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2. Solving the Synthetic Riddle of Colloidal 2D PbTe Nanoplatelets with Tunable Near-Infrared Emission

Author

Biesterfeld, Leon, Vochezer, Mattis T., Kögel, Marco, Zaluzhnyy, Ivan A., Rosebrock, Marina, Klepzig, Lars F., Leis, Wolfgang, Seitz, Michael, Meyer, Jannik C., and Lauth, Jannika

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Near-infrared emitting colloidal two-dimensional (2D) PbX (X=S, Se) nanoplatelets have emerged as interesting materials with strong size quantisation in the thickness dimension. They act as model systems for efficient charge carrier multiplication and hold potential as intriguing candidates for finer-based photonic quantum applications. However, synthetic access to the third family member, 2D PbTe, remains elusive due to a challenging precursor chemistry. Here, we report a direct synthesis for 2D PbTe nanoplatelets (NPLs) with unable photoluminescence (PL, 910-1460 nm (1.36-0.85 eV), PLQY 1-15 %), based on aminophosphine precursor chemistry. Ex-situ transamination of tris(dimethylamino)phosphine telluride with octylamine is confirmed by 31P NMR and yields a reactive tellurium precursor for the formation of 2D PbTe NPLs at temperatures as low as 0 {\deg}C. The PL position of the PbTe NPLs is unable by controlling the Pb:Te ration in the reaction. GIWAXS confirms the 2D geometry of the NPLs and the formation of superlattices. The importance of a post-synthetic passivation of the PbTe NPLs by PbI2 to ensure colloidal stability of the otherwise oxygen sensitive samples is supported by X-ray photoelectron spectroscopy. Our results expand and complete the row of lead chalcogenide-based 2D NPLs, opening up new ways for further pushing the optical properties of 2D NPLs into the infrared and toward technologically relevant wavelengths.

Published
2024
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3. An Overview of Major Occupational Lung Diseases

Author

Ufelle, Alexander C., Bernardo, Angela Mattis, and Williams, Adelle

Subjects
Lung diseases -- Care and treatment -- Risk factors -- Prevention, Occupational diseases -- Risk factors -- Prevention -- Care and treatment
Abstract

Occupational lung diseases result from worker exposure in occupational settings to unhealthy environments and agents, such as silica dust, antigens, coal dust, washed coal/mixed dust, asbestos, and beryllium. Most of these conditions have long latency periods, with disease manifesting years after exposure. Approximately 1 million workers in the U.S. report occupational exposure-related illnesses, including respiratory diseases. We evaluated major occupational lung diseases via an extensive literature review and present here advances in diagnosing major occupational lung diseases, preventive strategies, and regulatory considerations for maintaining a healthy workforce. We include the widely studied occupational lung diseases asbestosis, coal workers' pneumoconiosis and all other pneumoconioses, silicosis, byssinosis, malignant mesothelioma, hypersensitivity pneumonitis, and work-related asthma. The chemistry and physical properties of exposed materials play a role in the severity and pathogenesis of most occupational lung diseases. Knowledge of clinical signs and symptoms of these diseases, exposure history, and consensus diagnostic tools and criteria are crucial for accurate diagnosis, early detection, management, and improved outcomes. Further, regulatory agencies and other interested parties need to develop new and improved surveillance strategies, exposure limits, and technological and industrial safety measures, as well as implement regulations to guide industries and provide recommendations to protect all workers and reduce disease burden. Keywords: diagnosis, disease burden, occupational lung diseases, prevention, regulations, latency period, Introduction Widely studied occupational lung diseases include asbestosis, coal workers' pneumoconiosis (CWP) and all pneumoconioses, silicosis, byssinosis, malignant mesothelioma, hypersensitivity pneumonitis, asthma, chronic obstructive pulmonary disease (COPD), and lung cancer [...]

