bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-term outcomes in patients with immune checkpoint inhibitor induced pneumonitis
Authors: Davis Puente Andrea , Johnson Douglas B , Bastarache Julie A 
Published: May 2023

IntroductionImmune checkpoint inhibitors (ICI) have improved outcomes for patients with many malignancies. However, these treatments are associated with immune-related adverse events, including pulmonary toxicity (pneumonitis). Pneumonitis is associated with significant short-term morbidity and mortality, but long-term outcomes are not well described.MethodsWe used the Vanderbilt Synthetic Derivative, a deidentified electronic health record database of >2.5 million patients seen at Vanderbilt, to identify patient charts that included treatment with pembrolizumab, nivolumab, ipilimumab, ipilimumab and nivolumab, atezolizumab or durvalumab by keyword search and ICD-10 codes for acute respiratory failure and/or bronchoalveolar lavage. We manually reviewed these charts and identified 78 subjects who met criteria for probable pneumonitis which included patients presenting with symptoms (dyspnoea, hypoxia, cough) and/or CT imaging consistent with this diagnosis. We collected data on demographics, ICI regimen, hospital admissions and long-term survival.ResultsOf the 78 patients (48 males; median age 64 (range 28–81)), 52 patients required at least 1 hospital admission related to pneumonitis. A total of 25 patients experienced poor short-term outcomes (including 6 referred to hospice, 11 discharged to rehabilitation and 9 deaths). There was no association with these outcomes by patient age (p=0.96), sex (p=0.60), smoking status (p=0.63) or cancer type (p=0.13). Median duration of follow-up was 8.3 months (range 0.2–110.6 months), and 29 patients (37%) were alive at last follow-up. Patients admitted to the hospital were more likely to die (p=0.002) and less likely to receive additional treatment (p<0.0001) or survive for ≥12 months with no evidence of disease (p=0.02). There were no differences in long-term outcomes for patients with underlying pulmonary comorbidities.DiscussionICI-pneumonitis has a high likelihood of causing hospitalisation and poor outcomes, including death. While there appears to be no difference in outcomes for patients with underling pulmonary comorbidities, those requiring admission have worse outcomes.
Variability in firearm injury among major pediatric trauma centers across the USA
Authors: Fraser Doh Kiesha , Chaudhary Sofia , Ruest Stephanie M , Shaahinfar Ashkon , Chun Thomas , Cooper Nicholas , Fein Joel , Feng Alayna , Feske-Kirby Katherine , Figueroa Janet , Gutman Colleen K , Grupp-Phelan Jacqueline , Kanaan Ghid , Keathley Nora , Khan Naghma , McGlamry Katherine , Myers Sage , Nance Michael , Russell Katherine , Rowker Kelli , Sheline Erica , Simon Harold K , Morris Claudia R 
Published: May 2023

ObjectivesIn 2020, firearm injuries surpassed automobile collisions as the leading cause of death in US children. Annual automobile fatalities have decreased during 40 years through a multipronged approach. To develop similarly targeted public health interventions to reduce firearm fatalities, there is a critical need to first characterize firearm injuries and their outcomes at a granular level. We sought to compare firearm injuries, outcomes, and types of shooters at trauma centers in four pediatric health systems across the USA.MethodsWe retrospectively extracted data from each institution’s trauma registry, paper and electronic health records. Study included all patients less than 19 years of age with a firearm injury between 2003 and 2018. Variables collected included demographics, intent, resources used, and emergency department and hospital disposition. Descriptive statistics were reported using medians and IQRs for continuous data and counts with percentages for categorical data. χ2test or Fisher’s exact test was conducted for categorical comparisons.ResultsOur cohort (n=1008, median age 14 years) was predominantly black and male. During the study period, there was an overall increase in firearm injuries, driven primarily by increases in the South (S) site (β=0.11 (SE 0.02), p=<0.001) in the setting of stable rates in the West and decreasing rates in the Northeast and Mid-Atlantic sites (β=−0.15 (SE 0.04), p=0.002; β=−0.19 (SE0.04), p=0.001). Child age, race, insurance type, resource use, injury type, and shooter type all varied by regional site.ConclusionThe incidence of firearm-related injuries seen at four sites during 15 years varied by site and region. The overall increase in firearm injuries was predominantly driven by the S site, where injuries were more often unintentional. This highlights the need for region-specific data to allow for the development of targeted interventions to impact the burden of injury.Level of Evidence: II, retrospective study
Efficient Estimation of Pairwise Effective Resistance
Authors: Yang Renchi , Tang Jing 
Published: May 2023

Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as recommender systems, combinatorial optimization, molecular chemistry, and electric power networks. Existing techniques towards pairwise effective resistance estimation either trade approximation guarantees for practical efficiency, or vice versa. In particular, the state-of-the-art solution is based on a multitude of Monte Carlo random walks, rendering it rather inefficient in practice, especially on large graphs.
Motivated by this, this paper first presents an improved Monte Carlo approach, AMC, which reduces both the length and amount of random walks required without degrading the theoretical accuracy guarantee, through careful theoretical analysis and an adaptive sampling scheme. Further, we develop a greedy approach, GEER, which combines AMC with sparse matrix-vector multiplications in an optimized and non-trivial way. GEER offers significantly improved practical efficiency over AMC without compromising its asymptotic performance and accuracy guarantees. Extensive experiments on multiple benchmark datasets reveal that GEER is orders of magnitude faster than the state of the art in terms of computational time when achieving the same accuracy.
Ground Truth Inference for Weakly Supervised Entity Matching
Authors: Wu Renzhi , Bendeck Alexander , Chu Xu , He Yeye 
Published: May 2023

Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine learning (ML) models currently achieve state-of-the-art matching performance; however, they require a large number of labeled examples, which are often expensive or infeasible to obtain. This has inspired us to approach data labeling for EM using weak supervision. In particular, we use the labeling function abstraction popularized by Snorkel, where each labeling function (LF) is a user-provided program that can generate many noisy match/non-match labels quickly and cheaply. Given a set of user-written LFs, the quality of data labeling depends on a labeling model to accurately infer the ground-truth labels. In this work, we first propose a simple but powerful labeling model for general weak supervision tasks. Then, we tailor the labeling model specifically to the task of entity matching by considering the EM-specific transitivity property.
The general form of our labeling model is simple while substantially outperforming the best existing method across ten general weak supervision datasets. To tailor the labeling model for EM, we formulate an approach to ensure that the final predictions of the labeling model satisfy the transitivity property required in EM, utilizing an exact solution where possible and an ML-based approximation in remaining cases. On two single-table and nine two-table real-world EM datasets, we show that our labeling model results in a 9% higher F1 score on average than the best existing method. We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Authors: Wu Ziniu , Negi Parimarjan , Alizadeh Mohammad , Kraska Tim , Madden Samuel 
Published: May 2023

Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on simplified assumptions leading to ineffective cardinality estimates or build large models to understand the complicated data distributions, leading to long planning times and a lack of generalizability across queries.
In this paper, we propose a new framework FactorJoin for estimating join queries. FactorJoin combines the idea behind the classical join-histogram method to efficiently handle joins with the learning-based methods to accurately capture attribute correlation Specifically, FactorJoin scans every table in a DB and builds single-table conditional distributions during an offline preparation phase. When a join query comes, FactorJoin translates it into a factor graph model over the learned distributions to effectively and efficiently estimate its cardinality.
Unlike existing learning-based methods, FactorJoin does not need to de-normalize joins upfront or require executed query workloads to train the model. Since it only relies on single-table statistics, FactorJoin has a small space overhead and is extremely easy to train and maintain. In our evaluation, FactorJoin can produce more effective estimates than the previous state-of-the-art learning-based methods, with 40x less estimation latency, 100x smaller model size, and 100x faster training speed at comparable or better accuracy. In addition, FactorJoin can estimate 10,000 sub-plan queries within one second to optimize the query plan, which is very close to the traditional cardinality estimators in commercial DBMS.
Robust and Transferable Log-based Anomaly Detection
Authors: Jia Peng , Cai Shaofeng , Ooi Beng Chin , Wang Pinghui , Xiong Yiyuan 
Published: May 2023

