bluuebunny/crossref_metadata_2025_split huggingface dataset:
A compatible mixed finite element method for large deformation analysis of two‐dimensional compressible solids in spatial configuration
Authors: Jahanshahi Mohsen 
Published: March 2022

AbstractIn this article, a new mixed finite element formulation is presented for the analysis of two‐dimensional compressible solids in finite strain regime. A three‐field Hu–Washizu functional, with displacement, displacement gradient and stress tensor considered as independent fields, is utilized to develop the formulation in spatial configuration. Certain constraints are imposed on displacement gradient and stress tensor so that they satisfy the required continuity conditions across the boundary of elements. From theoretical standpoint, simplex elements are best suited for the application of continuity constraints. The techniques that are proposed to implement the constraints facilitate their automatic imposition and, hence, they can be regarded as an important feature of the work. Since the exterior calculus provides the basis for the developments presented herein, the relevant topics are discussed within the context of the work. Various technical aspects of the formulation are described in detail. These aspects help to illuminate the mathematical formulation that might seem vague in the first place and, more importantly, they help to provide an efficient implementation for ensuing developments. The performance of the mixed finite element method is studied through benchmark numerical examples and it is compared with other similar elements. It is shown that the element has excellent convergence properties and it is numerically stable, especially for problems where classical first order elements demonstrate stiff or unstable behavior.
A New Parameterized Monogamy Relation between Entanglement and Equality
Authors: Jin Zhi‐Xiang , Fei Shao‐Ming , Li‐Jost Xianqing , Qiao Cong‐Feng 
Published: March 2022

AbstractA generalized definition of the monogamy relation for entanglement measures is provided. A monogamy equality rather than the usual inequality is presented based on the monogamy weight, from which monogamy relations are given satisfied by the αth power of the entanglement measures. Taking concurrence as an example, the significance and advantages of these relations are further demonstrated. In addition, it is shown that monogamy relations can be recovered by considering multiple copies of states for every non‐additive entanglement measure that violates the inequalities. It is also demonstrated that such relations for tripartite states can be generalized to multipartite systems.
Autism, the Criminal Justice System, and Transition to Adulthood
Authors: Richman Kenneth A. , Krause-Jensen Katrine , Rodogno Raffaele 
Published: March 2022

AbstractThe criminal justice system is set up for the purpose of regulating people’s behavior. If an individual violates a criminal norm, sanctions should reflect that person’s mental capacity in understanding what they did was legally wrong. When an autistic individual commits a crime, questions may be raised as to the responsibility of key social/state actors for failing to provide structures that would facilitate accepted behavior in this special category of citizens. Many of the infractions that autistic individuals are likely to commit come as a result of failed interactions with the wider neurotypical society as well as with law-enforcement officials. If the general public were more sensitive and educated about some of the issues raised here, and if (parts of) the public realm were less stressful and more autism friendly, there may well be fewer problematic incidents involving autistic people.
Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
Authors: Atanda Sikiru Adeniyi , Govindan Velu , Singh Ravi , Robbins Kelly R. , Crossa Jose , Bentley Alison R. 
Published: March 2022

Abstract
Key message
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget.

Abstract
Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
A Convolutional Neural Network (CNN) classification to identify the presence of pores in powder bed fusion images
Authors: Ansari Muhammad Ayub , Crampton Andrew , Garrard Rebecca , Cai Biao , Attallah Moataz 
Published: March 2022