Published
2024

4. The Impact of Aging on Occupational Lung Disease

Author

Ufelle, Alexander C., Williams, Adelle, and Bernardo, Angela Mattis

Subjects
Lung diseases -- Demographic aspects -- Prognosis -- Risk factors, Occupational diseases -- Demographic aspects -- Prognosis -- Risk factors, Aged -- Employment
Abstract

The majority of occupational lung diseases have long latency periods, with disease manifesting at an older age. We conducted an extensive literature review and analyzed data for age-related mortality, the impact of age on the lungs and occupational lung diseases, changes in the workforce, and considerations for surveillance to maintain a healthy workforce. Age is a major factor in occupational lung diseases, which in turn predisposes older adults to other health conditions and reduces their quality of life and life expectancy. More specifically, age-related changes in the lungs increase the susceptibility to environmental exposure-induced lung injuries and are linked to poor prognosis. Data from the Work-Related Lung Disease Surveillance Report published by the National Institute for Occupational Safety and Health (NIOSH, 2008) indicated that death from occupational lung diseases among U.S. residents peaked in the age group 75-84 years. Interestingly, the age group 65-74 years recorded a substantial number of deaths, second to the age group 75-84 years. This trend represents a shift in U.S. mortality for all diseases, which peaks at >85 years. Improved surveillance and early detection will be instrumental in reducing the burden of occupational lung injury in older adults, many of whom are staying in the workforce longer. Keywords: aging, latency period, mortality, occupational lung disease, surveillance, Introduction Advanced age is the major risk factor for death for all age-related adult chronic diseases (Schneider et al., 2021) and for developing chronic lung conditions such as chronic obstructive [...]

Published
2024

9. 'I Help My Parents by Using Some of My FAFSA Money': A Qualitative Exploration of Pandemic-Related Stress among Community College Students

Author

Jacquelyn Chin, Samantha Mattis, Josceline Acosta, Amaranta Ramirez, David Rivera, Adrian Valadez, Kathleen Baca Leanos, Isaiah Jones, and Alison Cerezo

Abstract

The coronavirus (COVID-19) global pandemic upended the academic trajectories and opportunities of many community college students in the United States. While four-year universities have seen an uptick in applications in the 2021-2022 academic year, community colleges have experienced a sharp decrease in enrollment, signifying significant challenges to students' life circumstances. In May 2020, we sampled 116 community college students (M[subscript age] 23.08; SD = 7.17) attending Hispanic Serving Institutions in California. The online survey gathered: (a) in-depth demographic information, including pandemic-related changes to students' academic and financial standing, and (b) qualitative data on pandemic-related stress. Given the novelty of the COVID-19 pandemic in May 2020, the overarching goal of this study was to conduct a broad needs assessment of community college students' experiences. Data highlighted significant socio-emotional challenges, financial hardship, and both current and projected academic delays. Community college students described severe challenges across financial, socio-emotional and academic areas. Data that capture first-hand qualitative descriptions of COVID-19 stress are necessary to inform the allocation of critical resources and policies to improve the well-being of community college students.

Published
2024
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10. Quantum control of a cat-qubit with bit-flip times exceeding ten seconds

Author

Réglade, Ulysse, Bocquet, Adrien, Gautier, Ronan, Cohen, Joachim, Marquet, Antoine, Albertinale, Emanuele, Pankratova, Natalia, Hallén, Mattis, Rautschke, Felix, Sellem, Lev-Arcady, Rouchon, Pierre, Sarlette, Alain, Mirrahimi, Mazyar, Campagne-Ibarcq, Philippe, Lescanne, Raphaël, Jezouin, Sébastien, and Leghtas, Zaki

Subjects
Quantum Physics
Abstract

Quantum bits (qubits) are prone to several types of errors due to uncontrolled interactions with their environment. Common strategies to correct these errors are based on architectures of qubits involving daunting hardware overheads. A hopeful path forward is to build qubits that are inherently protected against certain types of errors, so that the overhead required to correct remaining ones is significantly reduced. However, the foreseen benefit rests on a severe condition: quantum manipulations of the qubit must not break the protection that has been so carefully engineered. A recent qubit - the cat-qubit - is encoded in the manifold of metastable states of a quantum dynamical system, thereby acquiring continuous and autonomous protection against bit-flips. Here, in a superconducting circuit experiment, we implement a cat-qubit with bit-flip times exceeding 10 seconds. This is a four order of magnitude improvement over previous cat-qubit implementations. We prepare and image quantum superposition states, and measure phase-flip times above 490 nanoseconds. Most importantly, we control the phase of these quantum superpositions without breaking bit-flip protection. This experiment demonstrates the compatibility of quantum control and inherent bit-flip protection at an unprecedented level, showing the viability of these dynamical qubits for future quantum technologies.

