bluuebunny/crossref_metadata_2025 huggingface dataset:
Persistently high TB prevalence in Nairobi County neighbourhoods, 2015–2022
Authors: Ong'ang'o Jane R. , Ross Jennifer , Kiplimo Richard , Kerama Cheryl , Tram Khai Hoan , Zifodya Jerry S. , Mukiri Nellie , Nyadimo Erick , Njoroge Martha , Ronoh Aiban , Kathure Immaculate , Kirathe Dickson , Hawn Thomas R. , Nduba Videlis , Horne David J.
Published: February 2025

National and sub-national population-based surveys, when performed at intervals, may assess important changes in TB prevalence. In 2022 we re-surveyed nine Nairobi County neighbourhoods that were previously surveyed in 2015. We aimed to determine pulmonary TB prevalence, compare prevalence to 2015 estimates, and evaluate changes in risk groups. Participants who reported cough of any duration and/or whose chest x-ray suggested TB submitted sputum for smear microscopy, Xpert Ultra, and liquid culture. We defined prevalent TB as Mycobacterium tuberculosis detection by sputum Xpert or culture, excepting individuals who were only trace positive. Our methods differed from 2015, which used solid media, Xpert MTB/RIF, and cough duration >2 weeks. We calculated TB prevalence using random-effects logistic regression models with missing value imputations and inverse probability weighting. In 2022 among 6369 participants, 1582 submitted ≥1 sputum sample, among whom 42 (2.7%) had TB, a weighted TB prevalence of 806/100,000 (95% confidence interval (CI), 518–1096). An additional 31 (2.0%) participants tested Ultra trace-positive/culture-negative. For comparison to 2015, we excluded 2022 participants (n = 2) whose only criterion for sputum was cough <2 weeks. There was no evidence for a decline in overall TB prevalence from 2015 to 2022. TB prevalence among men was high (1301/100,000) and remained high compared to 2015 (p-value <0.05). The age group with the highest estimated prevalence remained people ages 45–54 years. Among people with prevalent TB who reported cough, 76% had not sought health care. Dissimilar from other serial surveys that showed declines in TB prevalence, we found persistently high TB prevalence over a 7-year period in Nairobi County. Limitations of this study include changes in methodology between the two surveys and complex effects of the COVID-19 pandemic.
Genetic evidence for amlodipine’s protective role in gastroesophageal reflux disease: A focus on CACNB2
Authors: Zhang Liuzhao , Chu Quanwang , Jiang Shuyue , Shao Bo
Published: February 2025

Objective

This study aims to elucidate the causal relationship between genetically predicted amlodipine use and the risk of gastroesophageal reflux disease (GERD) using a bidirectional Mendelian Randomization (MR) approach and to explore the underlying genetic and molecular mechanisms through functional enrichment analysis and the construction of a competing endogenous RNA (ceRNA) network.

Methods

Publicly available GWAS datasets from the Neale Lab consortium were used, including data on amlodipine (13,693 cases, 323,466 controls) and GERD (14,316 cases, 322,843 controls). Genome-wide significant SNPs were selected as instrumental variables and clustered by linkage disequilibrium. MR analysis was conducted using R software with all five methods. Sensitivity analyses assessed pleiotropy and heterogeneity. Drug target genes were analyzed using GO and KEGG pathways. GeneMANIA was used for network visualization, and a ceRNA network was constructed with Cytoscape. Differential gene expression analysis on GERD-related datasets from GEO validated the findings.

Results

The MR analysis indicated a significant negative association between genetically predicted amlodipine use and GERD risk (IVW OR = 0.872, 95% CI = 0.812–0.937, P = 0.0002). Sensitivity analyses confirmed the robustness of these findings, showing no evidence of pleiotropy or heterogeneity. The enrichment analysis identified key biological processes and pathways involving calcium ion transport and signaling. The ceRNA network highlighted core targets such as CACNB2, which were further validated by differential expression analysis intersecting drug target genes with GERD-related gene expression changes.

Conclusion

This study provides robust evidence of a protective effect of amlodipine against GERD, supported by genetic and molecular analyses. The findings suggest that calcium channel blockers like amlodipine could be repurposed for GERD treatment. The identification of CACNB2 and other core targets in the ceRNA network offers novel insights into the pathophysiology of GERD and potential therapeutic targets, paving the way for personalized medicine approaches to improve patient outcomes.
Active tuberculosis in household contacts of bacteriologically confirmed pulmonary tuberculosis patients: A multicenter study finding the ‘Missed One’ in Central Ethiopia
Authors: Seid Getachew , Alemu Ayinalem , Diriba Getu , Hailu Michael , Wondimu Amanuel , Tadesse Mengistu , Tadesse Gemechu , H Mariam Solomon , Gumi Balako
Published: February 2025

Background

There was a ‘missing millions’ gap between the incidence of tuberculosis (TB) cases and the notified cases. In many TB high-burden countries, only about 25% of household contacts (HHCs) completing household TB evaluation and 20–89% of eligible contacts did not adhere to TB screening. The study was conducted to assess the yield of door-to-door TB household contact investigation among household contact of bacteriologically confirmed pulmonary TB cases in central Ethiopia.

Methods

This cross-sectional study was carried out in selected health facilities of central Ethiopia from January 1, 2023 to December 3, 2023.All sequential voluntary bacteriologically confirmed TB patients and their HHCs without discrimination by age were included in the study. Xpert Ultra assay and TB culture were used to investigate active TB from sputum sample. Spearman’s correlation analysis was used to determine the correlation between the index case cycle threshold value and the corresponding HHCs. Multivariable logistic regression analysis was done to investigate the associated risk factors for active TB in HHCs.

Results

Among 967 HHCs claimed by 303 index cases (259 drug susceptible TB (DS-TB) and 44 multi-drug resistance TB (MDR/RR-TB)), 902(93.07%) HHCs had received baseline symptom-based TB evaluation. Presumptive TB was identified in 20.17% (182) of the evaluated HHCs and 13(1.44%) were diagnosed with active TB. Eleven HHCs (7.24%; 95% CI: 3.85–12.9%) from DS-TB index case contacts and 2 (6.67%; 95% CI: 1.16–23.51) from MDR/RR-TB indexes HHCs were found to be MTB detected Rifampicin resistance not detected cases. The Xpert ultra assay results revealed an 84.62% (95% CI: 57.77–95.68) Rifampicin drug resistance concordance between the index case and the corresponding HHC. Active TB was significantly associated with night sweating and sharing a bed with the index patient, P-value < 0.05.

Conclusion

Home-to-home TB contact screening have high active TB yield and implementable in both rural and urban areas of the nation only by mentoring and motivating the health extension workers. Proximity to bacteriologically confirmed TB patient for long time exposes household contacts for active TB. Scheduling convenient times and last-mile service delivery to contacts is very important to address the missed active TB cases in the community.
Investigation of severe dengue outbreak in Maumere, East Nusa Tenggara, Indonesia: Clinical, serological, and virological features
Authors: Santoso Marsha S. , Nara Mario B. R. , Nugroho Dwi Kurniawan , Yohan Benediktus , Purnama Asep , Boro Angela M. B. , Hayati Rahma F. , Gae Erlinda P. , Denis Dionisius , Rana Bunga , Hibberd Martin L. , Sasmono R. Tedjo
Published: February 2025

Background

Dengue, an acute febrile disease caused by dengue virus (DENV) infection, is endemic to Indonesia. During early 2020, an outbreak of severe dengue occurred in Maumere, East Nusa Tenggara province, a region with low dengue endemicity with limited data on the characteristics of the circulating DENV. By 18 March 2020, 1396 cases were reported with 14 fatalities. Investigation was conducted to understand the cause and characteristics of the outbreak.

Methods

Sera were collected from 133 patients with dengue-like symptoms through random sampling at TC Hillers Hospital, Maumere during outbreak between February and June 2020. Dengue was confirmed using NS1 and/or RT-PCR detection. Serological status was determined using IgG/IgM ELISA and plaque reduction neutralization test (PRNT). DENV serotyping and genome sequencing were performed to identify the DENV serotype and genotype.

Results

We recruited suspected dengue patients attending the hospital during the outbreak. Dengue was confirmed in 72.2% (96/133), while 18.8% (25/133) were diagnosed as probable dengue. Children under 18 years old accounted for 85.1% (103/121) of dengue cases. Severe dengue accounted for 94.2% (81/86) of cases. Secondary infections made up 92.6% (112/121) of cases. Serotyping detected 87.3% (62/71) as DENV-3, 7.0% (5/71) as DENV-4, 2.8% (2/71) as DENV-1, and 2.8% (2/71) as DENV-2. Phylogenetic analysis revealed close evolutionary relationship of Maumere DENV to viruses from other Indonesian regions, especially Bali and Kupang. PRNT on DENV-3 secondary infections patients detected the presence of DENV-2 and DENV-4 neutralizing antibodies.

Conclusion

The severe dengue outbreak in Maumere is caused by DENV-3 introduced from nearby islands. The high proportion of secondary infections likely contributes to the severity of the disease. The high percentage of anti-dengue neutralizing antibodies for multiple serotypes and the high proportion of anti-dengue IgG in young children suggests a history of dengue transmission with a high infection rate in the area.
Targeting beta-lactamase activity with Oxacyclohexadecan-2-one in carbapenem-resistant uropathogenic E. coli: A molecular simulation approach
Authors: Balaji Priyanka , N Madhana Priya , Bhaskar M. Emmanuel , R Gnanasambandan , Paul Solomon F. D. , R. Magesh
Published: February 2025

Urinary tract infections caused by uropathogenic Escherichia coli (E. coli) are a global health concern, with rising rates and antibiotic resistance demanding novel treatments. Therefore, in this study, we explored the potential of Oxacyclohexadecan-2-one obtained from Moringa oleifera (M. oleifera) seed, as antibacterial agent against three majorly prevalent carbapenemase-producing E. coli proteins, blaNDM-1 (New Delhi metallo-betalactamase-1), blaNDM-5 (New Delhi metallo-betalactamase-5) and blaOXA-48 (Oxacillinase-48) from the strains Ecw3, EC-114 and T20 respectively. The ethanolic extract of M. oleifera seed was subjected to GC-MS, identifying 135 compounds. PyRx virtual screening, identified the top 10 ligands for each protein following the Rule of 5 and ProTox classes V and VI, with Oxacyclohexadecan-2-one (PubChem ID: 235414) showing best binding affinity across all 3 proteins with an optimized dose (LD50) of 5000mg/kg. Hence, molecular docking was carried out for ligand 235414 along with Imipenem, belonging to the same class V toxicity class with an optimized dose (LD50) of 5000mg/kg. Imipenem is a commonly used FDA drug to treat UTIs, which served as the control in the study. Oxacyclohexadecan-2-one showed higher binding affinity for the beta-lactamase proteins with a docking score of -6.45 kcal/mol, -6.05 kcal/mol and -7.34 kcal/mol compared to -3.41 kcal/mol, -3.99 kcal/mol and -6.36 kcal/mol of Imipenem for NDM-1, NDM-5 and OXA-48 respectively. Dynamic Simulation was performed for 100 ns for Oxacyclohexadecan-2-one and Imipenem bound protein complexes to determine the stability, fluctuations, compactness, bond interaction, solvent accessibility area, free energy landscape and the binding free energy. The results of molecular docking and dynamics were promising for the Oxacyclohexadecan-2-one, suggesting its potent inhibitory effect against the beta-lactamase producing proteins.
An integrated approach for advanced vehicle classification
Authors: Liu Rui , Wen Shiyuan , Xing Yufei
Published: February 2025

This study is dedicated to addressing the trade-off between receptive field size and computational efficiency in low-level vision. Conventional neural networks (CNNs) usually expand the receptive field by adding layers or inflation filtering, which often leads to high computational costs. Although expansion filtering was introduced to reduce the computational burden, the resulting receptive field is only a sparse sampling of the tessellated pattern in the input image due to the grid effect. To better trade-off between the size of the receptive field and the computational efficiency, a new multilevel discrete wavelet CNN model (DWAN) is proposed in this paper. The DWAN introduces a four-level discrete wavelet transform in the convolutional neural network architecture and combines it with Convolutional Block Attention Module (CBAM) to efficiently capture multiscale feature information. By reducing the size of the feature maps in the shrinkage subnetwork, DWAN achieves a wider sensory field coverage while maintaining a smaller computational cost, thus improving the performance and efficiency of visual tasks. In addition, this paper validates the DWAN model in an image classification task targeting fine categories of automobiles. Significant performance gains are observed by training and testing the DWAN architecture that includes CBAM. The DWAN model can identify and accurately classify subtle features and differences in automotive images, resulting in better classification results for the automotive fine-grained category. This validation result further demonstrates the effectiveness and robustness of the DWAN model in vision tasks and lays a solid foundation for its generalization to practical applications.
Deformation monitoring and safety stability evaluation study of high-altitude limestone dumps
Authors: Dong Jianjun , Guo Yawen , Mei Yuan , Gao Ke
Published: February 2025

We employed synthetic aperture radar interferometry (InSAR) to assess the slope stability of a high-altitude landfill in Sangri County, Shannan, Tibet. To address the unique climatic conditions of high-altitude regions, the InSAR deformation monitoring model was enhanced to mitigate the effects of temperature and rainfall. The accuracy of InSAR monitoring in high-elevation slopes was validated by comparison with GNSS RTK measurements. Both Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) techniques were applied to monitor and evaluate slope deformation at the landfill site. The findings indicate that the average error between the improved InSAR model and GNSS measurements is 0.28 mm, with no statistically significant difference. The maximum slope displacement exceeds 20 mm when rainfall exceeds 300 mm, reaching the blue warning threshold. From 2018 to 2022, the deformation rate of the high-altitude landfill ranged from 0 to − 9.00 mm/a, classified as slip category VII. Significant deformation was observed during the rainy season, while the slope remained stable during dry periods, suggesting that rainfall is a primary trigger for slope deformation. A certain hysteresis effect in the deformation response to rainfall was also identified. The results demonstrate that InSAR technology offers comprehensive and dynamic monitoring capabilities for high-altitude slopes and serves as an effective tool for slope stability management in challenging environments.
Effect of using home-based dynamic intermittent pneumatic compression therapy during periods of physical activity on functional and vascular health outcomes in chronic stroke: A randomized controlled clinical trial
Authors: Faulkner James , Paine Eloise , Hudson Nick , Hannah Scott , Dennis-Jones Amy , Martinelli Louis , Hobbs Helen
Published: February 2025

Background

Intermittent pneumatic compression (IPC) therapy may benefit stroke patients by eliciting more intensive training sessions that may result in better health, mobility and ultimately quality of life. The purpose of this randomized controlled trial was to assess the effect of using a home-based IPC device on functional outcomes and vascular health in individuals with chronic stroke.

Methods

Thirty-one stroke survivors (64.3 ± 14.3y; 4.3 ± 2.7y since stroke) completed pre- and post-intervention assessments of functional capacity (six-minute walk test [6MWT], timed-up-and-go, 10m walk test), vascular health (pulse wave analysis, carotid-femoral pulse wave velocity), and physical activity. Following the pre-assessment, individuals were randomly assigned to either a daily, 12-week, home-based IPC group, or to a usual care control (CON) group. Outcomes were assessed using analysis of covariance (ANCOVA), controlling for age and any baseline differences.

Results

Following ANCOVA, a significant increase in 6MWT walking distance was observed post-assessment for the IPC (Mean ±  SD [95%CI]; 188 ±  19 m [177–199m]) but not the CON group (167 ±  19 m [157–178m]) (p <  0.05). A significant reduction in peripheral systolic blood pressure was reported at the post-assessment for the IPC group (136.2 ±  8.0 mmHg [131.9–140.4 mmHg]) but not for CON (142.2 ±  8.0 mmHg [138.1–144.6 mmHg]) (p <  0.05). Similar findings were observed for central systolic blood pressure. Physical activity levels significantly increased at the post-assessment for IPC (1857 ±  879 MET·min−1·week−1 [1390–2325 MET·min−1·week−1]) but not for the CON group (1161 ± 879 MET·min−1·week−1 [677–1645 MET·min−1·week−1]), while for time spent sitting, a significantly greater reduction was observed at the post-assessment for the IPC group (396 ±  86 mins [350–442 mins]) compared to CON (486 ± 86 mins [439–534 mins]) (both p <  0.05).

Conclusions

The observed improvements in functional mobility, cardiovascular health, increased physical activity and reduced sedentary time demonstrates important clinical implications of ‘home-based’ IPC therapy as a clinical training aid for stroke rehabilitation. Home-based IPC therapy could serve as an adjunct to conventional rehabilitation, however, further research is needed to determine whether IPC therapy can sustain or improve function over time for individuals in the chronic stage of recovery.
King John V Library at Coimbra: History and construction, architecture and art
Authors: Ferreira Sílvia
Published: February 2025

A presente publicação revela a obra inédita do historiador de arte norte­?americano Robert C. Smith (1912-1975) dedicada à Biblioteca Joanina de Coimbra, nas vertentes da sua história, arquitetura e arte. Estabelece a cronologia das obras da Biblioteca desde os seus primórdios até às intervenções de restauro da antiga DGEM, entre 1943-45, desvenda os artistas que nela laboraram e procura a autoria do seu projeto. Apresenta-se em edição crítica bilíngue da obra original, datável de 1974, no âmbito da atualização dos seus conteúdos.
Forecasting with Excel
Authors: Grech Victor
Published: February 2025

Introduction: Time series analysis is used by statisticians to make predictions from time-ordered data. This is crucial for planning for the future. The inclusion of little-known forecasting function in Excel™ has brought this type of analysis within the ability of less mathematically sophisticated individuals, including doctors. There are two main models for time series analysis: ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing. This paper will demonstrate how the ubiquitous Excel facilitates a little-known sophisticated forecasting technique that employs the latter and presents a facilitating spreadsheet. Methods: Excel’s FORECAST.ETS function was invoked with supporting macros. Results: A bespoke spreadsheet was created that would prompt for data to be pasted in columns A and B, formatted as a valid date in A and data in B. After error trapping and a horizon date, the FORECAST.ETS function calculates forecasts with 95% CI and a line graph. The FORECAST.ETS.CONFINT was also invoked using a macro to obtain a 95, 96, 97, 98 and 99% confidence intervals table. Discussion: Forecasting is vital in all fields, including the medical field, for innumerable reasons. Statisticians are capable of far more sophisticated time series analyses and techniques and may use multiple techniques that are beyond the competence of ordinary clinicians. However, the sophisticated Excel tool described in this paper allows simple forecasting by anyone with some knowledge of this ubiquitous software. It is hoped that the spreadsheet included with this paper helps to encourage colleagues to engage with this simple-to-use Excel function.
Colloid Cyst of the Third Ventricle: A Case Report
Authors: Kopal Aleš , Preis Jiří , Ungermann Leoš , Ehler Edvard , Štětkářová Ivana
Published: February 2025

Colloid cyst of the third ventricle (CC) represents approximately 1% of intracranial tumours and 20% of intraventricular tumours. CC usually occurs between 20 and 50 years of age. During the first decade of life, it is diagnosed very rarely (1–2%). It can be most commonly found in the anterior part of the third ventricle at the foramen of Monro (1). It is often visualised during the computed tomography (CT) examination as a hyperdense focal lesion, it has variable change of the signal during magnetic resonance imaging (MRI) (2). CC has a benign character, however, a strategic position which may lead to acute hydrocephalus, intracranial hypertension syndrome, consciousness disorder, and even sudden death. This peracute hydrocephalus is an indication to an acute neurosurgical procedure (3).
Optimizing herbicide use: Can drone technology revolutionize weed management in rice?
Authors: Bibhudutta M , Ashirbachan M , Triptesh M , Chandrasekhar S , Sahadeva S , Rajesh K S , Vishal G K , Dwity R S
Published: February 2025

This study investigates the standardization of herbicide dosage for drone applications in rice cultivation, specifically for the herbicide mixture triafamone 20% + ethoxysulfuran 10%. It compares the efficacy of drone-applied herbicide at 70% of the recommended dose (RDH) with traditional knapsack sprayer applications, where 67.5 g a.i. ha-1 is considered the 100% dose. The research analyzes several factors, including growth parameters, rice yield, weed density, biomass, and weed control efficiency (WCE) at 30, 60, and 90 days after sowing (DAS). The study utilized a randomized block design with 10 treatments and three replicates, which included weed-free and weedy checks, along with varying herbicide doses. Results show that the weed-free check exhibited the lowest weed density and biomass with the highest WCE. Among drone-applied treatments, 70% RDH proved most effective, maintaining low weed densities and biomass similar to the weed-free check, and achieving WCE values of 94-95%. Higher doses (100%, 90%, 80% RDH) caused phytotoxicity, hindering initial crop growth and resulting in higher weed competition and lower WCE (71-73%). The study highlights that the 70% RDH spray via drone not only reduces herbicide costs by 30%, but also improves crop-weed dynamics, rice yield, net income, benefit-cost ratio, and energy efficiency, promoting sustainable agricultural practices in rice cultivation.
Assessing the relationship between hardseedness and seed morphometrics in Blackgram
Authors: Menaka C , Pozhilarasi S , Yuvaraja A , Djanaguiraman M , Vanitha C , Raja K
Published: February 2025

Seed dormancy in black gram (Vigna mungo) was studied about seed size (weight, length, width) and their impact on hard seed formation and imbibition rate during the summer and Kharif seasons at NPRC, Vamban, Pudukkottai. The seeds of blackgram obtained during the summer season showed a significant negative correlation with hard seed occurrence (r = -0.639, P ? 0.01) and a positive correlation with imbibition rate (r = 0.451, P ? 0.05), Whereas seed width had a weaker negative correlation with hard seed occurrence (r = 0.357) and a positive correlation with imbibition rate (r = 0.273). In addition, the strongest correlation was found between hard seed occurrence and imbibition rate (r = 0.802, P ? 0.01). The seed length of the blackgram harvested during the kharif season had a stronger negative correlation with hard seed occurrence (r = -0.790, P ? 0.01) and a positive correlation with imbibition rate (r = 0.555, P ? 0.01). Seed width also showed significant correlations with hard seed occurrence (r = -0.673, P ? 0.01) and imbibition rate (r = 0.613, P ? 0.01). At the same time, the obtained seed weight correlations were generally weak. The results also proved a seasonal difference in the hard seedness of blackgram, which confirmed the occurrence of hard seed during the summer compared to the Kharif season. Thus, the study's findings highlight the importance of seed morphometrics and imbibition in predicting the presence of hard seeds in blackgram.
The Case for DBMS Live Patching
Authors: Fruth Michael , Scherzinger Stefanie
Published: February 2025

Traditionally, when the code of a database management system (DBMS) needs to be updated, the system is restarted, and database clients suffer downtime, or the provider instantiates hot-standby instances and rolls over the workload. We investigate a third option, live patching of the DBMS binary. For certain code changes, live patching allows to modify the application code in memory, without restart. The memory state and all client connections can be maintained. Although live patching has been explored in the operating systems research community, it remains a blind spot in DBMS research. In this
Experiment, Analysis & Benchmark
article, we systematically explore this field from the DBMS perspective. We discuss what distinguishes database management systems from generic multi-threaded applications when it comes to live patching. We then propose domain-specific strategies for injecting quiescence points into the DBMS source code so that threads can safely migrate to the patched process version. We experimentally investigate the interplay between the query workload and different quiescence methods, monitoring both transaction throughput and tail latencies. We show that live patching can be a viable option for updating database management systems, since database providers can make informed decisions w.r.t. the latency overhead on the client side.
InBox: Recommendation with Knowledge Graph using Interest Box Embedding
Authors: Xu Zezhong , Qu Yincen , Zhang Wen , Liang Lei , Chen Huajun
Published: February 2025

Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim to identify user interests based on historical interactions and recommend suitable items. However, existing works overlook two key challenges: (1) an interest corresponds to a potentially large set of related items, and (2) the lack of explicit, fine-grained exploitation of KG information and interest connectivity. This leads to an inability to reflect distinctions between entities and interests when modeling them in a single way. Additionally, the granularity of concepts in the knowledge graphs used for recommendations tends to be coarse, failing to match the fine-grained nature of user interests. This homogenization limits the precise exploitation of knowledge graph data and interest connectivity. To address these limitations, we introduce a novel embedding-based model called InBox. Specifically, various knowledge graph entities and relations are embedded as points or boxes, while user interests are modeled as boxes encompassing interaction history. Representing interests as boxes enables containing collections of item points related to that interest. We further propose that an interest comprises diverse basic concepts, and box intersection naturally supports concept combination. Across three training steps, InBox significantly outperforms state-of-the-art methods like HAKG and KGIN on recommendation tasks. Further analysis provides meaningful insights into the variable value of different KG data for recommendations.
Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks
Authors: Wang Yiqi , Yuan Long , Zhang Wenjie , Chen Zi , Lin Xuemin , Liu Qing
Published: February 2025

Top-
k
Nearest Neighbors (
k
NN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been proposed to speedup the query processing. However, even with the current state-of-the-art approach, long query processing delays persist, along with significant space overhead and prohibitively long indexing time. In this paper, we depart from the complex index designs prevalent in existing literature and propose a simple index named KNN-Index. With KNN-Index, we can answer a
k
NN query optimally and progressively with small and size-bounded index. To improve the index construction performance, we propose a bidirectional construction algorithm which can effectively share the common computation during the construction. Theoretical analysis and experimental results on real road networks demonstrate the superiority of KNN-Index over the state-of-the-art approach in query processing performance, index size, and index construction efficiency.
Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB
Authors: Abduvakhobov Abduvoris , Jensen Søren Kejser , Pedersen Torben Bach , Thomsen Christian
Published: February 2025

