bluuebunny/crossref_metadata_2025_split huggingface dataset:
Total Wrist Replacement Outcomes in the Asian Pacific Population – A Single-Centre Result
Authors: WAN Raymond Chung-Wai , MAK Michael Chu-Kay , TSE Wing-Lim , CHAU Wai-Wang , HO Pak-Cheong 
Published: December 2023

Background: Total wrist replacement (TWR) is rarely done in the Asia–Pacific region. The aim of this study is to report the surgical outcomes and experience of TWR in patients with advanced arthritis. Methods: This is a retrospective review of all TWR patients in the Department of Orthopaedics and Traumatology, Prince of Wales Hospital, Hong Kong, which is a university tertiary centre, from January 2004 to March 2023. Recorded demographic parameters include gender, age upon surgery, pathology, types of implants and follow-up period. The surgical outcome parameters include range of motion, grip strength, wrist function assessment, radiological and clinical complications and any related secondary operations. Postoperative X-ray and clinical notes were reviewed. All wrist function assessments were performed by specialised occupational therapists according to protocol. Results: The study included a total of 12 wrists of 10 patients, all Chinese–Asian, with a mean age of 61.4 years at surgery. Larsen grade V arthritis constituted 50% and grade IV 16.7% of the patients, amongst which 33% had volar subluxation. The mean follow-up period was 97.4 months (21–205 months). The mean grip strength was 64.2% of the unaffected side. The mean postoperative Disabilities of Arm, Shoulder and Hand (DASH) score was 41.12% and patient-rated wrist/hand evaluation (PRWE) score 18.0. Complication incidence was 16.67% for loosening, 8.3% for metallosis and 8.3% for infection. One patient required conversion to total wrist arthrodesis due to metallosis. No patient suffered from dislocation, periprosthetic fracture and infection. Conclusions: TWR is an effective and safe alternative to total wrist arthrodesis with comparable outcomes. Our series outcomes are satisfactory and in line with literature. With meticulous soft tissue release and balancing, volar subluxation can also be corrected and may not be a contraindication. Level of Evidence: Level IV (Therapeutic)
ChainKV: A Semantics-Aware Key-Value Store for Ethereum System
Authors: Chen Zehao , Li Bingzhe , Cai Xiaojun , Jia Zhiping , Ju Lei , Shao Zili , Shen Zhaoyan 
Published: December 2023

The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for persistence. However, blockchain semantics are ignored during this process, making the blockchain storage suffer from heavy read/write amplification problems. Moreover, as the Ethereum network scales up, tremendous data further exacerbates its storage burden. Until now, most studies have focused on sharding, data archiving, decentralized distributed storage, etc., to mitigate the burden of the storage layer. However, the incompatibility between Ethereum semantics and the characteristics of the storage engine is ignored.
In this paper, we present ChainKV, a new semantics-aware storage paradigm to improve the storage management performance for the Ethereum system. Firstly, based on Ethereum blockchain semantics, ChainKV separately stores different types of data in multiple storage zones in the KV store to mitigate the read/write amplification problem. Secondly, following the mechanism of the verification process in the authenticated data structure (ADS), a new ADS data transformer is proposed to exploit the data locality when persisting ADS. Moreover, a new space gaming caching policy is adopted to coordinate the cache space management for two independent storage zones. Finally, we propose an optional lightweight node crash recovery mechanism to eliminate functional redundancy between the Ethereum protocol and the storage engine. The experimental results indicate that ChainKV outperforms the prior Ethereum systems by up to 1.99× and 4.20× for synchronization and query operations, respectively
ALP: Adaptive Lossless floating-Point Compression
Authors: Afroozeh Azim , Kuffo Leonardo X. , Boncz Peter 
Published: December 2023

IEEE 754 doubles do not exactly represent most real values, introducing rounding errors in computations and [de]serialization to text. These rounding errors inhibit the use of existing lightweight compression schemes such as Delta and Frame Of Reference (FOR), but recently new schemes were proposed: Gorilla, Chimp128, PseudoDecimals (PDE), Elf and Patas. However, their compression ratios are not better than those of general-purpose compressors such as Zstd; while [de]compression is much slower than Delta and FOR.
We propose and evaluate ALP, that significantly improves these previous schemes in both speed and compression ratio (Figure 1). We created ALP after carefully studying the datasets used to evaluate the previous schemes. To obtain speed, ALP is designed to fit vectorized execution. This turned out to be key for also improving the compression ratio, as we found in-vector commonalities to create compression opportunities. ALP is an adaptive scheme that uses a strongly enhanced version of PseudoDecimals [31] to losslessly encode doubles as integers if they originated as decimals, and otherwise uses vectorized compression of the doubles' front bits. Its high speeds stem from our implementation in scalar code that auto-vectorizes, using building blocks provided by our FastLanes library [6], and an efficient two-stage compression algorithm that first samples row-groups and then vectors.
Efficient Approximation Framework for Attribute Recommendation
Authors: Chen Xingguang , Zhang Fangyuan , Huang Jinchao , Wang Sibo 
Published: December 2023

