bluuebunny/crossref_metadata_2025_split huggingface dataset:
The influence of small amounts of shear reinforcement on the shear‐transferring mechanisms in RC beams: An analysis based on refined experimental measurements
Authors: Autrup Frederik , Jørgensen Henrik Brøner , Hoang Linh Cao 
Published: August 2022

AbstractSmall amounts of shear reinforcement are often assumed to increase the shear capacity of RC beams, compared to an identical beam without shear reinforcement. However, in a recent experimental campaign, the shear capacity of beams with a shear reinforcement ratio below the minimum requirements according to the design standards turned out to be similar to identical beams without shear reinforcement. This paper presents a detailed analysis of why the shear capacity may be similar for beams without‐ and beams with small amounts of shear reinforcement. This includes the influence of small amounts of shear reinforcement on the shear behaviour and shear‐transferring mechanisms. The analysis shows that the crack development is more severe at the ultimate load for beams with a small amount of shear reinforcement compared to beams without shear reinforcement. This more severe crack development is shown to cause an overestimation of the shear contribution from aggregate interlock when applying a well‐known constitutive model often used for beams without shear reinforcement. Therefore, a new expression for the aggregate interlock stresses is proposed. A comparison of the proposed expression with Mixed‐Mode crack opening tests shows a good agreement with the test for both small and large crack openings. By applying the proposed expression on the measured crack kinematics it is shown that for a large shear contribution from aggregate interlock the shear contribution from the shear reinforcement is very limited and as the aggregate interlock stresses decrease the shear contribution from the shear reinforcement increases. This shift in the governing shear‐transferring mechanism can help to improve the requirements for the minimum shear reinforcement often found in the design standards.
IGFBP2 function as a novel biomarker for active lupus nephritis
Authors: Li Hui , Liang Jun , Gao Yingying , Liu Min , Xia Nan , Kong Wei , Zheng Lisha , Zhang Yikun , Li Zutong , Chen Hongwei , Liu Shanshan , Sun Lingyun 
Published: August 2022

Abstract
In search for new targets for the diagnosis and treatment of lupus nephritis (LN), we employed TMT-liquid chromatography-triple quadrupole mass spectrometry (TMT-LC–MS/MS) combined with RNA-seq and identified a panel of proteins that was dysregulated both at protein level and mRNA level in active LN patients compared with healthy controls. We chose to study the role of IGFBP2 since it is a relatively understudied protein in the context of LN. We further validated that IGFBP2 significantly increased and correlated with SLE activity index in active LN patients. The receiver operator characteristic (ROC) curve suggested that plasma IGFBP2 had a high diagnostic efficiency for distinguishing between inactive and active LN patients (AUC = 0.992; 95% CI = 0.974–1.000; P < 0.001). We demonstrated neutralizing IGFBP2-downregulated CD4+ T cell activation, upregulated the ratio of Treg, downregulated AKT/mTOR/4E-BP1 pathway, and significantly improved nephritis in MRL/lpr mice. In all, our work demonstrated IGFBP2 as a biomarker specific for active LN and blocking IGFBP2 could be a new target for treating LN.

Key messages

Plasma IGFBP2 is a promising diagnostic marker for distinguishing stable LN from active LN, and it is also a predictor for the poor prognosis of LN.

Blockade of IGFBP2 can significantly improve the pathological damage of LN.

IGFBP2 may regulate activation of CD4+ T and Treg ratio.

Neutralizing IGFBP2 downregulates AKT/mTOR/4E-BP1 pathway.
Incidental pulmonary embolism in patients with cancer: prevalence, underdiagnosis and evaluation of an AI algorithm for automatic detection of pulmonary embolism
Authors: Wiklund Peder , Medson Koshiar , Elf Johan 
Published: August 2022

Abstract
Objectives
To assess the prevalence of reported and unreported incidental pulmonary embolism (iPE) in patients with cancer, and to evaluate an artificial intelligence (AI) algorithm for automatic detection of iPE.

Methods
Retrospective cohort study on patients with cancer with an elective CT study including the chest between 2018-07-01 and 2019-06-30. All study reports and images were reviewed to identify reported and unreported iPE and were processed by the AI algorithm.