AbstractThis study aims to detect seeded porosity during metal additive manufacturing by employing convolutional neural networks (CNN). The study demonstrates the application of machine learning (ML) in in-process monitoring. Laser powder bed fusion (LPBF) is a selective laser melting technique used to build complex 3D parts. The current monitoring system in LPBF is inadequate to produce safety-critical parts due to the lack of automated processing of collected data. To assess the efficacy of applying ML to defect detection in LPBF by in-process images, a range of synthetic defects have been designed into cylindrical artefacts to mimic porosity occurring in different locations, shapes, and sizes. Empirical analysis has revealed the importance of accurate labelling strategies required for data-driven solutions. We formulated two labelling strategies based on the computer-aided design (CAD) file and X-ray computed tomography (XCT) scan data. A novel CNN was trained from scratch and optimised by selecting the best values of an extensive range of hyper-parameters by employing a Hyperband tuner. The model’s accuracy was 90% when trained using CAD-assisted labelling and 97% when using XCT-assisted labelling. The model successfully spotted pores as small as 0.2mm. Experiments revealed that balancing the data set improved the model’s precision from 89% to 97% and recall from 85% to 97% compared to training on an imbalanced data set. We firmly believe that the proposed model would significantly reduce post-processing costs and provide a better base model network for transfer learning of future ML models aimed at LPBF micro-defects detection.
Umweltrechtliche Aspekte des Genehmigungsverfahrens der Gaspipeline Nord Stream 2 in Deutschland und Russland
Authors: Rohmann Ina 
Published: March 2022

ZusammenfassungDer Beitrag befasst sich mit umweltrechtlichen Aspekten des Genehmigungsverfahrens der umstrittenen
Gaspipeline Nord Stream 2. Er knüpft an den bereits zur ersten Nord Stream-Pipeline erschienenen Aufsatz
von Kathrin Kim (NuR 2009, 170–178) an, nimmt neben dem deutschen Abschnitt der Pipeline aber auch
deren russischen Teil in den Fokus. Zwischenabschnitte im finnischen, schwedischen und dänischen Teil
bleiben außerhalb der Betrachtung, ebenso wie Fragen der Zertifizierung der Pipeline, die für
ihre, derzeit nicht vorgesehene, Inbetriebnahme ausschlaggebend sind.
Nach Auffassung der Autorin erfolgte die Genehmigung der Pipelineabschnitte auf beiden Seiten im Einklang
mit den geltenden Vorschriften – auch wenn die Kritik an den Vorschriften als solchen teilweise nicht
unberechtigt ist.
The effects of berberine on inflammatory markers in Chinese patients with metabolic syndrome and related disorders: a meta‑analysis of randomized controlled trials
Authors: Lu Yuqiong , Zhang Xiwen , He Jiafang , Dai Zhanjing , Shi Penghua , Lu Yun , Chang Feng 
Published: March 2022

Abstract
Background
A meta-analysis of randomized controlled trials (RCTs) was conducted to systematically evaluate the effects of berberine on the inflammatory markers of metabolic syndrome (MetS) and related disorders.

Method
Databases that were searched from inception to October 2020 included PubMed, Web of Science, the Cochrane Library, CNKI, VIP, WanFang Data, and ClinicalTrials.gov. Two reviewers independently selected articles and extracted data. The pooled evaluations were entered and analyzed in Review Manager 5.3.

Results
Of the 7387 publications screened, 52 studies were included, and the related trials involved 4616 patients. Pooled estimates showed that the use of berberine could significantly reduce the concentration level of C-reactive protein (CRP) [standardized mean difference (SMD) = − 1.54, 95% confidence intervals (CI) − 1.86, − 1.22, p < 0.05], tumor necrosis factor-α (TNF-α) [SMD = − 1.02, 95% CI − 1.27, − 0.77, p < 0.05], and interleukin 6 (IL-6) [SMD = − 1.17, 95% CI − 1.53, − 0.81, p < 0.05] among patients with MetS and related disorders. However, it did not affect the level of interleukin 1β (IL-1β) [SMD = − 0.81, 95% CI − 1.80, 0.17, p = 0.11].

