bluuebunny/crossref_metadata_2025_split huggingface dataset:
3D Printing of Robust High‐Performance Conducting Polymer Hydrogel‐Based Electrical Bioadhesive Interface for Soft Bioelectronics
Authors: Yu Jiawen , Wan Rongtai , Tian Fajuan , Cao Jie , Wang Wen , Liu Qi , Yang Hanjun , Liu Jingcheng , Liu Ximei , Lin Tao , Xu Jingkun , Lu Baoyang 
Published: December 2023

AbstractElectrical bioadhesive interface (EBI), especially conducting polymer hydrogel (CPH)‐based EBI, exhibits promising potential applications in various fields, including biomedical devices, neural interfaces, and wearable devices. However, current fabrication techniques of CPH‐based EBI mostly focus on conventional methods such as direct casting, injection, and molding, which remains a lingering challenge for further pushing them toward customized practical bioelectronic applications and commercialization. Herein, 3D printable high‐performance CPH‐based EBI precursor inks are developed through composite engineering of PEDOT:PSS and adhesive ionic macromolecular dopants within tough hydrogel matrices (PVA). Such inks allow the facile fabrication of high‐resolution and programmable patterned EBI through 3D printing. Upon successive freeze‐thawing, the as‐printed PEDOT:PSS‐based EBI simultaneously exhibits high conductivity of 1.2 S m−1, low interfacial impedance of 20 Ω, high stretchability of 349%, superior toughness of 109 kJ m−3, and satisfactory adhesion to various materials. Enabled by these advantageous properties and excellent printability, the facile and continuous manufacturing of EBI‐based skin electrodes is further demonstrated via 3D printing, and the fabricated electrodes display excellent ECG and EMG signal recording capability superior to commercial products. This work may provide a new avenue for rational design and fabrication of next‐generation EBI for soft bioelectronics, further advancing seamless human‐machine integration.
N‐Type Small Molecule Electron Transport Layers with Excellent Surface Energy and Moisture Resistance Siloxane for Non‐Fullerene Organic Solar Cells
Authors: Li Yubing , Zhou Dan , Han Liangjing , Quan Jianwei , Wang Fang , Yang Xufang , Hu Lin , Wang Jianru , Xu Haitao , Chen Lie 
Published: December 2023

AbstractElectron transport layers (ETLs) generally contain polar groups for enhancing performance and reducing the work function. Nevertheless, the polar group with high surface energy may cause inferior interfacial compatibility, which challenges the ETLs to balance stability and performance. Here, two conjugated small molecules of ETLs with low surface energy siloxane, namely PDI‐Si and PDIN‐Si, are synthesized. The siloxane with low surface energy not only enhances the interfacial compatibility between ETLs and active layers but also improves the moisture‐proof stability of the device. Impressively, the amine‐functionalized PDIN‐Si can simultaneously exhibit conspicuous n‐type self‐doping properties and outstanding moisture‐proof stability. The optimization of interfacial contact and morphology enables the PM6:Y6‐based OSC with PDIN‐Si to achieve a power conversion efficiency (PCE) of 15.87%, which is slightly superior to that of classical ETL PDINO devices (15.27%), and when the PDIN‐Si film thickness reaches 28 nm, the PCE remains at 13.19% (≈83%), which indicates that PDIN‐Si has satisfactory thickness insensitivity to facilitate roll‐to‐roll processing. Excitingly, after 120 h of storage in an environment with humidity above 45%, the unencapsulated device with PDIN‐Si as ETL remains at 75% of the initial PCE value, while the device with PDINO as ETL is only 50%.
Baupraktische Bestimmung der Einschraubtiefe von Schrauben
Authors: Hofstetter Rolf 
Published: December 2023

AbstractMit dem nachfolgenden Beitrag wird ein Konzept zur baupraktischen Bestimmung der Einschraubtiefe von Schrauben in unterschiedlichen Materialkombinationen vorgestellt. Die rechnerische Ermittlung der Einschraubtiefe erfolgt anhand von bereits existierenden Veröffentlichungen bzw. Richtlinien. Da die bisherige Vorgehensweise sehr komplex ist, wird ein vereinfachtes baupraktisches Konzept erarbeitet und vorgestellt. Die Bestimmung wird für übliche Materialkombinationen (Schraube zu Einschraubuntergrund) im Bauwesen erarbeitet bzw. dargestellt.
Electrochemical C−H/C−C Bond Oxygenation: A Potential Technology for Plastic Depolymerization
Authors: Rani Sadia , Aslam Samina , Lal Kiran , Noreen Sobia , Alsader Khadeeja Ali Mohammed , Hussain Riaz , Shirinfar Bahareh , Ahmed Nisar 
Published: December 2023