Trend analysis is a fundamental type of analytical query in online analytical processing (OLAP) systems. In trend analysis, a key step is to identify k valuable attributes whose distributions in two subsets under different predicates significantly differ for further investigation, where the difference is measured by metric functions. However, the exact solution that involves scanning all records is prohibitively expensive, particularly when handling large datasets in the era of big data. To minimize unnecessary data access, the existing state-of-the-art solution TopKAttr adopts sampling to avoid the expensive data scan. However, their solution still has two main drawbacks. Firstly, their solution is tailored only for two limited metric functions: the Earth Mover distance and Euclidean distance, and cannot be generalized to more complicated metric functions. Besides, their solution still aims to return the exact top-k answers via the sampling method, which still causes high running costs as shown in our experiment.Motivated by these limitations, we propose a general approximation framework for attribute recommendation that efficiently returns the top-k attributes with theoretical guarantees while supporting an extensive range of metric functions, such as the Kolmogorov-Smirnov test (KS-test), Chebyshev distance, the Earth Mover distance, Euclidean distance, and with the potential to more metrics. The key to our framework is a new bound estimation strategy that can be applied to a wide spectrum of metrics, as we listed above. Based on our estimation framework, we further devise an efficient approximation algorithm with theoretical guarantees to answer the top-k queries, which is widely used in attribute recommendation. Extensive experiments on four real large datasets show that our framework gains up to an order of magnitude speed-up and consistently high accuracy compared to TopKAttr, providing a promising alternative for attribute recommendation in OLAP systems.
MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying
Authors: Wang Zhiqi , Shao Zili 
Published: December 2023

LSM-based key-value stores have been leveraged in many state-of-the-art data-intensive applications as storage engines. As data volume scales up, a cost-efficient approach is to deploy these applications on hybrid cloud storage with hot/cold separation, which splits the LSM-tree into two parts and thus brings new challenges on how to split and how to close the significant performance gap between these two parts. Existing LSM-tree key-value stores mainly focus on the optimizations of local storage, which incurs sub-optimal performance when directly applied to hybrid storage.
In this paper, we present MirrorKV for efficient compaction and querying on hybrid cloud storage. First, based on the capacities of fast and slow cloud storage, MirrorKV vertically separates hot/cold data of different levels stored in different cloud storage with different compaction mechanisms. To avoid compaction in slow storage being the bottleneck of the write path, MirrorKV proposes a novel virtual split to only compact the metadata during the compaction, which postpones the actual compaction until it reaches deep enough levels. Second, to reduce accessing slow storage during querying, MirrorKV horizontally separates keys and values into two mirrored LSM-trees to differentiate caching priorities; the maintained tree structures preserve the data locality for efficient sequential reading without incurring the overhead of the traditional key-value separation solutions. Finally, MirrorKV leverages cached data to guide the compaction where the hot data is retained in the fast storage while the cold data is compacted to deeper levels in slow storage. Compared with RocksDB-cloud, MirrorKV achieves 2.4× higher random insertion throughput, 29% higher random read throughput, and 99% less compaction time.
NOCAP: Near-Optimal Correlation-Aware Partitioning Joins
Authors: Zhu Zichen , Hu Xiao , Athanassoulis Manos 
Published: December 2023

Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution.
In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the optimal partitioning using a new cost-based analysis of partitioning-based joins that is tailored for primary key - foreign key (PK-FK) joins, one of the most common join types. This optimal partitioning strategy has a high memory cost, thus, we further derive an approximate algorithm that has tunable memory cost and leads to near-optimal results. Our algorithm, termed NOCAP (Near-Optimal Correlation-Aware Partitioning) join, outperforms the state of the art for skewed correlations by up to 30%, and the textbook Grace Hash Join by up to 4×. Further, for a limited memory budget, NOCAP outperforms HHJ by up to 10%, even for uniform correlation. Overall, NOCAP dominates state-of-the-art algorithms and mimics the best algorithm for a memory budget varying from below √||relation|| to more than ||relation||.
Deep Learning–Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell–Cell Interaction Dynamics in the Tumor Microenvironment
Authors: Liu Bodong , Zhu Yanting , Yang Zhenye , Yan Helen H.N. , Leung Suet Yi , Shi Jue 
Published: December 2023

