bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perspectives of aminoacylases in biocatalytic synthesis of N-acyl-amino acids surfactants
Authors: Haeger Gerrit , Wirges Jessika , Bongaerts Johannes , Schörken Ulrich , Siegert Petra 
Published: October 2024

AbstractMany industrial processes are performed using harmful chemicals. The current technical synthesis ofN-acyl-amino acids relies on acyl chlorides, which are typically obtained from phosgene chemistry. A greener alternative is the application of whole cells or enzymes to carry out synthesis in an environmentally friendly manner. Aminoacylases belong to the hydrolase family and the resolution of racemic mixtures ofN-acetyl-amino acids is a well-known industrial process. Several new enzymes accepting long-chain fatty acids as substrates were discovered in recent years. This article reviews the synthetic potential of aminoacylases to produce biobasedN-acyl-amino acid surfactants. The focus lays on a survey of the different types of aminoacylases available for synthesis and their reaction products. The enzymes are categorized according to their protein family classification and their biochemical characteristics including substrate spectra, reaction optima and process stability, both in hydrolysis and under process conditions suitable for synthesis. Finally, the benefits and future challenges of enzymaticN-acyl-amino acid synthesis with aminoacylases will be discussed.Key points• Enzymatic synthesis of N-acyl-amino acids, biobased surfactants by aminoacylases.
Prognostic utility of mid-regional pro-adrenomedullin and growth differentiation factor 15 in patients undergoing transfemoral transcatheter aortic valve implantation
Authors: Piayda Kerstin , Keranov Stanislav , Schulz Luisa , Arsalan Mani , Liebetrau Christoph , Kim Won-Keun , Hofmann Felsix J. , Bauer Pascal , Voss Sandra , Troidl Christian , Sossalla Samuel T. , Hamm Christian W. , Nef Holger M. , Dörr Oliver 
Published: October 2024

Abstract
Background
Risk prediction in patients with severe, symptomatic aortic stenosis (AS) undergoing transcatheter aortic valve implantation (TAVI) remains an unsolved issue. In addition to classical risk scoring systems, novel circulating biomarkers like mid-regional pro-adrenomedullin (MR-proADM) and growth differentiation factor 15 (GDF-15) may be of value in assessing risk.

Methods
Consecutive patients undergoing elective transfemoral TAVI were included in this prospective observational study. Baseline information, imaging findings, blood samples, and clinical outcomes were collected. Blood levels of the classical biomarkers interleukin-6 (IL-6) and high-sensitivity C-reactive peptide (hsCRP) and of the novel biomarkers MR-proADM and GDF-15 were measured and their predictive utility for mortality assessed.

Results
The study cohort consisted of 92 patients undergoing TAVI. The median age was 80.7 years [IQR 77.2;83.3], and 48 (52.2%) were male. Analysis of the area under the curve (AUC) of the receiver-operating characteristics showed that the hsCRP levels discriminated poorly (AUC 0.66, 95% CI [0.52;0.8], p = 0.027), whereas all other biomarkers reached a higher level of discrimination (IL-6: AUC 0.76, 95% CI [0.66;0.86], p < 0.001; MR-proADM: AUC 0.73, 95% CI [0.61;0.85], p = 0.002; GDF-15: AUC 0.73, 95% CI [0.61;0.85], p = 0.002). Kaplan–Meier analysis in conjunction with Youden J-statistics yielded the optimal cutoff points for each biomarker to predict survival: IL-6 4.65 pg/mL, hsCRP 12.9 mg/L, MR-proADM 1.02 nmol/L, and GDF-15 2400.1 pg/mL.

Conclusion
Novel circulating biomarkers like MR-proADM and GDF-15 may provide additional value in predicting survival after TAVI.
Silexan in anxiety, depression, and related disorders: pharmacological background and clinical data
Authors: Kasper Siegfried , Eckert Anne 
Published: October 2024

AbstractWe present a narrative review of clinical trials investigating the anxiolytic and antidepressant effects of silexan, an active substance derived from lavender oil and summarize nonclinical findings from pharmacological studies supporting its therapeutic use. Six studies investigated the efficacy of the lavender oil in patients with subthreshold and generalized anxiety disorders as well as in mixed anxiety and depressive disorder (MADD). Furthermore, we present data indicating that silexan may influence sleep quality as well as anxiety or depressive disorders in individuals with post-COVID-19. Silexan taken orally at a daily dose of 80 mg for 10 weeks was significantly superior to placebo in reducing psychic and somatic symptoms of anxiety and was as effective as 0.5 mg/d lorazepam and 20 mg/d paroxetine. In patients with mild or moderate major depression, silexan was superior to placebo and comparably effective to 50 mg/d sertraline. Significant antidepressant effects were also observed in MADD and depression co-morbid with anxiety. The herbal product had a beneficial effect on activities of daily living and health-related quality of life. Adverse events associated with silexan in clinical trials were limited to eructation and mild, transient gastrointestinal complaints. The herbal product was not associated with drug interactions, sedation, sleep disturbance, dependence and abuse potential, sexual dysfunction, weight gain or withdrawal symptoms. Silexan was therefore safe and effective in subthreshold and syndromal anxiety disorders and in major depression.
Mesh topology-based spurious pressure stabilization in 3D finite elasticity using Voronoi tessellations
Authors: Sauren Bjorn , Klinkel Sven 
Published: October 2024