Conclusion
Overall, the use of berberine in patients with MetS and related disorders appeared to significantly decrease several inflammatory markers. Further multi-center and rigorous investigations with larger patient populations are encouraged to confirm the effect of berberine on MetS and related disorders.
Inside school turnaround: What drives success?
Authors: Hill Kirsten Lee , Desimone Laura , Wolford Tonya , Reitano Adrienne , Porter Andrew 
Published: March 2022

AbstractThis study proposes an empirically grounded theory of how school reform implementation relates to effectiveness, useful for developing and studying many approaches to school reform both in the U.S. and abroad, and also for assessing how policymakers and implementers might leverage various aspects of implementation to create effective school improvement models at scale. Guided by a new framework that links Bryk and colleagues’ (2010) five essential supports and as reported by Desimone’s (2002) adaptation of Porter and colleagues’ (1986) policy attributes theory, we use a mixed-methods approach to study the implementation and effectiveness of school turnaround efforts in the School District of Philadelphia. We explore the relationships among key turnaround model components, approaches to model implementation, and academic achievement using a matched comparison design and estimating a series of regressions. Qualitative methods are used to contextualize findings and offer explanatory hypotheses.
The impact of a telehealth platform on ABA-based parent training targeting social communication in children with autism spectrum disorder
Authors: Ferguson Jenny , Dounavi Katerina , Craig Emma A. 
Published: March 2022

AbstractInterventions based upon applied behaviour analysis (ABA) have been shown to be best practice for children with autism spectrum disorder. However, in many parts of the world there is a shortage of appropriately trained behaviour analysts. Telehealth is a potential solution to increasing access to ABA. Our study assessed the use of telehealth to provide parent training in naturalistic teaching strategies designed to increase child communication skills. Five parent child dyads took part in the training, utilising didactic training and synchronous coaching. Parents could be trained to a high level of fidelity and viewed the training favourably. Children showed variable gains in communication and improved positive affect. The project was cost effective in comparison with traditional training models.
Atomic Ordering at the Interfaces Between Liquid Aluminum and Polar AlN{0 0 0 1} Substrates
Authors: Fang C. M. , Fan Z. 
Published: March 2022

Abstract
AlN particles are formed in liquid Al metals/alloys during liquid-dealing and casting. They may act as potential nucleation sites during solidification. Along its [0 0 0 1] axis, AlN consists of Al3+N3- dipolar double-atom layers in the ionic model. Thus, the AlN{0 0 0 1} substrates are terminated by either an Al3+ or a N3- layer, being polar. Here we investigate the atomic ordering at the interfaces between liquid-aluminum and AlN{0 0 0 1} using an ab initio molecular dynamics technique. We have observed a rich variety of interfacial chemistry and identified an ordered Al layer terminating the substrates. The newly formed terminating Al atoms are positively charged. The liquid Al adjacent to the interfaces exhibit strong layering but weak in-plane ordering. The obtained information helps get insight into the role of aluminum nitride as potential nucleation sites in solidification of Al-metals, and further enriches our knowledge about nucleation
Serum Levels of Growth-Associated Protein-43 and Neurotrophin-3 in Childhood Epilepsy and Their Relation to Zinc Levels
Authors: Bakri Ali Helmi , Hassan Mohammed H. , Ahmed Ahmed El-Abd , Alotaibi Ghallab , Halim Pola Rafat , Abdallah Ahmed Alamir Mahmoud , Rashwan Nagwan I. 
Published: March 2022