Abstract

The three-dimensional (3D) tumor microenvironment (TME) comprises multiple interacting cell types that critically impact tumor pathology and therapeutic response. Efficient 3D imaging assays and analysis tools could facilitate profiling and quantifying distinctive cell–cell interaction dynamics in the TMEs of a wide spectrum of human cancers. Here, we developed a 3D live-cell imaging assay using confocal microscopy of patient-derived tumor organoids and a software tool, SiQ-3D (single-cell image quantifier for 3D), that optimizes deep learning (DL)–based 3D image segmentation, single-cell phenotype classification, and tracking to automatically acquire multidimensional dynamic data for different interacting cell types in the TME. An organoid model of tumor cells interacting with natural killer cells was used to demonstrate the effectiveness of the 3D imaging assay to reveal immuno-oncology dynamics as well as the accuracy and efficiency of SiQ-3D to extract quantitative data from large 3D image datasets. SiQ-3D is Python-based, publicly available, and customizable to analyze data from both in vitro and in vivo 3D imaging. The DL-based 3D imaging analysis pipeline can be employed to study not only tumor interaction dynamics with diverse cell types in the TME but also various cell–cell interactions involved in other tissue/organ physiology and pathology.

Significance:
A 3D single-cell imaging pipeline that quantifies cancer cell interaction dynamics with other TME cell types using primary patient-derived samples can elucidate how cell–cell interactions impact tumor behavior and treatment responses.
Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences
Authors: Aiello Bowles Erin J. , Kroenke Candyce H. , Chubak Jessica , Bhimani Jenna , O'Connell Kelli , Brandzel Susan , Valice Emily , Doud Rachael , Theis Mary Kay , Roh Janise M. , Heon Narre , Persaud Sonia , Griggs Jennifer J. , Bandera Elisa V. , Kushi Lawrence H. , Kantor Elizabeth D. 
Published: December 2023

Abstract

Background:
We updated algorithms to identify breast cancer recurrences from administrative data, extending previously developed methods.

Methods:
In this validation study, we evaluated pairs of breast cancer recurrence algorithms (vs. individual algorithms) to identify recurrences. We generated algorithm combinations that categorized discordant algorithm results as no recurrence [High Specificity and PPV (positive predictive value) Combination] or recurrence (High Sensitivity Combination). We compared individual and combined algorithm results to manually abstracted recurrence outcomes from a sample of 600 people with incident stage I–IIIA breast cancer diagnosed between 2004 and 2015. We used Cox regression to evaluate risk factors associated with age- and stage-adjusted recurrence rates using different recurrence definitions, weighted by inverse sampling probabilities.

Results:
Among 600 people, we identified 117 recurrences using the High Specificity and PPV Combination, 505 using the High Sensitivity Combination, and 118 using manual abstraction. The High Specificity and PPV Combination had good specificity [98%, 95% confidence interval (CI): 97–99] and PPV (72%, 95% CI: 63–80) but modest sensitivity (64%, 95% CI: 44–80). The High Sensitivity Combination had good sensitivity (80%, 95% CI: 49–94) and specificity (83%, 95% CI: 80–86) but low PPV (29%, 95% CI: 25–34). Recurrence rates using combined algorithms were similar in magnitude for most risk factors.

Conclusions:
By combining algorithms, we identified breast cancer recurrences with greater PPV than individual algorithms, without additional review of discordant records.

Impact:
Researchers should consider tradeoffs between accuracy and manual chart abstraction resources when using previously developed algorithms. We provided guidance for future studies that use breast cancer recurrence algorithms with or without supplemental manual chart abstraction.
GellingBodies: Understanding the Mechanisms Underlying Arsenic Trioxide Action
Authors: Salomoni Paolo 
Published: December 2023

Summary:The study by Bercier and colleagues investigates the mechanisms of action of arsenic trioxide (ATO). The authors find that ATO promotes transition of PML nuclear bodies to a gel-like state via the PML trimerization domain and a critical cysteine residue. Overall, this work sheds new light onto how PML–RARα, the oncogene of APL, is targeted by ATO for disease eradication.See related article by Bercier et al., p. 2548 (6).
Highlights of FDA Oncology Approvals in 2023: Bispecific T-cell Engagers, Pediatric Indications, and Inclusive Drug Development
Authors: Donaldson Joshua M. , Seddiq Marjilla , Fusco Michael J. , Singla Shyamli , Pamuk Gulsum E. , Lee-Alonzo Rosa J. , Mixter Bronwyn D. , Goldberg Kirsten B. , Amiri-Kordestani Laleh , de Claro R. Angelo , Drezner Nicole , Gormley Nicole J. , Kanapuru Bindu , Lemery Steven J. , Fashoyin-Aje Lola A. , Richardson Nicholas C. , Singh Harpreet , Suzman Daniel L. , Theoret Marc R. , Kluetz Paul G. , Pazdur Richard 
Published: December 2023