Modern wind turbines are monitored by sensors that generate massive amounts of high frequency time series that are ingested on the edge and then transferred to the cloud where they are stored and analyzed. This results in at least four challenges: (1) Limited hardware makes efficient ingestion necessary to keep up; (2) Limited bandwidth makes data compression necessary; (3) High storage costs as all data must be stored; and (4) Low data quality due to lossy compression methods without error bounds. Practitioners currently use solutions that only solve some of these. In this paper, we evaluate the Time Series Management System ModelarDB, a solution that meets all four challenges by efficiently managing time series across the entire pipeline. We compare it to three commonly used alternatives and evaluate different aspects of them in a realistic edge-to-cloud scenario with real-life datasets. For lossless compression, ModelarDB achieves up to 2x better compression and 1.2x better transfer efficiency. For lossy compression, ModelarDB achieves up to 4.6x better compression and 10x better transfer efficiency, or similar compression with orders of magnitude less error.
TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges
Authors: Qiu Ruizhong , Jang Jun-Gi , Lin Xiao , Liu Lihui , Tong Hanghang
Published: February 2025

Given an evolving tensor time series and multiple time ranges, how can we compute Tucker decomposition for each time range efficiently and accurately? Tucker decomposition has been widely used in a variety of applications to obtain latent factors of tensor data. For example, Tucker decomposition on air pollution data allows us to analyze and compare air pollution patterns between different locations during different periods of time. In these applications, a common need is to compute Tucker decomposition for a given time range. Furthermore, real-world tensor time series are typically evolving in the time dimension. Such needs call for a data structure that can efficiently and accurately support range queries of Tucker decomposition and stream updates. Unfortunately, existing methods do not support either range queries or stream updates. For methods that do not support range queries, they have to re-compute from scratch for each query. Not until 2021 has a data structure called Zoom-Tucker been proposed to support range queries via block-wise preprocessing. However, Zoom-Tucker does not support stream updates and, more critically, suffers from a reluctant efficiency-accuracy tradeoff --- a large block size causes inaccuracy, while a small block size leads to inefficiency. This challenging problem has remained open for years prior to our work. To solve this challenging problem, we propose TUCKET, a data structure that can efficiently and accurately handle both range queries and stream updates. Our key idea is to design a new data structure that we call a
stream segment tree
by generalizing the
segment tree
, a data structure that was originally invented for computational geometry. For a range query of length
L
, our TUCKET can find
O
(log
L
) nodes (called the
hit set
) from the tree and efficiently stitch their preprocessed decompositions to answer the range query. We also propose an algorithm to optimally prune the hit set via an approximation of subtensor decomposition. For the
T
-th stream update, our TUCKET modifies only amortized
O
(1) nodes and only
O
(log
T
) nodes in the worst case. Extensive evaluation demonstrates that our TUCKET consistently achieves the highest efficiency and accuracy across four large-scale datasets. Our TUCKET achieves at least 3 times lower latency and at least 1.4 times smaller reconstruction error than Zoom-Tucker on all datasets. The full version can be found at https://github.com/q-rz/TUCKET/blob/main/TUCKET-Full.pdf.
Dynamic Graph Databases with Out-of-Order Updates
Authors: Anadiotis Angelos Christos , Khan Muhammad Ghufran , Manolescu Ioana
Published: February 2025

Several real-time applications rely on dynamic graphs to model and store data arriving from multiple streams. Providing both high ingestion rate and efficient analytics with transactional guarantees is challenging, even more so when updates may be received
out-of-order
at the database. In this work, we propose HAL, a novel in-memory dynamic graph database design, addressing these challenges. HAL outperforms comparable systems by a factor of up to 73× in terms of update processing throughput and up to 357× for analytics, while being the first to support out-of-order updates.
ELEET: Efficient Learned Query Execution over Text and Tables
Authors: Urban Matthias , Binnig Carsten
Published: February 2025

In this paper, we present ELEET, a novel execution engine that allows one to seamlessly query and process text as a first-class citizen along with tables. To enable such a seamless integration of text and tables, ELEET leverages learned multi-modal operators (MMOps) such as joins and unions that seamlessly combine structured with unstructured textual data. While large language models (LLM) such as GPT-4 are interesting candidates to enable such learned multimodal operations, we deliberately do not follow this trend to enable MMOps, since it would result in high overhead at query runtime. Instead, to enable MMOps, ELEET comes with a more efficient small language model (SLM) that is targeted to extract structured data from text. Thanks to our novel architecture and pre-training procedure, the ELEET-model enables high-accuracy extraction with low overheads. In our evaluation, we compare query execution based on ELEET to baselines leveraging LLMs such as GPT-4 and show that ELEET can speed up multi-modal queries over tables and text by up to 575× without sacrificing accuracy.
Intervalos de referencia de parámetros hematológicos en población chilena adulta y en la etnia mapuche
Authors: Letelier Pablo J. , Chicahual Carolina A. , Arroyo Nicolás F. , Monsalves Daniel P. , Boguen Rodrigo E. , Guzmán Neftalí H.
Published: February 2025

Objetivos

Los intervalos de referencia (IR) son una herramienta esencial para apoyar la toma de decisiones clínicas. Estos pueden presentar variaciones intra e interindividuales asociadas a diferencias genéticas y factores medioambientales. Dado que la población de Chile está compuesta por múltiples grupos étnicos, estas variables adquieren aún mayor relevancia. El objetivo del presente estudio es establecer IR para diferentes parámetros hematológicos en la población chilena y la etnia mapuche.