AbstractHerein, we provide eco‐friendly and safely operated electrocatalytic methods for the selective oxidation directly or with water, air, light, metal catalyst or other mediators serving as the only oxygen supply. Heavy metals, stoichiometric chemical oxidants, or harsh conditions were drawbacks of earlier oxidative cleavage techniques. It has recently come to light that a crucial stage in the deconstruction of plastic waste and the utilization of biomass is the selective activation of inert C(sp3)−C/H(sp3) bonds, which continues to be a significant obstacle in the chemical upcycling of resistant polyolefin waste. An appealing alternative to chemical oxidations using oxygen and catalysts is direct or indirect electrochemical conversion. An essential transition in the chemical and pharmaceutical industries is the electrochemical oxidation of C−H/C−C bonds. In this review, we discuss cutting‐edge approaches to chemically recycle commercial plastics and feasible C−C/C−H bonds oxygenation routes for industrial scale‐up.
RETRACTED: Calcitriol combined with high‐calcium and high‐phosphorus diet induces vascular calcification model in chronic kidney disease rats
Authors: Wang Yujing , Han Wenlong , Zhong Yuxiang , Li Wenning , Liu Qiang 
Published: December 2023

AbstractBackgroundCardiovascular diseases represent a significant complication arising from chronic kidney disease (CKD). Vascular calcification is an important risk factor for cardiovascular diseases. Reducing vascular calcification is therefore critical to reducing mortality in CKD patients.HypothesisThis study aims to establish a vascular calcification model in rats with CKD by administering subcutaneous injections of calcitriol in combination with a high‐calcium and high‐phosphorus diet.MethodsThe rats were divided into the CKD vascular calcification model group (subtotal nephrectomy+ [SNx+]) and the sham‐operated control group (subtotal nephrectomy− [SNx−]). The rats in the SNx(+) group were administered high‐calcium and high‐phosphorus feeds following a 5/6 nephrectomy. Calcitriol (1 μg/kg, three times a week) was injected subcutaneously at weeks 0, 4, 8, and 12 after the operation. Measurements of body weight, urine, serum biochemical indicators and vascular calcification level were conducted in rats.Results(1) Compared with the SNx(−) group, rats in the SNx(+) group experienced an increase in 24‐h urine output, urinary phosphorus, and urinary microprotein excretion, along with the development of severe anemia. Additionally, there was a notable elevation in serum phosphorus, blood urea nitrogen, blood creatinine, fibroblast growth factor 23 (FGF‐23), and intact parathyroid hormone levels, accompanied by severe hypoproteinemia at week 12. (2) The results of micro‐compuyed tomography (μCT) and alizarin S staining of the thoracic aorta demonstrated an increase in vascular calcification in the SNx(+) group. (3) The expression levels of vascular calcification‐related proteins were increased.ConclusionsThe administration of calcitriol combined with a high‐calcium and high‐phosphorus diet was found to induce vascular calcification in CKD rats, leading to a disturbance in mineral metabolism. Vascular calcification was effectively induced in CKD rats after 12 weeks of modeling, thereby presenting a novel approach for establishing a vascular calcification model in CKD rats, helping to elucidate this clinical condition and its underlying molecular mechanisms.
Neuroprotective effect of ketamine and sevoflurane against TNF‐α induced cognitive impairment
Authors: Wu Yuanshui , Zhang Meilan , Ke Hongyan , Xu Juanjuan , Li Huanhuan , Ni Xiaohong 
Published: December 2023