Published
2023
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13. Prospective de novo drug design with deep interactome learning

Author

Atz, Kenneth, Cotos, Leandro, Isert, Clemens, Håkansson, Maria, Focht, Dorota, Hilleke, Mattis, Nippa, David F., Iff, Michael, Ledergerber, Jann, Schiebroek, Carl C. G., Romeo, Valentina, Hiss, Jan A., Merk, Daniel, Schneider, Petra, Kuhn, Bernd, Grether, Uwe, and Schneider, Gisbert

Published
2024
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17. Fluoride-related changes in the fetal cord blood proteome; a pilot study

Author

Sami T. Tuomivaara, Susan J. Fisher, Steven C. Hall, Dana E. Goin, Aras N. Mattis, and Pamela K. Den Besten

Subjects
Industrial medicine. Industrial hygiene, RC963-969, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Fluoride exposure during pregnancy has been associated with various effects on offspring, including changes in behavior and IQ. To provide clues to possible mechanisms by which fluoride may affect human fetal development, we completed proteomic analyses of cord blood serum collected from second-trimester pregnant women residing in northern California, USA. Objective To identify changes in cord blood proteins associated with maternal serum fluoride concentration in pregnant women. Methods The proteomes of 19 archived second-trimester cord blood samples from women living in northern California, USA, and having varied serum fluoride concentrations, were analyzed by quantitative mass spectrometry. The 327 proteins that were quantified were characterized by their abundance relative to maternal serum fluoride concentration, and subjected to pathway analyses using PANTHER and Ingenuity Pathway Analysis processes. Results Pathway analyses showed significant increases in process related to reactive oxygen species and cellular oxidant detoxification, associated with increasing maternal serum fluoride concentrations. Pathways showing significant decreases included complement cascade, suggesting alterations in alterations in process associated with inflammation. Conclusion Maternal fluoride exposure, as measured by serum fluoride concentrations in a small, but representative sample of women from northern California, USA, showed significant changes in the second trimester cord blood proteome relative to maternal serum fluoride concentration.

Published
2024
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18. Spatial tumor immune microenvironment phenotypes in ovarian cancer

Author

Claudia Mateiou, Lavanya Lokhande, Lan Hoa Diep, Mattis Knulst, Elias Carlsson, Sara Ek, Karin Sundfeldt, and Anna Gerdtsson

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Immunotherapy has largely failed in ovarian carcinoma (OC), likely due to that the vast tumor heterogeneity and variation in immune response have hampered clinical trial outcomes. Tumor-immune microenvironment (TIME) profiling may aid in stratification of OC tumors for guiding treatment selection. Here, we used Digital Spatial Profiling combined with image analysis to characterize regions of spatially distinct TIME phenotypes in OC to assess whether immune infiltration pattern can predict presence of immuno-oncology targets. Tumors with diffuse immune infiltration and increased tumor-immune spatial interactions had higher presence of IDO1, PD-L1, PD-1 and Tim-3, while focal immune niches had more CD163 macrophages and a preliminary worse outcome. Immune exclusion was associated with presence of Tregs and Fibronectin. High-grade serous OC showed an overall stronger immune response and presence of multiple targetable checkpoints. Low-grade serous OC was associated with diffuse infiltration and a high expression of STING, while endometrioid OC had higher presence of CTLA-4. Mucinous and clear cell OC were dominated by focal immune clusters and immune-excluded regions, with mucinous tumors displaying T-cell rich immune niches.

