bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Intelligent Optimized Compression Framework for Columnar Database
Authors: Jadhawar B. A. , Sharma Narendra 
Published: January 2025

Instead of storing data in rows, a columnar database is a type of Database Management System (DBMS). To speed up the processing and reply to a question, a columnar database’s job is to efficiently write and read data to and from hard disc storage. One of the most crucial methods in the creation of column-oriented database systems is compression. For columns with Zero-length string types, all heavier and light-in-weight compression techniques have limitations. Processing of transactions online, these databases are substantially more effective for online analytical processing than for online transactional processing. This indicates that although they are made to examine transactions, they are not very effective at updating them. To overcome these issues a Zero Length Recurrent based Fruit Fly Optimization (ZLRFF) model is used. Additionally, a reduction technique is known as ZLRFF was designed to achieve a high compression ratio and allow straight lookups on compressed material without decompression first. ZLRFF’s main goal is to divide a Zero-length string written column vertically into smaller columns that can each be compressed using a separate lightweight compression technique. To search directly on compressed data, we also provide a search technique we call FF-search. Extensive testing demonstrates that ZLRFF supports direct searching on compressed data in addition to achieving a decent compression ratio, which enhances query performance.
ICE: Incremental Subspace Clustering of High-Dimensional Categorical Data
Authors: Pang Ning , Zhang Chaowei , Zhang Jifu , Qin Xiao 
Published: January 2025

Subspace clustering is an effective way to analyze high-dimensional data. The main problems of the conventional subspace clustering techniques are as follows: first, conventional clustering methods can not describe categorical attribute space in more detail; second, most subspace-clustering techniques failure to process dynamic data effectively; finally, lack of effective noise recognition leads to the decline of the efficiency of incremental subspace-clustering analysis. We address the above problems by an incremental subspace-clustering algorithm — called ICE. With attribute subspace constructed by a rough set-based weight computing method, ICE obtains clustering results through initial and incremental clustering stage. Utilizing the original cluster results generated from initial clustering stage, we adopt merging and splitting operation to dynamic adjust cluster-structure in incremental clustering stage. Before achieving the final results, a polymerization-based noise recognition technique is employed to automatically identify noise from sparse clusters without human threshold intervention. We implement ICE on synthetic and real-world datasets. The experimental results reveal that incremental subspace-clustering method can achieves satisfactory performance on extensibility, accuracy and robustness.
Hospital Variability in the Use of Vasoactive Agents in Patients Hospitalized for Acute Decompensated Heart Failure for Clinical Phenotypes
Authors: Shiraishi Yasuyuki , Niimi Nozomi , Kohsaka Shun , Harada Kazumasa , Kohno Takashi , Takei Makoto , Jimba Takahiro , Nakano Hiroki , Matsuda Junya , Shindo Akito , Kitano Daisuke , Tsukamoto Shigeto , Koba Shinji , Yamamoto Takeshi , Takayama Morimasa 
Published: January 2025

BACKGROUND:
The absence of practice standards in vasoactive agent usage for acute decompensated heart failure has resulted in significant treatment variability across hospitals, potentially affecting patient outcomes. This study aimed to assess temporal trends and institutional differences in vasodilator and inotrope/vasopressor utilization among patients with acute decompensated heart failure, considering their clinical phenotypes.

METHODS:
Data were extracted from a government-funded multicenter registry covering the Tokyo metropolitan area, comprising consecutive patients hospitalized in intensive/cardiovascular care units with a primary diagnosis of acute decompensated heart failure between January 2013 and December 2021. Clinical phenotypes, that is, pulmonary congestion or tissue hypoperfusion, were defined through a comprehensive assessment of clinical signs and symptoms, vital signs, and laboratory findings. We assessed the frequency and temporal trends in phenotype-based drug utilization of vasoactive agents and investigated institutional characteristics associated with adopting the phenotype-based approach using generalized linear mixed-effects models, with random intercepts to account for hospital-level variability.