Log messages provide a valuable source of runtime information for ensuring the safety and consistency of systems. Recently, many machine learning and deep learning methods have been proposed to automatically detect anomalous log messages, obviating the need for manual detection by experts. However, we find that in practice, the effectiveness of existing learning-based methods is severely affected by incomplete information and distribution shift. Specifically, each log message can actually be parsed into a fixed number of key information fields, while existing methods analyze log messages using only the log event information and ignore other useful information fields that can be critical to anomaly detection. Further, the distribution of real-world log messages changes continuously due to the dynamic nature of the runtime environment and thus, a detection model conventionally trained based on the unrealistic i.i.d. assumption may not provide the expected and consistent performance.
In this paper, we present a robust and transferable anomaly detection framework RT-Log to address the above problems. To perform a comprehensive analysis of log messages, we introduce an adaptive relation modeling technique, which captures feature interactions among log information fields selectively and dynamically for effective and interpretable log representations. To establish its robustness and transferability, we propose a general environment generalization technique for learning the environment invariant representations that can generalize across different runtime environments. We evaluate the anomaly detection performance of RT-Log on large real-world datasets. Extensive experimental results demonstrate that RT-Log consistently outperforms state-of-the-art methods by a significant margin under different settings.
Discovering Similarity Inclusion Dependencies
Authors: Kaminsky Youri , Pena Eduardo H. M. , Naumann Felix 
Published: May 2023

Inclusion dependencies (INDs) are a well-known type of data dependency, specifying that the values of one column are contained in those of another column. INDs can be used for various purposes, such as foreign-key candidate selection or join partner discovery. The traditional notion of INDs is based on clean data, where the dependencies hold without exceptions. Unfortunately, data often contain errors, preventing otherwise valid INDs from being discovered. A typical response to this problem is to relax the dependency definition using a similarity measure to account for minor data errors, such as typos or different formatting. While this relaxation is known for functional dependencies, for inclusion dependencies no such relaxation has been defined.
We formally introduce similarity inclusion dependencies, which relax the inclusion by demanding the existence only of sufficiently similar values. Similarity inclusion dependencies can fulfill traditional IND use cases, such as foreign-key candidate discovery, even in the presence of dirty data. We present Sawfish, the first algorithm to discover all similarity inclusion dependencies in a given dataset efficiently. Our algorithm combines approaches for the discovery of traditional INDs and string similarity joins with a novel sliding-window approach and lazy candidate validation. Our experimental evaluation shows that Sawfish can outperform a baseline by a factor of up to 6.5.
RLS Side Channels: Investigating Leakage of Row-Level Security Protected Data Through Query Execution Time
Authors: Dar Chen , Hershcovitch Moshik , Morrison Adam 
Published: May 2023

Many modern use cases of relational databases involve multi-tenancy. To allow a tenant to only access its data, relational database systems (RDBMSs) introduced row-level security (RLS). RLS enables specifying per-row access controls, which the database enforces by rewriting tenant queries to add an RLS policy filter that filters out rows the tenant is not allowed to view. Unfortunately, while RLS blocks queries from returning unauthorized data, side-effects of query execution can form a side-channel that leaks information about such secret data.
This paper investigates how RLS query execution time can leak information about rows that the querying tenant is restricted from viewing. We show that in PostgreSQL and SQL Server, an attacker can craft index-using queries to learn whether a value they are not authorized to view exists in an RLS-protected table, and in some cases, how many times such a value exists in the table. Our attack succeeds in a realistic cloud setting: we successfully attack managed PostgreSQL and SQL Server database instances on AWS from virtual machines in the same and different data centers.
To block the RLS time side-channel, we design a data-oblivious query scheme for the case of unique keys. We also analyze the trade-offs created by the data-oblivious approach for non-unique keys.
To facilitate the evaluation of RLS attacks and defenses, we introduce a benchmark that supports multi-tenancy and RLS, which are not supported by established benchmarks such as YCSB. We implement our solution in PostgreSQL and show that it achieves security with minimal performance impact.
Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware
Authors: Schönberger Manuel , Scherzinger Stefanie , Mauerer Wolfgang 
Published: May 2023

