bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neurodevelopmental Delay and Associated Factors among Preterm Infants Aged 6 to 24 months Adjusted Gestation Age in Two Urban Hospitals in Uganda
Authors: Nalwoga Joyce , Nakibuuka Victoria , Manirakiza Leonard , Namata Tracy Tushabe , Sebunya Robert 
Published: February 2025

ABSTRACTIntroductionNeurodevelopmental delay has been reported among preterm infants who survive beyond the neonatal period. However, there is paucity of data regarding neurodevelopmental outcomes in preterm infants in Africa, including Uganda.ObjectivesThis study aimed at determining the prevalence and factors associated with neurodevelopment delay (NDD) in preterm infants aged 6 to 24 months of adjusted gestation age.MethodsA cross-sectional study was conducted among 206 preterm infants, aged between 6 and 24 months of adjusted gestation age in the preterm follow up clinics at St. Francis Hospital Nsambya and Mulago Specialized Women and Neonatal Hospital in Kampala-Uganda from 1stJanuary 2021 to 1stJanuary 2022.ResultsThe prevalence of NDD was 13.6% (28/206), with social delay comprising 12.1% (25/206), fine motor delay 11.7% (24/206), language delay 10.7% (22/206) and gross motor delay 7.8% (16/206). Factors associated with NDD included microcephaly [aPR=6.2, CI: 2.6-33.5, P=0.0002], severe acute malnutrition (SAM) [aPR=4.6, CI: 1.87-12.56, P=0.021], incomplete immunization [aPR=2.8, CI: 1.23-4.76, P=0.013], neonatal sepsis [aPR=3.8, CI: 1.1-9.3, P=0.026], neonatal hypoglycemia [aPR=6.2, CI: 1.8-16.4, P=0.002], lack of caretaker social support [aPR=8.3, CI: 2.43-37.9, P=0.002] and families with more than 5 children [aPR=6.8, CI: 2.24-22.7, P=0.002]ConclusionsThe 13.6% prevalence of NDD is noteworthy, with the social and fine motor domains affected most. Modifiable factors like malnutrition, lack of caretaker social support and incomplete immunization should be screened and addressed to reduce NDD among preterm infants in Uganda.
Trends in the Distribution ofP Valuesin Epidemiology Journals: DecreasedPHacking or Increased Power?
Authors: Ackley Sarah F. , Andrews Ryan M. , Seaman Christopher , Flanders Michael , Chen Ruijia , Wang Jingxuan , Lopes Grissel , Sims Kendra D. , Buto Peter , Ferguson Erin , Allen Isabel Elaine , Glymour M. Maria 
Published: February 2025

AbstractEpidemiologists have advocated for reporting confidence intervals and deemphasizingPvalues to address long-standing concerns about null-hypothesis statistical-significance testing,Phacking, and reproducibility. It is unknown if efforts to reduce reliance onPvalues have altered the distribution ofPvalues. For 21,377 abstracts published 2000-2024 in 4 major epidemiology journals, two-sidedPvalues (N=25,360) were calculated from estimates and confidence intervals scraped using ChatGPT’s 4o model. We evaluated trends over time to determine whether the empirical distribution ofPvalues changed. We fitted to expectedPvalue distributions and simulated these distributions with and without assuming changes in statistical power over time. AveragePvalues decreased from 2000 to 2024, as did the fraction just below the 0.05 threshold. Fits to models indicate that statistical power increased. Increasing power would reduce averagePvalue while also decreasingPvalues near the 0.05 threshold—precisely the trends observed in epidemiology journals. Although the frequency ofPvalues near the 0.05 threshold has declined modestly, this likely reflects increases in statistical power rather than decreases inPhacking.
PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications
Authors: Kim Yeasul , Marić Ivana , Kashiwagi Chloe M. , Han Lichy , Chung Philip , Reiss Jonathan D , Butcher Lindsay D. , Caoili Kaitlin J. , Berson Eloïse , Xue Lei , Espinosa Camilo , James Tomin , Shome Sayane , Xie Feng , Ghanem Marc , Seong David , Chang Alan L , Reincke S Momsen , Mataraso Samson , Shu Chi-Hung , Francesco Davide De , Becker Martin , Kumar Wasan M , Wong Ron , Gaudilliere Brice , Angst Martin S , Shaw Gary M , Bateman Brian T , Stevenson David K , Prince Lance S , Aghaeepour Nima 
Published: February 2025

AbstractWhile medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects.Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet’s value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.
Unraveling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic Investigation of Biological Databases and Text Mining
Authors: Babaiha Negin Sadat , Geissler Stefan , Nibart Vincent , Güvenilir Heval Atas , Bharadhwaj Vinay Srinivas , Kodamullil Alpha Tom , Klein Juergen , Jacobs Marc , Hofmann-Apitius Martin 
Published: February 2025