Results
One thousand sixty-nine patients (1892 studies) were included. Per study, iPE was present in 75 studies (4.0%), of which 16 (21.3%) were reported. Unreported iPE had a significantly lower number of involved vessels compared to reported iPE, with a median of 2 (interquartile range, IQR, 1–4) versus 5 (IQR 3–9.75), p < 0.001. There were no significant differences in age, cancer type, or attenuation of the main pulmonary artery. The AI algorithm correctly identified 68 of 75 iPE, with 3 false positives (sensitivity 90.7%, specificity 99.8%, PPV 95.6%, NPV 99.6%). False negatives occurred in cases with 1–3 involved vessels. Of the unreported iPE, 32/59 (54.2%) were proximal to the subsegmental arteries.

Conclusion
In patients with cancer, the prevalence of iPE was 4.0%, of which only 21% were reported. Greater than 50% of unreported iPE were proximal to the subsegmental arteries. The AI algorithm had a very high sensitivity and specificity with only three false positives, with the potential to increase the detection rate of iPE.

Key Points
• In a retrospective single-center study on patients with cancer, unreported iPE were common, with the majority lying proximal to the subsegmental arteries.
• The evaluated AI algorithm had a very high sensitivity and specificity, so has the potential to increase the detection rate of iPE.
Early marker of ocular neurodegeneration in children and adolescents with type 1 diabetes: the contributing role of polymorphisms in mir146a and mir128a genes
Authors: Piona Claudia , Costantini Silvia , Zusi Chiara , Cozzini Tiziano , Pedrotti Emilio , Marigliano Marco , Fornari Elena , Maguolo Alice , Morandi Anita , Maffeis Claudio 
Published: August 2022

Abstract
Background
Early ocular neurodegenerative signs of diabetic neuropathy (DN) can be found in children and adolescents with type 1 diabetes (T1D). No data are available on the potential role of polymorphisms in miRNAs genes in predisposing T1D subjects to these signs.

Aims
To determine whether MIR146A rs2910164 and MIR128A rs11888095 polymorphisms are associated with early retinal and corneal neurodegenerative changes in pediatric patients with T1D.

Methods
A total of 140 T1D children/adolescents underwent spectral domain-optical coherence tomography (SD-OCT) and in vivo confocal microscopy (IVCM) with measurement of retinal and corneal nerve fiber parameters. Risk factors for diabetes complications (diabetes duration, blood pressure, HbA1c) were recorded. Genotyping of rs2910164 and rs1188095 SNPs and genotype–phenotype association analysis were performed.

Results
The C allele of rs2910164 in MIR146A was associated with higher values of IVCM parameters and minimum rim width (MRW) of the peripapillary region of optic nerve head measured in the retina, whereas the T allele of rs1188095 in MIR128A was associated with a significant impairment of them. Multiple regression analysis showed that MIR146A and MIR128A polymorphisms were significantly associated with corneal nerve fiber length (beta = 0.225 and − 0.204, respectively) and other IVCM parameters, independently from age, diabetes duration, HbA1c and systolic blood pressure percentile. Similar results were found for MRW (beta = 0.213 and − 0.286, respectively).

Conclusions
These results provide new insight into the genetic predisposition to DN showing that two polymorphisms in MIR146A and MIR128A genes could significantly contribute to the development of early ocular preclinical signs of DN.
Factors associated with re-admission in the year after acute postpartum psychiatric treatment
Authors: Taylor Billie Lever , Sweeney Angela , Potts Laura C. , Trevillion Kylee , Howard Louise M. 
Published: August 2022

Abstract
Purpose
To examine factors associated with being re-admitted in the year after discharge from acute postpartum psychiatric treatment.

Methods
Secondary data analysis of information collected from mothers who were admitted to acute psychiatric services in the year after childbirth between 2013 and 2017. We carried out univariable analyses and multivariable hierarchical logistic regression to examine risk factors for women’s re-admission to acute psychiatric care (inpatient or community crisis care) in the year following discharge.