The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for quantum algorithms. We present the first quantum implementation of join ordering, one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes conforming to current quantum annealers. Yet, we also confirm that limits of early-stage technology are quickly reached.
Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future QPUs, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and problem size.
In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
SAT-boosted Tabu Search for Coloring Massive Graphs
Authors: Schidler André , Szeider Stefan 
Published: May 2023

Graph coloring is the problem of coloring the vertices of a graph with as few colors as possible, avoiding monochromatic edges. It is one of the most fundamental NP-hard computational problems. For decades researchers have developed exact and heuristic methods for graph coloring. While methods based on propositional satisfiability (SAT) feature prominently among these exact methods, the encoding size is prohibitive for large graphs. For such graphs, heuristic methods have been proposed, with tabu search among the most successful ones.
In this article, we enhance tabu search for graph coloring within the SAT-based local improvement (SLIM) framework. Our hybrid algorithm incrementally improves a candidate solution by repeatedly selecting small subgraphs and coloring them optimally with a SAT solver. This approach scales to dense graphs with several hundred thousand vertices and over 1.5 billion edges. Our experimental evaluation shows that our hybrid algorithm beats state-of-the-art methods on large dense graphs.
Abstract 5467: Single-cell analysis of CD34-enriched blood cells reveals early prognostic markers of myelodysplastic syndromes
Authors: Meiri Eti , Saar-Furer Nili , Rappoport Nimrod , Pollock Sarah , Lithwick-Yanai Gila , Shipony Zohar , Iremadze Nika , Lipson Doron , Tanay Amos , Shlush Liran 
Published: May 2023

Abstract
Myelodysplastic Syndromes (MDS) are a group of blood malignancies characterized by aberrant differentiation of hematopoietic stem and progenitor cells (HSPC) in the bone marrow that results in inefficient hematopoiesis and high risk of transformation to acute myeloid leukemia. In order to detect aberrant HSC differentiation in blood samples sensitively and accurately, we explored the use of scRNA-seq of CD34-enriched cells from peripheral blood in identifying differentiation trajectories that are abnormal in patient samples versus expected normal development.
A total of 685,000 CD34-enriched PBMCs from 93 samples were analyzed using 10X 3’ v3 scRNA-seq library preparation. For increased efficiency, the samples were multiplexed in groups of 5 individuals and were later identified based on individual natural genetic variation using a custom genotyping assay or low-pass WGS. We then sequenced these 24 libraries on a UG100 sequencer to yield an average of 20,000 reads per cell. Six of these libraries were also sequenced on a NovaSeq. Comparison between scRNA profiles generated on the two different sequencers yielded highly similar results, including a similar ability to demultiplex the patient samples using genetic markers, accurately quantify gene modules and determine cell populations.To analyze the scRNA data, we used a novel computational framework, Metacell, to reconstruct metacell modules for both healthy and disease samples allowing us to find variability in the differentiation process between the disease samples and the normal baseline state. Two patients with MDS which had transformed to acute myelogenous leukemia (AML) demonstrated unique and distinct metacell clusters that were significantly separate from normal HSPC differentiation patterns, demonstrating distinct clusters with elevated levels of BCL2 which is an existing therapy target in secondary AML. Our results demonstrate that scRNA-seq analysis of peripheral blood HSPCs samples can be used to detect aberrations in HSC development in MDS patients and serve as a prognostic tool for stratification of patients with aggressive disease and drug response. Given the cost-efficiency of the entire process, we believe this is one of the first examples of the potential practical utility of single-cell analysis in a clinical setting.
Citation Format: Eti Meiri, Nili Saar-Furer, Nimrod Rappoport, Sarah Pollock, Gila Lithwick-Yanai, Zohar Shipony, Nika Iremadze, Doron Lipson, Amos Tanay, Liran Shlush. Single-cell analysis of CD34-enriched blood cells reveals early prognostic markers of myelodysplastic syndromes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5467.