Métodos

Se seleccionó una muestra de 356 adultos (entre 18 y 65 años), de los cuales 146 pertenecían a la etnia mapuche, utilizando el método indirecto a posteriori a partir de la base de datos del laboratorio clínico UC Temuco. El análisis se realizó considerando el sexo y la etnia. Los valores atípicos se detectaron mediante la prueba de Tukey, mientras que los IR se establecieron aplicando el método no paramétrico recomendado por la IFCC.

Resultados

La mediana de edad de la muestra global de la población general fue de 35 años en mujeres y 36 años en hombres. Se observaron diferencias estadísticamente significativas (p<0,05) por sexo en los parámetros dependientes de la hemoglobina y del recuento de plaquetas. En el análisis por etnicidad, se observaron diferencias significativas en el recuento de glóbulos rojos, hemoglobina y hematocrito (p<0,0001).

Conclusiones

Este estudio demuestra que los intervalos de referencia hematológicos varían según el sexo y la etnicidad, lo cual debería ser considerado en una población multiétnica. Este hallazgo contribuye a un mejor conocimiento de las características individuales de cada persona, facilitando una interpretación clínica más precisa.
Galen’s Typology of Organs
Authors: Ezrokhi Dmitry , Lewis Orly
Published: February 2025

This paper examines Galen’s insistence that the stomach and heart, despite their anatomical and physiological similarities to muscles, are not muscles. Through analyzing key passages in Galen’s works, we show that this claim is rooted in a consistent tripartite distinction between organs: Psychic Moving Organs (muscles), Natural Moving Organs (e.g. stomach, heart), and Natural Immobile Organs (e.g. liver, kidneys). We argue that this classification is grounded in anatomical differences between flesh and fiber that Galen deems salient enough to support further physiological explanations and corroborate his philosophical-psychological commitments. By tracing the empirical foundations and theoretical motivations for these distinctions, we shed light on the relationship between Galen’s anatomical practices and his physiology and psychology.
Proofs of two conjectures involving sums of normalized Narayana numbers
Authors: Goldman Ron , Simeonov Plamen , Zafiris Vasilis
Published: February 2025

Narayana numbers are well-known and have many applications in the field of combinatorics. The Narayana numbers form a triangular array, where the sum of the nth row is the nth Catalan number. Here we normalize the Narayana numbers by dividing each entry in the nth row by the nth Catalan number. Now each row of these normalized Narayana numbers defines a discrete probability distribution. We investigate two new properties of these normalized Narayana numbers: instead of summing along the rows, we derive the limit of the sums along the columns and the limit of the sums along the short diagonals.
Navigating nephropathy and nephrotoxicity: understanding pathophysiology unveiling clinical manifestations, and exploring treatment approaches
Authors: Javed Aysha , Mahmood Tarique , Tiwari Reshu , Ahsan Farogh , Ansari Vaseem Ahamad , Bano Shahzadi , Uzzam Khan Mohd Masih , Khan Anas
Published: February 2025

Nephropathy and nephrotoxicity are significant causes of renal impairment, with both conditions contributing to a substantial global healthcare burden. Nephropathy encompasses a range of kidney diseases, including glomerulonephritis, diabetic nephropathy, hypertensive nephropathy, and polycystic kidney disease, each with distinct pathophysiological mechanisms and clinical manifestations. Nephrotoxicity, often induced by environmental toxins or medications, can lead to acute kidney injury (AKI) or chronic kidney disease (CKD) if not properly managed. Understanding the underlying processes and risk factors for kidney damage is crucial to preventing and treating these conditions effectively. However, while considerable research exists on each of these topics, there remains a gap in integrated knowledge regarding the diverse pathophysiological pathways, the role of early detection, and the optimal management strategies for nephropathy and nephrotoxicity. While substantial research has been conducted on nephropathy and nephrotoxicity, there remains a year gap in fully understanding the diverse mechanisms of kidney damage across different etiologies and their long-term implications for renal health. This work aimed to fill this gap by synthesizing current research on the pathophysiology, clinical manifestations, and treatment approaches for both nephropathy and nephrotoxicity. Major findings highlight the importance of early detection through biomarkers, the need for vigilant monitoring of renal function, and the role of avoiding nephrotoxic agents in both medical and environmental contexts. The paper also emphasizes the critical role of individualized treatment approaches, including the careful use of medications to prevent drug-induced nephropathy.
Catalytic synthesis of DHHB over home-made catalysts and its kinetic calculations
Authors: Hui-Fen Xin , Bai-Cheng Feng , Jin-Bo Wu , Ri-Hong Liu , Rong Qu , Yan Jin
Published: February 2025