AbstractIn the present study, neuroprotective effect of sevoflurane in combination with ketamine was investigated on TNF‐α induced necroptosis of neurons and cognitive impairment in the rat model. The results demonstrated that exposure to TNF‐α/z‐VAD led to a significant decrease in viability of HT‐22 neuronal cells. However, incubation of HT‐22 cells with ketamine plus sevoflurane inhibited decrease in viability induced by TNF‐α/z‐VAD exposure. The increase in production of ROS by TNF‐α/z‐VAD exposure in HT‐22 cells was effectively suppressed on pre‐treatment with ketamine plus sevoflurane. Moreover, suppression of TNF‐α/z‐VAD induced ROS production in HT‐22 cells by ketamine plus sevoflurane pretreatment was higher in comparison to ketamine or sevoflurane treatment alone. Treatment of HT‐22 cells with ketamine plus sevoflurane suppressed TNF‐α/z‐VAD induced increase in RIP1 and p‐MLKL protein expression. Ketamine plus sevoflurane treatment effectively reversed decrease in movement speed as well as total distance traveled in TNF‐α injected rats. The number of neurons in rat hippocampus injected with TNF‐α showed a significant decrease more specifically in carbonic anhydrase‐3 region. However, no significant change in the density of neurons was observed in the hippocampus of rats pretreated with ketamine plus sevoflurane by TNF‐α injection. The increase in expression of p‐MLKL and p‐RIP3 by TNF‐α injection was effectively reversed in rats on treatment with ketamine plus sevoflurane. In silico studies revealed that ketamine interacts with p‐MLKL protein in different confirmations with the binding affinities ranging from −9.7 to −8.4 kcal/mol. It was found that ketamine binds to p‐MLKL protein by interacting with alanine (ALA A:295), proline (PRO A:306), glutamine (GLN A: 307) and isoleucine (ILE A:293) amino acid residues. In summary, ketamine plus sevoflurane combination alleviates TNF‐α/z‐VAD induced decrease in viability of HT‐22 cells in vitro and rat hippocampus neurons in vivo. Moreover, ketamine plus sevoflurane combination prevented TNF‐α injection induced cognitive impairment in rats. Therefore, sevoflurane plus ketamine combination can be developed as a potential therapeutic regimen for treatment of isoflurone induced cognitive impairment.
Durum wheat heat tolerance loci defined via a north–south gradient
Authors: Sall Amadou Tidiane , Kabbaj Hafssa , Menoum Sidi ould Ely , Cisse Madiama , Geleta Mulatu , Ortiz Rodomiro , Bassi Filippo M. 
Published: December 2023

AbstractThe global production of durum wheat (Triticum durum Desf.) is hindered by a constant rise in the frequency of severe heat stress events. To identify heat‐tolerant germplasm, three different germplasm panels (“discovery,” “investigation,” and “validation”) were studied under a range of heat‐stressed conditions. Grain yield (GY) and its components were recorded at each site and a heat stress susceptibility index was calculated, confirming that each 1°C temperature rise corresponds to a GY reduction in durum wheat of 4.6%–6.3%. A total of 2552 polymorphic single nucleotide polymorphisms (SNPs) defined the diversity of the first panel, while 5642 SNPs were polymorphic in the “investigation panel.” The use of genome‐wide association studies revealed that 36 quantitative trait loci were associated with the target traits in the discovery panel, of which five were confirmed in a “subset” tested imposing heat stress by plastic tunnels, and in the investigation panel. A study of allelic combinations confirmed that Q.icd.Heat.003‐1A, Q.icd.Heat.007‐1B, and Q.icd.Heat.016‐3B are additive in nature and the positive alleles at all three loci resulted in a 16% higher GY under heat stress. The underlying SNPs were converted into kompetitive allele specific PCR markers and tested on the validation panel, confirming that each explained up to 9% of the phenotypic variation for GY under heat stress. These markers can now be used for breeding to improve resilience to climate change and increase productivity in heat‐stressed areas.
Eosinophilic esophagitis is associated with increased risk of later inflammatory bowel disease in a nationwide Swedish population cohort
Authors: Uchida Amiko M. , Garber John J. , Pyne Ashley , Peterson Kathryn , Roelstraete Bjorn , Olén Ola , Halfvarson Jonas , Ludvigsson Jonas F. 
Published: December 2023