AbstractIn this paper, we present a mesh topology-based stabilization approach to suppress spurious pressure modes in 3D nearly-incompressible finite elasticity. The focus lies on a mixed formulation with lowest-order approximation for the displacement and pressure fields. Motivated by the fact that the popular H1/P0 element does not fulfill the inf-sup condition, all possible local spurious pressure modes are derived on a patch of elements. The nullspace method is used to determine all spurious pressure solutions. From this, the topological requirements of the finite element mesh are established. We conclude that no more than four elements are allowed to intersect in the same vertex to overcome local checkerboarding. To fulfill this requirement, we employ non-degenerate 3D Voronoi diagrams with several different site distributions. These result in random, centroidal, and honeycomb Voronoi meshes. The resulting convex polyhedral elements are discretized by a polyhedral mixed finite element based on the lowest possible interpolation pair. The numerical examples illustrate that spurious pressure modes do not occur for any degree of mesh refinement as long as the topological mesh requirements are met. Furthermore, it is shown that the numerical inf-sup test is passed. By violating the topological requirements, it is shown that a stable pressure field cannot be guaranteed and the checkerboard phenomenon is provoked.
Machine Learning Approach for Rock Mass Classification with Imbalanced Database of TBM Tunnelling in Himalayan Geology
Authors: Katuwal Tek Bahadur , Panthi Krishna Kanta , Basnet Chhatra Bahadur 
Published: October 2024

AbstractThe geological condition of the Himalayan region is very complex and challenging. So far, empirical and analytical approaches for rock mass characterization have been a common practice in the Himalayas. Due to the limitations of input parameters and governing equations in design practices, rock mass characterization in tunnel boring machine (TBM) excavated tunnels is crucial. This research introduces robust machine learning (ML) approaches to predict rock mass quality conditions in complex geological environments, leveraging a large database of TBM parameters and rock mass rating (RMR) values. To do so, a total of 6879 stable phase TBM cycle data were collected from 12 km long tunnel in Nepal. The pre-processed parameters were randomly split into a training set (80%) and a testing set (20%). Seven individual classifiers consisting of logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and bagging, and stacking ensemble classifier were exploited with optimal hyperparameters. The comprehensive assessment carried out has shown that the ensemble classifier gave highest overall accuracy as compared to other individual classifiers. More importantly, the synthetic minority over-sampling technique (SMOTE) performs better to handle the imbalanced database, while the RF and stacking classifier demonstrated the best prediction performance with accuracy of 92%. Moreover, for the minority rock mass class, the RF shows better performance compared to stacking classifier. The authors emphasize that the effective application of ML-based data-driven approach shows substantial potential for rock mass characterization in TBM tunnelling.
Green Financing, Energy Transformation, and the Moderating Effect of Digital Economy in Developing Countries
Authors: Nepal Rabindra , Liu Yang , Dong Kangyin , Jamasb Tooraj 
Published: October 2024

AbstractThe energy sector in many developing nations faces the difficulty of insufficient financing throughout the low-carbon transition, highlighting the importance of international green financing in alleviating financial constraints. The advancement of digital technology could facilitate green financing for energy transition in the digital economy, but this statement lacks empirical evidence. The primary objective of this research is to investigate the impact of international green financing on low-carbon energy transformation in developing nations. Additionally, we investigate the moderating role of digital economy between the two. Our findings validate the favorable impact of international green financing on low-carbon energy transformation, and this impact is particularly evident for hydro and wind energy consumption. We show that this beneficial effect is greater for low-income countries or regions with high levels of energy transition. We also provide evidence of the positive moderation effects of digital economy and find that its effects are still present in the transition to hydro and wind energy. This research helps to broaden green financing channels for the energy sector in developing countries, especially from the perspective of digital economy.
Numerical investigation of the late-time tails of the solutions of the Fackerell–Ipser equation
Authors: Rácz István , Tóth Gábor Zsolt 
Published: October 2024

