bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical models on bone cell homeostasis and kinetics in the presence of electric fields: a review
Authors: Lee Poh Soo , Sriperumbudur Kiran K , Dawson Jonathan , van Rienen Ursula , Appali Revathi 
Published: November 2024

Abstract
The role of bioelectricity in regulating various physiological processes has attracted increasing scientific interest in implementing exogenous electrical stimulations as a therapeutic approach. In particular, electrical stimuli are used clinically in pre-/post-surgery patient care for the musculoskeletal tissues. The reported potential of electric fields (EF) to regulate bone cell homeostasis and kinetics in vitro has further provoked more studies in this field of research. Various customised apparatuses have been developed, and a range of parameters for the applied EFs have been investigated in vitro with bone cells or mesenchymal stem cells. Additionally, biomaterials with conductive or piezo-electric properties have been designed to complement the enhancing effects of the EF on bone regeneration. Despite much research, there remained a significant gap in knowledge due to the diverse range of EF parameters available. Mathematical models are built to facilitate further understanding and zero in on an effective range of EF parameters in silico. However, the diverse range of EF parameters, experimental conditions, and reported analytical output of different works of literature were reported to possess significant variance, making it challenging to accurately model the field in silico. This review categorises the existing experimental approaches and the parameters used to distinguish the potential variables that apply to mathematical modelling. Furthermore, we will discuss existing modelling approaches and models available in the literature. With this, we will concisely highlight the need to categorise EF parameters, osteogenic differentiation initiators and research output.
Exploring temporal dynamics of ice melting through dynamic laser speckle imaging
Authors: S H Keerthana , Radhakrishnan P , Mujeeb A 
Published: November 2024

Abstract
Dynamic laser speckle imaging (DLSI) is an emerging non-destructive imaging technique used for characterizing rough surfaces. High spatio-temporal resolution and cost effective nature make this imaging technique more attractive in optical metrology. In this study, DLSI is utilized to monitor the intermittent dynamics of ice during its melting process. In DLSI, image processing algorithms such as Time History of Speckle Patterns and Co-Occurrence Matrices are generally employed to process the speckle images. However, these algorithms are limited to gather information from specific regions of the speckle images. To overcome this limitation, frequency domain algorithms are employed to analyse the activity across all frequency bands. In this work, we utilized Wavelet-Based Entropy for the analysis of speckle images captured from the surface of ice during its melting process. Wavelet-Based Entropy is a powerful frequency domain tool for exploring dynamic processes, offering superior capability in capturing spatial and spectral variations, making it particularly well-suited for the analysis of speckle images. The results were also compared with Probability density function and Co-Occurrence Matrices and demonstrated consistent agreement.
Fusion of improved RRT and ant colony optimization for robot path planning
Authors: Chang Zhen , Wang Yi , Cai Ying , Li Siquan , Gao Fangzheng 
Published: November 2024

Abstract
To address the issues of poor guidance at the beginning of the Ant Colony Optimization (ACO) algorithm, non-smooth paths, and its tendency to fall into local optima, this paper proposes a path planning approach based on the Rapidly-exploring Random Tree (RRT) and Ant Colony Optimization (ACO). Firstly, obstacles are inflated to set a safety distance, and a differentiated pheromone distribution is created using the sub-optimal trajectory produced by the improved RRT, guiding the initial direction of the ant colony. Secondly, dynamic strategies are introduced into the evaporation coefficient and heuristic factor, adjusting their weights according to the number of iterations to enhance the attraction of the target point to the ants. Then, a reward-punishment mechanism is used to update the pheromone, solving the problem of local optima. Finally, a pruning optimization strategy based on the maximum turning angle is employed to remove redundant nodes, making the path smoother. Multiple simulation results confirm that the algorithm possesses good global search capabilities and robustness under various conditions.
Illuminating new and known relations between knot invariants
Authors: Craven Jessica , Hughes Mark , Jejjala Vishnu , Kar Arjun 
Published: November 2024

Abstract
We automate the process of machine learning correlations between knot invariants. For nearly 200 000 distinct sets of input knot invariants together with an output invariant, we attempt to learn the output invariant by training a neural network on the input invariants. Correlation between invariants is measured by the accuracy of the neural network prediction, and bipartite or tripartite correlations are sequentially filtered from the input invariant sets so that experiments with larger input sets are checking for true multipartite correlation. We rediscover several known relationships between polynomial, homological, and hyperbolic knot invariants, while also finding novel correlations which are not explained by known results in knot theory. These unexplained correlations strengthen previous observations concerning links between Khovanov and knot Floer homology. Our results also point to a new connection between quantum algebraic and hyperbolic invariants, similar to the generalized volume conjecture.
Urban scaling of water and electricity demand across the United States
Authors: Adul Joy , Bhaskar Chiluveru Vijay , Obringer Renee 
Published: November 2024

Abstract
As urban populations continue to grow, ensuring an adequate supply of water and electricity will be imperative. However, these resources are generally extracted in rural areas, creating tension during periods of limited availability. It has been argued, however, that urban areas are more efficient users of various resources in spite of their large populations. Here, we test this argument for water and electricity consumption across 46 US cities. Leveraging urban scaling theory, we show that water and electricity consumption scale sublinearly with population. This suggests that cities are using water and electricity more efficiently as their population increases. Further, the results show that this sublinear scaling exists regardless of season or year. Nonetheless, there were cities that deviated from the expected consumption value that would have been predicted by this model. We explored the role that precipitation and temperature might have on these deviates and found that temperature, in particular, can help explain why certain cities consume more electricity than expected based on their population. Understanding the relationship between consumption patterns and population is critical for planning investments for future infrastructure systems that will need to service higher populations with more limited resources.
Impact of the urban heat island on heat-related mortality in the Tokyo Metropolitan Area, Japan
Authors: He Jinyu , Madaniyazi Lina , Chua Paul L C , Kim Yoonhee , Milojevic Ai , Oka Kazutaka , Honda Yasushi , Hashizume Masahiro 
Published: November 2024

Abstract
With global temperatures rising, urban heat islands (UHIs) intensify heat exposure in cities, potentially elevating heat-related mortality. However, the specific burden of UHI-attributable mortality remains inadequately quantified. This study investigates the impact of UHIs on heat-related mortality in the Tokyo Metropolitan Area (TMA), Japan, from 2010 to 2019. Daily time series data on maximum and mean ambient temperatures and all-cause mortality were obtained for 149 municipalities. Urban municipalities were identified by the presence of densely inhabited districts, with adjacent non-urban municipalities at similar elevations serving as a baseline (Zone 1). UHI intensity was quantified by calculating municipality-specific UHI anomalies (UHIa) as the deviation of daily maximum temperatures in each urban municipality from the average mean temperatures of Zone 1. Based on UHIa values, urban municipalities were grouped into nine zones (Zones 2–10). Heat-mortality relationships across zones were estimated using distributed lag nonlinear models, with relative risks (RRs) calculated at 30 °C using 26 °C as the reference. Heat-related mortality was computed for observed and counterfactual scenarios, the latter assuming urban temperatures matched non-urban levels. Differences in estimated mortality represented UHI-attributable impacts in the TMA. A mean UHIa difference of 0.72 °C was observed between Zones 2 and 10, with UHIa values ranging from 4.77 °C to 5.49 °C. No significant heterogeneity in RRs was observed across zones. UHI-attributable mortality was estimated at 2207 deaths (95% empirical confidence interval (eCI): 1200, 3119), accounting for 30.0% of urban heat-related deaths from 2010 to 2019. Zone-specific UHI-attributable mortality ranged from 9 deaths (95% eCI: 5, 13) to 593 deaths (95% eCI: 323, 839). These findings highlight the potential health impact of UHIs in the TMA and underscore the importance of integrating UHI effects into future temperature-mortality assessments to better understand urban heat-related health risks.
World delirium awareness and quality survey in 2023—a worldwide point prevalence study
Authors: Lindroth Heidi , Liu Keibun , Szalacha Laura , Ashkenazy Shelly , Bellelli Giuseppe , van den Boogaard Mark , Caplan Gideon , Chung Chi Ryang , Elhadi Muhammed , Gurjar Mohan , Heras-La-Calle Gabriel , Hoffman Magdalena , Jeitziner Marie-Madlen , Krewulak Karla , Mailhot Tanja , Morandi Alessandro , Nawa Ricardo Kenji , Oh Esther S , Collet Marie Oxenboell , Paulino Maria Carolina , von Haken Rebecca , Nydahl Peter ,   For the WDAD Study Team
Published: November 2024

Abstract

Background
Delirium, an acute brain dysfunction, is proposed to be highly prevalent in clinical care and shown to significantly increase the risk of mortality and dementia.

Objectives
To report on the global prevalence of clinically documented delirium and delirium-related clinical practices in wards caring for paediatric and adult patients in healthcare facilities.

Design
A prospective, cross-sectional, 39-question survey completed on World Delirium Awareness Day, 15 March 2023.

Participants
Clinicians or researchers with access to clinical data.

Main Outcome and Measure
The primary outcome was the prevalence of clinically documented delirium at 8:00 a.m. (4 h) and 8:00 p.m. (±4 h). Secondary outcomes included delirium-related care practices and barriers to use. Descriptive statistics were calculated and multilevel modelling was completed.

Results
1664 wards submitted surveys from 44 countries, reporting on delirium assessments at 8:00 a.m. (n = 36 048) and 8:00 p.m. (n = 32 867); 61% reported use of validated delirium assessment tools. At 8:00 a.m., 18% (n = 2788/15 458) and at 8:00 p.m., 17.7% (n = 2454/13 860) were delirium positive. Top prevention measures were pain management (86.7%), mobilisation (81.4%) and adequate fluids (80.4%). Frequently reported pharmacologic interventions were benzodiazepines (52.7%) and haloperidol (46.2%). Top barriers included the shortage of staff (54.3%), lack of time to educate staff (48.6%) and missing knowledge about delirium (38%).

Conclusion and Relevance
In this study, approximately one out of five patients were reported as delirious. The reported high use of benzodiazepines needs further evaluation as it is not aligned with best-practice recommendations. Findings provide a benchmark for future quality improvement projects and research.
Local adaptation and phenotypic plasticity in two forest understorey herbs in response to forest management intensity
Authors: Møller Charlotte , March-Salas Martí , De Frenne Pieter , Scheepens J F 
Published: November 2024

Abstract
Local adaptation is a common phenomenon that helps plant populations to adjust to broad-scale environmental heterogeneity. Given the strong effect of forest management on the understorey microenvironment and often long-term effects of forest management actions, it seems likely that understorey herbs may have locally adapted to the practiced management regime and induced environmental variation. We investigated the response of Anemone nemorosa and Milium effusum to forest management using a transplant experiment along a silvicultural management intensity gradient. Genets were sampled from sites with contrasting management intensities and transplanted sympatrically, near allopatrically and far allopatrically along the management intensity gradient to test for local adaptation and phenotypic plasticity, as well as to sites where the species were absent to test for recruitment versus dispersal limitations. We then measured survival and fitness traits over two growing seasons. We found only little evidence of local adaptation in A. nemorosa and M. effusum, whereas various traits in both species showed linear plastic changes in response to transplantation along the forest management intensity gradient. Furthermore, A. nemorosa performed worse when transplanted to unoccupied sites, suggesting recruitment limitation, whereas M. effusum performed better in unoccupied sites, suggesting dispersal limitation. Altogether, our results underpin the importance of forest management to indirectly drive phenotypic variation among populations of forest plants.
Improved prediction of post-translational modification crosstalk within proteins using DeepPCT
Authors: Huang Yu-Xiang , Liu Rong 
Published: November 2024

Abstract

Motivation
Post-translational modification (PTM) crosstalk events play critical roles in biological processes. Several machine learning methods have been developed to identify PTM crosstalk within proteins, but the accuracy is still far from satisfactory. Recent breakthroughs in deep learning and protein structure prediction could provide a potential solution to this issue.

Results
We proposed DeepPCT, a deep learning algorithm to identify PTM crosstalk using AlphaFold2-based structures. In this algorithm, one deep learning classifier was constructed for sequence-based prediction by combining the residue and residue pair embeddings with cross-attention techniques, while the other classifier was established for structure-based prediction by integrating the structural embedding and a graph neural network. Meanwhile, a machine learning classifier was developed using novel structural descriptors and a random forest model to complement the structural deep learning classifier. By integrating the three classifiers, DeepPCT outperformed existing algorithms in different evaluation scenarios and showed better generalizability on new data owing to its less distance dependency.

Availability and implementation
Datasets, codes, and models of DeepPCT are freely accessible at https://github.com/hzau-liulab/DeepPCT/.
Sparse Neighbor Joining: rapid phylogenetic inference using a sparse distance matrix
Authors: Kurt Semih , Bouchard-Côté Alexandre , Lagergren Jens 
Published: November 2024

Abstract

Motivation
Phylogenetic reconstruction is a fundamental problem in computational biology. The Neighbor Joining (NJ) algorithm offers an efficient distance-based solution to this problem, which often serves as the foundation for more advanced statistical methods. Despite prior efforts to enhance the speed of NJ, the computation of the n2 entries of the distance matrix, where n is the number of phylogenetic tree leaves, continues to pose a limitation in scaling NJ to larger datasets.

Results
In this work, we propose a new algorithm which does not require computing a dense distance matrix. Instead, it dynamically determines a sparse set of at most O(n log n) distance matrix entries to be computed in its basic version, and up to O(n log 2n) entries in an enhanced version. We show by experiments that this approach reduces the execution time of NJ for large datasets, with a trade-off in accuracy.

Availability and implementation
Sparse Neighbor Joining is implemented in Python and freely available at https://github.com/kurtsemih/SNJ.
Attenuation of Ampullary Anoctamin 1 by the peritoneal fluid in rhesus macaques with spontaneous endometriosis
Authors: Luo Fangzhou , Slayden Ov 
Published: November 2024

Abstract
Altered peristaltic and ciliary dysfunction is a feature of females with endometriosis. To further explore this premise, we examined the ampulla of rhesus macaques (Macaca mulatta) with and without spontaneous endometriosis for the expression of adenylate kinase 7 (AK7), a mitochondrial-dwelling nucleotide converting enzyme with critical roles in cellular kinesis, forkhead protein box J1 (FOXJ1), a marker of cilia abundance, and Anoctamin 1 (ANO1) as a marker of both smooth muscle contraction and ciliogenesis. We further performed an in vitro experiment that treated ampullary segments with peritoneal fluid from animals with and without endometriosis. We report significantly downregulated expression of ANO1 in the ampulla of monkeys with endometriosis (in vivo), and in the ampullary segments exposed to peritoneal fluid of animals with endometriosis. We did not observe statistically significant differences in the expression of AK7 or FOXJ1 both in vivo and in vitro. This highlights potentially essential roles of ANO1 in the oviduct, the dampening of which may lead to a specific subtype of endometriosis-caused subfertility.
On Rosenbaum’s rank-based matching estimator
Authors: Cattaneo Matias D , Han Fang , Lin Zhexiao 
Published: November 2024

Summary
In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between componentwise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that Rosenbaum’s rank-based matching estimator, when coupled with a regression adjustment, enjoys the properties of double robustness and semiparametric efficiency without the need to enforce restrictive covariate moment assumptions. Our theoretical findings further emphasize the statistical virtues of employing ranks for estimation and inference, more broadly aligning with the insights put forth by Peter Bickel in his 2004 Rietz lecture.
Lewy pathology formation in patient-derived GBA1 Parkinson’s disease midbrain organoids
Authors: Frattini Emanuele , Faustini Gaia , Lopez Gianluca , Carsana Emma V , Tosi Mattia , Trezzi Ilaria , Magni Manuela , Soldà Giulia , Straniero Letizia , Facchi Daniele , Samarani Maura , Martá-Ariza Mitchell , De Luca Chiara M G , Vezzoli Elena , Pittaro Alessandra , Stepanyan Astghik , Silipigni Rosamaria , Rosety Isabel , Schwamborn Jens C , Sardi Sergio P , Moda Fabio , Corti Stefania , Comi Giacomo P , Blandini Fabio , Tritsch Nicolas X , Bortolozzi Mario , Ferrero Stefano , Cribiù Fulvia M , Wisniewski Thomas , Asselta Rosanna , Aureli Massimo , Bellucci Arianna , Di Fonzo Alessio 
Published: November 2024

Abstract
Fibrillary aggregation of α-synuclein in Lewy body inclusions and nigrostriatal dopaminergic neuron degeneration define Parkinson’s disease neuropathology. Mutations in GBA1, encoding glucocerebrosidase, are the most frequent genetic risk factor for Parkinson’s disease. However, the lack of reliable experimental models able to reproduce key neuropathological signatures has hampered the clarification of the link between mutant glucocerebrosidase and Parkinson’s disease pathology. Here, we describe an innovative protocol for the generation of human induced pluripotent stem cell-derived midbrain organoids containing dopaminergic neurons with nigral identity that reproduce characteristics of advanced maturation. When applied to patients with GBA1-related Parkinson’s disease, this method enabled the differentiation of midbrain organoids recapitulating dopaminergic neuron loss and fundamental features of Lewy body pathology observed in human brains, including the generation of α-synuclein fibrillary aggregates with seeding activity that also propagate pathology in healthy control organoids. Still, we observed that the retention of mutant glucocerebrosidase in the endoplasmic reticulum and increased levels of its substrate glucosylceramide are determinants of α-synuclein aggregation into Lewy body-like inclusions. Consistently, the reduction of glucocerebrosidase activity accelerated α-synuclein pathology by promoting fibrillary α-synuclein deposition. Finally, we demonstrated the efficacy of ambroxol and GZ667161 – two modulators of the glucocerebrosidase pathway in clinical development for the treatment of GBA1-related Parkinson’s disease – in reducing α-synuclein pathology in this model, supporting the use of midbrain organoids as a relevant pre-clinical platform for investigational drug screening.
Neuropathic pain relief and altered brain networks after dorsal root entry zone microcoagulation in patients with spinal cord injury
Authors: Falci Scott , Morse Leslie , Berliner Jeffrey , Murakami Mario , Welch Abigail , Barnkow David , Nguyen Nguyen , Battaglino Ricardo , Linnman Clas 
Published: November 2024

Abstract
Spinal cord injury (SCI) below-level neuropathic pain is a difficult condition to treat both pharmacologically and surgically. Successful treatment using surgically created lesions of the spinal cord dorsal root entry zone (DREZ), guided by intramedullary monitoring of neuronal electrical hyperactivity, has shown that DREZs both cephalad and caudal to the level of injury can be the primary generators of SCI below-level pain. Below-level pain perception follows a unique somatotopic map of DREZ pain generators, and neuronal transmission to brain pain centres can occur primarily through sympathetic nervous system (SNS) pathways. This study evaluated changes in brain resting-state and task-based functional magnetic resonance imaging responses before and after neuroelectrically guided DREZ microcoagulation surgery. Eight persons with clinically complete SCI who suffered chronic, severe and unrelenting below-level neuropathic pain refractory to all pharmacological management were investigated before and after the surgical intervention. Baseline differences between DREZ subjects, group-matched low pain SCI and healthy controls were observed in medial primary somatosensory and motor cortex connectivity to the hippocampus, amygdala and medial prefrontal cortex. The DREZ surgery led to short-term (12 days) almost complete pain relief in all participants and long-term (1+ year) pain relief in all participants receiving DREZ lesioning both cephalad and caudal to the level of injury (six out of eight participants). Follow-up 12 days post-operatively indicated that DREZ surgery normalized prior negative functional coupling between primary sensory (S1) and motor (M1) cortices to the hippocampus, amygdala and the medial prefrontal cortex, increased M1 to putamen and amygdala connectivity and decreased limbic to cerebellar connectivity. DREZ hyperactivity was found both cephalad and caudal to the level of injury. The regional distribution of hyperactive regions corresponded not to classical dermatomes but rather mapped on to intermediolateral (IML) cell column end organ innervation of body regions of below-level pain perception, consistent with a non-classical SNS-mediated somatotopic map of DREZ below-level pain generators. The results indicate that neuroelectrically guided DREZ microcoagulation alters a medial prefrontal–somatosensory–limbic network that is separate from classical pain pathways. This provides further evidence that below-level SCI pain originates in hyperactive DREZs and can be relayed to the brain via the SNS.