Published
2024
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19. You can bring plankton to fecal indicator organisms, but you cannot make the plankton graze: particle contribution to E. coli and MS2 inactivation in surface waters

Author

Lauren C. Kennedy, Ava M. Mattis, and Alexandria B. Boehm

Subjects
fecal indicator organism, persistence, grazer, plankton, surface water, environmental water, Microbiology, QR1-502
Abstract

ABSTRACT Organisms that are associated with feces (“fecal indicator organisms”) are monitored to assess the potential for fecal contamination of surface water bodies in the United States. However, the effect of the complex mixtures of chemicals and the natural microbial community within surface water (“particles”) on fecal indicator organism persistence is not well characterized. We aimed to better understand how particles, including biological (e.g., potential grazers) and inert (e.g., minerals) types, affect the fecal indicator organisms Escherichia coli K-12 (“E. coli”) and bacteriophage MS2 in surface waters. A gradient of particles captured by a 0.2-µm-pore-size filter (“large particles”) was generated, and the additional particles and dissolved constituents that passed through the filter were deemed “small particles.” We measured the ratio of MS2 and E. coli that survived over a 24-h incubation period for each condition (0%–1,000% large-particle concentration in raw water) and completed a linear regression that included large- and small-particle coefficients. Particles were characterized by quantifying plankton, total bacterial cells, and total solids. E. coli and MS2 persistence was not significantly affected by large particles, but small particles had an effect in most waters. Small particles in higher-salinity waters had the largest, negative effect on E. coli and MS2 survival ratios: Significant small-particle coefficients ranged from −1.7 to −5.5 day−1 in the marine waters and −0.89 to −3.2 day−1 in the fresh and estuarine waters. This work will inform remediation efforts for impaired surface water bodies.IMPORTANCEMany surface water bodies in the United States have organisms associated with fecal contamination that exceed regulatory standards and prevent safe recreation. The process to remediate impaired water bodies is complicated because these fecal indicator organisms are affected by the local environmental conditions. For example, the effect of particles in surface water on fecal indicator concentrations are difficult to quantify in a way that is comparable between studies and water bodies. We applied a method that overcomes this limitation to assess the effects of large particles, including natural plankton that could consume the seeded fecal indicator organisms. Even in environmental water samples with diverse communities of plankton present, no effect of large particles on fecal indicator concentrations was observed. These findings have implications for the interpretation and design of future studies, including that particle characterization of surface water may be necessary to assess the fate of fecal indicators.

Published
2024
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20. Umkämpfte Meilensteine - Rechtsstaatlichkeitskonditionalität bei NextGenerationEU

Author

Mattis Leson

Subjects
Europäischer Aufbauplan, 2020, Konditionalität, NextGenerationEU, Rechtsstaatlichkeit, Rechtsstaatsmechanismus, Law
Abstract

Am 28. August 2024 haben vier Richterverbände bekanntgegeben, dass sie Berufung gegen den Medel-Beschluss des Gerichts der Europäischen Union eingelegt haben. Das Verfahren verdient bereits deshalb Aufmerksamkeit, weil es wieder einmal das ewige Thema des Individualrechtsschutzes vor den Gerichten der Europäischen Union betrifft. Darüber hinaus – und von noch größerer Relevanz – wirft die Rechtssache wichtige, unbeantwortete materielle Fragen der Rechtsstaatskonditionalität auf. Insbesondere steht die Wirksamkeit des Instituts erneut auf dem Prüfstand – verhandelt erstmals im Kontext von NextGenerationEU.