AbstractThe late-time behaviour of the solutions of the Fackerell–Ipser equation (which is a wave equation for the spin-zero component of the electromagnetic field strength tensor) on the closure of the domain of outer communication of sub-extremal Kerr spacetime is studied numerically. Within the Kerr family, the case of Schwarzschild background is also considered. Horizon-penetrating compactified hyperboloidal coordinates are used, which allow the behaviour of the solutions to be observed at the event horizon and at future null infinity as well. For the initial data, pure multipole configurations that have compact support and are either stationary or non-stationary are taken. It is found that with such initial data the solutions of the Fackerell–Ipser equation converge at late times either to a known static solution (up to a constant factor) or to zero. As the limit is approached, the solutions exhibit a quasinormal ringdown and finally a power-law decay. The exponents characterizing the power-law decay of the spherical harmonic components of the field variable are extracted from the numerical data for various values of the parameters of the initial data, and based on the results a proposal for a Price’s law relevant to the Fackerell–Ipser equation is made. Certain conserved energy and angular momentum currents are used to verify the numerical implementation of the underlying mathematical model. In the construction of these currents a discrete symmetry of the Fackerell–Ipser equation, which is the product of an equatorial reflection and a complex conjugation, is also taken into account.
Trauma-informed Financial Empowerment Programming Associated with Improved Financial Well-being
Authors: Weida Emily B. , Carroll-Scott Amy , Le-Scherban Felice , Bloom Sandra , Chilton Mariana 
Published: October 2024

AbstractFinancial well-being and overall health are significantly linked, especially among those in poverty who have been exposed to violence and suffer from unaddressed trauma. Yet existing public assistance programs fail to address the presence or impact of trauma and adversity. Built specifically for families living in poverty who experience adversity, the Building Wealth and Health Network (the Network) provides a space for families to heal from the effects of trauma while also building social networks and economic security. The sample for this study was primarily Black (91%) women (92%) in Philadelphia with at least one child. A repeated measures linear regression model was performed via a Difference-in-Differences approach to test differences in financial well-being scores between two groups (full participation vs. low/no participation) at two time points (baseline vs 3 months, and baseline vs 6 months). We use this program as a field study to better understand the financial well-being of program participants who took part in fewer or more program sessions. Those who participated in more sessions reported greater increases in two measures of financial well-being at three months and six months post baseline, when compared to those with low or no participation.
The Impact of Caregiving on Informal Caregivers of People with Dementia: Family Functioning, Burden, and Burnout
Authors: Brites Rute , Brandão Tânia , Nunes Odete , Hipólito João , Pires Catarina Tomé 
Published: October 2024

AbstractCaregiving is a complex occupation, with a significant impact for informal caregivers (IC). Stress-process models propose a framework that considers that this impact depends on primary and secondary stressors, but also on the IC situation appraisal. This work aimed to verify: whether being, or not, an IC of an individual with dementia influenced the relationship between family functioning and burnout; the association between neuropsychiatric symptoms of the individual with dementia and IC burnout and whether the IC burden and perceived family functioning had a mediating role in such relationship. This cross-sectional study investigated differences in family functioning and its association with burnout between IC and non-IC. For IC specifically, the study examined a mediation model to explore the possibility of neuropsychiatric symptoms indirectly affecting IC burnout through the impact on family functioning and burden. Measures included the Copenhagen Burnout Inventory, the McMaster Family Assessment Device, the Neuropsychiatric Inventory, and the Zarit Burden Interview. Results showed an association between poorer family function and high burnout, specifically in IC. They also showed that burden mediated the relationship between neuropsychiatric symptoms and burnout. The findings offer a significant contribution to the growing knowledge about the relationship between stressors associated with informal caregiving in dementia context, such as neuropsychiatric symptoms and its outcomes, like burnout.
Scheduling of e-commerce packaging machines: blocking machines and their impact on the performance–waste tradeoff
Authors: Briskorn Dirk , Boysen Nils , Zey Lennart 
Published: October 2024

AbstractTo streamline their fulfillment processes, many e-commerce retailers today use automated packaging machines for their outbound parcels. An important performance–waste tradeoff is associated with these machines: To reduce packaging waste when handling different sized goods, packaging machines should be able to handle different carton sizes. However, more carton sizes lead to a more involved scheduling process, so that the throughput performance deteriorates (and vice versa). To investigate this tradeoff, this paper develops scheduling procedures for a specific type of packaging machine, called blocking machines. These packaging machines provide multiple back-to-back packaging devices, each continuously processing a dedicated carton size, but blocking each other whenever incoming goods are not properly ordered according to carton sizes on the infeed conveyor. To reduce the resulting throughput loss, we derive various scheduling problems for optimizing the inflow of goods, provide a thorough analysis of the computational complexity, and derive an exact dynamic programming approach that is polynomial in the number of orders to be packed. This allows us to solve even large real-world instances to proven optimality with which we can analyze the performance–waste tradeoff of blocking machines.
Intolerance of Uncertainty as a Central Influence on Social Media Use: A School-Based Program for Adolescents
Authors: Einstein Danielle A. , McMaugh Anne , Rapee Ronald M. , McEvoy Peter , Fraser Madeleine I. , Abbott Maree , Mansell Warren , Karin Eyal 
Published: October 2024