RESULTS:

Among 37 293 patients (median age, 80 years; 43.7% female), 88.6% and 21.2% had pulmonary congestion and tissue hypoperfusion status, respectively. Throughout the study period, both overall and phenotype-based vasodilator utilizations showed significant declines, with overall usage dropping from 61.4% in 2013 to 48.6% in 2021 (
P
trend
<0.001). Conversely, no temporal changes were observed in overall inotrope/vasopressor utilization from 24.6% in 2013 to 25.8% in 2021 or the proportion of phenotype-based utilization. Notably, there was considerable variability in phenotype-based drug utilization among hospitals, with a median ranging from 48.3% to 77.8%. In multivariable-adjusted models, a higher number of board-certified cardiologists were significantly associated with lower rates of phenotype-based vasodilator utilization and reduced inappropriate inotrope/vasopressor utilization, while tertiary care hospitals were linked to more appropriate inotrope/vasopressor utilization.

CONCLUSIONS:
Substantial variability existed among hospitals in phenotype-based drug utilization of vasoactive agents for patients with acute decompensated heart failure, highlighting the need for standardized treatment protocols.

REGISTRATION:

URL:
https://www.umin.ac.jp/ctr/index.htm
; Unique identifier: UMIN000013128.
Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations
Authors: Silver Max M. , Lundquist Julie K. 
Published: January 2025

Abstract
Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

Significance Statement
The purpose of this study is to understand if the occurrences of fire weather are changing in Colorado. Using a 22-yr observational dataset representative of one location, we fail to find a significant trend in fire weather occurrences over this period. However, when we examine a reanalysis dataset for the same time period and for a longer 67-yr time period, fire weather occurrences seem to be increasing. We find that reanalysis often underestimates wind speed when compared to observations. The coarser spatial and temporal resolution of reanalysis data do not fully explain the bias. This conflict between observations and reanalysis data suggests that more long-term observational datasets are required to accurately assess fire risk.
Healthcare Resource Utilization Among Patients With Generalized Pustular Psoriasis: The Impact of Flares and Disease Severity
Authors: Noe Megan H. , Lavasani Layla , Rasouliyan Lawrence , Laouri Marianne , Rhoads Jamie L. W. 
Published: January 2025

Background Generalized pustular psoriasis (GPP) is a rare, chronic, often unpredictable, severe multisystemic autoinflammatory skin disease from which patients can experience flares, episodes of widespread eruptions of painful, sterile pustules often accompanied by systemic symptoms. The impact of GPP flares and underlying GPP severity on the healthcare resource utilization (HCRU) is not well characterized. Objective To quantify HCRU among US GPP patients by flare status and underlying severity. Methods Outpatient electronic health record (EHR) data (2017-2023) from the OMNY Health platform were linked with claims. Patients were indexed at first EHR GPP diagnosis code and followed for 1 year. GPP flares were identified from a previously developed algorithm. All-cause hospitalizations, emergency department/urgent care (ED/UC), and outpatient visits were summarized by flare status and underlying severity. Pharmacy and total gross charges were described by number of flares. Results A total of 335 patients were included. Patients who flared in the follow-up period (n = 205) had more hospitalizations than patients who did not flare (n = 130; 12.2% vs 6.9%; mean: 0.26 vs 0.09). ED/UC visits were similar between groups (22.9% vs 27.7%; mean: 0.54 vs 0.45), while outpatient visits were greater among patients who did not flare (69.8% vs 78.5%; mean: 5.37 vs 6.56). For patients with 0, 1, and ≥2 flares with HCRU, mean pharmacy charges ($19,887, $25,180, and $57,674, respectively) and total gross charges ($29,196, $40,079, and $52,940, respectively) increased monotonically. Conclusion GPP patients who flared and had more severe disease had greater HCRU and charges.
Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data
Authors: Fries Anya H. , Choi Eunji , Han Summer S. 
Published: January 2025

Abstract

Background
To effectively monitor long-term outcomes among cancer patients, it is critical to accurately assess patients’ dynamic prognosis, which often involves utilizing multiple data sources (e.g., tumor registries, treatment histories, and patient-reported outcomes). However, challenges arise in selecting features to predict patient outcomes from high-dimensional data, aligning longitudinal measurements from multiple sources, and evaluating dynamic model performance.

Methods
We provide a framework for dynamic risk prediction using the penalized landmark supermodel (penLM) and develop novel metrics (
$$\:\overline{AUC}_{w}\:$$
and
$$\:\overline{BS}_{w}\:$$
) to evaluate and summarize model performance across different timepoints. Through simulations, we assess the coverage of the proposed metrics’ confidence intervals under various scenarios. We applied penLM to predict the updated 5-year risk of lung cancer mortality at diagnosis and for subsequent years by combining data from SEER registries (2007–2018), Medicare claims (2007–2018), Medicare Health Outcome Survey (2006–2018), and U.S. Census (1990–2010).

Results
The simulations confirmed valid coverage (~ 95%) of the confidence intervals of the proposed summary metrics. Of 4,670 lung cancer patients, 41.5% died from lung cancer. Using penLM, the key features to predict lung cancer mortality included long-term lung cancer treatments, minority races, regions with low education attainment or racial segregation, and various patient-reported outcomes beyond cancer staging and tumor characteristics. When evaluated using the proposed metrics, the penLM model developed using multi-source data (
$$\:\overline{AUC}_{w}\:$$
of 0.77 [95% confidence interval: 0.74–0.79]) outperformed those developed using single-source data (
$$\:\overline{AUC}_{w}\:$$
range: 0.50–0.74).

Conclusions
The proposed penLM framework with novel evaluation metrics offers effective dynamic risk prediction when leveraging high-dimensional multi-source longitudinal data.
Application of GUHA data mining method in cohort data to explore paths associated with premature death: a 29-year follow-up study
Authors: Nosraty Lily , Turunen Esko , Kyrönlahti Saila , Nygård Clas-Håkan , KC Prakash , Neupane Subas 
Published: January 2025

Abstract

Background and Method
This study set out to identify the factors and combinations of factors associated with the individual’s premature death, using data from the Finnish Longitudinal Study on Ageing Municipal Employees (FLAME) which involved 6,257 participants over a 29-year follow-up period. Exact dates of death were obtained from the Finnish population register. Premature death was defined as a death occurring earlier than the age- and sex-specific actuarial life expectancy indicated by life tables for 1981, as the baseline, with the threshold period of nine months. Explanatory variables encompassed sociodemographic characteristics, health and functioning, health behaviors, subjective experiences, working conditions, and work abilities. Data were mined using the General Unary Hypothesis Automaton (GUHA) method, implemented with LISp-Miner software. GUHA involves an active dialogue between the user and the LISp-Miner software, with parameters tailored to the data and user interests. The parameters used are not absolute but depend on the data to be mined and the user’s interests.

Results
Over the follow-up period, 2,196 deaths were recorded, of which 70.4% were premature. Seven single factors and 67 sets of criteria (paths) were statistically significantly associated with premature mortality, passing the one-sided Fisher test. Single predicates of premature death included smoking, consuming alcohol a few times a month or once a week, poor self-rated fitness, incompetence to work and poor assured workability in two years’ time, and diseases causing work disability. Notably, most of the factors selected as single predicates of premature mortality did not appear in the multi-predicate paths. Factors appearing in the paths were smoking more than 20 cigarettes a day, symptoms that impaired functioning, past smoking, absence of musculoskeletal diseases, poor self-rated health, having pain, male sex, being married, use of medication, more physical strain compared to others, and high life satisfaction, intention to retire due to reduced work ability caused by diseases and demanding work. Sex-specific analysis revealed similar findings.

Conclusion
The findings indicate that associations between single predictors and premature mortality should be interpreted with caution, even when adjusted for a limited number of other factors. This highlights the complexity of premature mortality and the need for comprehensive models considering multiple interacting factors.
The effect of transcranial direct current stimulation and inhibitory control training on depression and anxiety among post-stroke individuals
Authors: Kazinczi Csaba , Szepfalusi Noemi , Nemeth Viola Luca , Holczer Adrienn , Jakab Katalin , Vecsei Laszlo , Klivenyi Peter , Must Anita , Racsmany Mihaly 
Published: January 2025

Abstract

Background
Recent research has highlighted the role of fronto-parietal brain networks and cognitive control in mood disorders. Transcranial direct current stimulation (tDCS) and computer-based cognitive training are used in post-stroke rehabilitation. This study examined the combined effects ofof computer-based inhibitory control training (ICCT) and anodal tDCS on post-stroke depression and anxiety.

Methods
Thirty-five participants were randomly assigned to one of three groups: active tDCS treatment (A), sham tDCS treatment with ICCT (T), or active tDCS with ICCT (AT), for a duration of ten days. Primary outcome measures included the Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HAM-D), and Spielberger’s State-Trait Anxiety Inventory (STAI-S/T). Statistical analysis was performed using a Mixed-model Analysis of Variance, with supplementary Bayesian analysis.

Results
The AT group showed a significant improvement in BDI scores (p < .001), whereas no significant effects were observed on the HAM-D, STAI-T, or STAI-S scales.

Conclusions
The combination of tDCS and ICCT reduced depressive symptoms as measured by the BDI; while no significant effects were found with either treatment alone. Further research is needed to explore the mechanisms behind the synergistic effects in the treatment of post-stroke mood disorders.
The association of travel distance and other patient characteristics with breast cancer stage at diagnosis and treatment completion at a rural Rwandan cancer facility
Authors: Bhangdia Kayleigh , Natarajan Abirami , Rudolfson Niclas , Verguet Stéphane , Castro Marcia C. , Dusengimana Jean-Marie Vianney , Shyirambere Cyprien , Schleimer Lauren E. , Shulman Lawrence N. , Umwizerwa Aline , Kigonya Catherine , Butonzi John , MacDuffie Emily , Fadelu Temidayo , O’Neil Daniel S. , Nguyen Cam , Mpunga Tharcisse , Keating Nancy L. , Pace Lydia E. 
Published: January 2025

Abstract

Background
Butaro Cancer Center of Excellence (BCCOE) was founded to serve Rwanda’s rural low-income population, providing subsidized cancer diagnosis and treatment with transport stipends for the lowest-income patients. We examined whether travel distance to BCCOE was associated with advanced-stage diagnoses and treatment completion.

Methods
We conducted a retrospective cohort study using medical record data from BCCOE patients with pathologically-confirmed breast cancer from 2012–2016. Women with no prior surgery were included in the stage analysis; those with non-metastatic disease were included in the treatment analysis. We calculated travel distances using spatial analytic software and used multivariable logistic regression to examine the association of distance and other patient characteristics with late-stage diagnoses and treatment completion within one year of diagnosis.

Results
The analytic cohort for stage included 426 patients; 75.1% had late-stage (stage 3 or 4) disease. In univariable analyses, patients residing in BCCOE’s surrounding district had a lower proportion of late-stage diagnoses compared to those residing outside the district (57.9% v 76.8%, p = 0.02). In adjusted analyses, odds of late-stage diagnosis were 2.46 (95% CI:1.21–5.12) times higher among those in distance quartile 4 (> 135.8 km) versus 1 (< 55.7 km); the effect of distance was less strong in sensitivity analyses excluding patients from BCCOE’s surrounding district. Patients from sectors with > 50% poverty had 2.33 times higher odds of late-stage diagnoses (95% CI:1.07–5.26) relative to those with poverty < 30%. In the treatment completion cohort (n = 348), 49.1% of patients completed surgery and chemotherapy within a year. In adjusted analyses, travel distance and poverty were not linearly associated with treatment completion.

Conclusions
At Rwanda’s first public cancer facility, sector-level poverty and longer travel distances were associated with late-stage breast cancer diagnoses, but less clearly associated with treatment completion, perhaps partly due to travel stipends provided to the lowest-income individuals undergoing treatment. Our findings support further investigation into wider use of travel stipends to facilitate early diagnosis and treatment completion.
Contribution of vitamin B 6 deficiency to anemia in children on regular hemodialysis
Authors: Atia Fatma , Bazaraa Hafez , Abdelatty Yara , Lotfy Sarah , Eryan Eman 
Published: January 2025

Abstract

Background
Anemia is prevalent among pediatric patients diagnosed with end-stage kidney disease (ESKD). In addition, erythropoiesis-stimulating agents (ESA) and iron supplementation are considered the cornerstones in the management of anemia. However, a significant proportion of patients remain anemic. Vitamin B6 plays a vital role in the biosynthesis of heme and may be deficient in children with chronic kidney disease (CKD), particularly those on regular hemodialysis (HD). We aimed to evaluate serum vitamin B6 concentration in those children and determine its correlation with anemia indices.

Methods
The current cross-sectional study included 39 children on regular HD and 43 healthy controls. Clinical data were collected, including anthropometric measurements, blood pressure, iron, and erythropoietin therapy. Laboratory investigations included hemoglobin (Hb) indices, iron profile, and vitamin B6 level.

Results
The median Hb level in our cohort was 10.5 g/dL, and nine patients had Hb levels at or above the target Hb of 11 g/dl. They had a median Serum Vitamin B6 concentration of 28.2 ng/ml (IQR = 22.8–52.9) compared to a median of 27.5 ng/ml(IQR = 20–34) in controls, with no substantial differences between both groups. There was no statistically significant correlation between vitamin B6 and Hb levels or erythropoietin dose.

Conclusion
It is evident that the majority of our patients did not meet the desired threshold for anemia control. However, it is noteworthy that the average hemoglobin (Hb) level approached the intended target. The incidence of Vitamin B6 deficiency was not found to be statistically significant within our study population. Therefore, we could not establish a correlation between vitamin B6 deficiency and anemia in children on HD.
Association of LPCAT1-rs8352 genetic variant with susceptibility and severity of pediatric bronchial asthma: a case-control study
Authors: Mohany Khalid M. , Gamal Yasser , Raheem Yaser F. Abdel 
Published: January 2025

Abstract

Background
This study aimed to investigate the possible association of LPCAT1-rs8352 genetic variant (single nucleotide change C to G) with the onset and severity of pediatric asthma. Additionally, the study examined the influence of LPCAT1-rs8352 genotypes on asthma-related biomarkers including blood eosinophils count (BEC), eosinophil cationic protein (ECP), high-sensitivity C-reactive protein (hs-CRP), and immunoglobulin E (IgE) and on lung function [forced expiratory volume in one second (FEV1) and the forced vital capacity (FVC)].

Patients and methods
The study included ninety-six participant grouped into two groups: G1 (46 asthmatics) and G2 (50 healthy controls). ECP, hs-CRP, and total IgE serum levels were measured using their corresponding ELISA kits. Neonatal blood DNA was extracted using the Gene JET™ Whole Blood Genomic DNA Purification Mini Kit. Genotyping was performed by RT-PCR.

Results
A significantly higher proportion of individuals in G1 had the LPCAT1-rs8352 CC and GC genotypes compared to G2 (p < 0.001). Individuals with the CC genotype exhibited significantly more severe asthma, along with elevated levels of BEC, ECP, hs-CRP, and total IgE. Those with the GC genotype demonstrated a similar, though less severe, pattern, followed by individuals with the GG genotype. The FEV1 and FVC values showed the opposite trend, with individuals having the GG genotype exhibiting the highest lung function values.

Conclusion
The LPCAT1-rs8352 allele C is associated with pediatric asthma onset and severity. Further research on the LPCAT1 genetic variants may provide a deeper understanding of pediatric bronchial asthma mechanisms and lead to improved management strategies.