bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prospects of detecting deviations to Kerr geometry with radiation reaction effects in EMRIs
Authors: Chowdhuri Abhishek , Bhattacharyya Arpan , Kumar Shailesh 
Published: April 2024

Abstract
Direct detection of gravitational waves and binary black hole mergers have proven to be
remarkable investigations of general relativity. In order to have a definitive answer as to
whether the black hole spacetime under test is the Kerr or non-Kerr, one requires accurate mapping
of the metric. Since EMRIs are perfect candidates for space-based detectors, Laser Interferometer
Space Antenna (LISA) observations will serve a crucial purpose in mapping the spacetime metric. In
this article, we consider such a study with the Johannsen spacetime that captures the deviations
from the Kerr black hole and further discuss their detection prospects. We analytically derive the
leading order post-Newtonian corrections in the average loss of energy and angular momentum fluxes
generated by a stellar-mass object exhibiting eccentric equatorial motion in the Johannsen
background. We further study the orbital evolution of the inspiralling object within the adiabatic
approximation. We lastly provide the possible detectability of deviations from the Kerr black hole
by estimating gravitational wave dephasing and highlight the crucial role of LISA observations.
A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction
Authors: Kumar Halder Anup , Agarwal Abhishek , Jodkowska Karolina , Plewczynski Dariusz 
Published: March 2024

Abstract
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.
scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics
Authors: Zheng Chuanyang , Wang Yixuan , Cheng Yuqi , Wang Xuesong , Wei Hongxin , King Irwin , Li Yu 
Published: March 2024

Abstract
Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel’s performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.
A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts
Authors: Pan Jie , Zhang Zhen , Li Ying , Yu Jiaoyang , You Zhuhong , Li Chenyu , Wang Shixu , Zhu Minghui , Ren Fengzhi , Zhang Xuexia , Sun Yanmei , Wang Shiwei 
Published: March 2024

Abstract
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.
Benchmarking digital PCR partition classification methods with empirical and simulated duplex data
Authors: Chen Yao , De Spiegelaere Ward , Trypsteen Wim , Gleerup David , Vandesompele Jo , Lievens Antoon , Vynck Matthijs , Thas Olivier 
Published: March 2024

Abstract
Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters (‘rain’) as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.
FAIR Header Reference genome: a TRUSTworthy standard
Authors: Wright Adam , Wilkinson Mark D , Mungall Christopher , Cain Scott , Richards Stephen , Sternberg Paul , Provin Ellen , Jacobs Jonathan L , Geib Scott , Raciti Daniela , Yook Karen , Stein Lincoln , Molik David C 
Published: March 2024

Abstract
The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR’s design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.
Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity
Authors: Thrift William John , Perera Jason , Cohen Sivan , Lounsbury Nicolas W , Gurung Hem R , Rose Christopher M , Chen Jieming , Jhunjhunwala Suchit , Liu Kai 
Published: March 2024

Abstract
Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide–MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.
DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage
Authors: Li Yiming , Zhang Haoling , Chen Yuxin , Shen Yue , Ping Zhi 
Published: March 2024

Abstract
DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin–yang codec is developed.
The effect of parathyroid hormone lowering by etelcalcetide therapy on calcification propensity and calciprotein particles in hemodialysis patients
Authors: Thiem Ursula , Lenz Jakob , Haller Maria C , Pasch Andreas , Smith Edward R , Cejka Daniel 
Published: March 2024

ABSTRACT

Background
This study investigated whether parathyroid hormone (PTH) lowering with etelcalcetide, and the consequent effects on mineral and bone metabolism, could improve serum calcification propensity (T50 time) and decrease calciprotein particle (CPP) load in hemodialysis patients with secondary hyperparathyroidism.

Methods
In this single-arm, prospective, dose-escalation proof-of-principle study, hemodialysis patients received etelcalcetide at 2.5 mg/dialysis session with increments of 2.5 mg every 4 weeks to a maximum dose of 15 mg three times a week or until a pre-specified safety endpoint was reached, followed by an 8-week wash-out phase.

Results
Out of 36 patients recruited (81% male, 62 ± 13 years), 16 patients completed the study per protocol with a mean maximum tolerated dose of etelcalcetide of 9.5 ± 2.9 mg/dialysis session. With escalating doses of etelcalcetide, PTH and serum calcium levels significantly decreased (P &lt; 0.0001). While there was no significant change in T50 times or serum phosphate levels, etelcalcetide did yield significant and consistent reductions in serum levels of endogenous calciprotein monomers [−35.4 (−44.4 to −26.5)%, P &lt; 0.0001], primary [−22.4 (−34.5 to −10.3)%, P &lt; 0.01] and secondary CPP [−29.1 (−45.7 to −12.4)%, P &lt; 0.01], an effect that was reversed after therapy withdrawal. Serum levels of osteoclastic markers significantly decreased with escalating doses of etelcalcetide, while levels of the osteoblastic marker remained stable.

Conclusions
Lowering of PTH with etelcalcetide did not result in statistically significant changes in T50. By contrast, homogenous reductions in serum levels of calciprotein monomers, primary and secondary CPP were observed.
Inflammatory-mediated atrial cardiomyopathy diagnosed using multimodality imaging and successfully treated with prednisolone: a case report
Authors: Larsen Birgitte Carbuhn , Al-Badri Kadhem Helo Abbas , Mellemkjær Søren , Bærentzen Steen , Oxlund Christina Stolzenburg , Pryds Kasper 
Published: March 2024

Abstract

Background
Atrial fibrillation is a common cardiac arrhythmia and often develops secondary to structural cardiac changes. Both the occurrence of atrial fibrillation and/or structural changes of the heart may lead to development of atrial cardiomyopathy and heart failure (HF). However, isolated atrial cardiomyopathy caused by focal atrial thickening is a rare condition, previously only described in case reports as a result of different aetiologies all linked to inflammation.

Case summary
A patient with inflammatory-mediated atrial cardiomyopathy causing atrial fibrillation and acute decompensated HF presented as isolated left atrial wall thickening on transoesophageal echocardiography. The diagnosis was confirmed using multimodality imaging with transthoracic and transoesophageal echocardiography, cardiac magnetic resonance imaging, positron emissions tomography/computer tomography scanning and intracardiac echocardiography-guided endomyocardial biopsy. Despite no specific histological aetiology, the observed atrial cardiomyopathy might be associated with type 1 diabetes mellitus. The patient in the present case was successfully treated with prednisolone.

Discussion
Diabetes mellitus is an important risk factor for developing atrial fibrillation and diabetic cardiomyopathy, due to reduced levels of anti-inflammatory and increased levels of proinflammatory cytokines causing cardiac inflammatory structural remodelling. The regression of the atrial thickening might be due to prednisolone’s anti-inflammatory effects and thereby ability to suppress atrial remodelling and reduce the occurrence of atrial fibrillation. However, the effect of prednisolone might only affect the non-manifested inflammatory-mediated atrial remodelling. Due to the rare occurrence of isolated atrial cardiomyopathy a multiple imaging approach during the diagnostic process and follow-ups are essential to determine the aetiology and effect of the treatment.
Estimating the timber value of a forest property using geographically balanced samples and unoccupied aerial vehicle data
Authors: Räty Janne , Heikkinen Juha , Kukkonen Mikko , Mehtätalo Lauri , Kangas Annika , Packalen Petteri 
Published: March 2024

Abstract
A common task in forestry is to determine the value of a forest property, and timber is the most valuable component of that property. Remotely sensed data collected by an unoccupied aerial vehicle (UAV) are suited for this purpose as most forest properties are of a size that permits the efficient collection of UAV data. These UAV data, when linked to a probability sample of field plots, enable the model-assisted (MA) estimation of the timber value and its associated uncertainty. Our objective was to estimate the value of timber (€/ha) in a 40-ha forest property in Finland. We used a systematic sample of field plots (n = 160) and 3D image point cloud data collected by an UAV. First, we studied the effects of spatial autocorrelation on the variance estimates associated with the timber value estimates produced using a field data-based simple expansion (EXP) estimator. The variance estimators compared were simple random sampling, Matérn, and a variant of the Grafström–Schelin estimator. Second, we compared the efficiencies of the EXP and MA estimators under different sampling intensities. The sampling intensity was varied by subsampling the systematic sample of 160 field plots. In the case of the EXP estimator, the simple random sampling variance estimator produced the largest variance estimates, whereas the Matérn estimator produced smaller variance estimates than the Grafström–Schelin estimator. The MA estimator was more efficient than the EXP estimator, which suggested that the reduction of sampling intensity from 160 to 60 plots is possible without deterioration in precision. The results suggest that the use of UAV data improves the precision of timber value estimates compared to the use of field data only. In practice, the proposed application improves the cost-efficiency of the design-based appraisal of a forest property because expensive field workload can be reduced by means of UAV data.
Can we obtain reliable seismic b-values for real-time catalogues?
Authors: Li Linxuan , Luo Gang 
Published: March 2024

SUMMARY
The seismic b-value in Gutenberg–Richter law is an important parameter in earthquake science research and earthquake risk assessment. People have tried to use b-values for short-term earthquake forecasts, and this requires the premise of estimating reliable b-values for real-time seismic catalogues. However, estimating b-values for real-time catalogues, which are usually of poor qualities, is usually faced with many difficulties and problems. In this study, through a series of numerical tests, we investigate the performance of three methods, including the commonly used maximum-likelihood estimation method and two relatively new b-value estimation methods, namely the b-positive and K–M slope methods, on calculating b-values for real-time seismic catalogues. We also apply these three methods to both observed seismic catalogues (the seismic sequence in the Costa Marchigiana, Italy, and a high-resolution early aftershock sequence of the 2023 two Mw ∼7.8 earthquakes in Türkiye) and synthetic real-time seismic catalogues. The results in this study show that it seems difficult to obtain accurate b-values for real-time earthquake catalogues by each of these three methods, but the combination of these methods may give a better judgment—if all three methods suggest that the change in b-value is significant, the probability of making a correct decision is very high. Facing the uncertainty of b-value estimation that always exists, we advocate exploring the effectiveness of standard b-value estimation strategies in practical applications.
Genome-wide transcriptome analysis reveals key regulatory networks and genes involved in the determination of seed hardness in vegetable soybean
Authors: Wang Congcong , Lin Jianyu , Bu Yuanpeng , Sun Ruidong , Lu Yang , Gai JunYi , Xing Han , Guo Na , Zhao Jinming 
Published: April 2024

Abstract
Seed hardness is an important quality trait of vegetable soybean. To determine the factors underlying seed hardness, two landraces with contrasting seed hardness, Niumaohuang (low seed hardness) and Pixiansilicao (high seed hardness), were selected from 216 soybean accessions originating from 26 provinces in China. The contents of the main components in vegetable soybean seeds such as water, soluble sugar, starch, protein and oil were measured, and transcriptome analyses performed during five stages of seed developmental. Transcriptome analysis indicates that during the middle and late stages of seed development, a large number of genes involved in the synthesis or degradation of starch, storage protein, and fatty acids were differentially expressed, leading to differences in the accumulation of stored substances during seed maturation among Niumaohuang and Pixiansilicao. The activity of cell proliferation and the formation of cell walls in the middle and late stages of seed development may also affect the hardness of seeds to a certain extent. In addition, weighted gene co-expression network analysis (WGCNA) was undertaken to identify co-expressed gene modules and hub genes that regulate seed hardness. Overexpression of a candidate seed hardness regulatory hub gene, GmSWEET2, resulted in increased seed hardness. In this study, the important role of GmSWEET2 in regulating the hardness of vegetable soybean seeds was verified and numerous potential key regulators controlling seed hardness and the proportion of seed components were identified, laying the groundwork for improving the texture of vegetable soybean.
A materialist reconsideration of international peace practice and its human and non-human constituents
Authors: Lakitsch Maximilian 
Published: April 2024

Abstract
The international community's sobering track record of sustainably reducing the number of armed conflicts worldwide, as well as the increasing involvement of non-human complexities in times of climate crisis suggest that current approaches to foster peace are too narrow and inflexible. So far, demands and calls for more flexible and adaptive peace practice made by policy-makers and researchers have not led to a related paradigm shift. In order to contribute to such a change of practice, this article advances a speculative analytical attempt that seeks to conceptually uncover new modes of engaging with the world for the purpose of peace. It builds on Baruch Spinoza's materialism and describes peace as intimately interrelated with the issue of political community. Following Spinoza, the article reconsiders the imaginary of political community as something that is constantly being rearticulated by a contingent complexity of human and non-human actors, and thus transcends the geographic scope of its formal borders as well as its epistemic scope across power hierarchies. Finally, the article introduces the human–non-human multitude as political subject that is able to detect and interpret the constituents of a political community, and therefore to maintain and sustain peace.