bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping health service coverage inequalities in Africa: a scoping review protocol
Authors: Karamagi Humphrey Cyprian , Ben Charif Ali , Afriyie Doris Osei , SY Sokona , Kipruto Hillary , Oyelade Taiwo , Droti Benson 
Published: May 2023

IntroductionAddressing inequities in health service coverage is a global priority, especially with the resurgence of interest in universal health coverage. However, in Africa, which has the lowest health service coverage index, there is limited information on the progress of countries in addressing inequalities related to health services. Thus, we seek to map the evidence on inequalities in health service coverage in Africa.Methods and analysisWe will conduct a scoping review following the Joanna Briggs Institute Manual for Evidence Synthesis. We preregistered this protocol with the Open Science Framework on 26 July 2022 (https://osf.io/zd5bt). We will consider any empirical research that assesses inequalities in relation to services for reproductive, maternal, newborn and child health (eg, family planning), infectious diseases (eg, tuberculosis treatment) and non-communicable diseases (eg, cervical cancer screening) in Africa. We will search MEDLINE, Embase, Web of Science, CINAHL, PsycINFO and Cochrane Library from their inception onwards. We will also hand-search Google and Global Index Medicus, and screen reference lists of relevant studies. We will evaluate studies for eligibility and extract data from included studies using pre-piloted and standardised forms. We will further extract a core set of health service coverage indicators, which are disaggregated by place of residence, race/ethnicity/culture, occupation, gender, religion, education, socioeconomic status and social capital plus equity stratifiers. We will summarise data using a narrative approach involving thematic syntheses and descriptive statistics. We will report our findings according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist.Ethics and disseminationEthical approval is not required as primary data will not be collected. This work will contribute to identifying knowledge gaps in the evidence of inequalities in health service coverage in Africa, and propose strategies that could help overcome current challenges. We will disseminate our findings to knowledge users through a publication in a peer-reviewed journal and organisation of workshops.
Patient–clinician dynamics in remote consultations: a qualitative study of cardiology and rheumatology outpatient clinics in the UK
Authors: Grey Elisabeth , Brown Frankie , Smith Paula , Springett Daniella , Augustine Dan , Sengupta Raj , Peacock Oliver , Gillison Fiona 
Published: May 2023

ObjectiveExplore the experiences of patients and clinicians in rheumatology and cardiology outpatient clinics during the first year of the COVID-19 pandemic, focusing on the impact of remote consultations on interpersonal dynamics.DesignQualitative study using semistructured interviews, conducted between February and June 2021.SettingThe rheumatology and cardiology departments of a general hospital in England, UK.ParticipantsAll clinicians and a convenience sample of 100 patients in each department who had taken part in a remote consultation in the past month were invited to take part. Twenty-five interviews were conducted (13 with patients, 12 with clinicians).ResultsThree themes were developed through the analysis: adapting to the dynamics of remote consultations, impact on the patient’s experience and impact on the clinician’s experience. The majority of remote consultations experienced by both patients and clinicians had been via telephone. Both clinicians and patients found remote consultations to be more business-like and focused, with the absence of pauses restricting time for reflection. For patients with stable, well-managed conditions, remote consultations were felt to be appropriate and could be more convenient than in-person consultations. However, the loss of visual cues meant some patients felt they could not give a holistic view of their condition and limited clinicians’ ability to gather and convey information. Clinicians adjusted their approach by asking more questions, checking understanding more frequently and expressing empathy verbally, but felt patients still shared fewer concerns remotely than in person; a perception with which patients concurred.ConclusionsThese findings highlight the importance of ensuring, for each patient, that remote care is appropriate. Future research should focus on developing ways to support both clinicians and patients to gather and provide all information necessary during remote consultations, to enhance communication and trust.
Effectiveness of adding motivational interviewing or a stratified
vocational advice intervention to usual case management on return to work
for people with musculoskeletal disorders: the MI-NAV randomised controlled
trial
Authors: Aanesen Fiona , Grotle Margreth , Rysstad Tarjei Langseth , Tveter Anne Therese , Tingulstad Alexander , Løchting Ida , Småstuen Milada C , van Tulder Maurits W , Berg Rigmor , Foster Nadine E , Wynne-Jones Gwenllian , Sowden Gail , Fors Egil , Bagøien Gunnhild , Hagen Roger , Storheim Kjersti , Øiestad Britt Elin 
Published: May 2023

Objectives
To evaluate if adding motivational interviewing (MI) or a stratified
vocational advice intervention (SVAI) to usual case management (UC),
reduced sickness absence over 6 months for workers on sick leave due to
musculoskeletal disorders.

Methods
We conducted a three-arm parallel pragmatic randomised controlled
trial including 514 employed workers (57% women, median age 49 (range
24–66)), on sick leave for at least 50% of their contracted work hours
for ≥7 weeks. All participants received UC. In addition, those
randomised to UC+MI were offered two MI sessions from social insurance
caseworkers and those randomised to UC+SVAI were offered vocational
advice from physiotherapists (participants with low/medium-risk for
long-term sickness absence were offered one to two sessions, and those
with high-risk were offered three to four sessions).

Results
Median sickness absence was 62 days, (95% CI 52 to 71) in the UC arm
(n=171), 56 days (95% CI 43 to 70) in the UC+MI arm (n=169) and 49 days
(95% CI 38 to 60) in the UC+SVAI arm (n=169). After adjusting for
predefined potential confounding factors, the results showed seven fewer
days in the UC+MI arm (95% CI −15 to 2) and the UC+SVAI arm (95% CI −16
to 1), compared with the UC arm. The adjusted differences were not
statistically significant.

Conclusions
The MI-NAV trial did not show effect on return to work of adding MI
or SVAI to UC. The reduction in sickness absence over 6 months was
smaller than anticipated, and uncertain due to wide CIs.

Trial registration number
NCT03871712.
About this title - Celebrating 100 Years of Female Fellowship of the Geological Society: Discovering Forgotten Histories
Authors: Burek C. V. , Higgs B. M. 
Published: May 2023

The Geological Society of London was founded in 1807. At the time, membership was restricted to men, many of whom became well-known names in the history of the geological sciences. On the 21 May 1919, the first female Fellows were elected to the Society, 112 years after its formation.
This Special Publication celebrates the centenary of that important event. In doing so it presents the often untold stories of pioneering women geoscientists from across the world who navigated male-dominated academia and learned societies, experienced the harsh realities of Siberian field-exploration, or responded to the strategic necessity of the ‘petroleum girls’ in early American oil exploration and production.
It uncovers important female role models in the history of science, and investigates why not all of these women received due recognition from their contemporaries and peers. The work has identified a number of common issues that sometimes led to original work and personal achievements being lost or unacknowledged, and as a consequence, to histories being unwritten.
iFlipper: Label Flipping for Individual Fairness
Authors: Zhang Hantian , Tae Ki Hyun , Park Jaeyoung , Chu Xu , Whang Steven Euijong 
Published: May 2023

As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the well-known individual fairness, which states that similar individuals should be treated similarly. While individual fairness can be improved when training a model (in-processing), we contend that fixing the data before model training (pre-processing) is a more fundamental solution. In particular, we show that label flipping is an effective pre-processing technique for improving individual fairness.
Our system iFlipper solves the optimization problem of minimally flipping labels given a limit to the individual fairness violations, where a violation occurs when two similar examples in the training data have different labels. We first prove that the problem is NP-hard. We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips. We also propose techniques for making the linear programming solution more optimal without exceeding the violations limit. Experiments on real datasets show that iFlipper significantly outperforms other pre-processing baselines in terms of individual fairness and accuracy on unseen test sets. In addition, iFlipper can be combined with in-processing techniques for even better results.
ClipSim: A GPU-friendly Parallel Framework for Single-Source SimRank with Accuracy Guarantee
Authors: Wu Tianhao , Cheng Ji , Zhang Chaorui , Hou Jianfeng , Chen Gengjian , Huang Zhongyi , Zhang Weixi , Han Wei , Bai Bo 
Published: May 2023