Results
Sixty-seven (24.1%) of 278 women were re-admitted in the year after discharge from acute care; the median number of days to re-admission was 86 (IQR 35–214), and women who were re-admitted accessed a median of two further acute services (IQR 1–3). In adjusted analyses, reporting a history of childhood trauma (aOR 1.02; 95% CI 1.00- 1.03, p = 0.036), a higher level of difficulties in the mother–infant bond (aOR 1.03; 95% CI 1.01–1.06, p = 0.009) and younger age (aOR 0.95; 95% CI 0.90–1.00, p = 0.066) were associated with re-admission.

Conclusion
This study confirms that the role of childhood adverse experiences on mental health is relevant for outcomes in women experiencing acute postpartum psychiatric episodes. Ongoing parent–infant bonding difficulties are also independently associated with re-admission. Perinatal mental health services therefore need to offer evidence-based interventions to address histories of trauma and to support parent–infant bonding to optimise mental health in women following discharge from acute psychiatric services. However, further research is needed to explore what other factors, not measured in our study, are also influential to re-admission.
Development of metal oxide incorporated Al-Zn-Sn sacrificial anodes processed by stir casting and heat treatment
Authors: Elsayed Abdelrahman , Nofal Adel , Attia Galal 
Published: August 2022

AbstractAluminum sacrificial anodes are currently the first choice for cathodic protection in numerous applications. The galvanic performance of aluminum-based sacrificial anodes is considerably enhanced by addition of certain alloying elements called activators. Recent researches proved that incorporation of specific metal oxides like MnO2, CeO2, RuO2, and IrO2 into the aluminum matrix could enhance the galvanic efficiency of aluminum anodes; however, the mechanism by which metal oxides improve galvanic properties of aluminum is still subject to discussion. The present work investigates the effect of incorporating commercially available low-cost manganese dioxide concentrate into Al-5Zn-0.1Sn sacrificial anodes in different volume fractions. It also studies the influence of heat treatment on anode’s galvanic performance by performing solution treatment at 3 different temperatures (250 °C, 400 °C, 550 °C). The electrochemical testing results proved an increase in efficiency of anodes incorporated with metal oxides and solution treated at 550 °C. The SEM imaging and EDX elemental mapping declared that the presence of SiO2 particles in the anode matrix which might cause effective and uniform corrosion of Al anodes and decreased non-coulombic losses.
Graphical abstract
Comment on the applicability of the Gurvich rule for estimation of pore volume in microporous zeolites
Authors: Farmahini Amir H. , Limbada Khalid , Sarkisov Lev 
Published: August 2022

AbstractThis comment seeks to establish a relation between two definitions of the pore volume of a microporous crystalline material. According to the first definition based on the Gurvich rule, the volume of the pores can be estimated from the saturated amount of vapour adsorbed, using the bulk liquid density of adsorbate as the conversion factor. The second definition is based on a purely geometric consideration of the porous space. With argon as the adsorbate and all-silica zeolite structures from the International Zeolite Association (IZA) database as the model adsorbents, we generate adsorption data using Grand Canonical Monte Carlo simulations and structural characteristics of the materials from the Poreblazer PB4.0 software. Under confinement in zeolitic pores, adsorbed argon forms structures very different from the liquid-like configurations. However, the pore volumes of these materials obtained from the Gurvich may deviate positively or negatively from the reference geometric value. Considering simply the geometric features of the materials, such as the pore volume itself or the pore size distribution, it proved to be difficult to anticipate how the volume from the Gurvich rule would deviate from the geometric volume for a particular structure. Overall, volume from the Gurvich rule agrees with the geometric volume within 25% error for 82% of the structures from the IZA database. As an additional outcome of this study, we provide a comprehensive database of textural characteristics and simulated argon adsorption data for all-silica zeolites, which can be used as reference values for the assessment of the quality of the microporous samples of all-silica zeolites in future experimental studies.
From inexact optimization to learning via gradient concentration
Authors: Stankewitz Bernhard , Mücke Nicole , Rosasco Lorenzo 
Published: August 2022

