 % of the temperature variance when combined). However, the low latitude of these sites shows exciting potential for regions south of 30&deg; N that are traditionally not targeted for temperature reconstructions. EWB also shows good potential to reconstruct hydroclimate parameters in some humid areas.</p

Biofilm formation and antibiotic sensitivity in Elizabethkingia anophelis

Elizabethkingia anophelis has recently gained global attention and is emerging as a cause of life-threatening nosocomial infections. The present study aimed to investigate the association between antimicrobial resistance and the ability to form biofilm among E. anophelis isolated from hospitalized patients in China. Over 10 years, a total of 197 non-duplicate E. anophelis strains were collected. Antibiotic susceptibility was determined by the standard agar dilution method as a reference assay according to the Clinical and Laboratory Standards Institute. The biofilm formation ability was assessed using a culture microtiter plate method, which was determined using a crystal violet assay. Culture plate results were cross-checked by scanning electron microscopy imaging analysis. Among the 197 isolates, all were multidrug-resistant, and 20 were extensively drug-resistant. Clinical E. anophelis showed high resistance to current antibiotics, and 99% of the isolates were resistant to at least seven antibiotics. The resistance rate for aztreonam, ceftazidime, imipenem, meropenem, trimethoprim-sulfamethoxazole, cefepime, and tetracycline was high as 100%, 99%, 99%, 99%, 99%, 95%, and 90%, respectively. However, the isolates exhibited the highest susceptibility to minocycline (100%), doxycycline (96%), and rifampin (94%). The biofilm formation results revealed that all strains could form biofilm. Among them, the proportions of strong, medium, and weak biofilm-forming strains were 41%, 42%, and 17%, respectively. Furthermore, the strains forming strong or moderate biofilm presented a statistically significant higher resistance than the weak formers (p &lt; 0.05), especially for piperacillin, piperacillin-tazobactam, cefepime, amikacin, and ciprofloxacin. Although E. anophelis was notoriously resistant to large antibiotics, minocycline, doxycycline, and rifampin showed potent activity against this pathogen. The data in the present report revealed a positive association between biofilm formation and antibiotic resistance, which will provide a foundation for improved therapeutic strategies against E. anophelis infections in the future

Knowledge Prompt-tuning for Sequential Recommendation

Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this issue by integrating side information while suffering from information loss. To summarize, we believe that a good recommendation system should utilize both general and domain knowledge simultaneously. Therefore, we introduce an external knowledge base and propose Knowledge Prompt-tuning for Sequential Recommendation (\textbf{KP4SR}). Specifically, we construct a set of relationship templates and transform a structured knowledge graph (KG) into knowledge prompts to solve the problem of the semantic gap. However, knowledge prompts disrupt the original data structure and introduce a significant amount of noise. We further construct a knowledge tree and propose a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem. We evaluate KP4SR on three real-world datasets, and experimental results show that our approach outperforms state-of-the-art methods on multiple evaluation metrics. Specifically, compared with PLM-based methods, our method improves NDCG@5 and HR@5 by \textcolor{red}{40.65\%} and \textcolor{red}{36.42\%} on the books dataset, \textcolor{red}{11.17\%} and \textcolor{red}{11.47\%} on the music dataset, and \textcolor{red}{22.17\%} and \textcolor{red}{19.14\%} on the movies dataset, respectively. Our code is publicly available at the link: \href{https://github.com/zhaijianyang/KP4SR}{\textcolor{blue}{https://github.com/zhaijianyang/KP4SR}.

Phylogenetic structure and formation mechanism of shrub communities in arid and semiarid areas of the Mongolian Plateau

The mechanisms of species coexistence within a community have always been the focus in ecological research. Community phylogenetic structure reflects the relationship of historical processes, regional environments, and interactions between species, and studying it is imperative to understand the formation and maintenance mechanisms of community composition and biodiversity. We studied the phylogenetic structure of the shrub communities in arid and semiarid areas of the Mongolian Plateau. First, the phylogenetic signals of four plant traits (height, canopy, leaf length, and leaf width) of shrubs and subshrubs were measured to determine the phylogenetic conservation of these traits. Then, the net relatedness index (NRI) of shrub communities was calculated to characterize their phylogenetic structure. Finally, the relationship between the NRI and current climate and paleoclimate (since the Last Glacial Maximum, LGM) factors was analyzed to understand the formation and maintenance mechanisms of these plant communities. We found that desert shrub communities showed a trend toward phylogenetic overdispersion; that is, limiting similarity was predominant in arid and semiarid areas of the Mongolian Plateau despite the phylogenetic structure and formation mechanisms differing across habitats. The typical desert and sandy shrub communities showed a significant phylogenetic overdispersion, while the steppified desert shrub communities showed a weak phylogenetic clustering. It was found that mean winter temperature (i.e., in the driest quarter) was the major factor limiting steppified desert shrub phylogeny distribution. Both cold and drought (despite having opposite consequences) differentiated the typical desert to steppified desert shrub communities. The increase in temperature since the LGM is conducive to the invasion of shrub plants into steppe grassland, and this process may be intensified by global warming

Learning Sparse Neural Networks with Identity Layers

The sparsity of Deep Neural Networks is well investigated to maximize the performance and reduce the size of overparameterized networks as possible. Existing methods focus on pruning parameters in the training process by using thresholds and metrics. Meanwhile, feature similarity between different layers has not been discussed sufficiently before, which could be rigorously proved to be highly correlated to the network sparsity in this paper. Inspired by interlayer feature similarity in overparameterized models, we investigate the intrinsic link between network sparsity and interlayer feature similarity. Specifically, we prove that reducing interlayer feature similarity based on Centered Kernel Alignment (CKA) improves the sparsity of the network by using information bottleneck theory. Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity. In other words, layers of our sparse network tend to have their own identity compared to each other. Experimentally, we plug the proposed CKA-SR into the training process of sparse network training methods and find that CKA-SR consistently improves the performance of several State-Of-The-Art sparse training methods, especially at extremely high sparsity. Code is included in the supplementary materials

Meta Architecture for Point Cloud Analysis

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets

MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation

Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally may distort the essential structural information of the target-domain data, leading to inferior performance. To address this issue, we firstly propose to introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. By innovatively adopting multiple anchors instead of a single centroid, both source and target domains can be better characterized as multimodal distributions, in which way more complementary and informative samples are selected from the target domain. With only a little workload to manually annotate these active samples, the distortion of the target-domain distribution can be effectively alleviated, achieving a large performance gain. In addition, a powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem and further improve the segmentation performance. Extensive experiments are conducted on public datasets, and the results demonstrate that the proposed approach outperforms state-of-the-art methods by large margins and achieves similar performance to the fully-supervised upperbound, i.e., 71.4% mIoU on GTA5 and 71.8% mIoU on SYNTHIA. The effectiveness of each component is also verified by thorough ablation studies.Comment: Accepted by TPAMI-IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2108.0801