SimRank is an important metric to measure the topological similarity between two nodes in a graph. In particular, single-source and top-k SimRank has numerous applications in recommendation systems, network analysis, and web mining, etc. Mathematically, given a vertex, the computation of single-machine and single-source SimRank mainly lies in matrix-matrix operations. However, it is almost impossible to directly compute on large graphs. Thus, existing works yield to two main operations: a series of random walks, and sparse matrix and dense vector multiplication operations. This brings about high computation cost for SimRank on large graphs. In real-world applications, there is always the query time and accuracy trade-off, which hinders the computation of high-precision SimRank on large-scale graphs.
To handle this problem, this paper proposesClipSim, the first GPU-friendly parallel framework that accelerates the single-source SimRank on GPU with accuracy guarantee. We design a novel data structure and GPU-friendly parallel algorithms for efficient computation of all the operations of SimRank on GPU. Moreover, our theoretical derivation enables ClipSim to largely reduce the number of random walks required for each node, while maintaining the same theoretical accuracy as the state-of-the-art algorithm, ExactSim. We conduct extensive experiments on real-world and synthetic datasets to demonstrate the accuracy and efficiency of ClipSim. The results show that compared with ExactSim, ClipSim obtains single-source SimRank vectors with the same accuracy and up to 160× faster computation time.
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
Authors: Kurmanji Meghdad , Triantafillou Peter 
Published: May 2023

Machine Learning (ML) is changing DBs as many DB components are being replaced by ML models. One open problem in this setting is how to update such ML models in the presence of data updates. We start this investigation focusing on data insertions (dominating updates in analytical DBs). We study how to update neural network (NN) models when new data follows a different distribution (a.k.a. it is "out-of-distribution" -- OOD), rendering previously-trained NNs inaccurate. A requirement in our problem setting is that learned DB components should ensure high accuracy for tasks on old and new data (e.g., for approximate query processing (AQP), cardinality estimation (CE), synthetic data generation (DG), etc.).
This paper proposes a novel updatability framework (DDUp). DDUp can provide updatability for different learned DB system components, even based on different NNs, without the high costs to retrain the NNs from scratch. DDUp entails two components: First, a novel, efficient, and principled statistical-testing approach to detect OOD data. Second, a novel model updating approach, grounded on the principles of transfer learning with knowledge distillation, to update learned models efficiently, while still ensuring high accuracy. We develop and showcase DDUp's applicability for three different learned DB components, AQP, CE, and DG, each employing a different type of NN. Detailed experimental evaluation using real and benchmark datasets for AQP, CE, and DG detail DDUp's performance advantages.
Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-Chief
Authors: Bornemann Leon , Bleifuß Tobias , Kalashnikov Dmitri V. , Nargesian Fatemeh , Naumann Felix , Srivastava Divesh 
Published: May 2023

We present role matching, a novel, fine-grained integrity constraint on temporal fact data, i.e., (subject, predicate, object, timestamp)-quadruples. A role is a combination of subject and predicate and can be associated with different objects as the real world evolves and the data changes over time. A role matching states that the associated object of two or more roles should always match across time. Once discovered, role matchings can serve as integrity constraints to improve data quality, for instance of structured data in Wikipedia[3]. If violated, role matchings can alert data owners or editors and thus allow them to correct the error. Finding all role matchings is challenging due both to the inherent quadratic complexity of the matching problem and the need to identify true matches based on the possibly short history of the facts observed so far.
To address the first challenge, we introduce several blocking methods both for clean and dirty input data. For the second challenge, the matching stage, we show how the entity resolution method Ditto[27] can be adapted to achieve satisfactory performance for the role matching task. We evaluate our method on datasets from Wikipedia infoboxes, showing that our blocking approaches can achieve 95% recall, while maintaining a reduction ratio of more than 99.99%, even in the presence of dirty data. In the matching stage, we achieve a macro F1-score of 89% on our datasets, using automatically generated labels.
INEv: In-Network Evaluation for Event Stream Processing
Authors: Akili Samira , Purtzel Steven , Weidlich Matthias 
Published: May 2023

