Extrinsically-Focused Evaluation of Omissions in Medical Summarization

The goal of automated summarization techniques (Paice, 1990; Kupiec et al, 1995) is to condense text by focusing on the most critical information. Generative large language models (LLMs) have shown to be robust summarizers, yet traditional metrics struggle to capture resulting performance (Goyal et al, 2022) in more powerful LLMs. In safety-critical domains such as medicine, more rigorous evaluation is required, especially given the potential for LLMs to omit important information in the resulting summary. We propose MED-OMIT, a new omission benchmark for medical summarization. Given a doctor-patient conversation and a generated summary, MED-OMIT categorizes the chat into a set of facts and identifies which are omitted from the summary. We further propose to determine fact importance by simulating the impact of each fact on a downstream clinical task: differential diagnosis (DDx) generation. MED-OMIT leverages LLM prompt-based approaches which categorize the importance of facts and cluster them as supporting or negating evidence to the diagnosis. We evaluate MED-OMIT on a publicly-released dataset of patient-doctor conversations and find that MED-OMIT captures omissions better than alternative metrics

PDBe-KB: a community-driven resource for structural and functional annotations.

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession

Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease

The FANTOM5 consortium utilised cap analysis of gene expression (CAGE) to provide an unprecedented insight into transcriptional regulation in human cells and tissues. In the current study, we have used CAGE-based transcriptional profiling on an extended dense time course of the response of human monocyte-derived macrophages grown in macrophage colony-stimulating factor (CSF1) to bacterial lipopolysaccharide (LPS). We propose that this system provides a model for the differentiation and adaptation of monocytes entering the intestinal lamina propria. The response to LPS is shown to be a cascade of successive waves of transient gene expression extending over at least 48 hours, with hundreds of positive and negative regulatory loops. Promoter analysis using motif activity response analysis (MARA) identified some of the transcription factors likely to be responsible for the temporal profile of transcriptional activation. Each LPS-inducible locus was associated with multiple inducible enhancers, and in each case, transient eRNA transcription at multiple sites detected by CAGE preceded the appearance of promoter-associated transcripts. LPS-inducible long non-coding RNAs were commonly associated with clusters of inducible enhancers. We used these data to re-examine the hundreds of loci associated with susceptibility to inflammatory bowel disease (IBD) in genome-wide association studies. Loci associated with IBD were strongly and specifically (relative to rheumatoid arthritis and unrelated traits) enriched for promoters that were regulated in monocyte differentiation or activation. Amongst previously-identified IBD susceptibility loci, the vast majority contained at least one promoter that was regulated in CSF1-dependent monocyte-macrophage transitions and/or in response to LPS. On this basis, we concluded that IBD loci are strongly-enriched for monocyte-specific genes, and identified at least 134 additional candidate genes associated with IBD susceptibility from reanalysis of published GWA studies. We propose that dysregulation of monocyte adaptation to the environment of the gastrointestinal mucosa is the key process leading to inflammatory bowel disease

p16-induced apoptosis in Ewing\u27s sarcoma

The tumor suppressor p16 is a negative regulator of the cell cycle, and acts by preventing the phosphorylation of RB, which in turn prevents the progression from G1 to S phase of the cell cycle. In addition to its role in the cell cycle, p16 may also be able to induce apoptosis in some tumors. Ewing\u27s sarcoma, a pediatric cancer of the bone and soft tissue, was used to study the ability of p16 to induce apoptosis due to the fact that p16 is often deleted in Ewing\u27s sarcoma tumors and may play a role in the oncogenesis or progression of this disease. The purpose of these studies was to determine whether introduction of p16 into Ewing\u27s sarcoma cells would induce apoptosis. We infected the Ewing\u27s sarcoma cell line TC71, which does not express p16, with adenovirus- p16 (Ad-p16). Ad-p16 infection led to the production of functional p16 as measured by the induction of G1 arrest. Ad-p16 infection induced as much as a 100% increase in G1 arrest compared to untreated cells. As measured by propidium iodide (PI) and Annexin V staining, Ad-p16 was able to induce apoptosis to levels 20–30 fold higher than controls. Furthermore, Ad-p16 infection led to loss of RB protein before apoptosis could be detected. The loss of RB protein was due to post-translational degradation of RB, which was inhibited by the addition of the proteasome inhibitors PS-341 and NPI-0052. Downregulation of RB with si-RNA sensitized cells to Ad-p16-induced apoptosis, indicating that RB protects from apoptosis in this model. This study shows that p16 leads to the degradation of RB by the ubiquitin/proteasome pathway, and that this degradation may be important for the induction of apoptosis. Given that RB may protect from apoptosis in some tumors, apoptosis-inducing therapies may be enhanced in tumors which have lost RB expression, or in which RB is artificially inactivated

Twister2 Demonstrations for BDEC2

Data-driven applications are essential to handle the ever-increasing volume, velocity, and veracity of data generated by sources such as the Web and Internet of Things (IoT) devices. Simulta- neously, an event-driven computational paradigm is emerging as the core of modern systems designed for database queries, data analytics, and on-demand applications. Modern big data pro- cessing runtimes and asynchronous many task (AMT) systems from high performance computing (HPC) community have adopted dataflow event-driven model. The services are increasingly mov- ing to an event-driven model in the form of Function as a Service (FaaS) to compose services. An event-driven runtime designed for data processing consists of well-understood components such as communication, scheduling, and fault tolerance. Different design choices adopted by these components determine the type of applications a system can support efficiently. We find that modern systems are limited to specific sets of applications because they have been designed with fixed choices that cannot be changed easily. In this paper, we present a loosely coupled component-based design of a big data toolkit where each component can have different imple- mentations to support various applications. Such a polymorphic design would allow services and data analytics to be integrated seamlessly and expand from edge to cloud to HPC environments

Increasing Stress to Induce Apoptosis in Pancreatic Cancer via the Unfolded Protein Response (UPR)

High rates of cell proliferation and protein synthesis in pancreatic cancer are among many factors leading to endoplasmic reticulum (ER) stress. To restore cellular homeostasis, the unfolded protein response (UPR) activates as an adaptive mechanism through either the IRE1α, PERK, or ATF6 pathways to reduce the translational load and process unfolded proteins, thus enabling tumor cells to proliferate. Under severe and prolonged ER stress, however, the UPR may promote adaptation, senescence, or apoptosis under these same pathways if homeostasis is not restored. In this review, we present evidence that high levels of ER stress and UPR activation are present in pancreatic cancer. We detail the mechanisms by which compounds activate one or many of the three arms of the UPR and effectuate downstream apoptosis and examine available data on the pre-clinical and clinical-phase ER stress inducers with the potential for anti-tumor efficacy in pancreatic cancer. Finally, we hypothesize a potential new approach to targeting pancreatic cancer by increasing levels of ER stress and UPR activation to incite apoptotic cell death