bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of off-label use of reduced-dose direct oral anticoagulants therapy in the treatment of pulmonary embolism comparable to standard-dose therapy
Authors: Yamazoe Shinji , Imai Hajime , Ogawa Yasuhiro , Kano Naoaki , Murase Yosuke , Mamiya Keita , Ikeda Tomoyo , Hiramatsu Kei , Torii Jun , Kawaguchi Katsuhiro 
Published: February 2024

AbstractDirect oral anticoagulants (DOACs) have been shown to be effective and safe in preventing pulmonary embolism recurrence. In this single-center retrospective observational study, we aimed to evaluate the efficacy and safety of reduced-dose DOACs in 86 consecutive patients with acute pulmonary embolism. Patients were divided into standard-dose and reduced-dose DOACs groups. Initial clot volume did not significantly differ between the two groups (standard-dose DOACs vs. reduced-dose DOACs, 18.8 [Q1–Q3 7.3–30.8] mL vs. 10.0 [Q1–Q3 3.2–27.9] mL, p = 0.1). Follow-up computed tomography (CT) within 30 days showed a higher rate of clot volume reduction or disappearance in the standard-dose group compared to the reduced-dose group (standard-dose DOACs vs. reduced-dose DOACs, 81.6% vs. 53.9%, p = 0.02). However, at the final follow-up CT, there was no significant difference in clot volume change between the two groups (standard-dose DOACs vs. reduced-dose DOACs, 91.5% vs. 82.0%, p = 0.19). Major bleeding occurred in two patients in the standard-dose group (4.3%) and three patients in the reduced-dose DOACs group (7.7%) (p = 0.5). In conclusion, while standard-dose DOACs demonstrated superior efficacy in early clot reduction, reduced doses of apixaban and edoxaban showed comparable efficacy and safety profiles in long-term treatment of acute pulmonary embolism in certain patients.
The effect of missing data on evolutionary analysis of sequence capture bycatch, with application to an agricultural pest
Authors: Featherstone Leo A. , McGaughran Angela 
Published: February 2024

AbstractSequence capture is a genomic technique that selectively enriches target sequences before high throughput next-generation sequencing, to generate specific sequences of interest. Off-target or ‘bycatch’ data are often discarded from capture experiments, but can be leveraged to address evolutionary questions under some circumstances. Here, we investigated the effects of missing data on a variety of evolutionary analyses using bycatch from an exon capture experiment on the global pest moth, Helicoverpa armigera. We added > 200 new samples from across Australia in the form of mitogenomes obtained as bycatch from targeted sequence capture, and combined these into an additional larger dataset to total > 1000 mitochondrial cytochrome c oxidase subunit I (COI) sequences across the species’ global distribution. Using discriminant analysis of principal components and Bayesian coalescent analyses, we showed that mitogenomes assembled from bycatch with up to 75% missing data were able to return evolutionary inferences consistent with higher coverage datasets and the broader literature surrounding H. armigera. For example, low-coverage sequences broadly supported the delineation of two H. armigera subspecies and also provided new insights into the potential for geographic turnover among these subspecies. However, we also identified key effects of dataset coverage and composition on our results. Thus, low-coverage bycatch data can offer valuable information for population genetic and phylodynamic analyses, but caution is required to ensure the reduced information does not introduce confounding factors, such as sampling biases, that drive inference. We encourage more researchers to consider maximizing the potential of the targeted sequence approach by examining evolutionary questions with their off-target bycatch where possible—especially in cases where no previous mitochondrial data exists—but recommend stratifying data at different genome coverage thresholds to separate sampling effects from genuine genomic signals, and to understand their implications for evolutionary research.
Safety and efficacy of new staple-line reinforcement in lung resection: a prospective study of 48 patients
Authors: Mitsui Suguru , Tanaka Yugo , Nishikubo Megumi , Doi Takefumi , Tane Shinya , Hokka Daisuke , Mitomo Yuji , Maniwa Yoshimasa 
Published: February 2024

Abstract
Purpose
To evaluate the safety and efficacy of new staple-line reinforcement (SLR) in pulmonary resection through a prospective study and to compare the results of this study with historical control data in an exploratory study.

Methods
The subjects of this study were 48 patients who underwent thoracoscopic lobectomy. The primary endpoint was air leakage from the staple line. The secondary endpoints were the location of air leakage, duration of air leakage, and postoperative pulmonary complications.