AbstractBackgroundEarlier studies on the possible association between eosinophilic esophagitis (EoE) and inflammatory bowel disease (IBD) have been contradictory.MethodsPatients with biopsy‐verified EoE diagnosed between 1990 and 2017 in Sweden (n = 1587) were age‐ and sex‐matched with up to five general population reference individuals (n = 7808). EoE was defined using pathology reports from all 28 pathology centers in Sweden (the ESPRESSO study). Multivariate Cox regression then estimated hazard ratios for future IBD. IBD was defined based on the international classification of disease codes and histopathology codes. In secondary analyses, sibling comparators were used to further reduce potential familial confounding. Additionally, we performed logistic regression examining earlier IBD in EoE.ResultsDuring follow‐up until 2020, 16 (0.01%) EoE patients and 21 (0.003%) general population reference individuals diagnosed with IBD, corresponding to a 3.5‐fold increased risk of future IBD (aHR = 3.56; 95% CI 1.79–7.11). EoE was linked to Crohn's disease (aHR = 3.39 [95% CI 1.02–9.60]) but not to ulcerative colitis (aHR = 1.37; 95% CI 0.38–4.86). Compared to their siblings, patients with EoE were at a 2.48‐fold increased risk of IBD (aHR = 2.48; 95% CI 0.92–6.70). Earlier IBD was 15 times more likely in EoE patients than in matched reference individuals (odds ratio, 15.39; 95% CI 7.68–33.59).ConclusionIn this nationwide cohort study, EoE was associated with a 3.5‐fold increased risk of later IBD diagnosis. This risk increase may be due to shared genetic or early environmental risk factors, but also surveillance bias could play a role.
Integration of Chemicals Market Data with Suspect Screening Using In Silico Tools to Identify Potential New and Emerging Risk Chemicals
Authors: Menger Frank , Andersson Patrik L. , Weiss Jana M. 
Published: December 2023

AbstractEarly identification of new and emerging risk chemicals (NERCs) is critical in protecting human and environmental health while chemical invention and production is growing on a global market. Chemicals market data is information on the production, import, and use of chemicals in materials and products. By integration of chemicals market data with suspect screening strategies NERCs could potentially be detected early. In silico tools play an important role in this integration to identify blind spots in current analytical approaches and in identification of the potentially most hazardous chemicals. This chapter starts with a brief presentation of the term “chemicals market data.” The integrated approach is then presented in three steps: (1) Data collection and curation, (2) Scoring, ranking, and filtering, and (3) Suspect screening. Each step is first presented conceptually and then exemplified with use cases from the authors. The use of chemicals market data provides a solid basis for identification of true NERCs with confidence, and true and false negative findings can more confidently be distinguished. Chemicals market data should be provided to authorities and researchers so that early warning systems for NERCs can be installed and analytical blind spots identified and addressed.
Efficient Mining of Volunteered Trajectory Datasets
Authors: Forsch Axel , Funke Stefan , Haunert Jan-Henrik , Storandt Sabine 
Published: December 2023

AbstractWith the ubiquity of mobile devices that are capable of tracking positions (be it via GPS or Wi-Fi/mobile network localization), there is a continuous stream of location data being generated every second. These location measurements are typically not considered individually but rather as sequences, each of which reflects the movement of one person or vehicle, which we call trajectory. This chapter presents new algorithmic approaches to process and visualize trajectories both in the network-constrained and the unconstrained case.
Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection
Authors: Reinders Christoph , Yang Michael Ying , Rosenhahn Bodo 
Published: December 2023

AbstractNeural networks have demonstrated great success; however, large amounts of labeled data are usually required for training the networks. In this work, a framework for analyzing the road and traffic situations for cyclists and pedestrians is presented, which only requires very few labeled examples. We address this problem by combining convolutional neural networks and random forests, transforming the random forest into a neural network, and generating a fully convolutional network for detecting objects. Because existing methods for transforming random forests into neural networks propose a direct mapping and produce inefficient architectures, we present neural random forest imitation—an imitation learning approach by generating training data from a random forest and learning a neural network that imitates its behavior. This implicit transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is differentiable, can be used as a warm start for fine-tuning, and enables end-to-end optimization. Experiments on several real-world benchmark datasets demonstrate superior performance, especially when training with very few training examples. Compared to state-of-the-art methods, we significantly reduce the number of network parameters while achieving the same or even improved accuracy due to better generalization.
Non Naturally Reductive Einstein Metrics on the Orthogonal Group via Real Flag Manifolds
Authors: Arvanitoyeorgos Andreas , Sakane Yusuke , Statha Marina 
Published: December 2023

