bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Sleep, Anxiety, Mood, and Cognitive Performance of Oceanic Rocket League Esports Athletes Competing in a Multiday Regional Event
Authors: Bonnar Daniel , Gradisar Michael , Kahn Michal , Richardson Cele 
Published: March 2024

The overall aim of the present study was to examine the daily patterns and relationships between sleep behavior, anxiety, mood (i.e., depression symptoms), and cognitive performance (i.e., reaction time) in esports athletes competing in an Oceanic Rocket League Championship Series regional event. Sixteen participants completed a daily sleep diary, an evening anxiety measure, and an afternoon mood measure and cognitive performance task. Measures were taken (a) precompetition, (b) across the competition days, and (c) postcompetition. We found that participants’ lights-out time was earliest across the competition nights. Sleep-onset latency gradually lengthened precompetition and across the competition nights, eventually exceeding normal limits. Wake after sleep onset was longest across the competition nights but remained within normal limits. Wake-up time was earliest at the start of the competition period but consistently late on most other days. Total sleep time was generally adequate but mildly reduced the night before the first day of competition. There was no significant relationship between anxiety and subsequent sleep nor a relationship between total sleep time and next day mood or cognitive performance. Future research should investigate whether these findings generalize to esports athletes from other games, at higher levels of competition, and to different start times.
Quantitative Hormone Analysis Reveals Sources of Variability in the Menstrual Cycle
Authors: Francis Gavin , Keay Nicola 
Published: March 2024

Although the fluctuations of hormones over the menstrual cycle are well recognized, this study investigated the variability in these patterns for individual women. This study examined a set of daily blood hormone results from an underlying data set of previous research. Hormones included follicle-stimulating hormone (FSH), luteinizing hormone, progesterone, and estradiol taken over one menstrual cycle in 20 women of reproductive age (age 20–36 years), with ultrasound-confirmed ovulation. Although every woman’s profile of hormone changes was consistent with the expected physiological sequence of events, there was notable variability in the timing and peak levels. Variability in the length of the follicular phase was greater than in the length of the luteal phase, with the greater part of the variability in the cycle length being explained by variability in the follicular phase. Lower levels of FSH at the beginning of the cycle were associated with a longer follicular phase. Variability in the timing of events around ovulation was relatively consistent across all women. Variability in the length of the luteal phase was associated with the duration of elevated levels of progesterone. Differences were seen in the extent to which FSH increased at the end of the cycle. This study suggests the variability of the first part of the follicular phase may be due to initial FSH changes. Understanding the variability of menstrual cycle hormones is relevant for all women. Identifying subclinical ovulatory disturbances and variability is important to understand female health, across different populations including menstruating women and female athletes.
Characterization of the skin microbiome in normal and cutaneous squamous cell carcinoma affected cats and dogs
Authors: Bromfield Jacoba I. , Zaugg Julian , Straw Rodney C. , Cathie Julia , Krueger Annika , Sinha Debottam , Chandra Janin , Hugenholtz Philip , Frazer Ian H. 
Published: March 2024

ABSTRACT

Human cutaneous squamous cell carcinomas (SCCs) and actinic keratoses (AK) display microbial dysbiosis with an enrichment of staphylococcal species, which have been implicated in AK and SCC progression. SCCs are common in both felines and canines and are often diagnosed at late stages leading to high disease morbidity and mortality rates. Although recent studies support the involvement of the skin microbiome in AK and SCC progression in humans, there is no knowledge of this in companion animals. Here, we provide microbiome data for SCC in cats and dogs using culture-independent molecular profiling and show a significant decrease in microbial alpha diversity on SCC lesions compared to normal skin (
P ≤
0.05). Similar to human skin cancer, SCC samples had an elevated abundance of staphylococci relative to normal skin—50% (6/12) had >50% staphylococci, as did 16% (4/25) of perilesional samples. Analysis of
Staphylococcus
at the species level revealed an enrichment of the pathogenic species
Staphylococcus felis
in cat SCC samples, a higher prevalence of
Staphylococcus pseudintermedius
in dogs, and a higher abundance of
Staphylococcus aureus
compared to normal skin in both companion animals. Additionally, a comparison of previously published human SCC and perilesional samples against the present pet samples revealed that
Staphylococcus
was the most prevalent genera across human and companion animals for both sample types. Similarities between the microbial profile of human and cat/dog SCC lesions should facilitate future skin cancer research.