Results
The incidence of intraoperative air leakage from the staple line was 6.3%. Three patients had prolonged air leakage as a postoperative pulmonary complication. No malfunction was found in patients who underwent SLR with the stapling device. When compared with the historical group, the SLR group had a significantly lower incidence of air leakage from the staple line (6.3% vs. 28.5%, P < 0.001) and significantly shorter indwelling chest drainage time (P = 0.049) and length of hospital stay (P < 0.001).

Conclusions
The use of SLR in pulmonary resection was safe and effective. When compared with conventional products, SLR could control intraoperative air leakage from the staple line and shorten time needed for indwelling chest drainage and the length of hospital stay.
Formally verifying decompositions of stochastic specifications
Authors: Hampus Anton , Nyberg Mattias 
Published: February 2024

AbstractAccording to the principles of compositional verification, verifying that lower-level components satisfy their specification ensures that the whole system satisfies its top-level specification. The key step is to ensure that the lower-level specifications constitute a correct decomposition of the top-level specification. In a non-stochastic context, such decomposition can be analyzed using techniques of theorem proving. In industrial applications, especially in safety-critical systems, specifications are often of stochastic nature, for example, giving a bound on the probability that a system failure will occur before a given time. A decomposition of such a specification requires techniques beyond traditional theorem proving. The first contribution of the paper is a theoretical framework that allows the representation of, and reasoning about, stochastic and timed behavior of systems as well as specifications for such behavior. The framework is based on traces that describe the continuous-time evolution of a system, and specifications are formulated using timed automata combined with probabilistic acceptance conditions. The second contribution is a novel approach to verifying decompositions of such specifications by reducing the problem to checking emptiness of the solution space for a system of linear inequalities.
Forecasting first-year student mobility using explainable machine learning techniques
Authors: Litmeyer Marie-Louise , Hennemann Stefan 
Published: February 2024

AbstractIn the context of regional sciences and migration studies, gravity and radiation models are typically used to estimate human spatial mobility of all kinds. These formal models are incorporated as part of regression models along with co-variates, to better represent regional specific aspects. Often, the correlations between dependent and independent variables are of non-linear type and follow complex spatial interactions and multicollinearity. To address some of the model-related obstacles and to arrive at better predictions, we introduce machine learning algorithm class XGBoost to the estimation of spatial interactions and provide useful statistics and visual representations for the model evaluation and the evaluation and interpretation of the independent variables. The methods suggested are used to study the case of the spatial mobility of high-school graduates to the enrolment in higher education institutions in Germany at the county-level. We show that machine learning techniques can deliver explainable results that compare to traditional regression modeling. In addition to typically high model fits, variable-based indicators such as the Shapley Additive Explanations value (SHAP) provide significant additional information on the differentiated and non-linear effect of the variable values. For instance, we provide evidence that the initial study location choice is not related to the quality of local labor-markets in general, as there are both, strong positive and strong negative effects of the local academic employment rates on the migration decision. When controlling for about 28 co-variates, the attractiveness of the study location itself is the most important single factor of influence, followed by the classical distance-related variables travel time (gravitation) and regional opportunities (radiation). We show that machine learning methods can be transparent, interpretable, and explainable, when employed with adequate domain-knowledge and flanked by additional calculations and visualizations related to the model evaluation.
Post-sepsis psychiatric disorder: Pathophysiology, prevention, and treatment
Authors: Li Dayong , Zhang Xujie , Lu Yuru , Jing Liang , Hu Hongjie , Song Yaqin , Wu Shuhui , Zhu Wei 
Published: February 2024