AbstractThe COVID-19 pandemic has produced an overwhelming volume of research, yet much of it remains focused on individual diseases, largely ignoring the complex relationships between comorbidities. Although extensive literature exists on both neurodegenerative diseases (NDDs), namely Alzheimer’s and Parkinson’s, and COVID-19, their intersection remains underexplored. Co-morbidity modeling is essential, as patients, particularly those hospitalized, often present with multiple conditions. This study addresses this gap by investigating the crosstalk between COVID-19 and NDDs using a combination of knowledge graphs built from curated biomedical datasets and text mining tools. We conducted comprehensive graph analyses, including path analysis, phenotype coverage assessment, and mapping of cellular and genetic factors, to examine how various Knowledge Graphs (KGs)—such as PrimeKG, DrugBank, OpenTargets, and those generated from natural language processing (NLP) methodologies—illuminate molecular and phenotypic relationships between these diseases. Our findings reveal significant variability in graph density and connectivity across datasets, each offering unique insights into the landscape of COVID-19 and NDD co-morbidities. By integrating structured biological data with unstructured textual data, this study aimed to optimize co-morbidity modeling, maximize recall for identifying potential co-morbidity mechanisms, and consolidate this information in a dedicated co-morbidity hypothesis database. Key genetic and inflammatory markers, particularly immune response genes, emerged as consistent features across multiple KGs, reinforcing their potential role in COVID-19–NDD interactions. This integrative approach advances our understanding of the underlying mechanisms linking these diseases and facilitates the identification of potential therapeutic targets.All data, methodologies, and detailed instructions for accessing the co-morbidity hypothesis database are publicly available and thoroughly documented at:https://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP.
Comprehensive Metagenomic Profiling of Diverse Microbiomes
Authors: Kakuk Balázs , Dörmő Ákos , Taifi Ahmed , Járay Tamás , Kurucsai Gábor , Gulyás Gábor , Prazsák István , Boldogkői Zsolt , Tombácz Dóra 
Published: February 2025

AbstractThe canine gut microbiome serves as a key model for veterinary and human health research, but inconsistent findings arise due to methodological variations. This study presents a three-part dataset to clarify how DNA extraction, primer selection, and sequencing platforms influence microbial profiling. First, we performed ultra-deep sequencing of a single dog fecal sample using five DNA isolation kits, multiple library protocols, and four sequencing platforms (Illumina MiSeq/NovaSeq, ONT MinION, PacBio Sequel IIe), enabling direct comparisons of 16S rRNA and shotgun sequencing techniques. Second, we analyzed 40 fecal samples from eight co-housed dogs using Zymo High-Molecular-Weight (ZHMW) and Zymo MagBead (ZMB) extraction kits to assess longitudinal extraction-kit effects. Third, we evaluated three 16S primer systems (standard ONT, PacBio, and modified ONT with degenerate bases) using synthetic mock communities and human/canine fecal samples to quantify primer biases. By integrating synthetic and biological replicates, this dataset provides a standardized resource for benchmarking bioinformatics pipelines and improving cross-study comparability. The study generated 75.31GB of new sequencing data: 43.451GB from ZHMW-ZMB comparisons, 22.611GB for primer evaluations, and 9.191GB from the single-sample analysis. Combined with 31.51GB of prior data, the total dataset exceeds 1061GB, including all analytical outputs. These resources enhance methodological transparency and accuracy in canine gut microbiome research across diverse laboratory workflows.
From Archaea to the atmosphere: remotely sensing Arctic methane
Authors: Varner Ruth K. , Cronin Dylan R. , Crill Patrick , Palace Michael , McCalley Carmody K. , Deng Jia , Herrick Christina , Kuhn McKenzie , Hodgkins Suzanne B. , McArthur Kellen , Singer Jessica DelGreco , Bolduc Benjamin , Li Yueh-Fen ,   The Archaea to Atmosphere (A2A) Project Team,   EMERGE Institute Coordinators, Li Changsheng , Tyson Gene , Frolking Steve , Chanton Jeffrey P. , Persson Andreas , Saleska Scott R. , Rich Virginia I. 
Published: February 2025

AbstractGlobal atmospheric methane concentrations are rapidly rising and becoming isotopically more depleted, implying an unresolved microbial contribution. Rising Arctic temperatures are variably altering soil methane cycling, causing consequential uncertainty in the atmospheric methane budget. We demonstrated in an Arctic wetland that below-ground microbiota and methane-cycling features parallelled above-ground plant communities. To upscale emissions, we applied machine learning to remote sensing data to identify habitats, which were assigned average emissions. To upscale dynamically, we incorporated climate data, remotely-sensed water table variation, and habitat classes into a temporally-resolved biogeochemical model, to predict methane flux and isotope dynamics. This accurately estimated more depleted 13C-methane than previously used for Arctic habitats in global source partitioning. Remote-sensing of these rapidly changing inaccessible landscapes can thus help constrain the role of the Arctic in ongoing changes in global methane emissions.
Repurposing AZD-5991 for inhibiting growth and biofilm formation ofStaphylococcus aureusby disrupting the cell membrane and targeting FabI
Authors: Tang Yuanyuan , Deng Han , Xu Zhichao , Yu Zhijian , Xiang Yong , Wen Zewen , Han Shiqing , Chen Zhong , Hou Tieying 
Published: February 2025

AbstractStaphylococcus aureusinfections have already become a major threat to public health worldwide. Drug resistance and biofilm formation are two critical factors that significantly affect the efficacy of the antimicrobial treatment ofS. aureusinfections using conventional antibiotics. Therefore, the discovery of novel antimicrobial agents with potent antibacterial and antibiofilm activity has become a hotspot in recent years. Here, we first demonstrated the remarkable inhibitory activity of AZD-5991, a selective Mcl-1 inhibitor, againstS. aureus. The MIC50and MIC90of AZD-5991 againstS. aureuswere 12.5 μM, and AZD-5991 significantly inhibited the growth ofS. aureusat a subinhibitory concentration of 1/2 × MIC. Besides, AZD-5991 displayed bactericidal activity and a robust capacity for inhibiting biofilm formation againstS. aureuswith very low drug toxicity against host cell lines. Our data demonstrated the disruption ofS.aureuscell integrity by AZD-5991 through membrane permeability assays and the ingredients of the bacterial phospholipids could neutralize the antibacterial activity of AZD-5991. Moreover, whole-genome sequencing and proteomic analysis were also applied to gain insights into the possible impact of AZD-5991 on the fatty metabolism ofS. aureus. Furthermore, the antibacterial activity of AZD-5991 was significantly elevated by exogenous fatty acids linoleic acid (C18:2 Δ 9,12) and arachidonic acid (C20:4 Δ 5,8,11,14). Lastly, the biolayer interferometry assay supported the direct interaction of AZD-5991 with FabI, a necessary protein related closely to bacterial growth and fatty acid metabolism. Conclusively, this study suggests that AZD-5991 could inhibit the planktonic growth and biofilm formation ofS. aureusby disrupting the cell membrane and targeting FabI. AZD-5991 might be a promising new antibiotic candidate for the antimicrobial treatment ofS. aureusinfections resistant to traditional clinical drugs.Graphical Abstract
Aggregate sales decline and cost stickiness: a multinational study
Authors: Wang Weiwei , Zheng Kenneth 
Published: February 2025

PurposeThis study investigates the implications of country-level aggregate sales decline on firms’ cost stickiness and explores how legal systems and cultural dimensions moderate this relationship.Design/methodology/approachWe analyze an international sample of firms from 39 countries over the 2003–2022 period using regression analysis to examine the relationship between aggregate sales decline and firms’ cost behavior.FindingsOur findings reveal that country-level aggregate sales decline reduces firms’ cost stickiness, in contrast to the extensively documented positive association between firm-level sales decline and cost stickiness. This reduction in cost stickiness is statistically and economically significant, regardless of whether firms individually experience consecutive sales declines. Moreover, the impact is significantly stronger for firms in common-law countries, countries with long-term orientation and countries with higher levels of individualism.Originality/valueThe study is the first multinational study to examine the effect of aggregate sales decline on cost stickiness. The study also offers insights into the role of legal systems and cultural dimensions in moderating this relationship.
A novel self-adaptive multivariate grey model with external intervention for port cargo throughput prediction
Authors: Li Xuemei , Sun Yuyu , Shi Yansong , Zhao Yufeng , Zhou Shiwei 
Published: February 2025

PurposeAccurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.Design/methodology/approachThis paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.FindingsVerification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.Originality/valueA novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.
Rethinking process improvement: from diffusion to translation
Authors: Al Hasan Rima , Micheli Pietro 
Published: February 2025

PurposeDespite firms’ growing investments, process improvement (PI) programs often fail to deliver the expected benefits. In this paper, we argue that the widespread adoption in PI research of a paradigm founded in positivism plays a primary role in deriving these conclusions and limits the development of PI theory and practice.Design/methodology/approachWe examine the main characteristics of the dominant paradigm in PI research and then propose an alternative perspective drawing on research in management innovation and complexity. Findings from two empirical case studies in a pharmaceutical and an automotive firm are reported to support our theoretical argument.FindingsThe proposed perspective highlights the interaction between various PI approaches – such as lean, Six Sigma and total quality management (TQM) – and the context-dependence and experiential aspects of PI. We argue that this perspective can better account for where, by whom and how PI approaches are shaped and used and, ultimately, can more effectively advance both theory and practice.Originality/valueThis study suggests that PI approaches should be considered as adaptable rather than static, in combination rather than as single entities and as continuously interpreted and translated by organizational actors rather than homogeneously diffused across companies and business units. In this paper, we discuss the substantial conceptual, methodological and practical implications of adopting this perspective.
“How does workplace spirituality influence organizational citizenship behavior in the hospitality industry?”: a person-organization fit perspective
Authors: Srivastava Shalini , Dhamija Pavitra , Madan Poornima 
Published: February 2025

Purpose
Using the person-organization (P-O) fit perspective, the present study explores the interlinkages between workplace spirituality (WPS) and organizational citizenship behavior (OCB). It further attempts to understand the mediating effects of psychological ownership and innovative work behavior (IWB) for the association.

Design/methodology/approach
Data was collected in three waves from 283 frontline hotel employees in the Delhi NCR region of India. Partial least square (PLS-structural equation modeling) was used to test the hypothesized model.

Findings
A significant association was found between WPS and OCB. Psychological ownership and IWB had a significant serial mediation effect on WPS and OCB relationship.

Practical implications
The involvement of spirituality in the workplace is one of the significant factors contributing to positive organizational performance from the perspective of perishable services. Hence, understanding and implementing best practices to encourage WPS and strengthening psychological ownership for favorable behavioral outcomes must be one of the significant priorities for human resource managers in the hospitality industry.

Originality/value
WPS is an under-explored area in the hospitality industry. The present study will be novel and critical in bridging the research gap wherein psychological ownership and IWBs mediate the relationship between WPS and OCB in the hospitality sector. Furthermore, the present study notably contributes to using person organizational fit theory for the hypothesized relationships between study variables for the hospitality sector employees in India.
Commercial real property instalment land contracts
Authors: Larsen James E. , Akhbari Marlena L. , Morris Brandon C.L. 
Published: February 2025

PurposeThis paper reports the results of an investigation of the use of instalment land contracts (ILCs) in the Montgomery County, Ohio commercial real property market.Design/methodology/approachGeneralised spatial two-stage least squares regression is used to analyse data from 164 ILC financed commercial property transactions and 918 traditionally financed commercial property transactions, all of which were initiated between January 1, 2016 and December 31, 2023.FindingsIn our sample, the use of ILCs for the acquisition of commercial real estate is concentrated on relatively low-value properties. A bargaining power imbalance was discovered; the price ILC vendees promised to pay was more than buyers using traditional financing paid for similar property.Practical implicationsThe results suggest that some commercial property investors use ILCs because they cannot qualify for a loan from an institutional lender, and some may benefit from the additional regulations that ILC critics have proposed.Originality/valueThis is the first published empirical study of ILC use in a local commercial property market.
Mitigating bias in long‐term terrestrial ecoacoustic studies
Authors: Jarrett David , Barnett Ross , Bradfer‐Lawrence Tom , Froidevaux Jérémy S. P. , Gibb Kieran , Guinet Pauline , Greenhalgh Jack , Heath Becky , Johnston Alison , Monfort José Joaquín Lahoz , Rogers Alex , Willis Stephen G. , Metcalf Oliver 
Published: February 2025

Abstract

Long‐term biodiversity monitoring is needed to track progress towards ambitious global targets to reduce species loss and restore ecosystems. The recent development of cheap and robust acoustic recording devices offers a cost‐effective means of gathering standardised long‐term datasets.

Accounting for sources of bias in ecological monitoring and research is a fundamental part of the study design process. To highlight this issue in the context of long‐term terrestrial ecoacoustic monitoring, here we collate and discuss sources of bias arising from (i) hardware devices, (ii) firmware, software and analysis tools and (iii) the deployment environment.

One important source of bias is unavoidable changes in recording hardware—to demonstrate how this potentially introduces bias, we present two case studies comparing the output from simultaneous recordings from different recorders.

To mitigate biases, we recommend effective documentation of environmental and hardware‐related variables, as well as a long‐term data storage strategy that facilitates reanalysis. Additionally, the use of regular calibration tests to measure variation in the acoustic detection space will facilitate analytical approaches or post‐hoc AI solutions that remove unwanted biases.

Synthesis and applications: The sources of bias and suggested mitigations described here will be of relevance to hardware manufacturers, ecological researchers and conservation practitioners. Researchers and conservation practitioners must be fully aware of relevant biases when designing long‐term ecoacoustic studies and should incorporate appropriate mitigations into their study design.