AbstractBackgroundEpilepsy is one of the most common neurological disorders, and it places a significant economic strain on the healthcare system around the world. Although the exact mechanism of epilepsy has yet to be illustrated, various pathogenic cascades involving neurotransmitters and trace elements have been reported. We aimed to investigate the serum levels of growth-associated protein-43 (GAP-43) and neurotrophin-3 (NT-3) among cohort of Egyptian children with epilepsy and correlate these biomarkers with their zinc levels.MethodsThis case–control study included 50 pediatric patients with epilepsy who were comparable with 50 controls. Neurological assessment and electroencephalogram (EEG) were done to all included children. Biochemical measurements of serum GAP-43 and NT-3 using enzyme linked immunosorbent assays (ELISA), and total antioxidant capacity (TAC) and zinc using colorimetric assays, were performed to all participants.ResultsThere was significantly frequent positive parental consanguinity among cases with significantly frequent generalized onset seizures (94%) than simple partial seizure (6%). There were significantly lower serum GAP-43 and zinc levels with significantly higher TAC among cases vs. the controls,p˂0.05 for all. There was no significant difference in the serum levels of NT-3 among epileptic children vs. the controls,p = 0.269. Serum Zn was positively correlated with GAP-43 level among epileptic children (r = 0.381,p = 0.006). Serum GAP-43 in diagnosing childhood epilepsy at cut-off point ≤ 0.6 ng/mL showed 78% sensitivity, 62% specificity, positive predictive value (PPV) = 50.6%, negative predictive value (NPP) = 84.9% withAUC = 0.574.ConclusionGAP-43 can be considered a sensitive good negative biomarker in childhood epilepsy which correlated positively with the zinc status.
How Machine Learning is Powering Neuroimaging to Improve Brain Health
Authors: Singh Nalini M. , Harrod Jordan B. , Subramanian Sandya , Robinson Mitchell , Chang Ken , Cetin-Karayumak Suheyla , Dalca Adrian Vasile , Eickhoff Simon , Fox Michael , Franke Loraine , Golland Polina , Haehn Daniel , Iglesias Juan Eugenio , O’Donnell Lauren J. , Ou Yangming , Rathi Yogesh , Siddiqi Shan H. , Sun Haoqi , Westover M. Brandon , Whitfield-Gabrieli Susan , Gollub Randy L. 
Published: March 2022

AbstractThis report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
Molecular Insights into Therapeutic Potentials of Hybrid Compounds Targeting Alzheimer’s Disease
Authors: Jana Ankit , Bhattacharjee Arkadyuti , Das Sabya Sachi , Srivastava Avani , Choudhury Akshpita , Bhattacharjee Rahul , De Swagata , Perveen Asma , Iqbal Danish , Gupta Piyush Kumar , Jha Saurabh Kumar , Ojha Shreesh , Singh Sandeep Kumar , Ruokolainen Janne , Jha Niraj Kumar , Kesari Kavindra Kumar , Ashraf Ghulam Md 
Published: March 2022

Abstract
Alzheimer’s disease (AD) is one of the most complex progressive neurological disorders involving degeneration of neuronal connections in brain cells leading to cell death. AD is predominantly detected among elder people (> 65 years), mostly diagnosed with the symptoms of memory loss and cognitive dysfunctions. The multifarious pathogenesis of AD comprises the accumulation of pathogenic proteins, decreased neurotransmission, oxidative stress, and neuroinflammation. The conventional therapeutic approaches are limited to symptomatic benefits and are ineffective against disease progression. In recent years, researchers have shown immense interest in the designing and fabrication of various novel therapeutics comprised of naturally isolated hybrid molecules. Hybrid therapeutic compounds are developed from the combination of pharmacophores isolated from bioactive moieties which specifically target and block various AD-associated pathogenic pathways. The method of designing hybrid molecules has numerous advantages over conventional multitarget drug development methods. In comparison to in silico high throughput screening, hybrid molecules generate quicker results and are also less expensive than fragment-based drug development. Designing hybrid-multitargeted therapeutic compounds is thus a prospective approach in developing an effective treatment for AD. Nevertheless, several issues must be addressed, and additional researches should be conducted to develop hybrid therapeutic compounds for clinical usage while keeping other off-target adverse effects in mind. In this review, we have summarized the recent progress on synthesis of hybrid compounds, their molecular mechanism, and therapeutic potential in AD. Using synoptic tables, figures, and schemes, the review presents therapeutic promise and potential for the development of many disease-modifying hybrids into next-generation medicines for AD.
Typ-VII-Sekretionssysteme: Ziele gegen Tuberkulose
Authors: Geibel Sebastian 
Published: March 2022