Complex event processing (CEP) detects situations of interest by evaluating queries over event streams. Once CEP is used in networked applications, the distribution of query evaluation among the event sources enables performance optimization. Instead of collecting all events at one location for query evaluation, sub-queries are placed at network nodes to reduce the data transmission overhead. Yet, existing techniques either place such sub-queries at exactly one node in the network, which neglects the benefits of truly distributed evaluation, or are agnostic to the network structure, which ignores transmission costs due to the absence of direct network links.
To overcome the above limitations, we propose INEV graphs for in-network evaluation of CEP queries with rich semantics, including Kleene closure and negation. Our idea is to introduce fine-granular routing of partial results of sub-queries as an additional degree of freedom in query evaluation: We exploit events already disseminated in the network as part of one sub-query, when evaluating another one. We show how to instantiate INEv graphs by splitting a query workload into sub-queries, placing them at network nodes, and forwarding of their results to other nodes. Also, we characterize INEv graphs that guarantee correct and complete query evaluation, and discuss their construction based on a cost model that unifies transmission and processing latency. Our experimental results indicate that INEv graphs can reduce transmission costs for distributed CEP by up to eight orders of magnitude compared to baseline strategies.
Pea Hash: A Performant Extendible Adaptive Hashing Index
Authors: Liu Zhuoxuan , Chen Shimin 
Published: May 2023

Hashing index is widely used to support efficient point operations. We observe that there is a conflict between performance and memory utilization goals. Existing hashing indices often have to trade off hash table access latency for better memory utilization. Moreover, many designs support only unique keys, and their performance is often suboptimal with skew workloads.
In this paper, we propose Pea Hash with two techniques to address the above two problems: (i) adaptive hashing strategy that holistically optimizes both access latency and memory utilization, and (ii) data-aware adaptive buckets that accommodate unique keys, and keys with various numbers of duplicates. We develop both an NVM-optimized Pea Hash and a DRAM-based Pea Hash index. Experiments on a machine equipped with Intel Optane DC Persistent memory show that compared to state-of-the-art NVM-optimized hashing indices, the NVM-optimized Pea Hash achieves up to 13.8x performance improvements with similar memory utilization. The DRAM-based Pea Hash outperforms existing in-DRAM hashing index designs, showing the generality of the proposed techniques.
BLOOP: Boolean Satisfiability-based Optimized Loop Pipelining
Authors: Fiege Nicolai , Zipf Peter 
Published: May 2023

Modulo scheduling is the premier technique for throughput maximization of loops in high-level synthesis by interleaving consecutive loop iterations. The number of clock cycles between data insertions is called the initiation interval (II). For throughput maximization, this value should be as low as possible; therefore, its minimization is the main optimization goal.
Despite its long historical existence, modulo scheduling always remained a relevant research topic over the years with many exact and heuristic algorithms available in the literature.
Nevertheless, we are able to leverage the scalability of modern Boolean Satisfiability (SAT) solvers to outperform state-of-the-art ILP-based algorithms for latency-optimal modulo scheduling for both integer and rational IIs. Our algorithm is able to compute valid modulo schedules for the whole CHStone and MachSuite benchmark suites, with 99% of the solutions being proven to be throughput optimal for a timeout of only 10 minutes per candidate II. For various time limits, not a single tested scheduler from the state of the art is able to compute more verified optimal solutions or even a single schedule with a higher throughput than our proposed approach. Using an HLS toolflow, we show that our algorithm can be effectively used to generate Pareto-optimal FPGA implementations regarding throughput and resource usage.
Functional and Clinical Characterization of Variants of Uncertain Significance Identifies a Hotspot for Inactivating Missense Variants in RAD51C
Authors: Hu Chunling , Nagaraj Anil Belur , Shimelis Hermela , Montalban Gemma , Lee Kun Y. , Huang Huaizhi , Lumby Carolyn A. , Na Jie , Susswein Lisa R. , Roberts Maegan E. , Marshall Megan L. , Hiraki Susan , LaDuca Holly , Chao Elizabeth , Yussuf Amal , Pesaran Tina , Neuhausen Susan L. , Haiman Christopher A. , Kraft Peter , Lindstrom Sara , Palmer Julie R. , Teras Lauren R. , Vachon Celine M. , Yao Song , Ong Irene , Nathanson Katherine L. , Weitzel Jeffrey N. , Boddicker Nicholas , Gnanaolivu Rohan , Polley Eric C. , Mer Georges , Cui Gaofeng , Karam Rachid , Richardson Marcy E. , Domchek Susan M. , Yadav Siddhartha , Hruska Kathleen S. , Dolinsky Jill , Weroha S. John , Hart Steven N. , Simard Jacques , Masson Jean Yves , Pang Yuan-Ping , Couch Fergus J. 
Published: May 2023

Abstract

Pathogenic protein-truncating variants of RAD51C, which plays an integral role in promoting DNA damage repair, increase the risk of breast and ovarian cancer. A large number of RAD51C missense variants of uncertain significance (VUS) have been identified, but the effects of the majority of these variants on RAD51C function and cancer predisposition have not been established. Here, analysis of 173 missense variants by a homology-directed repair (HDR) assay in reconstituted RAD51C−/− cells identified 30 nonfunctional (deleterious) variants, including 18 in a hotspot within the ATP-binding region. The deleterious variants conferred sensitivity to cisplatin and olaparib and disrupted formation of RAD51C/XRCC3 and RAD51B/RAD51C/RAD51D/XRCC2 complexes. Computational analysis indicated the deleterious variant effects were consistent with structural effects on ATP-binding to RAD51C. A subset of the variants displayed similar effects on RAD51C activity in reconstituted human RAD51C-depleted cancer cells. Case–control association studies of deleterious variants in women with breast and ovarian cancer and noncancer controls showed associations with moderate breast cancer risk [OR, 3.92; 95% confidence interval (95% CI), 2.18–7.59] and high ovarian cancer risk (OR, 14.8; 95% CI, 7.71–30.36), similar to protein-truncating variants. This functional data supports the clinical classification of inactivating RAD51C missense variants as pathogenic or likely pathogenic, which may improve the clinical management of variant carriers.

Significance:
Functional analysis of the impact of a large number of missense variants on RAD51C function provides insight into RAD51C activity and information for classification of the cancer relevance of RAD51C variants.
Elevated Mast Cell Abundance Is Associated with Enrichment of CCR2+ Cytotoxic T Cells and Favorable Prognosis in Lung Adenocarcinoma
Authors: Fan Fanfan , Gao Jian , Zhao Yue , Wang Jun , Meng Lu , Ma Jiaqiang , Li Teng , Han Han , Lai Jinglei , Gao Zhendong , Li Xiongfei , Guo Ran , Cao Zhiwei , Zhang Yang , Zhang Xiaoming , Chen Haiquan 
Published: May 2023

Abstract

Mast cells constitute indispensable immunoregulatory sentinel cells in the tumor microenvironment. A better understanding of the regulation and functions of mast cells in lung adenocarcinoma (LUAD) could uncover therapeutic approaches to reprogram the immunosuppressive tumor microenvironment. Here, we performed flow cytometry and single-cell RNA sequencing (scRNA-seq) of patient LUAD samples to comprehensively characterize LUAD-infiltrating mast cells. Mast cells exhibited functional heterogeneity and were enriched in LUAD with ground-glass opacity features (gLUAD). The mast cells in gLUAD exhibited proinflammatory and chemotactic properties while those in radiologically solid LUAD (sLUAD) were associated with tumor angiogenesis. Mast cells were an important source of CCL2 and correlated with the recruitment of CCR2+ CTL, a specific subcluster of preexhausted T cells with tissue-resident memory phenotype and enhanced cytotoxicity. Increased infiltration of mast cells and CCR2+ CTLs and their colocalization showed a strong association with favorable prognosis after surgery but were not associated with improved survival after chemotherapy. Collectively, these findings reveal a key role of mast cells in LUAD and their potential cross-talk with CTLs, suggesting that targeting mast cells may be an immunotherapeutic strategy for LUAD.

Significance:
Comprehensive characterization of mast cells in lung adenocarcinoma elucidates their heterogeneity and identifies interplay between mast cells and CCR2+ T cells that is associated with a favorable prognosis.