AbstractOptimization in machine learning typically deals with the minimization of empirical objectives defined by training data. The ultimate goal of learning, however, is to minimize the error on future data (test error), for which the training data provides only partial information. In this view, the optimization problems that are practically feasible are based on inexact quantities that are stochastic in nature. In this paper, we show how probabilistic results, specifically gradient concentration, can be combined with results from inexact optimization to derive sharp test error guarantees. By considering unconstrained objectives, we highlight the implicit regularization properties of optimization for learning.
Convergence of GHGs emissions in the long-run: aerosol precursors, reactive gases and aerosols—a nonlinear panel approach
Authors: Romero-Ávila Diego , Omay Tolga 
Published: August 2022

AbstractAnthropogenic emissions of reactive gases, aerosols and aerosol precursor compounds are responsible for the ozone hole, global warming and climate change, which have altered ecosystems and worsened human health. Environmental authorities worldwide have responded to these climate challenges through the 2030 Agenda for Sustainable Development. In this context, it is key to ascertain empirically whether emission levels are converging among the countries forming the industrialized world. In doing so, we focus on 23 industrialized countries using a novel dataset with ten series of annual estimates of anthropogenic emissions that include aerosols, aerosol precursor and reactive compounds, and carbon dioxide over the 1820–2018 period. We apply four state-of-the-art panel unit root tests that allow for several forms of time-dependent and state-dependent nonlinearity. Our evidence supports stochastic convergence following a linear process for carbon dioxide, whereas the adjustment is nonlinear for black carbon, carbon monoxide, methane, non-methane volatile organic compounds, nitrous oxide, nitrogen oxides and sulfur dioxide. In contrast, ammonia and organic carbon emissions appear to diverge. As for deterministic convergence, carbon dioxide converges linearly, while black carbon, carbon monoxide, nitrogen oxides, non-methane volatile organic compounds and sulfur dioxide adjust nonlinearly. Our results carry important policy implications concerning the achievement of SDG13 of the global 2030 Agenda for Sustainable Development, which appears to be feasible for the converging compounds.
Effect of the BMPR-II-SMAD3/MRTF pathway on proliferation and migration of ASMCs and the mechanism in asthma
Authors: Gu Wenbo , Lei Jiahui , Zhu He , Xiao Yali , Zhang Zhenping , Zhao Limin 
Published: August 2022

Abstract
Background
A variety of smooth muscle-specific genes and proteins, including SMAD3, BMPR-II, and MRTF, are involved in airway remodeling in asthma. As a receptor of bone morphogenetic protein (BMP) signaling, BMPR-II has important roles in airway remodeling in asthma. However, the underlying mechanism of BMPR-II in airway smooth muscle cells (ASMCs) in asthma remains incomplete.

Methods
Wistar rats were intraperitoneally injected with ovalbumin antigen suspension and aluminium hydroxide and, stimulated with ovalbumin nebulized inhalation to constructed asthma model. Primary ASMCs were isolated with collagenase I and identified by testing the α-SMA expression. Quantitative polymerase chain reaction (qPCR) and western blot assay were employed to detect the gene expression. CCK8, Transwell and Fluo-4 A assays were introduced to measure the cell viability, migration and intracellular Ca2+. Co-Immunoprecipitation (Co-IP) assay was applied to test the interaction among proteins.

Results
First, we observed significant increases in BMPR-II in asthmatic rat model and ASMCs at both the mRNA and protein levels. Second, we observed that silencing of siBMPR-II inhibited proliferation, migratory capacity and intracellular Ca2+ concentration in ASMCs. Furthermore, our study demonstrated that siBMPR-II inhibited the Smad3 expression and overexpression promoted the bioactivity of ASMCs. In addition, this study showed that p-Smad3 could interacted with MRTF and siMRTF inhibits the bioactivity of ASMCs. Finally, our results revealed BMPR-II-SMAD3/MRTF pathway affected the bioactivity of ASMCs.

Conclusions
This study indicates that the BMPR-II-SMAD3/MRTF signaling pathway is involved in the process of ASMCs remodeling, providing novel avenues for the identification of new therapeutic modalities.
A deep learning approach for parkinson’s disease severity assessment
Authors: Aşuroğlu Tunç , Oğul Hasan 
Published: August 2022

Abstract
Purpose
Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved.