IMPORTANCE

The progression of precancerous actinic keratosis lesions (AK) to cutaneous squamous cell carcinoma (SCC) is poorly understood in humans and companion animals, despite causing a significant burden of disease. Recent studies have revealed that the microbiota may play a significant role in disease progression.
Staphylococcus aureus
has been found in high abundance on AK and SCC lesions, where it secretes DNA-damaging toxins, which could potentiate tumorigenesis. Currently, a suitable animal model to investigate this relationship is lacking. Thus, we examined the microbiome of cutaneous SCC in pets, revealing similarities to humans, with increased staphylococci and reduced commensals on SCC lesions and peri-lesional skin compared to normal skin. Two genera that were in abundance in SCC samples have also been found in human oral SCC lesions. These findings suggest the potential suitability of pets as a model for studying microbiome-related skin cancer progression.
Landscapes on the edge: River intermittency in a warming world
Authors: McLeod Jonah S. , Whittaker Alexander C. , Bell Rebecca E. , Hampson Gary J. , Watkins Stephen E. , Brooke Sam A.S. , Rezwan Nahin , Hook Joel , Zondervan Jesse R. , Ganti Vamsi , Lyster Sinéad J. 
Published: March 2024

Abstract
Sediment transport in rivers is not steady through time. Highly intermittent river systems, which only transport bedload during the most significant flow events, are particularly sensitive to changes in climate and precipitation patterns. People and landscapes can be vulnerable to fluvial processes, and quantifying river intermittency is critical for assessing landscape response to projected changes in precipitation extremes due to climate change. We generated new constraints on recent to modern fluvial intermittency factors—the frequency at which bedload is mobilized in a river—based on field measurements in the Corinth Rift, Greece, and Holocene sediment accumulation rates. Results reveal some of the lowest documented intermittency factors to date, showing Mediterranean rivers can transport an entire annual sediment load in a rare storm event. Coupling intermittency calculations with historical flood and precipitation data indicates these rivers transport bedload during one storm every ~4 yr, associated with rainfall &gt;50 mm/d, and subsequent floods; this hydroclimate is typical across the Mediterranean region. Furthermore, climate models predict precipitation extremes will increase across Europe, and the frequency of events that surpass thresholds of sediment transport will increase significantly, potentially causing sediment loads to double by 2100 CE. As the area of arid land likely to host intermittent rivers also increases, sensitive landscapes are on the edge of significant geomorphic change, driven by global warming.
Challenges and lessons learned from a long-term postauthorisation safety study programme of rivaroxaban in Europe
Authors: Brobert Gunnar , Ruigomez Ana , Schink Tania , Voss Annemarie , Herings Ron , Smits Elisabeth , Swart Karin M A , Friberg Leif , Balabanova Yanina , Tarenz Christine , Suzart-Woischnik Kiliana , Soriano-Gabarró Montse , Vora Pareen , Homering Martin , Dyszynski Tomasz , Nagel Gerd , Amaya Pablo , García Rodríguez Luis Alberto 
Published: March 2024

