ODSum: New Benchmarks for Open Domain Multi-Document Summarization

Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set, there does not necessarily exist a correct answer for the retrieval, making it hard to measure the retrieving performance. We propose a rule-based method to process query-based document summarization datasets into ODMDS datasets. Based on this method, we introduce a novel dataset, ODSum, a sophisticated case with its document index interdependent and often interrelated. We tackle ODMDS with the \textit{retrieve-then-summarize} method, and the performance of a list of retrievers and summarizers is investigated. Through extensive experiments, we identify variances in evaluation metrics and provide insights into their reliability. We also found that LLMs suffer great performance loss from retrieving errors. We further experimented methods to improve the performance as well as investigate their robustness against imperfect retrieval. We will release our data and code at https://github.com/yale-nlp/ODSum

Finding disease-specific coordinated functions by multi-function genes: Insight into the coordination mechanisms in diseases

AbstractWe developed an approach using multi-function disease genes to find function pairs whose co-deregulation might induce a disease. Analyzing cancer genes, we found many cancer-specific coordinated function pairs co-deregulated by dysfunction of multi-function genes and other molecular changes in cancer. Studying two subtypes of cardiomyopathy, we found they show certain consistency at the functional coordination level. Our approach can also provide important information for finding novel disease genes as well as their mechanisms in diseases

Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain

Phosphoinositides (PIs) are lipid signaling molecules that operate by recruiting proteins to cellular membranes via PI recognition domains. The dominant PI of the plasma membrane is phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2]. One of only two PI(4,5)P2 recognition domains characterized in detail is the tubby domain. It is essential for targeting proteins into cilia involving reversible membrane association. However, the PI(4,5)P2 binding properties of tubby domains have remained enigmatic. Here, we used coarse-grained molecular dynamics simulations to explore PI(4,5)P2 binding by the prototypic tubby domain. The comparatively low PI(4,5)P2 affinity of the previously described canonical binding site is underpinned in a cooperative manner by a previously unknown, adjacent second binding site. Mutations in the previously unknown site impaired PI(4,5)P2-dependent plasma membrane localization in living cells and PI(4,5)P2 interaction in silico, emphasizing its importance for PI(4,5)P2 affinity. The two-ligand binding mode may serve to sharpen the membrane association-dissociation cycle of tubby-like proteins that underlies delivery of ciliary cargo

Dynamical Analysis of a Pest Management Model with Saturated Growth Rate and State Dependent Impulsive Effects

A new pest management mathematical model with saturated growth is proposed. The integrated pest management (IPM) strategy by introducing two state dependent pulses into the model is considered. Firstly, we analyze singular points of the model qualitatively and get the condition for focus point. Secondly, by using geometry theory of impulsive differential equation, the existence and stability of periodic solution of the system are discussed. Lastly, some examples and numerical simulations are given to illustrate our results

BMQE system: a MQ equations system based on ergodic matrix

In this paper, we propose a multivariate quadratic (MQ) equation system based on ergodic matrix (EM) over a finite field with q elements (denoted as F^q). The system actually implicates a problem which is equivalent to the famous Graph Coloring problem, and therefore is NP complete for attackers. The complexity of bisectional multivariate quadratic equation (BMQE) system is determined by the number of the variables, of the equations and of the elements of Fq, which is denoted as n, m, and q, respectively. The paper shows that, if the number of the equations is larger or equal to twice the number of the variables, and qn is large enough, the system is complicated enough to prevent attacks from most of the existing attacking schemes

Research progress on the role of the Wnt signaling pathway in pituitary adenoma

Pituitary adenoma (PA) is the third most common central nervous system tumor originating from the anterior pituitary, but its pathogenesis remains unclear. The Wnt signaling pathway is a conserved pathway involved in cell proliferation, Self-renewal of stem cells, and cell differentiation. It is related to the occurrence of various tumors, including PA. This article reviews the latest developments in Wnt pathway inhibitors and pathway-targeted drugs. It discusses the possibility of combining Wnt pathway inhibitors with immunotherapy to provide a theoretical basis for the combined treatment of PA

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin