bluuebunny/crossref_metadata_2025_split huggingface dataset:
International implementation of a care review process for mortality reviews: improving quality and safety virtually across the world
Authors: Cullinan Susan M , Yubeta Toni R , Al-Qadi Lara S , Bukhari Moatasiem I , Morgenthaler Timothy I 
Published: May 2023

Mortality reviews are intended to produce transparent, non-punitive personal and organisational learning that leads to systematic improvement in care. Mayo Clinic has a well-established care review process that has accomplished that objective within our system. The establishment of a new hospital, a joint venture between Mayo Clinic and Sheikh Shakhbout Medical City (SSMC) in Abu Dhabi, provided a unique opportunity to share this care review process internationally.During a baseline measurement period, only 78.3% of mortality reviews at SSMC were completed within 45 days, 16.7 percentage points below the target of 95%. A collaboration between SSMC and Mayo Clinic aimed to accelerate the design and implementation of a care review process system. Collaboration was constrained by travel restrictions imposed by COVID-19, language barriers, legal privacy concerns, and differing electronic health records.Mayo Clinic facilitated a 12-week virtual engagement with SSMC using weekly video meetings, education and training regarding Mayo Clinic’s care review process.The engagement led to implementation of weekly mortality review huddles, restructuring of the mortality review committee to be multidisciplinary, use of a standardised taxonomy to characterise opportunities to improve care and creation of an education/communication plan regarding identified improvement opportunities using change management strategies.After the care review process for mortality reviews was instituted, SSMC achieved and sustained a target of 100% of mortality reviews completed within 45 days. The new process resulted in improved mortality review indicators and provided quality feedback to staff with engagement in performance improvement efforts.A virtual collaboration led to successful implementation of a care review process and substantial gains in the effectiveness of the quality programme at SSMC. This could serve as a model to assist other organisations, even if in-person engagement is hindered.
An experimental comparison of stand management approaches to sudden oak death: prevention vs. restoration
Authors: Quiroga Gissella B. , Simler-Williamson Allison B. , Frangioso Kerri M. , Frankel Susan J. , Rizzo David M. , Cobb Richard C. 
Published: May 2023

Many coastal forests stretching from central California to southwest Oregon are threatened or have been impacted by the invasive forest pathogen Phytophthora ramorum, the cause of sudden oak death. We analyzed a set of stand-level forest treatments aimed at preventing or mitigating disease impacts on stand composition, biomass, and fuels using a before–after-control-intervention experiment with a re-evaluation after 5 years. We compared the effects of restorative management for invaded stands and preventative treatments for uninvaded forests with two stand-level experiments. The restorative treatments contrasted two approaches to mastication, hand-crew thinning, and thinning with pile burning with untreated controls replicated at three distinct sites ( N = 30), while the preventative treatments were limited to hand-crew thinning ( N = 10) conducted at a single site. Half of the restoration treatments had basal sprouts removed 2 and 4 years after treatment. All treatments significantly reduced stand density and increased average tree size without significantly decreasing total basal area, both immediately and 5 years after treatments. Preventative treatments did not reduce the basal area or density of timber species not susceptible to P. ramorum, suggesting the relative dominance of these species increased in accordance with host removal. Follow-up basal sprout removal in the restoration experiment appears to maintain treatment benefits for average tree size and may be associated with small decreases in stand density 5 years after initial treatment. Our study demonstrates that for at least 5 years, a range of common stand management practices can improve forest conditions threatened or impacted by sudden oak death.
A New Sparse Data Clustering Method Based On Frequent Items
Authors: Huang Qiang , Luo Pingyi , Tung Anthony K. H. 
Published: May 2023

Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of dimensions. While most existing clustering algorithms like k-Means work well on dense, numerical data, there exist relatively few algorithms that can cluster sets of sparse categorical features.
In this paper, we propose a new method called k-FreqItems that performs scalable clustering over high-dimensional, sparse data. To make clustering results easily interpretable, k-FreqItems is built upon a novel sparse center representation called FreqItem which will choose a set of high-frequency, non-zero dimensions to represent the cluster. Unlike most existing clustering algorithms, which adopt Euclidean distance as the similarity measure, k-FreqItems uses the popular Jaccard distance for comparing sets.
Since the efficiency and effectiveness of k-FreqItems are highly dependent on an initial set of representative seeds, we introduce a new randomized initialization method, SILK, to deal with the seeding problem of k-FreqItems. SILK uses locality-sensitive hash (LSH) functions for oversampling and identifies frequently co-occurred data in LSH buckets to determine a set of promising seeds, allowing k-FreqItems to converge swiftly in an iterative process. Experimental results over seven real-world sparse data sets show that the SILK seeding is around 1.1\sim3.2× faster yet more effective than the state-of-the-art seeding methods. Notably, SILK scales up well to a billion data objects on a commodity machine with 4 GPUs. The code is available at https://github.com/HuangQiang/k-FreqItems.
Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries
Authors: Liu Shang , Cong Gao , Feng Kaiyu , Gu Wanli , Zhang Fuzheng 
Published: May 2023

Geo-textual objects with both geographical location and textual description are gaining in prevalence. Over the past decades, substantial research has been conducted on spatial keyword queries, which integrate location into keyword-based querying of geo-textual content. However, existing proposals mostly focus on efficiency for processing spatial keyword queries, and little effort was made to address the effectiveness perspectives.
In this work, using two datasets with ground truth query results, we evaluate the effectiveness of standard spatial keyword queries. Our evaluation results show that the TkQ query that ranks objects by a weighted combination of spatial proximity and text relevance is the most effective. Motivated by the finding, we propose a Deep relevance with Weight learning (DrW) model to further improve the effectiveness of the retrieval ranking. DrW is featured with two novel ideas: First, we propose a neural network architecture to learn the text relevance matching over the local interaction between the query and geo-textual objects. Second, we find that a query-dependent weight to balance text relevance and spatial proximity in ranking can improve effectiveness, and we develop a learning-based method to learn the query-dependent weight. Experimental results reveal that our model outperforms state-of-the-art methods on effectiveness, with improvements up to 32.15%, 32.34%, and 33.00% in terms of NDCG@3, NDCG@5, and MRR.
EAR-Oracle: On Efficient Indexing for Distance Queries between Arbitrary Points on Terrain Surface
Authors: Huang Bo , Wei Victor Junqiu , Wong Raymond Chi-Wing , Tang Bo 
Published: May 2023

Due to the advancement of geo-positioning technology, the terrain data has become increasingly popular and has drawn a lot of research effort from both academia and industry. The distance computation on the terrain surface is a fundamental and important problem that is widely applied in geographical information systems and 3D modeling. As could be observed from the existing studies, online computation of the distance on the terrain surface is very expensive. All existing index-based methods are only efficient under the case where the distance query must be performed among a small set of predefined points-of-interest known apriori. But, in general cases, they could not scale up to sizable datasets due to their intolerable oracle building time and space consumption.
In this paper, we studied the arbitrary point-to-arbitrary point distance query on the terrain surface in which no assumption is imposed on the query points, and the distance query could be performed between any two arbitrary points. We propose an indexing structure, namely Efficient Arbitrary Point-to-Arbitrary Point Distance Oracle (EAR-Oracle), with theoretical guarantee on the accuracy, oracle building time, oracle size and query time. Our experiments demonstrate that our oracle enjoys excellent scalability and it scales up to enormous terrain surfaces but none of the existing index-based methods could be able to. Besides, it significantly outperforms all existing online computation methods by orders of magnitude in terms of the query time.
Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences
Authors: Ping Haoyue , Stoyanovich Julia 
Published: May 2023

It remains an open question how to determine the winner of an election when voter preferences are incomplete or uncertain. One option is to assume some probability space over the voting profile and select the Most Probable Winner (MPW) -- the candidate or candidates with the best chance of winning. In this paper, we propose an alternative winner interpretation, selecting the Most Expected Winner (MEW) according to the expected performance of the candidates.
We separate the uncertainty in voter preferences into the generation step and the observation step, which gives rise to a unified voting profile combining both incomplete and probabilistic voting profiles. We use this framework to establish the theoretical hardness of MEW over incomplete voter preferences, and then identify a collection of tractable cases for a variety of voting profiles, including those based on the popular Repeated Insertion Model (RIM) and its special case, the Mallows model. We develop solvers customized for various voter preference types to quantify the candidate performance for the individual voters, and propose a pruning strategy that optimizes computation. The performance of the proposed solvers and pruning strategy is evaluated extensively on real and synthetic benchmarks, showing that our methods are practical.
Efficiently Computing Join Orders with Heuristic Search
Authors: Haffner Immanuel , Dittrich Jens 
Published: May 2023

Join order optimization is one of the most fundamental problems in processing queries on relational data. It has been studied extensively for almost four decades now. Still, because of its NP hardness, no generally efficient solution exists and the problem remains an important topic of research. The scope of algorithms to compute join orders ranges from exhaustive enumeration, to combinatorics based on graph properties, to greedy search, to genetic algorithms, to recently investigated machine learning. A few works exist that use heuristic search to compute join orders. However, a theoretical argument why and how heuristic search is applicable to join order optimization is lacking.
In this work, we investigate join order optimization via heuristic search. In particular, we provide a strong theoretical framework, in which we reduce join order optimization to the shortest path problem. We then thoroughly analyze the properties of this problem and the applicability of heuristic search. We devise crucial optimizations to make heuristic search tractable. We implement join ordering via heuristic search in a real DBMS and conduct an extensive empirical study. Our findings show that for star- and clique-shaped queries, heuristic search finds optimal plans an order of magnitude faster than current state of the art. Our suboptimal solutions further extend the cost/time Pareto frontier.
Together is Better: Heavy Hitters Quantile Estimation
Authors: Shahout Rana , Friedman Roy , Ben Basat Ran 
Published: May 2023

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low.
In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand.
We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.
Learned Data-aware Image Representations of Line Charts for Similarity Search
Authors: Luo Yuyu , Zhou Yihui , Tang Nan , Li Guoliang , Chai Chengliang , Shen Leixian 
Published: May 2023

Finding line-chart images similar to a given line-chart image query is a common task in data exploration and image query systems, e.g. finding similar trends in stock markets or medical Electroencephalography images. The state-of-the-art approaches consider either data-level similarity (when the underlying data is present) or image-level similarity (when the underlying data is absent).
In this paper, we study the scenario that during query time, only line-chart images are available. Our goal is to train a neural network that can turn these line-chart images into representations that are aware of the data used to generate these line charts, so as to learn better representations. Our key idea is that we can collect both data and line-chart images to learn such a neural network (at training step), while during query (or inference) time, we support the case that only line-chart images are provided. To this end, we present LineNet, a Vision Transformer-based Triplet Autoencoder model to learn data-aware image representations of line charts for similarity search. We design a novel pseudo labels selection mechanism to guide LineNet to capture both data-aware and image-level similarity of line charts. We further propose a diversified training samples selection strategy to optimize the learning process and improve the performance. We conduct both quantitative evaluation and case studies, showing that LineNet significantly outperforms the state-of-the-art methods for searching similar line-chart images.
Abstract ND03: Discovery of ARV-766, an androgen receptor degrading PROTAC® for the treatment of men with metastatic castration resistant prostate cancer
Authors: Snyder Larry , Lee S H. , Neklesa T K. , Chen X , Dong H , Ferraro C , Gordon D A. , Macaluso J , Pizzano J , Wang J , Willard R R. , Vitale N , Peck R , Moore M D. , Crews C M. , Houston J , Crew A P. , Taylor I 
Published: May 2023

Abstract
Prostate cancer is the second leading cause of cancer death in men in the United States. The androgen receptor (AR) plays critical roles in both early disease and advanced prostate cancer. Current therapeutic approaches targeting the androgen/AR axis are initially effective, but castration resistant prostate cancer (CRPC) inevitably develops. CRPC is linked with increased AR activity via gene overexpression, amplification, and gain-of-function mutations. To address these mechanisms of AR-dependent prostate tumor growth, we have developed a novel therapeutic agent, ARV-766, a proteolysis targeting chimera (PROTAC®) that induces a protein-protein interaction between the AR and specific E3 ubiquitin ligase complexes, leading to the ubiquitination of AR and its subsequent degradation via the proteasome. In vitro, ARV-766 degrades AR in various prostate cancer cell lines, including those harboring resistance-conferring, clinically relevant point mutations, with a half-maximal degradation concentration (DC50) of &lt;1 nM in wild type VCaP. Importantly ARV-766 also maintains potency against the AR L702H mutant, which has been associated with resistance to some AR antagonists. In vivo, ARV-766 is orally bioavailable and robustly degrades AR with a &gt;90% observed maximum degradation (Dmax) at efficacious doses. ARV-766 significantly and dose-dependently inhibits tumor growth in murine LNCaP and VCaP xenograft models, including an enzalutamide-insensitive non-castrated VCaP model. These preclinical data supported the clinical development of ARV-766 for the treatment of men with metastatic CRPC. Selected pre-clinical data along with the chemical structure of ARV-766 will be presented.
Citation Format: Larry Snyder, S H. Lee, T K. Neklesa, X Chen, H Dong, C Ferraro, D A. Gordon, J Macaluso, J Pizzano, J Wang, R R. Willard, N Vitale, R Peck, M D. Moore, C M. Crews, J Houston, A P. Crew, I Taylor. Discovery of ARV-766, an androgen receptor degrading PROTAC® for the treatment of men with metastatic castration resistant prostate cancer [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 ND03.
Abstract ND04: BAY 2965501: A highly selective DGK- ζ inhibitor for cancer immuno-therapy with first-in-class potential
Authors: Offringa Rienk , Olesch Catherine , Cichon Frederik , Grees Mareike , Schmees Norbert , Roehn Ulrike , Prinz Florian , Guenther Judith , Stoeckigt Detlef , Erkelenz Michael , Nguyen Thi Thanh Uyen , Boemer Ulf , Werbeck Nicolas , Kunze Marc , Petersen Kirstin , Kirchhoff Dennis 
Published: May 2023

Abstract
The second messenger diacyl glycerol (DAG) plays a key role in T cell receptor downstream signaling and thereby in T-cell activation. Diacylglycerol kinase zeta (DGK-ζ) is a lipid kinase that can down-modulate T-cell activation by catalyzing the conversion of DAG to phosphatidic acid, thereby acting as a ligand independent, intracellular immune checkpoint. Inhibition of DGK- ζ offers the potential to enhance T cell priming against suboptimal tumor antigens and to overcome multiple immune-suppressive mechanisms in the tumor microenvironment.
Bayer AG in collaboration with the German Cancer Research Center (DKFZ) have developed BAY 2965501, a highly selective, orally available DGK-ζ inhibitor which shows a unique binding mode and good DMPK profile. The compound enhances the reactivity of human and mouse T-cells, both in the priming and the effector phase, and overcomes the inhibitory impact of suppressive factors, such as adenosine and prostaglandin E2. Oral dosing of BAY 2965501 shows T cell dependent efficacy in syngeneic, murine tumor models. Furthermore, this results in reactivation of exhausted T cells in vivo. Preclinical safety studies showed only mild findings, so that good clinical tolerability is expected. Collectively, the preclinical data support first in human (FiH) phase I testing of therapeutic potential.
A FiH trial with BAY 2965501 in patients with advanced solid tumors including NSCLC, gastric/GEJ AdCa, ccRCC, and melanoma patients, is currently enrolling patients (NCT05614102). This study will evaluate the safety, tolerability, maximum tolerated or administered dose, pharmacokinetics, pharmacodynamics, and tumor response profile of BAY 2965501.
Citation Format: Rienk Offringa, Catherine Olesch, Frederik Cichon, Mareike Grees, Norbert Schmees, Ulrike Roehn, Florian Prinz, Judith Guenther, Detlef Stoeckigt, Michael Erkelenz, Thi Thanh Uyen Nguyen, Ulf Boemer, Nicolas Werbeck, Marc Kunze, Kirstin Petersen, Dennis Kirchhoff. BAY 2965501: A highly selective DGK- ζ inhibitor for cancer immuno-therapy with first-in-class potential [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 ND04.
Abstract ND11: EO-3021: An antibody drug conjugate targeting CLDN18.2 expressing cancers
Authors: Dornan David 
Published: May 2023

Abstract
Claudin 18.2 is tight junction protein with a highly selective cell surface expression profile that is limited to the normal gastric mucosa. Importantly, it is often overexpressed in several types of cancers including gastric, pancreatic, esophageal, ovarian, and lung. There are currently no approved targeted therapies for claudin 18.2-expressing cancers. As such, we designed an antibody drug conjugate, EO-3021/SYSA1801 with the potential to selectively target and kill claudin 18.2-expressing cancer cells. EO-3021 is comprised of a novel tumor-targeting monoclonal antibody that selectively binds claudin 18.2 (Kd = 0.6 nM) that is conjugated to monomethyl auristatin E (MMAE). EO-3021 is internalized in claudin 18.2-expressing cells as well as demonstrates cell killing across a range of cancer cell lines with low (IC50 = 228 ng/ml), medium (IC50 = 7 ng/ml), and high levels of claudin 18.2 (IC50 = 28 ng/ml) expression. More specifically, claudin 18.2-expressing cells undergo G2/M cell cycle arrest and apoptosis upon EO-3021 treatment. EO-3021 demonstrates tumor regressions with a single dose across low, medium, and high claudin 18.2-expressing in vivo models derived from pancreatic (9 mg/kg), gastric (2 mg/kg), and lung cancers (8 mg/kg), respectively. In addition, a single dose of EO-3021 outperforms standard of care chemotherapies, such as cisplatin and gemcitabine, across in vivo models. Nonclinical toxicology studies suggested that EO-3021-mediated toxicities were reversible and manageable as well as afford a therapeutic index suitable for testing in humans. EO-3021/SYSA1801 is currently being evaluated in a Phase I dose escalation study (NCT05009966) in patients with claudin 18.2 positive advanced malignant solid tumors. A case study will be presented where a confirmed and durable partial response (66.7% tumor reduction) was observed following EO-3021 treatment.
Citation Format: David Dornan. EO-3021: An antibody drug conjugate targeting CLDN18.2 expressing cancers [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 ND11.