Published
2024
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21. Dentate gyrus granule cells are a locus of pathology in Scn8a developmental encephalopathy

Author

Wenxi Yu, Sophie F. Hill, Limei Zhu, Yiannos Demetriou, Faith Reger, Joanna Mattis, and Miriam H. Meisler

Subjects
hippocampus, snRNA-seq, Sodium channel, shRNA, Gene therapy, Epilepsy, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Gain-of-function mutations in SCN8A cause developmental and epileptic encephalopathy (DEE), a disorder characterized by early-onset refractory seizures, deficits in motor and intellectual functions, and increased risk of sudden unexpected death in epilepsy. Altered activity of neurons in the corticohippocampal circuit has been reported in mouse models of DEE. We examined the effect of chronic seizures on gene expression in the hippocampus by single-nucleus RNA sequencing in mice expressing the patient mutation SCN8A-p.Asn1768Asp (N1768D). One hundred and eighty four differentially expressed genes were identified in dentate gyrus granule cells, many more than in other cell types. Electrophysiological recording from dentate gyrus granule cells demonstrated an elevated firing rate. Targeted reduction of Scn8a expression in the dentate gyrus by viral delivery of an shRNA resulted in doubling of median survival time from 4 months to 8 months, whereas delivery of shRNA to the CA1 and CA3 regions did not result in lengthened survival. These data indicate that granule cells of the dentate gyrus are a specific locus of pathology in SCN8A-DEE.

Published
2024
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23. Examining Mental Health, Academic, and Economic Stressors during the COVID-19 Pandemic among Community College and 4-Year University Students

Author

Ramirez, Amaranta, Rivera, David B., Valadez, Adrian M., Mattis, Samantha, and Cerezo, Alison

Abstract

Objective: The COVID-19 global pandemic has created severe, long-lasting challenges to college students in the United States (US). In the present study, we assessed mental health symptomatology (depression, anxiety, life stress), academic challenges, and economic stress during the first wave of the Coronavirus pandemic. Method: A total sample of 361 college students (M[subscript age] = 22.26, SD = 5.56) was gathered from a community college (N = 134) and mid-size public university (N = 227) in Southwest US, both designated as Hispanic Serving Institutions. Results: Pearson and point biserial correlations indicated associations between mental health symptomatology, academic challenges, and economic stress, including expected delays in graduation. Multivariate analysis revealed that community college students had statistically significantly higher scores on anxiety F(1, 312) = 5.27, p = 0.02, [eta][superscript 2][subscript p] = 0.01 than 4-year university students, as well as key differences with respect to academic challenges. Chi Square analyses revealed that Latinx families experienced greater economic hardships, including job loss or reduced work hours (X[superscript 2] (1, N = 361) = 28.56, p = 0.00) than other ethnic/racial groups. Conclusions/Contributions: Findings revealed that community college students faced disparately negative mental health symptomatology, academic challenges, and economic stress during the first wave of the Coronavirus pandemic. Further, Latinx students' families experienced significant economic hardship that may have impacted students' academic progress and future planning.

Published
2023
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24. Universal and cultural factors shape body part vocabularies

Author

Annika Tjuka, Robert Forkel, and Johann-Mattis List

Subjects
Semantics, Body parts, Cross-linguistic comparison, Medicine, Science
Abstract

Abstract Every human has a body. Yet, languages differ in how they divide the body into parts to name them. While universal naming strategies exist, there is also variation in the vocabularies of body parts across languages. In this study, we investigate the similarities and differences in naming two separate body parts with one word, i.e., colexifications. We use a computational approach to create networks of body part vocabularies across languages. The analyses focus on body part networks in large language families, on perceptual features that lead to colexifications of body parts, and on a comparison of network structures in different semantic domains. Our results show that adjacent body parts are colexified frequently. However, preferences for perceptual features such as shape and function lead to variations in body part vocabularies. In addition, body part colexification networks are less varied across language families than networks in the semantic domains of emotion and colour. The study presents the first large-scale comparison of body part vocabularies in 1,028 language varieties and provides important insights into the variability of a universal human domain.