AbstractTo facilitate host infection, tuberculosis-causing mycobacteria employ five type VII secretion systems (T7SS) to orchestrate the timely secretion of effector proteins, which play central roles in their immune evasion strategy and ensure the uptake of essential nutrients. This article reviews our current structural and functional understanding of mycobacterial T7SS and discusses their potential as targets for novel antimicrobial strategies.
Zellspezifische Produktion von multiplen Therapeutika im Körper
Authors: Brücher Dominik , Freitag Patrick Christian , Plückthun Andreas 
Published: March 2022

AbstractMany conventional cancer therapies suffer from side effects and low efficacy, due to poor tumor localization of systemically injected therapeutics. Utilizing the adenoviral SHREAD platform, therapeutic genes were delivered specifically to cancer cells in vivo that express a defined, freely selectable cell receptor. Antibodies secreted by infected cells were visualized using a cell-clearing technology, revealing a high local concentration of the antibody within the tumor, with minimal concentration in peripheral healthy tissues.
Pilze zur wertsteigernden Holzfunktionalisierung
Authors: Schwarze Francis W. M. R. , Burgert Ingo 
Published: March 2022

AbstractWood decay fungi play an important role as recyclers of organic matter in the nutrient cycle. The risks of wood degradation by fungi are widely discussed, but little attention is given to the opportunity to apply wood decay fungi for wood functionalization, modification and/or wood protection. For this purpose, the mycelium (fungal network), spores, enzymes and polymers from various types of wood decay fungi are used.
Antimikrobielle Peptide – Inspiration aus der Natur
Authors: Rodríguez-Rojas Alexandro , Rolff Jens 
Published: March 2022

AbstractAntimicrobial peptides (AMPs) are ancient immune effectors and are considered as new anti-infective drugs. AMPs differ from conventional antibiotics in their pharmacodynamics, speed of killing and they do not increase mutagenesis. This results in a much lower probability of bacterial resistance evolution against AMPs. AMPs are usually expressed as synergistic cocktails. If AMPs are to be used in the clinic to tackle the antibiotic crisis, understanding their natural biology will be pivotal to prevent resistance.
Nanoschalen aus DNA fangen und neutralisieren Viren
Authors: Sigl Christian , Dietz Hendrik 
Published: March 2022

AbstractThe current pandemic has highlighted the need for new antiviral therapies, to respond to existing and emerging diseases transmitted by viral vectors. We developed a novel antiviral approach that is based on neutralizing viruses by trapping and encapsulation in artificial nano-shells. The surrounding shells prevent the interaction of viruses with host cells and thus interrupt an essential step in the lifecycle of most viruses.
Ein biologisches Lungenmodell mit Forschungspotenzial
Authors: Guenat Olivier , Zamprogno Pauline 
Published: March 2022

AbstractStandard in vitro models fail to reproduce the complex cellular microenvironment of the human lung, whereas lung animal models poorly predict drug response in humans. A powerful alternative to model various aspects of the air-blood barrier is lung-on-chips using a thin and porous polymeric membrane. Researchers from the University of Bern have developed a new generation lung-on-chip that mimics an array of alveoli based on a biological membrane, on which patient cells are cultured, opening new potentials for lung research, drug screening and personalized medicine.
Mitochondrien als universelle Sensoren der akuten Hypoxie?
Authors: Nolte Anika , Pak Oleg , Sommer Natascha 
Published: March 2022

AbstractAdaptation to acute hypoxia through cardiorespiratory responses is mediated by specialized cells in the carotid body and pulmonary vasculature to optimize systemic arterial oxygenation. Acute oxygen sensing thus is a fundamental pre-requisite for aerobic life. Recent studies unravelled basic oxygen sensing mechanisms involving the mitochondrial cytochrome c oxidase subunit 4 isoform 2 that regulates the release of mitochondrial reactive oxygen species and subsequent acute hypoxic responses.