AbstractThis study evaluated a classroom program to address Intolerance of uncertainty (IU) in secondary school students in Australia. IU is a transdiagnostic vulnerability factor for a range of psychological difficulties. A universal classroom program aimed to increase student understanding of feelings of uncertainty and explored socioemotional factors that could influence behaviors when facing uncertainty. Classroom teachers in two high schools were trained to deliver an 18-lesson program under naturalistic classroom conditions. Relative to the control group, the intervention lowered IU at post-test, but not at a later follow-up. The only socioemotional outcome measure to demonstrate a concurrent change was social media use. Limitations of the study were associated with the naturalistic classroom intervention, including the high level of attrition. Future studies may need to address broader factors in the students’ context that could influence intolerance of uncertainty.
Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images
Authors: Nievergeld Arjet , Çetinkaya Bünyamin , Maas Esther , van Sambeek Marc , Lopata Richard , Awasthi Navchetan 
Published: October 2024

AbstractUltrasound (US)-based patient-specific rupture risk analysis of abdominal aortic aneurysms (AAAs) has shown promising results. Input for these models is the patient-specific geometry of the AAA. However, segmentation of the intraluminal thrombus (ILT) remains challenging in US images due to the low ILT-blood contrast. This study aims to improve AAA and ILT segmentation in time-resolved three-dimensional (3D + t) US images using a deep learning approach. In this study a “no new net” (nnU-Net) model was trained on 3D + t US data using either US-based or (co-registered) computed tomography (CT)-based annotations. The optimal training strategy for this low-contrast data was determined for a limited dataset. The merit of augmentation was investigated, as well as the inclusion of low-contrast areas. Segmentation results were validated with CT-based geometries as the ground truth. The model trained on CT-based masks showed the best performance in terms of DICE index, Hausdorff distance, and diameter differences, covering a larger part of the AAA. With a higher accuracy and less manual input the model outperforms conventional methods, with a mean Hausdorff distance of 4.4 mm for the vessel and 7.8 mm for the lumen. However, visibility of the lumen-ILT interface remains the limiting factor, necessitating improvements in image acquisition to ensure broader patient inclusion and enable rupture risk assessment of AAAs in the future.
Graphical abstract
Demographic, Clinical, Psychosocial, and Behavioral Predictors of Continuous Glucose Monitor Use in Adults with Type 2 Diabetes
Authors: Morrow Emily L. , Spieker Andrew J. , Greevy Robert A. , Roddy McKenzie K. , Mayberry Lindsay S. 
Published: October 2024

Abstract
Background
Continuous glucose monitor (CGM) use is increasing rapidly among people with type 2 diabetes, although little is known about predictors of CGM use beyond clinical and demographic information available in electronic medical records. Behavioral and psychosocial characteristics may also predict CGM use.

Objective
We examined clinical, psychosocial, and behavioral characteristics that may predict CGM use in adults with type 2 diabetes.

Design
This longitudinal observational study comprised a secondary analysis of data collected in a larger trial. Enrollment included HbA1c tests and surveys assessing demographic, clinical, psychosocial, and behavioral characteristics. We queried participants regarding their CGM use during the study on their final self-report surveys, 15 months post-enrollment.

Participants
Participants were 245 community-dwelling adults with type 2 diabetes recruited from primary care.

Approach
We used logistic regression to predict CGM use during the 15-month trial period from baseline characteristics.

Key Results
Around one-third of participants (37.1%; 91/245) started CGM. Predictors of starting CGM in bivariate models included younger age, higher socioeconomic status, insulin use, higher HbA1c, and more diabetes distress. When including all potential predictors in a single multivariable model, only younger age (aOR = 0.95, p = 0.001), insulin use (aOR = 2.33, p = 0.006), and higher socioeconomic status (aOR = 0.44, p = 0.037) were significant predictors. Despite the association between higher HbA1c and CGM use, neither diabetes self-care behaviors nor diabetes self-efficacy significantly predicted CGM use. Of participants who tried a CGM, 14.3% (13/91) had stopped, with cost being the most-cited reason.

Conclusions
Even when including behavioral and psychological characteristics, younger age, using insulin, and higher socioeconomic status remain key predictors of CGM use. These findings emphasize the importance of access and affordability for people who may benefit from CGM. Providers should not bias their introduction of CGM towards those with (perceived or actual) optimal or sub-optimal self-care behaviors.