Published
2024
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25. Prospective de novo drug design with deep interactome learning

Author

Kenneth Atz, Leandro Cotos, Clemens Isert, Maria Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff, Jann Ledergerber, Carl C. G. Schiebroek, Valentina Romeo, Jan A. Hiss, Daniel Merk, Petra Schneider, Bernd Kuhn, Uwe Grether, and Gisbert Schneider

Subjects
Science
Abstract

Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Published
2024
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26. AMPK stimulation inhibits YAP/TAZ signaling to ameliorate hepatic fibrosis

Author

Mahbubul H. Shihan, Sachin Sharma, Carson Cable, Vijaya Prathigudupu, Alina Chen, Aras N. Mattis, and Jennifer Y. Chen

Subjects
Medicine, Science
Abstract

Abstract Hepatic fibrosis is driven by the activation of hepatic stellate cells (HSCs). The Hippo pathway and its effectors, YAP and TAZ, are key regulators of HSC activation and fibrosis. However, there is a lack of mechanistic understanding of YAP/TAZ regulation in HSCs. Here we show that AMPK activation leads to YAP/TAZ inhibition and HSC inactivation in vitro, while the expression of a kinase-inactive mutant reversed these effects compared to wild type AMPKɑ1. Notably, the depletion of LATS1/2, an upstream kinase of YAP/TAZ signaling, rescues YAP/TAZ activation, suggesting that AMPK may be mediating YAP/TAZ inhibition via LATS1/2. In the carbon tetrachloride mouse model of fibrosis, pharmacologic activation of AMPK in HSCs inhibits YAP/TAZ signaling and reduces fibrosis. The findings implicate AMPK as a critical regulator of YAP/TAZ signaling and HSC inactivation and highlight AMPK activation as a therapeutic target for the treatment of hepatic fibrosis.

Published
2024
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27. Grouping sounds into evolving units for the purpose of historical language comparison [version 2; peer review: 2 approved]

Author

Cristian Juárez, Frederic Blum, Johann-Mattis List, and Nathan W. Hill

Subjects
historical language comparison, phonetic transcription, representation of speech sounds, eng, Science, Social Sciences
Abstract

Computer-assisted approaches to historical language comparison have made great progress during the past two decades. Scholars can now routinely use computational tools to annotate cognate sets, align words, and search for regularly recurring sound correspondences. However, computational approaches still suffer from a very rigid sequence model of the form part of the linguistic sign, in which words and morphemes are segmented into fixed sound units which cannot be modified. In order to bring the representation of sound sequences in computational historical linguistics closer to the research practice of scholars who apply the traditional comparative method, we introduce improved sound sequence representations in which individual sound segments can be grouped into evolving sound units in order to capture language-specific sound laws more efficiently. We illustrate the usefulness of this enhanced representation of sound sequences in concrete examples and complement it by providing a small software library that allows scholars to convert their data from forms segmented into sound units to forms segmented into evolving sound units and vice versa.

Published
2024
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28. The Good, the Bad, and the Clever: Faking Ability as a Socio-Emotional Ability?

Author

Geiger, Mattis, Bärwaldt, Romy, and Wilhelm, Oliver

Abstract

Socio-emotional abilities have been proposed as an extension to models of intelligence, but earlier measurement approaches have either not fulfilled criteria of ability measurement or have covered only predominantly receptive abilities. We argue that faking ability--the ability to adjust responses on questionnaires to present oneself in a desired manner--is a socio-emotional ability that can broaden our understanding of these abilities and intelligence in general. To test this theory, we developed new instruments to measure the ability to fake bad (malingering) and administered them jointly with established tests of faking good ability in a general sample of n = 134. Participants also completed multiple tests of emotion perception along with tests of emotion expression posing, pain expression regulation, and working memory capacity. We found that individual differences in faking ability tests are best explained by a general factor that had a large correlation with receptive socio-emotional abilities and had a zero to medium-sized correlation with different productive socio-emotional abilities. All correlations were still small after controlling these effects for shared variance with general mental ability as indicated by tests of working memory capacity. We conclude that faking ability is indeed correlated meaningfully with other socio-emotional abilities and discuss the implications for intelligence research and applied ability assessment.