AbstractPost-sepsis psychiatric disorder, encompassing anxiety, depression, post-traumatic stress disorder and delirium, is a highly prevalent complication secondary to sepsis, resulting in a marked increase in long-term mortality among affected patients. Regrettably, psychiatric impairment associated with sepsis is frequently disregarded by clinicians. This review aims to summarize recent advancements in the understanding of the pathophysiology, prevention, and treatment of post-sepsis mental disorder, including coronavirus disease 2019-related psychiatric impairment. The pathophysiology of post-sepsis psychiatric disorder is complex and is known to involve blood–brain barrier disruption, overactivation of the hypothalamic–pituitary–adrenal axis, neuroinflammation, oxidative stress, neurotransmitter dysfunction, programmed cell death, and impaired neuroplasticity. No unified diagnostic criteria for this disorder are currently available; however, screening scales are often applied in its assessment. Modifiable risk factors for psychiatric impairment post-sepsis include the number of experienced traumatic memories, the length of ICU stay, level of albumin, the use of vasopressors or inotropes, daily activity function after sepsis, and the cumulative dose of dobutamine. To contribute to the prevention of post-sepsis psychiatric disorder, it may be beneficial to implement targeted interventions for these modifiable risk factors. Specific therapies for this condition remain scarce. Nevertheless, non-pharmacological approaches, such as comprehensive nursing care, may provide a promising avenue for treating psychiatric disorder following sepsis. In addition, although several therapeutic drugs have shown preliminary efficacy in animal models, further confirmation of their potential is required through follow-up clinical studies.
Intraoperative haemodynamic monitoring and management of adults having non-cardiac surgery: Guidelines of the German Society of Anaesthesiology and Intensive Care Medicine in collaboration with the German Association of the Scientific Medical Societies
Authors: Saugel Bernd , Annecke Thorsten , Bein Berthold , Flick Moritz , Goepfert Matthias , Gruenewald Matthias , Habicher Marit , Jungwirth Bettina , Koch Tilo , Kouz Karim , Meidert Agnes S , Pestel Gunther , Renner Jochen , Sakka Samir G , Sander Michael , Treskatsch Sascha , Zitzmann Amelie , Reuter Daniel A 
Published: February 2024

AbstractHaemodynamic monitoring and management are cornerstones of perioperative care. The goal of haemodynamic management is to maintain organ function by ensuring adequate perfusion pressure, blood flow, and oxygen delivery. We here present guidelines on “Intraoperative haemodynamic monitoring and management of adults having non-cardiac surgery” that were prepared by 18 experts on behalf of the German Society of Anaesthesiology and Intensive Care Medicine (Deutsche Gesellschaft für Anästhesiologie und lntensivmedizin; DGAI).
Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers
Authors: Haynes Sophie Crawford , Johnston Pamela , Elyan Eyad 
Published: February 2024

AbstractThe generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-19 from chest x-rays (CXRs). However, despite the many outstanding metrics reported, such models have failed to achieve widespread adoption into clinical settings. The significant risk of real-world generalisation failure has repeatedly been cited as one of the most critical concerns, and is a concern that extends into general medical image modelling. In this study, we propose a new dataset protocol and, using this, perform a thorough cross-dataset evaluation of deep neural networks when trained on a small COVID-19 dataset, comparable to those used extensively in recent literature. This allows us to quantify the degree to which these models can generalise when trained on challenging, limited medical datasets. We also introduce a novel occlusion evaluation to quantify model reliance on shortcut features. Our results indicate that models initialised with ImageNet weights then fine-tuned on small COVID-19 datasets, a standard approach in the literature, facilitate the learning of shortcut features, resulting in unreliable, poorly generalising models. In contrast, pre-training on related CXR imagery can stabilise cross-dataset performance. The CXR pre-trained models demonstrated a significantly smaller generalisation drop and reduced feature dependence outwith the lung region, as indicated by our occlusion test. This paper demonstrates the challenging problem of model generalisation, and the need for further research on developing techniques that will produce reliable, generalisable models when learning with limited datasets.
The distinctly zetetic significance of disagreement
Authors: Pharr Quentin 
Published: February 2024

AbstractRecent debates about disagreement’s significance have largely focused on its epistemic significance. However, given how much attention has already been paid to its epistemic significance, we might well wonder: what significance might disagreement have when we consider other related normative domains? And, in particular, what significance might it have when we consider the broader domain of inquiry, or what some thinkers have called either the “zetetic” or “erotetic” domain? In response, this paper suggest three things. Firstly, it suggests how we might clarify the relations among the epistemic, erotetic, and zetetic domains of normativity, given their potential differences and incompatibilities. Then, it suggests that disagreement’s significance within inquiry can either be tied to erotetic norms or to either of two sorts of zetetic norms: vindication-directed or possession-directed norms. And finally, it suggests preferred answers to the question of what disagreement’s distinctly zetetic significance might be, given the participating inquirers’ ordinarily-conceived zetetic standings and how their sets of dialectically accessible evidence might compare.
DIF Analysis with Unknown Groups and Anchor Items
Authors: Wallin Gabriel , Chen Yunxiao , Moustaki Irini 
Published: February 2024

Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption – the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an L1\documentclass[12pt]{minimal}
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Characterization of a novel amidohydrolase with promiscuous esterase activity from a soil metagenomic library and its application in degradation of amide herbicides
Authors: Sun Shengwei , Chen Wanqi , Peng Kailin , Chen Xueyingzi , Chen Jinju 
Published: February 2024

AbstractAmide herbicides have been extensively used worldwide and have received substantial attention due to their adverse environmental effects. Here, a novel amidohydrolase gene was identified from a soil metagenomic library using diethyl terephthalate (DET) as a screening substrate. The recombinant enzyme, AmiH52, was heterologously expressed in Escherichia coli and later purified and characterized, with the highest activity occurring at 40 ℃ and pH 8.0. AmiH52 was demonstrated to have both esterase and amidohydrolase activities, which exhibited highly specific activity for p-nitrophenyl butyrate (2669 U/mg) and degrading activity against several amide herbicides. In particular, it displayed the strongest activity against propanil, with a high degradation rate of 84% at 8 h. A GC–MS analysis revealed that propanil was transformed into 3,4-dichloroaniline (3,4-DCA) during this degradation. The molecular interactions and binding stability were then analyzed by molecular docking and molecular dynamics simulation, which revealed that several key amino acid residues, including Tyr164, Trp66, Ala59, Val283, Arg58, His33, His191, and His226, are involved in the specific interactions with propanil. This study provides a function-driven screening method for amide herbicide hydrolase from the metagenomic libraries and a promising propanil-degrading enzyme (AmiH52) for potential applications in environmental remediation.
Assessment of Zn and Cu in piglets’ liver and kidney: impact in fecal Enterococcus spp.?
Authors: Donato Maria M. , Assis Gabriela , Cardoso Olga , Oliveiros Bárbara , Freitas Andreia , Ramos Fernando 
Published: February 2024

AbstractZinc and copper have been used as growth promotors in alternative to antibiotics in pig’s diet. The aim was the ascertainment of the Zn and Cu concentrations in piglets’ liver and kidney and their impact in the reduced susceptibility to Zn, Cu, and antibiotics in enterococci, used as microbiota biomarker. Zn and Cu were determined in the livers and kidneys of 43 piglets slaughtered in Portugal, by flame atomic absorption spectrometry. Enterococci were isolated from feces for determining the identification of species (E. faecalis, E. faecium, and Enterococcus spp.); susceptibility to vancomycin, ciprofloxacin, linezolid, tigecycline, ampicillin, imipenem, and metals; and Cu tolerance genes. In piglets with Zn and Cu high or toxic levels, enterococci had reduced susceptibility to ions, reinforced by the presence of Cu tolerance genes and by resistance to antibiotics. The study relevance is to show the relationship between these metals’ levels and decreased susceptibility to Cu, Zn, and antibiotics by enterococci. From the results, it could be supposed that the piglets were being fed with high doses of Zn and Cu which could select more resistant bacteria to both antibiotics and metals that could spread to environment and humans.
Reciprocal Illumination and the Discovery of Fractal Patterns in Religious Diversity
Authors: Schmidt-Leukel Perry 
Published: February 2024

AbstractThis essay presents Arvind Sharma’s concept of “reciprocal illumination” as an innovative defense of interreligious comparison, showing that the comparative approach is still meaningful despite its currently widespread critique. In discussing Sharma’s concept, the essay focuses on the internal diversity of religious traditions, asking whether “reciprocal illumination” is possible because religious diversity is apparently not entirely at random but displays recurrent patterns and structures of a fractal nature. The existence of fractal patterns would explain very well not merely why “reciprocal illumination” is possible at all, but especially in what sense it fosters interreligious learning as part of the growing field of interreligious theology. The latter aspect is investigated by relating Sharma’s three types of “reciprocal illumination” to Catherine Cornille’s recent classification of six forms of interreligious learning. It will be argued that interreligious learning and reciprocal illumination are likely to lead to a radical change in religious self-understanding, perceiving one’s own tradition as a unique, internally diverse, and equally valid part and component of a larger diverse web of religious phenomena.