AbstractWe obtain new invariant Einstein metrics on the compact Lie groups $${{\,\textrm{SO}\,}}(n)$$

SO

(
n
)

which are not naturally reductive. This is achieved by using the real flag manifolds $${{\,\textrm{SO}\,}}(k_1+\cdots +k_p)/{{\,\textrm{SO}\,}}(k_1)\times \cdots \times {{\,\textrm{SO}\,}}(k_p)$$

SO

(

k
1

+
⋯
+

k
p

)

/

SO

(

k
1

)

×
⋯
×

SO

(

k
p

)

and by imposing certain symmetry assumptions in the set of all left-invariant metrics on $${{\,\textrm{SO}\,}}(n)$$

SO

(
n
)

.
A Functional Approach to Interpreting the Role of the Adjoint Equation in Machine Learning
Authors: Fekete Imre , Molnár András , Simon Péter L. 
Published: December 2023

AbstractThe connection between numerical methods for solving differential equations and machine learning has been revealed recently. Differential equations have been proposed as continuous analogues of deep neural networks, and then used in handling certain tasks, such as image recognition, where the training of a model includes learning the parameters of systems of ODEs from certain points along their trajectories. Treating this inverse problem of determining the parameters of a dynamical system that minimize the difference between data and trajectory by a gradient-based optimization method presents the solution of the adjoint equation as the continuous analogue of backpropagation that yields the appropriate gradients. The paper explores an abstract approach that can be used to construct a family of loss functions with the aim of fitting the solution of an initial value problem to a set of discrete or continuous measurements. It is shown, that an extension of the adjoint equation can be used to derive the gradient of the loss function as a continuous analogue of backpropagation in machine learning. Numerical evidence is presented that under reasonably controlled circumstances the gradients obtained this way can be used in a gradient descent to fit the solution of an initial value problem to a set of continuous noisy measurements, and a set of discrete noisy measurements that are recorded at uncertain times.
The impact of chronic obstructive pulmonary disease on surgical outcomes after surgery for an acute abdominal diagnosis
Authors: Kassahun Woubet Tefera , Babel Jonas , Mehdorn Matthias 
Published: December 2023

Abstract
Purpose
The current study was undertaken to describe the independent contribution of chronic obstructive pulmonary disease (COPD) to the risk of postoperative morbidity and in-hospital mortality among patients undergoing surgery for an acute abdominal diagnosis.

Methods
Patients who underwent emergency abdominal procedures were identified from the electronic database of the Department of Visceral, Transplantation, Thoracic and Vascular Surgery of our institution. To evaluate differences in surgical risk associated with COPD, patients with COPD were matched for age, sex, and type of surgery with an equal number of controls who did not have COPD. Logistic regression was performed to evaluate the univariate and multivariate associations between the independent variables, including COPD and outcome variables.

Results
Between January 2012 and December 2022, 3519 patients undergoing abdominal emergency surgery were identified in our abdominal surgical department. After removing ineligible cases, 201 COPD cases with an equal number of matched controls remained for analysis. The prevalence of COPD after the exclusion of ineligible cases was 5.7%. There were statistically significant differences in the rate of postoperative pulmonary complications (PPCs [57.7% vs. 35.8%; P < 0.001]), ventilator dependence (VD [63.2% vs. 46.3%; P < 0.001]), thromboembolic events (TEEs [22.9% vs. 12.9%; P = 0.009]), and in-hospital mortality (41.3% vs. 30.8%; P = 029) for patients with and without COPD. Independent of other covariates, the presence of COPD was not associated with a significantly increased risk of in-hospital mortality (OR, 1.16; 95% CI 0.70–1.97; P = 0.591) but was associated with an increased risk of PPCs (OR, 2.49; 95% CI 1.41–4.14; P = 0.002) and VD (OR, 2.26; 95% CI 1.22–4.17; P = 0.009).

Conclusions
Preexisting COPD may alter a patient’s risk of PPCs and VD. However, it was not associated with an increased risk of in-hospital mortality.
Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences
Authors: de Lourdes Berrios Cintrón María , Broomandi Parya , Cárdenas-Escudero Jafet , Cáceres Jorge O. , Galán-Madruga David 
Published: December 2023

AbstractThe aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.