ObjectivesTo describe opportunities and challenges experienced from the four pharmacoepidemiological database studies included in the rivaroxaban post authorisation safety study (PASS) programme and propose ways to maximise the value of population-based observational research when addressing regulatory requirements.DesignPASS programme of rivaroxaban carried out as part of the regulatory postapproval commitment to the European Medicines Agency.SettingClinical practice in Germany, the Netherlands, Sweden and the UK (electronic health records)—undertaken by pharmacoepidemiology research teams using country-specific databases with different coding structures.Participants355 152 patients prescribed rivaroxaban and 338 199 patients prescribed vitamin K antagonists.ResultsTwo major challenges that were encountered throughout the lengthy PASS programme were related to: (1) finalising country-tailored study designs before the extent of rivaroxaban uptake was known, and (2) new research questions that arose during the programme (eg, those relating to an evolving prescribing landscape).RecommendationsWe advocate the following strategies to help address these major challenges (should they arise in any future PASS): conducting studies based on a common data model that enable the same analytical tools to be applied when using different databases; maintaining early, clear, continuous communication with the regulator (including discussing the potential benefit of studying drug use as a precursor to planning a safety study); consideration of adaptive designs whenever uncertainty exists and following an initial period of data collection; and setting milestones for the review of study objectives.
Exposure to famine during early life and the risk of MAFLD during adulthood: evidence from the China Multi-Ethnic Cohort (CMEC) study
Authors: Yin Jianzhong , Zhang Nichang , Feng Yuemei , Meng Qiong , Zhang Teng , Hong Rudan , Zhang Xuehui 
Published: March 2024

BackgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease, affecting nearly one-third of the global population. The relationship between early-life famine exposure and MAFLD remains unclear in the multiethnic region of less-developed southwest China.MethodsA total of 18 558 participants who came from the baseline survey of the China Multi-Ethnic Cohort Study in Yunnan were included. Participants were divided into four groups according to birth year, including non-exposed (1962–1978 and 1939–1943), fetal exposed (1959–1961), childhood exposed (1949–1958) and adolescence exposed (1943–1949). Logistic regression analysis was used to explore the relationship between famine exposure in early life and the risk of MAFLD in adulthood.ResultsExperiencing the shock of early-life exposure to famine would affect adulthood MAFLD. Exposure to famine during fetal life and childhood increased the risk of MAFLD in adulthood, with this association being particularly pronounced in Bai populations. Moreover, famine exposure in males during fetal life raised the risk of MAFLD in adulthood.ConclusionWe suggest that adequate nutrition in early life may be beneficial in preventing MAFLD in adulthood. The prevention of chronic liver disease should adopt a whole-life strategy by extending the prevention window beginning from fetal life.
Evaluating the implementation of the Primary Health Integrated Care Project for Chronic Conditions: a cohort study from Kenya
Authors: Mugo Richard , Pliakas Triantafyllos , Kamano Jemima , Sanga Leah Anku , Nolte Ellen , Gasparrini Antonio , Barasa Edwine , Etyang Anthony , Perel Pablo 
Published: March 2024

IntroductionIn Kenya, non-communicable diseases (NCDs) are estimated to account for almost one-third of all deaths and this is likely to rise by over 50% in the next 10 years. The Primary Health Integrated Care for Chronic Conditions (PIC4C) project aims to strengthen primary care by integrating comprehensive NCD care into existing HIV primary care platform. This paper evaluates the association of PIC4C implementation on clinical outcomes.MethodsOutcomes included proportion of new patients, systolic blood pressure (SBP), fasting plasma glucose (FPG), diastolic blood pressure, hypertension control, random plasma glucose, diabetes control, viral load and HIV viral suppression. We used interrupted time series and binomial regression with random effects for facility-level data and generalised mixed-effects regression for visit-level data to examine the association between PIC4C and outcomes between January 2017 and December 2021. We conducted sensitivity analysis with restrictions on sites and the number of visits.ResultsData from 66 641 visits of 13 046 patients with hypertension, 24 005 visits of 7267 patients with diabetes and 84 855 visits of 21 186 people with HIV were analysed. We found evidence of association between PIC4C and increase in proportion of new patients per month with hypertension (adjusted OR (aOR) 1.57, 95% CI 1.39 to 1.78) and diabetes (aOR 1.31, 95% CI 1.19 to 1.45), small increase in SBP (adjusted beta (aB) 1.7, 95% CI 0.8 to 2.7) and FPG (aB 0.6, 95% CI 0.0 to 1.1). There was no strong evidence of association between PIC4C and viral suppression (aOR 1.20, 95% CI 0.98 to 1.47). In sensitivity analysis, there was no strong evidence of association between PIC4C and SBP (aB 1.74, 95% CI −0.70 to 4.17) or FPG (aB 0.52, 95% CI −0.64 to 1.67)ConclusionsPIC4C implementation was associated with increase in proportion of new patients attending clinics and a slight increase in SBP and FPG. The immediate post-PIC4C implementation period coincided with the COVID-19 pandemic, which is likely to explain some of our findings.
Application of bedside HINTS, ABCD2score and truncal ataxia to differentiate cerebellar–brainstem stroke from vestibular neuritis in the emergency room
Authors: Liu Xinmin , Li Zhaoxia , Ju Yi , Zhao Xingquan 
Published: March 2024

Background and purposeAcute vestibular syndrome (AVS) typically manifests as isolated dizziness or vertigo with no apparent neurological impairments. However, distinguishing life-threatening stroke from innocuous peripheral vestibular lesions in the emergency room (ER) remains challenging. This study aimed to explore the ability of the head impulse-nystagmus-test of skew (HINTS) combined with truncal ataxia or ABCD2score to differentiate stroke from peripheral vestibular disease in patients with AVS in the ER.MethodsWe prospectively recruited 121 patients with AVS from December 2022 to June 2023, 69 of whom presented with vestibular neuritis (VN) and the remaining with posterior circulation stroke (PCS). We analysed the HINTS results, truncal ataxia and ABCD2score and compared the sensitivity and specificity among HINTS, truncal ataxia, ABCD2score and their combinations using the McNemar test for paired samples.ResultsHINTS combined with grade 2-3 truncal ataxia achieved significantly higher sensitivity than that of isolated HINTS in differentiating PCS from VN (100% vs 88.5%, p=0.031). The specificity of HINTS plus grade 2-3 truncal ataxia did not significantly differ from that of isolated HINTS (p=0.125); however, the combination of ABCD2score and HINTS did not improve the diagnostic accuracy. The sensitivity of ABCD2score ≥4 plus grade 2-3 truncal ataxia was significantly higher than those of isolated ABCD2score ≥4 or isolated grade 2-3 truncal ataxia (p=0.016 and p<0.001, respectively) and not significantly lower than that of isolated HINTS (p=0.508).ConclusionCompared with the ABCD2score, the truncal ataxia is of more valuable assistance to HINTS in differentiating PCS. Although the combination of ABCD2score and truncal ataxia has a significant implication, it is not a replacement for HINTS.
Unveiling public perception of AI ethics: an exploration on Wikipedia data
Authors: Wei Mengyi , Feng Yu , Chen Chuan , Luo Peng , Zuo Chenyu , Meng Liqiu 
Published: March 2024

AbstractArtificial Intelligence (AI) technologies have exposed more and more ethical issues while providing services to people. It is challenging for people to realize the occurrence of AI ethical issues in most cases. The lower the public awareness, the more difficult it is to address AI ethical issues. Many previous studies have explored public reactions and opinions on AI ethical issues through questionnaires and social media platforms like Twitter. However, these approaches primarily focus on categorizing popular topics and sentiments, overlooking the public’s potential lack of knowledge underlying these issues. Few studies revealed the holistic knowledge structure of AI ethical topics and the relations among the subtopics. As the world’s largest online encyclopedia, Wikipedia encourages people to jointly contribute and share their knowledge by adding new topics and following a well-accepted hierarchical structure. Through public viewing and editing, Wikipedia serves as a proxy for knowledge transmission. This study aims to analyze how the public comprehend the body of knowledge of AI ethics. We adopted the community detection approach to identify the hierarchical community of the AI ethical topics, and further extracted the AI ethics-related entities, which are proper nouns, organizations, and persons. The findings reveal that the primary topics at the top-level community, most pertinent to AI ethics, predominantly revolve around knowledge-based and ethical issues. Examples include transitions from Information Theory to Internet Copyright Infringement. In summary, this study contributes to three points, (1) to present the holistic knowledge structure of AI ethics, (2) to evaluate and improve the existing body of knowledge of AI ethics, (3) to enhance public perception of AI ethics to mitigate the risks associated with AI technologies.
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting
Authors: Li Yingze , Wang Hongzhi , Liu Xianglong 
Published: March 2024

