bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction
Authors: Burkhart Michael C. , Lee Liz Y. , Vaghari Delshad , Toh An Qi , Chong Eddie , Chen Christopher , Tiňo Peter , Kourtzi Zoe 
Published: May 2024

AbstractPredicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.
Effects of Eimeria acervulina infection on the luminal and mucosal microbiota of the cecum and ileum in broiler chickens
Authors: Campos Philip M. , Miska Katarzyna B. , Jenkins Mark C. , Yan Xianghe , Proszkowiec-Weglarz Monika 
Published: May 2024

AbstractCoccidiosis, an intestinal disease caused by Eimeria parasites, is responsible for major losses in the poultry industry by impacting chicken health. The gut microbiota is associated with health factors, such as nutrient exchange and immune system modulation, requiring understanding on the effects of Eimeria infection on the gut microbiota. This study aimed to determine the effects of Eimeria acervulina infection on the luminal and mucosal microbiota of the cecum (CeL and CeM) and ileum (IlL and IlM) at multiple time points (days 3, 5, 7, 10, and 14) post-infection. E. acervulina infection decreased evenness in CeL microbiota at day 10, increased richness in CeM microbiota at day 3 before decreasing richness at day 14, and decreased richness in IlL microbiota from day 3 to 10. CeL, CeM, and IlL microbiota differed between infected and control birds based on beta diversity at varying time points. Infection reduced relative abundance of bacterial taxa and some predicted metabolic pathways known for short-chain fatty acid production in CeL, CeM, and IlL microbiota, but further understanding of metabolic function is required. Despite E. acervulina primarily targeting the duodenum, our findings demonstrate the infection can impact bacterial diversity and abundance in the cecal and ileal microbiota.
Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction
Authors: Wild Romina , Sozio Emanuela , Margiotta Riccardo G. , Dellai Fabiana , Acquasanta Angela , Del Ben Fabio , Tascini Carlo , Curcio Francesco , Laio Alessandro 
Published: May 2024

AbstractClinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features that is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical and are generally available only for a fraction of the patients. We apply this algorithm to a data set of $$\sim$$
∼
1300 patients treated for COVID-19 in Udine hospital before October 2021. Using this approach, we find combinations of features which, if used in combination, are maximally informative of the clinical fate and of the severity of the disease. The optimal number of features, which is determined automatically, turns out to be between 10 and 15. These features can be measured at admission. The approach can be used also if the features are available only for a fraction of the patients, does not require imputation and, importantly, is able to automatically select features with small inter-feature correlation. Clinical insights deriving from this study are also discussed.
Magnetic iron oxide nanoparticle enhanced microwave pretreatment for anaerobic digestion of meat industry sludge
Authors: Jákói Zoltán Péter , Hodúr Cecilia , Beszédes Sándor 
Published: May 2024

AbstractOur study investigates the effects of iron oxide (Fe3O4) nanoparticles combined microwave pretreatment on the anaerobic digestibility and soluble chemical oxygen demand (SCOD) of meat industry sludge. One of our main objectives was to see whether the different microwave-based pretreatment procedures can enhance biogas production by improving the biological availability of organic compounds. Results demonstrated that combining microwave irradiation with magnetic iron oxide nanoparticles considerably increased SCOD (enhancement ratio was above 1.5), the rate of specific biogas production, and the total cumulative specific biogas volume (more than a threefold increment), while having no negative effect on the biomethane content. Furthermore, the assessment of the sludge samples’ dielectric properties (dielectric constant and loss factor measured at the frequency of 500 MHz) showed a strong correlation with SCOD changes (r = 0.9942, R2 = 0.99), offering a novel method to evaluate pretreatment efficiency.
Low-complexity continuous-variable quantum key distribution with true local oscillator using pilot-assisted frequency locking
Authors: Ruiz-Chamorro Andres , Garcia-Callejo Aida , Fernandez Veronica 
Published: May 2024

AbstractIn the domain of continuous variable quantum key distribution (CV-QKD), a significant challenge arises in achieving precise frequency synchronization, an issue commonly termed as frequency locking. This involves matching the optical frequencies of both the quantum signal laser and the local oscillator laser for accurate symbol demodulation during the exchange of quantum keys. As such, implementations today still grapple with maintaining precise synchronization between sender and receiver frequencies, occasionally hindering the efficiency and reliability of the information exchange. Addressing this challenge, we present and empirically validate a novel approach to CV-QKD by incorporating a pilot tone-assisted frequency locking algorithm to enhance stability when using a locally generated local oscillator (LLO) at the receiver. The proposed design leverages software-based optimization techniques, thereby eliminating the need for high-speed electronic stabilization devices and achieving efficient performance at typical repetition rates. Specifically, the introduction of the pilot tone algorithm allows us to effectively mitigate phase fluctuations and preserve the integrity of the quantum signals during transmission without resorting to time-multiplexed reference pulses or fast-locking electronics in the lasers. Our results suggest the potential for achieving secure key rates of up to 1 Mb/s over a 50 km single-mode fiber when using these techniques, offering promising insights into the feasibility of high-rate, low-complexity CV-QKD implementations under realistic conditions.
Identification of candidate biomarkers for GBM based on WGCNA
Authors: Sun Qinghui , Wang Zheng , Xiu Hao , He Na , Liu Mingyu , Yin Li 
Published: May 2024

AbstractGlioblastoma multiforme (GBM), the most aggressive form of primary brain tumor, poses a considerable challenge in neuro-oncology. Despite advancements in therapeutic approaches, the prognosis for GBM patients remains bleak, primarily attributed to its inherent resistance to conventional treatments and a high recurrence rate. The primary goal of this study was to acquire molecular insights into GBM by constructing a gene co-expression network, aiming to identify and predict key genes and signaling pathways associated with this challenging condition. To investigate differentially expressed genes between various grades of Glioblastoma (GBM), we employed Weighted Gene Co-expression Network Analysis (WGCNA) methodology. Through this approach, we were able to identify modules with specific expression patterns in GBM. Next, genes from these modules were performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using ClusterProfiler package. Our findings revealed a negative correlation between biological processes associated with neuronal development and functioning and GBM. Conversely, the processes related to the cell cycle, glomerular development, and ECM-receptor interaction exhibited a positive correlation with GBM. Subsequently, hub genes, including SYP, TYROBP, and ANXA5, were identified. This study offers a comprehensive overview of the existing research landscape on GBM, underscoring the challenges encountered by clinicians and researchers in devising effective therapeutic strategies.
Impact of pilot diesel injection timing on performance and emission characteristics of marine natural gas/diesel dual-fuel engine
Authors: Zhang Xiao , Gao Jianqun , Fan Dawei , Yang Qizheng , Han Fangjun , Yu Hongliang 
Published: May 2024

AbstractIn diesel-ignited natural gas marine dual-fuel engines, the pilot diesel injection timing (PDIT) determines the premixing time and ignition moment of the combustible mixture in the cylinder. The PDIT plays a crucial role in the subsequent development of natural gas flame combustion. In this paper, four PDITs (− 8 °CA, − 6 °CA, − 4 °CA, and − 2 °CA) were studied. The results show that the advancement of PDIT increased the engine's power, thermal efficiency, and natural gas flame spread velocity, and increased NO emissions and CH4 emissions of the marine engine. The PDIT affected the ignition delay period and the rapid combustion period to a greater extent than the slow combustion period and the post combustion period. With each 2 °CA advancement of PDIT, the engine's power increased by 69.87 kW, thermal efficiency increased by 0.42%, radial flame spread velocity increased by 2 m/s, axial flame spread velocity increased by 1.7 m/s, NO emissions increased by 6.1%, and CH4 emissions increased by 3.75%.
Prebiotic membrane structures mimic the morphology of alleged early traces of life on Earth
Authors: Jordan Seán F. , van Zuilen Mark A. , Rouillard Joti , Martins Zita , Lane Nick 
Published: May 2024

AbstractElucidating compositions of the first cell membranes requires experiments with molecules and chemical conditions representative of early Earth. The molecules used are described as ‘prebiotically plausible’, i.e., they could have formed through abiotic reactions before the emergence of biology. Similarly, the chemical properties of solutions in which these membranes are formed (e.g., pH, temperature, ionic strength) must represent early Earth environments. Here, using confocal and transmission electron microscopy combined with population morphometry, we show that prebiotically plausible molecules, in solutions representative of Hadean submarine alkaline hydrothermal vents, form microstructures with substantial morphological diversity. The microstructures hold the potential for use as analogues of prebiotic processes in the rock record. Additionally, many of the structures are morphologically similar to purported early microfossils, highlighting limitations of morphological interpretation in these studies. Detailed analyses of abiotic microstructures are essential for understanding the earliest life on Earth, and for interpretation of potential biosignatures from extra-terrestrial bodies.
Guided and unguided internet-delivered psychodynamic therapy for social anxiety disorder: A randomized controlled trial
Authors: Mechler Jakob , Lindqvist Karin , Magnusson Kristoffer , Ringström Adrián , Krafman Johan Daun , Alvinzi Pär , Kassius Love , Sowa Josefine , Andersson Gerhard , Carlbring Per 
Published: May 2024

AbstractSocial Anxiety Disorder (SAD) is highly prevalent and debilitating disorder. Treatments exist but are not accessible and/or helpful for all patients, indicating a need for accessible treatment alternatives. The aim of the present trial was to evaluate internet-delivered psychodynamic therapy (IPDT) with and without therapist guidance, compared to a waitlist control condition, in the treatment of adults with SAD. In this randomized, clinical trial, we tested whether IPDT was superior to a waitlist control, and whether IPDT with therapeutic guidance was superior to unguided IPDT. Participants were recruited nationwide in Sweden. Eligible participants were ≥ 18 years old and scoring ≥ 60 on the Liebowitz Social Anxiety Scale self-report (LSAS-SR) whilst not fulfilling any of the exclusion criteria. Included participants were randomly assigned to IPDT with guidance (n = 60), IPDT without guidance (n = 61), or waitlist (n = 60). The IPDT intervention comprised eight self-help modules based on affect-focused dynamic therapy, delivered over 8 weeks on a secure online platform. The primary outcome was SAD symptoms severity measured weekly by the LSAS-SR. Primary analyses were calculated on an intention-to-treat sample including all participants randomly assigned. Secondary outcomes were depressive symptoms, generalized anxiety, quality of life, emotion regulation and defensive functioning. At post-treatment, both active treatments were superior to the waitlist condition with guided treatment exhibiting larger between group effects than unguided treatment (d = 1.07 95% CI [0.72, 1.43], p < .001 and d = 0.61, 95% CI [0.25, 0.98], p = .0018) on the LSAS-SR respectively. Guided IPDT lead to larger improvements than unguided IPDT (d = 0.46, 95% CI [0.11, 0.80], p < .01). At post-treatment, guided IPDT was superior to waitlist on all secondary outcome measures. Unguided IPDT was superior to waitlist on depressive symptoms and general anxiety, but not on emotion regulation, self-compassion or quality of life. Guided IPDT was superior to unguided PDT on depressive symptoms, with a trend towards superiority on a measure of generalized anxiety. At six and twelve month follow-up there were no significant differences between guided and unguided IPDT. In conclusion, IPDT shows promising effects in the treatment of SAD, with larger benefits from guided IPDT compared to non-guided, at least at post-treatment. This finding increases the range of accessible and effective treatment alternatives for adults suffering from SAD. The study was prospectively registered at ClinicalTrials (NCT05015166).
Power-law productivity of highly biodiverse agroecosystems supports land recovery and climate resilience
Authors: Funabashi Masatoshi 
Published: May 2024

