Temporal Trends and Spatial Variabilities of PCB concentrations in Lake Trout from Lake Superior from 1995 to 2013

It has been frequently reported that concentrations of PCBs in the Great Lakes fish have declined dramatically since their ban on production and use in 1979 in the United States, although some studies suggested that recent rates of decline are leveling off. In order to examine the temporal trends and spatial variabilities of PCB concentrations in lake trout (Salvelinus namaycush) from Lake Superior during the past two decades, statistical analyses were performed on fish sample data collected by two national agencies (U.S. EPA, Environment and Climate Change Canada) and three state agencies (Michigan Department of Environmental Quality, Minnesota Department of Natural Resources, Wisconsin Department of Natural Resources) from both the United States and Canada from 1976 to 2013. Because of a change in PCB analytical methodology in the mid-1990s, intercomparison between data recorded by the previous technique and the improved technique is not feasible. Because most organochlorine compounds are easily bound to fatty tissues in fish, lipid content has been commonly considered as a predictor of PCB levels. Also, larger fish were assumed to have higher PCBs in their bodies. Multiple linear regression analyses, setting time, lipid content, and fish length as three independent variables, revealed that lipid content had little impact on PCB concentrations at all sites except Whitefish Bay since 1995, which is in contrast to some previous studies. However, a strong positive correlation between PCBs and fish length, in good agreement with previous research, was observed at all sites except Whitefish Bay over the same period. It has been discovered that PCB concentrations vary among several sampling locations within Lake Superior. The general pattern was that the western sites had significantly higher concentrations than the eastern sites. When the entire historical record was analyzed, temporal trends were evident in all datasets. However, only at Keweenaw Point (U.S. EPA) was significant (p = 0.0005) declining trends in total PCB concentrations observed after 1995. In Wisconsin sites, the declining trend was marginal significant (p = 0.04) during the same period. In other locations, no temporal trends were found but large annual fluctuations occurred for unknown reasons. PCB concentrations at most sites have not achieved the reduction target of 100 ng/g ww for wildlife and human health protection established by the U.S. EPA. It is still difficult to predict when fish will be able to be consumed without limitation in this region

Caustic graphene plasmons with Kelvin angle

A century-long argument made by Lord Kelvin that all swimming objects have an effective Mach number of 3, corresponding to the Kelvin angle of 19.5 degree for ship waves, has been recently challenged with the conclusion that the Kelvin angle should gradually transit to the Mach angle as the ship velocity increases. Here we show that a similar phenomenon can happen for graphene plasmons. By analyzing the caustic wave pattern of graphene plasmons stimulated by a swift charged particle moving uniformly above graphene, we show that at low velocities of the charged particle, the caustics of graphene plasmons form the Kelvin angle. At large velocities of the particle, the caustics disappear and the effective semi-angle of the wave pattern approaches the Mach angle. Our study introduces caustic wave theory to the field of graphene plasmonics, and reveals a novel physical picture of graphene plasmon excitation during electron energy-loss spectroscopy measurement.Comment: 15 pages, 4 figure

A Dynamic Shift-Share Analysis on the China’s R&D: A Structure Analysis

To evaluate the R&D development in China, we can inspect both the R&D expenditure and the research talent pool. In this paper, we analyze the structure of the researcher groups by using dynamic shift-share analysis (DSSA). The DSSA results show that there is still much room to improve in the structure of research group. The provinces/municipalities from eastern China did not perform well in engineering and education researcher groups while the provinces/municipalities from central and western China perform well in engineering, agriculture and education researcher groups. We suggest that the government planners should implement more effective measures to improve the structure of the researcher groups in order to spend the R&D fund wisely and attract more extra fund in R&D

Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs can achieve consistently better performance. Existing ensemble methods for LLMs mainly focus on reward model ranking of outputs, leading to significant computation overhead. To combat this issue, we revisit the complementary potential of LLMs and further elaborate it by mining latent expertise with off-the-shelf reward models. We propose Zooter, a reward-guided routing method distilling rewards on training queries to train a routing function, which can precisely distribute each query to the LLM with expertise about it. We also integrate a tag-based label enhancement to mitigate noise from uncertainty when using rewards as silver supervision. Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods. We evaluate Zooter on a comprehensive benchmark collection with 26 subsets on different domains and tasks. Zooter outperforms the best single model on average and ranks first on 44% of tasks, even surpassing multiple reward model ranking methods

#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models

Foundation language models obtain the instruction-following ability through supervised fine-tuning (SFT). Diversity and complexity are considered critical factors of a successful SFT dataset, while their definitions remain obscure and lack quantitative analyses. In this work, we propose InsTag, an open-set fine-grained tagger, to tag samples within SFT datasets based on semantics and intentions and define instruction diversity and complexity regarding tags. We obtain 6.6K tags to describe comprehensive user queries. Then we analyze popular open-sourced SFT datasets and find that the model ability grows with more diverse and complex data. Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data. The resulting models, TagLM, outperform open-source models based on considerably larger SFT data evaluated by MT-Bench, echoing the importance of query diversity and complexity. We open-source InsTag in https://github.com/OFA-Sys/InsTag

Panax notoginseng

To investigate the therapeutic effects of PN on intestinal inflammation and microvascular injury and its mechanisms, dextran sodium sulfate- (DSS-) or iodoacetamide- (IA-) induced rat colitis models were used. After colitis model was established, PN was orally administered for 7 days at daily dosage of 1.0 g/kg. Obvious colonic inflammation and mucosal injuries and microvessels were observed in DSS- and IA-induced colitis groups. DAI scores, serum concentrations of VEGFA121, VEGFA165, VEGFA165/VEGFA121, IL-6, and TNF-α, and expression of Rap1GAP and TSP1 proteins in the colon were significantly higher while serum concentrations of IL-4 and IL-10 and MVD in colon were significantly lower in the colitis model groups than in the normal control group. PN promoted repair of colonic mucosal injury and microvessels, attenuated inflammation, and decreased DAI scores in rats with colitis. PN also decreased the serum concentrations of VEGFA121, VEGFA165, VEGFA165/VEGFA121, IL-6, and TNF-α and increased the serum concentrations of IL-4 and IL-10, with the expression of Rap1GAP and TSP1 proteins in colonic mucosa being downregulated. The constituents of PN were identified with HPLC-DAD. To sum up, PN could promote repair of injuries of colonic mucosa and microvessels via downregulating VEGFA isoforms and inhibiting Rap1GAP/TSP1 signaling pathway

M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images

The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones for this task, which ignore the inherent multi-scale hierarchical data structure of digital pathology images. To address this limit, we propose M2ORT, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images through a decoupled multi-scale feature extractor. Different from traditional models that are trained with one-to-one image-label pairs, M2ORT accepts multiple pathology images of different magnifications at a time to jointly predict the gene expressions at their corresponding common ST spot, aiming at learning a many-to-one relationship through training. We have tested M2ORT on three public ST datasets and the experimental results show that M2ORT can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs). The code is available at: https://github.com/Dootmaan/M2ORT/

Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl