Implications of Dam Removal: Modeling Streamflow in Lansing, Michigan Using the Soil and Water Assessment Tool

This paper uses hydrologic modeling methods to determine the effects of dam removal in Lansing, Michigan, on the streamflow of the Grand River, flooding risks, and flood mitigation strategies. In Michigan, more than one-half of the state’s dam infrastructure is more than 50 years old, and more than one-third are classified as having a moderate-to high-risk potential. Lansing, Michigan, contains two moderate-to high-risk dams along the Grand River that are a significant hazard to the surrounding community in the event of structural failure. This research utilizes the Soil and Water Assessment Tool (SWAT) to model the impacts of the Moores Park Dam and the North Lansing Dam on streamflow in the greater Lansing area. The purpose of using SWAT was to represent baseline streamflow conditions in the Grand River, compare the differences in streamflow magnitude between baseline conditions and a dam-out environment, and interpret the implications of modeling results for mitigation and management strategies in the study area. Our model exhibited similar streamflow patterns to USGS historical data, with overestimation errors during calibration and validation stemming from groundwater infiltration inaccuracies. The dams-out model for streamflow was higher than the baseline model for streamflow; however, both model iterations require further calibration and validation for the magnitude differences to be considered statistically significant. Despite issues of model calibration and validation, and ongoing model adjustments for accurately representing heavily impounded watershed, the results of this study provide a template for the City of Lansing to adapt their flood mitigation strategies in the study area and further calibrate SWAT with improved sediment, nutrient, and dam attribute data

Fusion-Eval: Integrating Evaluators with LLMs

Evaluating Large Language Models (LLMs) is a complex task, especially considering the intricacies of natural language understanding and the expectations for high-level reasoning. Traditional evaluations typically lean on human-based, model-based, or automatic-metrics-based paradigms, each with its own advantages and shortcomings. We introduce "Fusion-Eval", a system that employs LLMs not solely for direct evaluations, but to skillfully integrate insights from diverse evaluators. This gives Fusion-Eval flexibility, enabling it to work effectively across diverse tasks and make optimal use of multiple references. In testing on the SummEval dataset, Fusion-Eval achieved a Spearman correlation of 0.96, outperforming other evaluators. The success of Fusion-Eval underscores the potential of LLMs to produce evaluations that closely align human perspectives, setting a new standard in the field of LLM evaluation

RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting

Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final text results rather than intermediate revisions, it might be challenging for them to perform text rewriting tasks. Most studies in the rewriting tasks focus on a particular transformation type within the boundaries of single sentences. In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures expressed through natural languages including 1) generating rewriting instruction data from Wiki edits and public corpus through instruction generation and chain-of-thought prompting; 2) collecting comparison data for reward model training through a new ranking function. To facilitate this research, we introduce OpenRewriteEval, a novel benchmark covers a wide variety of rewriting types expressed through natural language instructions. Our results show significant improvements over a variety of baselines. The public repository is available on GitHub under Google Research (https://github.com/google-research/google-research/tree/master/rewritelm)

Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

Cloth-changing person reidentification (ReID) is a newly emerging research topic that is aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID is still challenging due to impressionable pedestrian representations. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is fully utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but also are suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is further proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on five public clothing person ReID datasets demonstrate that the proposed IGCL significantly outperforms SOTA methods and that the extracted feature is more robust, discriminative, and clothing-irrelevant

Study of Microbiomes in Aseptically Collected Samples of Human Breast Tissue Using Needle Biopsy and the Potential Role of in situ Tissue Microbiomes for Promoting Malignancy

Mounting evidence suggests that changes in microbiome are linked to development of cancer and its aggressiveness. Microbiome profiles in human breast tissue previously presumed to be sterile, have recently been characterized using high-throughput technologies. Recent findings of microbiome variation between benign and malignant disease provides a rationale for exploring microbiomes associated with cancer during tumor progression. We assessed microbiomes of aseptically collected human breast tissue samples in this study, using needle biopsy from patients with benign and malignant tumors of different histological grading, using 16S rRNA gene amplicon sequencing. This is consistent with previous studies, and our results identified distinct microbiome profiles in breast tissues from women with cancer as compared to women with benign breast disease in Chinese cohorts. The enriched microbial biomarkers in malignant tissue included genus Propionicimonas and families Micrococcaceae, Caulobacteraceae, Rhodobacteraceae, Nocardioidaceae, Methylobacteriaceae, which appeared to be ethno-specific. Further, we compared microbiome profiles in malignant tissues of three different histological grades. The relative abundance of family Bacteroidaceae decreased and that of genus Agrococcus increased with the development of malignancy. KEGG pathways inferred by PICRUSt showed that biotin and glycerophospholipid metabolism had significant differences in all three grades. Glycerophospholipid and ribosome biogenesis increased in grade III tissue as compared to grades I and II. Flavonoid biosynthesis significantly decreased in grade III tissue. The specific correlation of these potential microbial biomarkers and indicated pathways with advanced disease could have broad implications in the diagnosis and staging of breast cancer. Further large-cohort investigation of the breast cancer microbiome and its potential mechanism in breast cancer development are essential

The Effect of Polyvalent Staphylococcus Vaccine on the Gene Expression of IL-8, IFN-γ, IL-1R and Phagocytes in Mice Spleen

Objectives: To study the effect of Polyvalent staphylococcus vaccine on cellular immune function in mice. Methods: Forty SD kunming species of mice were randomly divided into low dose group (500 million/ml bacteria), middle dose group (1 billion/ml bacteria), high dose group (2 billion/ml bacteria) and control group, Mice were immunized with different doses of Polyvalent staphylococcus vaccine. The expression of IL-8, IFN - γ and IL-1R genes in spleen tissues of mice in different doses of experimental group and control group were detected by RT-PCR, and the phagocytic effect of phagocytes in abdominal cavity of mice was detected by abdominal smear Results: Compared with the normal control group, the expression level of IL-8, IFN - γ, IL-1R gene and phagocytic function of spleen in the experimental group were significantly higher than those in the control group (P < 0.01), the effect in the middle dose group were the best. Conclusion: Polyvalent staphylococcus vaccine can significantly improve the expression level of IL-8, IFN - γ, IL-1R gene in mouse spleen, and enhance the phagocytic function of phagocytes, and its effect showed dose-dependent manner