Summary:
Cancer drug development remained robust in 2023. Highlights of U.S. drug approvals this year include new immunotherapies and targeted drug development in adult and pediatric patients as well as patients with rare diseases.
External Validation of Proposed American Heart Association Algorithm for Cardiovascular Screening Before Kidney Transplantation
Authors: Nielsen Marie Bodilsen , Dahl Jonathan Nørtoft , Jespersen Bente , Ivarsen Per , Birn Henrik , Winther Simon 
Published: December 2023

BackgroundScreening for cardiovascular disease is currently recommended before kidney transplantation. The present study aimed to validate the proposed algorithm by the American Heart Association (AHA‐2022) considering cardiovascular findings and outcomes in kidney transplant candidates, and to compare AHA‐2022 with the previous recommendation (AHA‐2012).Methods and ResultsWe applied the 2 screening algorithms to an observational cohort of kidney transplant candidates (n=529) who were already extensively screened for coronary heart disease by referral to cardiac computed tomography between 2014 and 2019. The cohort was divided into 3 groups as per the AHA‐2022 algorithm, or into 2 groups as per AHA‐2012. Outcomes were degree of coronary heart disease, revascularization rate following screening, major adverse cardiovascular events, and all‐cause death. Using the AHA‐2022 algorithm, 69 (13%) patients were recommended for cardiology referral, 315 (60%) for cardiac screening, and 145 (27%) no further screening. More patients were recommended cardiology referral or screening compared with the AHA‐2012 (73% versus 53%;P<0.0001). Patients recommended cardiology referral or cardiac screening had a higher risk of major adverse cardiovascular events (hazard ratio [HR], 5.5 [95% CI, 2.8–10.8]; and HR, 2.1 [95% CI, 1.2–3.9]) and all‐cause death (HR, 12.0 [95% [CI, 4.6–31.4]; and HR, 5.3 [95% CI, 2.1–13.3]) compared with patients recommended no further screening, and were more often revascularized following initial screening (20% versus 7% versus 0.7%;P<0.001).ConclusionsThe AHA‐2022 algorithm allocates more patients for cardiac referral and screening compared with AHA‐2012. Furthermore, the AHA‐2022 algorithm effectively discriminates between kidney transplant candidates at high, intermediate, and low risk with respect to major adverse cardiovascular events and all‐cause death.
Estimating rectal temperature in exercising horses in a competition environment using infrared thermal imaging
Authors: Marlin D.J. , Reynolds H. , Mukai K. , Neil K. , Akerstrom G. 
Published: December 2023

Abstract
Short-term intense and or prolonged medium intensity exercise in conditions of moderate-high environmental temperature or temperature and humidity can result in moderate to marked hyperthermia. This in turn may result in decreased exercise capacity, earlier onset of fatigue and increased risk of falls or injury. Early identification of moderate-severe hyperthermia is important to allow appropriate intervention, either before, during or after exercise. Infra-red thermal imaging (IRT) allows measurement of horse surface temperature non-invasively, however it is generally considered to be unreliable in outdoor environments. Collection of pilot data suggested that this modality warranted further investigation. IRT Images were obtained from horses during training and competition at the 2019 Ready Steady Tokyo Olympic equestrian test event (Wet Bulb Globe Temperature [WBGT] > 28 °C), during and after the cross-country at the 2019 Burghley Horse Trials CCI 5* (WBGT 12-15 °C), the 2019 Adelaide International Horse Trials CCI 4* (WBGT ∼ 20 °C) and during the 2020 Tokyo Olympic Games (held in 2021; WBGT > 28 °C). A total of 87 paired hindquarter IRT images and rectal temperature measurements were obtained. When all data from all 4 events were pooled the bias and limits of agreement were −0.3 ± 1.1 °C (n = 87), i.e. IRT underestimated rectal temperature. When the data from Ready Steady Tokyo 2019 and Tokyo 2020 were combined as both events took place in similar thermal environmental conditions) the bias and limits of agreement were −0.1 ± 0.8 °C (n = 45). For 29 paired hindquarter thermal images, where a second image was obtained within 15 s, there was no significant difference between the mean of the first and second images (Image 1: 38.9 ± 0.9 °C versus Image 2: 38.7 ± 0.8 °C; ; n = 29). In conclusion, IRT imaging in environmental conditions of high temperature or high temperature and humidity may be useful in identifying horses with moderate to severe hyperthermia.
Limited Role of Absolute Humidity in Intraurban Heat Variability
Authors: Waugh Darryn W. , Zaitchik Benjamin , Scott Anna A. , Ibsen Peter C. , Jenerette G. Darrel , Schatz Jason , Kucharik Christopher J. 
Published: December 2023

Abstract
Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Significance Statement
Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.