Scientists' political behaviors are not driven by individual-level government benefits.

Is it appropriate for scientists to engage in political advocacy? Some political critics of scientists argue that scientists have become partisan political actors with self-serving financial agendas. However, most scientists strongly reject this view. While social scientists have explored the effects of science politicization on public trust in science, little empirical work directly examines the drivers of scientists' interest in and willingness to engage in political advocacy. Using a natural experiment involving the U.S. National Science Foundation Graduate Research Fellowship (NSF-GRF), we causally estimate for the first time whether scientists who have received federal science funding are more likely to engage in both science-related and non-science-related political behaviors. Comparing otherwise similar individuals who received or did not receive NSF support, we find that scientists' preferences for political advocacy are not shaped by receiving government benefits. Government funding did not impact scientists' support of the 2017 March for Science nor did it shape the likelihood that scientists donated to either Republican or Democratic political groups. Our results offer empirical evidence that scientists' political behaviors are not motivated by self-serving financial agendas. They also highlight the limited capacity of even generous government support programs to increase civic participation by their beneficiaries

On the Pareto Front of Multilingual Neural Machine Translation

In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual models with various model sizes, data sizes, and language directions, we find it interesting that the performance of certain translation direction does not always improve with the increase of its weight in the multi-task optimization objective. Accordingly, scalarization method leads to a multitask trade-off front that deviates from the traditional Pareto front when there exists data imbalance in the training corpus, which poses a great challenge to improve the overall performance of all directions. Based on our observations, we propose the Double Power Law to predict the unique performance trade-off front in MNMT, which is robust across various languages, data adequacy, and the number of tasks. Finally, we formulate the sample ratio selection problem in MNMT as an optimization problem based on the Double Power Law. In our experiments, it achieves better performance than temperature searching and gradient manipulation methods with only 1/5 to 1/2 of the total training budget. We release the code at https://github.com/pkunlp-icler/ParetoMNMT for reproduction.Comment: NeurIPS 202

Revisiting IP-to-AS mapping for AS-level traceroute

ABSTRACT On the way to obtaining accurate AS-level traceroute paths, lots of efforts have focused on the improvement of the original IP-to-AS mapping table which was extracted from BGP routing tables. One of those efforts is called pair matching which refines the original mapping table by maximizing the number of matched pairs of traceroute and BGP AS paths from the same AS to the same destination. However, in the existing pair-matching-based method, the granularity for mapping is prefix, i.e. that the IP addresses in the same /24 prefix always belong to the same AS or set of ASes, which may yield ambiguity and does not hold in some cases. In this paper, we revisit the IP-to-AS mapping with IP-address granularity by allowing IP addresses in the same prefix to be mapped to different ASes

Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) have been widely used due to their advanced reasoning skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual understanding and reasoning capabilities. We investigate whether state-of-the-art MLLMs can handle embodied decision-making in an end-to-end manner and whether collaborations between LLMs and MLLMs can enhance decision-making. To address these questions, we introduce a new benchmark called PCA-EVAL, which evaluates embodied decision-making from the perspectives of Perception, Cognition, and Action. Additionally, we propose HOLMES, a multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs to gather multimodal information for informed decision-making. We compare end-to-end embodied decision-making and HOLMES on our benchmark and find that the GPT4-Vision model demonstrates strong end-to-end embodied decision-making abilities, outperforming GPT4-HOLMES in terms of average decision accuracy (+3%). However, this performance is exclusive to the latest GPT4-Vision model, surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate that powerful MLLMs like GPT4-Vision hold promise for decision-making in embodied agents, offering new avenues for MLLM research. Code and data are open at https://github.com/pkunlp-icler/PCA-EVAL/.Comment: FMDM@NeurIPS2023, Code and data: https://github.com/pkunlp-icler/PCA-EVAL

Case report: Paralytic ileus resulted from nirmatrelvir/ritonavir-tacrolimus drug-drug interaction in a systemic lupus erythematosus patient with COVID-19

Patients with systemic autoimmune rheumatic diseases are at a high risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and effective antiviral treatments including nirmatrelvir/ritonavir can improve their outcomes. However, there might be potential drug-drug interactions when these patients take nirmatrelvir/ritonavir together with immunosuppressants with a narrow therapeutic window, such as tacrolimus and cyclosporine. We present a case of paralytic ileus resulting from tacrolimus toxicity mediated by the use of nirmatrelvir/ritonavir in a patient with systemic lupus erythematosus (SLE). A 37-year-old female SLE patient was prescribed nirmatrelvir/ritonavir without discontinuing tacrolimus. She presented to the emergency room with symptoms of paralytic ileus including persistent abdominal pain, nausea, and vomiting, which were verified to be associated with tacrolimus toxicity. The blood concentration of tacrolimus was measured >30 ng/mL. Urgent medical intervention was initiated, while tacrolimus was withheld. The residual concentration was brought within the appropriate range and tacrolimus was resumed 8 days later. Physicians must be aware of the potential DDIs when prescribing nirmatrelvir/ritonavir, especially to those taking immunosuppresants like tacrolimus

Combined linkage and association mapping reveals candidates for Scmv1, a major locus involved in resistance to sugarcane mosaic virus (SCMV) in maize

Background Sugarcane mosaic virus (SCMV) disease causes substantial losses of grain yield and forage biomass in susceptible maize cultivars. Maize resistance to SCMV is associated with two dominant genes, Scmv1 and Scmv2, which are located on the short arm of chromosome 6 and near the centromere region of chromosome 3, respectively. We combined both linkage and association mapping to identify positional candidate genes for Scmv1. Results Scmv1 was fine-mapped in a segregating population derived from near-isogenic lines and further validated and fine-mapped using two recombinant inbred line populations. The combined results assigned the Scmv1 locus to a 59.21-kb interval, and candidate genes within this region were predicted based on the publicly available B73 sequence. None of three predicted genes that are possibly involved in the disease resistance response are similar to receptor-like resistance genes. Candidate gene–based association mapping was conducted using a panel of 94 inbred lines with variable resistance to SCMV. A presence/absence variation (PAV) in the Scmv1 region and two polymorphic sites around the Zmtrx-h gene were significantly associated with SCMV resistance. Conclusion Combined linkage and association mapping pinpoints Zmtrx-h as the most likely positional candidate gene for Scmv1. These results pave the way towards cloning of Scmv1 and facilitate marker-assisted selection for potyvirus resistance in maize

Diagnostic value of circulating miR-155 for breast cancer: a meta-analysis

BackgroundsThe value of circulating microRNA (miR)-155 for breast cancer (BC) diagnosis may differ in different studies. Therefore, we conducted this systematic review and meta-analysis to evaluate the potential application of circulating miR-155 in the diagnosis of BC.MethodsArticles published before December 2023 and in English were searched in these databases: PubMed, Web of Science, Medline, EMBASE and Google Scholar. A summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were calculated from the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) of each study. Additionally, the summary receive-operating characteristics (SROC) curve was constructed to summarize the TP and FP rates.ResultsThe pooled parameters calculated were as follows: sensitivity, 0.93 (95% CI: 0.83-0.97); specificity, 0.85 (95% CI: 0.74-0.92); PLR, 6.4 (95% CI: 3.4-11.9); NLR, 0.09 (95% CI: 0.04-0.20); and DOR, 74 (95% CI: 22-247). The analysis showed a significant heterogeneity (sensitivity, I2 = 95.19%, p < 0.001; specificity, I2 = 95.29%, p < 0.001; DOR, I2 = 92.9%, p < 0.001). The SROC curve was with an area under curve (AUC) of 0.95 (95% CI: 0.93-0.97).ConclusionCirculating miR-155 has a potential in the diagnosis of BC