Methods
We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest.

Results
Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity.

Conclusion
This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.
Evolving knowledge in surgical oncology of pancreatic cancer: from theory to clinical practice—a fifteen-year journey at a tertiary referral centre
Authors: Casadei Riccardo , Ricci Claudio , Ingaldi Carlo , Alberici Laura , De Raffele Emilio , Barcia Bianca , Mosconi Cristina , Diegoli Margherita , Di Marco Mariacristina , Brandi Giovanni , Zagari Rocco Maurizio , Pagano Nico , Eusebi Leonardo Henry , Serra Carla , Migliori Marina , Guido Alessandra , Santini Donatella , Rosini Francesca , Malvi Deborah , Minni Francesco 
Published: August 2022

AbstractPancreatic ductal adenocarcinoma (PDAC) is an increasing disease having a poor prognosis. The aim of the present study was to evaluate the effect of different models of care for pancreatic cancer in a tertiary referral centre in the period 2006–2020. Retrospective study of patients with PDAC observed from January 2006 to December 2020. The demographic and clinical data, and data regarding the imaging techniques used, preoperative staging, management, survival and multidisciplinary tumour board (MDTB) evaluation were collected and compared in three different periods characterised by different organisation of pancreatic cancer services: period A (2006–2010); period B (2011–2015) and period C (2016–2020). One thousand four hundred seven patients were analysed: 441(31.3%) in period A; 413 (29.4%) in B and 553 (39.3%) in C. The proportion of patients increased significantly, from 31.3% to 39.3% (P = 0.032). Body mass index (P = 0.033), comorbidity rate (P = 0.002) and Karnofsky performance status (P < 0.001) showed significant differences. Computed tomography scans (P < 0.001), endoscopic ultrasound (P < 0.001), fine needle aspiration, fine needle biopsy (P < 0.001), and fluorodeoxyglucose-positron emission tomography/computed tomography (P < 0.001) increased; contrast-enhanced ultrasound (P = 0.028) decreased. The cTNM was significantly different (P < 0.001). The MDTB evaluation increased significantly (P < 0.001). Up-front surgery and exploratory laparotomy decreased (P < 0.001), neoadjuvant treatment increased (P < 0.001). The present study showed the evolving knowledge in surgical oncology of pancreatic cancer at a tertiary referral centre over the time. The different models of care of pancreatic cancer, in particular the introduction of the MDTB and the institution of a pancreas unit to the decision-making process seemed to be influential.
Effect of wet Hydrogen Sulfide on Carbon Steels Degradation in Refinery Based on Case Study
Authors: Orlikowski Juliusz , Jazdzewska Agata , Uygur Ilyas , Gospos Radoslaw , Olczak Tomasz , Darowicki Kazimierz 
Published: August 2022

AbstractMaterial degradation caused by wet hydrogen sulfide (Wet H2S) is one of the major issues in refineries. Carbon steel is the main construction material used in refineries and is subjected not only to thickness loss but the hydrogen is the major concern. H2S leads to high atomic hydrogen penetration in steel which causes degradation in the form of blistering and/or cracking (HIC/SOHIC) and stress cracking in the area of welds and heat affected zones. Mechanism is unusually dangerous due to high difficulty of threat assessment, which is mostly based on ultrasonic measurements or calculations based on the API standards. This research is based on evaluation of the real threat of wet H2S degradation based on examination of construction materials after 41 years of exploitation in refinery. The comparison of the theoretical calculation based on API standards and experience of the degradation of the real objects was characterized. Laboratory measurements include mechanical tests involving the elongation in the function of tensile stress and deformation, with analysis of material hardness and gas chromatography analysis. The results are complemented by studies of the chemical composition of the streams and analytical studies of the hydrogen content in the material obtained by the gas chromatography method. The tests showed compliance of the standard analysis of the corrosion risk with the laboratory tests performed on the real samples. Research leads to indication of the destructive methods which can be used on the materials obtained during scheduled material replacements or installation modernizations.