The modern database has many precise and approximate counting requirements. Nevertheless, a solitary multidimensional index or cardinality estimator is insufficient to cater to the escalating demands across all counting scenarios. Such approaches are constrained either by query selectivity or by the compromise between query accuracy and efficiency.
We propose CardIndex, a unified learned structure to solve the above problems. CardIndex serves as a versatile solution that not only functions as a multidimensional learned index for accurate counting but also doubles as an adaptive cardinality estimator, catering to varying counting scenarios with diverse requirements for precision and efficiency. Rigorous experimentation has showcased its superiority. Compared to the state-of-the-art (SOTA) autoregressive data-driven cardinality estimation baselines, our structure achieves training and updating times that are two orders of magnitude faster. Additionally, our CPU-based query estimation latency surpasses GPU-based baselines by two to three times. Notably, the estimation accuracy of low-selectivity queries is up to 314 times better than the current SOTA estimator. In terms of indexing tasks, the construction speed of our structure is two orders of magnitude faster than RSMI and 1.9 times faster than R-tree. Furthermore, it exhibits a point query processing speed that is 3%-17% times faster than RSMI and 1.07 to 2.75 times faster than R-tree and KDB-tree. Range queries under specific loads are 20% times faster than the SOTA indexes.
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models
Authors: Dargahi Nobari Arash , Rafiei Davood 
Published: March 2024

Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are required, or the mappings go beyond simple textual transformations.
In this paper, we study the potentials of deep neural models for transforming tables for joinability. In particular, we cast the problem as a prediction task and develop a framework that leverages large deep-learning language models to transform tabular data from a source formatting to a desired target representation. Our framework can efficiently learn the patterns for mapping a source formatting into an expected target using just a few examples, which can then be used for tasks such as table joining, filling in missing values, and error detection. Compared to state-of-the-art mapping and joining approaches, our framework delivers noticeably more accurate and scalable performance on both real-world and synthetic datasets. Our experimental evaluation also shows that the performance of the proposed framework using our fine-tuned model is at par or better than large language models such as GPT-3, despite the significant difference in size, and that using large language models within our framework improves their performance.
Towards Buffer Management with Tiered Main Memory
Authors: Hao Xiangpeng , Zhou Xinjing , Yu Xiangyao , Stonebraker Michael 
Published: March 2024

The scaling of per-GB DRAM cost has slowed down in recent years. Recent research has suggested that adding remote memory to a system can further reduce the overall memory cost while maintaining good performance. Remote memory (i.e., tiered memory), connected to host servers via high-speed interconnect protocols such as RDMA and CXL, is expected to deliver 100x (less than 1µs) lower latency than SSD and be more cost-effective than local DRAM through pooling or adopting cheaper memory technologies.Tiered memory opens up a large number of potential use cases within database systems. But previous work has only explored limited ways of using tiered memory. Our study provides a systematic study for DBMS to build tiered memory buffer management with respect to a wide range of hardware performance characteristics. Specifically, we study five different indexing designs that leverage remote memory in different ways and evaluate them through a wide range of metrics including performance, tiered-memory latency sensitivity, and cost-effectiveness. In addition, we propose a new memory provisioning strategy that allocates an optimal amount of local and remote memory for a given workload. Our evaluations show that while some designs achieve higher performance than others, no design can win in all measured dimensions.