DHHB (n-hexyl 2-(4-N,N-diethylamino-2-hydroxybenzoyl) benzoate) is a recently developed UV absorber that demonstrates a high degree of safety, excellent efficiency, and favorable solubility properties. In this paper a catalyst was prepared to catalyze the esterification of DHHB with homemade sulfonic acid modified polystyrene microspheres coated with silica instead of conventional H2SO4. And the catalyst was characterized by scanning electron microscopy (SEM) and infrared spectroscopy (FT-IR). The effects of different conditions on the reaction rate and equilibrium conversion were also investigated using conventional H2SO4 as a benchmark. The results showed that the catalytic activity of the catalyst did not decrease significantly after 10 consecutive cycles, and the yield of DHHB could reach 90.21 % after 2 h of reaction at 150 °C. Meanwhile, The kinetic experimental data obtained were correlated using a proposed homogeneous phase model. The synthesis of DHHB is a heat-absorbing reaction with heat of reaction and activation energies of 4.6537 kJ/mol, 43.2157 kJ/mol and 37.5735 kJ/mol for the forward and reverse reactions, respectively. It was verified that the proposed homogeneous reaction kinetic model accurately described the kinetic processes of the DHHB esterification reaction.
The myth of meaning: generative AI as language-endowed machines and the machinic essence of the human being
Authors: Paolucci Claudio
Published: February 2025

This article explores the intersection of generative artificial intelligence (AI) and human cognition through semiotics, proposing that generative AI offers a lens through which the essence of human being is revealed and through which semiotic enunciation and meaning can be radically reevaluated. Drawing on semiotic, philosophical, and neurodevelopmental frameworks, it argues that generative AI, as exemplified by language-endowed systems like ChatGPT and others, challenges traditional notions of meaning, subjectivity, and intelligence. By tracing the evolution of enunciation theories and their application to AI, the paper asserts that these machines reveal a machinic essence intrinsic to human beings – our reliance on external cognitive aids and hybridization with the environment that makes us “natural-born cyborgs.” Indeed AI, far from merely simulating intelligence, exposes the limits of human-centric models of meaning and the myths underlying our conceptualization of cognition and language.
Fortschritte der magnetometrischen Durchflussmessung: Grundsatznachweis der Fließgeschwindigkeitsmessung und Einfluss des Strömungsprofils auf die RF-Pulseffizienz
Authors: Schmieder Leonhard
Published: February 2025

Dieser Artikel befasst sich mit den Fortschritten in der Forschung zur magnetometrischen Durchflussmessung. In diesem neuen Messverfahren werden Methoden der Kernspinresonanz und Magnetometrie im Erd- bis Nullfeld-Regime verwendet, um die Fließgeschwindigkeit eines wasserstoffhaltigen Fluids nicht-invasiv mithilfe neuartiger Quantenmagnetometer zu bestimmen. Nach einer anfänglichen Demonstration der Durchflussmessfähigkeiten wird das Verfahren analysiert, um den Einfluss experimenteller Bedingungen auf die Radiofrequenz-Pulseffizienz in strömenden Medien zu bestimmen. Es wird festgestellt, dass das Strömungsprofil eine dominante Rolle bei der Effizienz der RF-Pulse spielt, was eine Basis für zukünftige Forschungen zur Sensitivität der magnetometrischen Durchflussmessung gegenüber Strömungsprofilen bildet.
Distinct patterns of PV and SST GABAergic neuronal activity in the basal forebrain during olfactory-guided behavior in mice
Authors: Moss Elizabeth H. , Tantry Evelyne K. , Le Elaine , Chin Pey-Shyuan , Ambrosi Priscilla , Brandel-Ankrapp Katie L. , Arenkiel Benjamin R.
Published: February 2025

Sensory perception relies on the flexible detection and interpretation of stimuli across variable contexts, conditions, and behavioral states. The basal forebrain is a hub for behavioral state regulation, supplying dense cholinergic and GABAergic projections to various brain regions involved in sensory processing. Of GABAergic neurons in the basal forebrain, parvalbumin (PV) and somatostatin (SST) subtypes serve opposing roles towards regulating behavioral states. To elucidate the role of basal forebrain circuits in sensory-guided behavior, we investigated GABAergic signaling dynamics during odor-guided decision-making in male and female mice. We used fiber photometry to record cell type-specific basal forebrain activity during an odor discrimination task and correlated temporal patterns of PV and SST neuronal activity with olfactory task performance. We found that while both PV-expressing and SST-expressing GABAergic neurons were excited during trial initiation, PV neurons were selectively suppressed by reward whereas SST neurons were excited. Notably, chemogenetic inhibition of BF SST neurons modestly altered decision bias to favor reward-seeking while optogenetic inhibition of BF PV neurons during odor presentations improved discrimination accuracy. Together, these results suggest that the bidirectional activity of GABAergic basal forebrain neuron subtypes distinctly influence perception and decision-making during olfactory guided behavior.

Significance statementThis study reveals distinct roles for basal forebrain GABAergic neurons in odor perception and odor-guided decision-making. Fiber photometry shows that basal forebrain parvalbumin-expressing neurons are selectively suppressed by rewards, while somatostatin-expressing neurons are activated, establishing the unique recruitment of these GABAergic neurons during behavioral reinforcement. Chemogenetic and optogenetic interventions demonstrate divergent roles for these neuronal subtypes in reward-seeking behavior and odor perception. This research provides new insights into how GABAergic neurons in the basal forebrain shape sensory perception and decision-making.