AbstractTransformative change in primary food production is urgently needed in the face of climate change and biodiversity loss. Although there are a growing number of studies aimed at global policymaking, actual implementations require on-site analyses of social feasibility anchored by ecological rationale. This article reports the in-depth characterizations of low-input mixed polyculture of highly diverse crops managed on the self-organization of ecosystems, which performed better compared to conventional monoculture methods in Japan and Burkina Faso. Analyses on crop productivity and diversity showed that the primary production of ecosystems followed a power law, and through the underlying mechanisms excelled in (1) promoting diversity and total quantity of products along with the rapid increase of in-field biodiversity, especially useful for the recovery of local regime shift in a semi-arid environment; (2) a fundamental reduction of inputs and environmental load; and (3) ecosystem-based autonomous adaptation of the crop portfolio to climatic variability. The overall benefits imply substantial possibilities of a new typology of sustainable farming for smallholders sensitive to climate change, which could overcome the historical trade-off between productivity and biodiversity based on the human-guided augmentation of ecosystems.
Relationship between radar reflectivity thresholds and very low frequency/low frequency total lightning for thunderstorm identification
Authors: Huang Yijun , Wang Jianguo , Cai Li , Fan Yadong , Wang Hongbin , Zhang Tao 
Published: May 2024

AbstractTo improve our knowledge of the spatio‐temporal correspondence between radar reflectivity and VLF/LF total lightning data, this paper proposes indexes to quantify the horizontal distribution and temporal evolution characteristics between radar composite reflectivity images and total lightning density map, lightning clusters and radar cells with different radar reflectivity thresholds (30, 35, 40, 45 and 50 dBZ), respectively. It is found that the number of radar grid cells with radar reflectivity over 30 dBZ is ten times the number of lightning grid cells. At the identification stage, the lightning activity regions in a radar cell account for less than 30% using thresholds of 30, 35 and 40 dBZ, respectively, and the radar cell has more than one lightning cluster, which means that the mesoscale convective systems typically have more than one draft, and drafts are only small part of the convection. The majority of the centroid deviations is less than 10 km, indicating that there are some shifts between electrically active regions and convective regions. Results suggest that VLF/LF total lightning data are consistent with radar data and total lightning data can be used individually on a smaller spatio‐temporal scale than radar data.
A group target track‐before‐detect approach using two‐stage strategy with maximum‐likelihood probabilistic data association
Authors: Bu Leiru , Rao Bin , Song Dan 
Published: May 2024

AbstractIn tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.
Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential
Authors: Shekhar Saraf Chandra , Bielewicz Paweł 
Published: May 2024

Context. Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift.
Aims. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements.
Methods. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method.
Results. We show that even with simple Gaussian errors with a standard deviation of σ(z) = 0.02(1 + z), the galaxy auto-power spectra in tomographic bins are offset by 2 − 15%. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations < 5%. As a result, the σ8 parameter deviates between 0.2 − 1.2σ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects.
Conclusions. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and σ8 are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.
Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic
Authors: Kim Keungoui , Kogler Dieter F. , Maliphol Sira 
Published: May 2024

AbstractGlobal scientific output is expanding exponentially, which in turn calls for a better understanding of the science of science and especially how the boundaries of scientific fields expand through processes of emergence. The present study proposes the application of embedded topic modeling techniques to identify new emerging science via knowledge recombination activities as evidenced through the analysis of research publication metadata. First, a dataset is constructed from metadata derived from the Web of Science Core Collection database. The dataset is then used to generate a global map representing a categorical scientific co-occurrence network. A research field is defined as interdisciplinary when multiple science categories are listed in its description. Second, the co-occurrence networks are subsequently compared between periods to determine changing patterns of influence in light of interdisciplinarity. Third, embedded topic modeling enables unsupervised association of interdisciplinary classification. We present the results of the analysis to demonstrate the emergence of global interdisciplinary sciences and further we perform qualitative validation on the results to identify what the sources of the emergent areas are. Based on these results, we discuss potential applications for identifying emergence through the merging of global interdisciplinary domains.