Published
2021

32. CircHTT(2,3,4,5,6) — co-evolving with the HTT CAG-repeat tract — modulates Huntington's disease phenotypes

Author

Morandell, Jasmin, Monziani, Alan, Lazioli, Martina, Donzel, Deborah, Döring, Jessica, Oss Pegorar, Claudio, D’Anzi, Angela, Pellegrini, Miguel, Mattiello, Andrea, Bortolotti, Dalia, Bergonzoni, Guendalina, Tripathi, Takshashila, Mattis, Virginia B., Kovalenko, Marina, Rosati, Jessica, Dieterich, Christoph, Dassi, Erik, Wheeler, Vanessa C., Ellederová, Zdenka, Wilusz, Jeremy E., Viero, Gabriella, and Biagioli, Marta

Published
2024
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34. Synthesis of N-acyl carbazoles, phenoxazines and acridines from cyclic diaryliodonium salts

Author

Nils Clamor, Mattis Damrath, Thomas J. Kuczmera, Daniel Duvinage, and Boris J. Nachtsheim

Subjects
carbazoles, heteroaromatics, iodanes, metal-catalyzed, one-pot reaction, Science, Organic chemistry, QD241-441
Abstract

N-Acyl carbazoles can be efficiently produced through a single-step process using amides and cyclic diaryliodonium triflates. This convenient reaction is facilitated by copper iodide in p-xylene, using the commonly found activating ligand diglyme. We have tested this method with a wide range of amides and iodonium triflates, proving its versatility with numerous substrates. Beyond carbazoles, we also produced a variety of other N-heterocycles, such as acridines, phenoxazines, or phenazines, showcasing the robustness of our technique. In a broader sense, this new method creates two C–N bonds simultaneously based on a mono-halogenated starting material, thus allowing heterocycle formation with diminished halogen waste.

Published
2024
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35. A comparative wordlist for investigating distant relations among languages in Lowland South America

Author

Frederic Blum, Carlos Barrientos, Roberto Zariquiey, and Johann-Mattis List

Subjects
Science
Abstract

Abstract The history of the language families in Lowland South America remains an understudied area of historical linguistics. Panoan and Tacanan, two language families from this area, have frequently been proposed to descend from the same ancestor. Despite ample evidence in favor of this hypothesis, not all scholars accept it as proven beyond doubt. We compiled a new lexical questionnaire with 501 basic concepts to investigate the genetic relation between Panoan and Tacanan languages. The dataset includes data from twelve Panoan, five Tacanan, and four other languages which have previously been suggested to be related to Pano-Tacanan. Through the transparent annotation of grammatical morphemes and partial cognates, our dataset provides the basis for testing language relationships both qualitatively and quantitatively. The data is not only relevant for the investigation of the ancestry of Panoan and Tacanan languages. Reflecting the state of the art in computer-assisted approaches for historical language comparison, it can serve as a role model for linguistic studies in other areas of the world.

Published
2024
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40. Large-Scale Network-Based Observations of a Saharan Dust Event across the European Continent in Spring 2022

Author

Christina-Anna Papanikolaou, Alexandros Papayannis, Marilena Gidarakou, Sabur F. Abdullaev, Nicolae Ajtai, Holger Baars, Dimitris Balis, Daniele Bortoli, Juan Antonio Bravo-Aranda, Martine Collaud-Coen, Benedetto de Rosa, Davide Dionisi, Kostas Eleftheratos, Ronny Engelmann, Athena A. Floutsi, Jesús Abril-Gago, Philippe Goloub, Giovanni Giuliano, Pilar Gumà-Claramunt, Julian Hofer, Qiaoyun Hu, Mika Komppula, Eleni Marinou, Giovanni Martucci, Ina Mattis, Konstantinos Michailidis, Constantino Muñoz-Porcar, Maria Mylonaki, Michail Mytilinaios, Doina Nicolae, Alejandro Rodríguez-Gómez, Vanda Salgueiro, Xiaoxia Shang, Iwona S. Stachlewska, Horațiu Ioan Ștefănie, Dominika M. Szczepanik, Thomas Trickl, Hannes Vogelmann, and Kalliopi Artemis Voudouri

Subjects
EARLINET, lidar, aerosols, Saharan dust, optical properties, CALIPSO, Science
Abstract

Between 14 March and 21 April 2022, an extensive investigation of an extraordinary Saharan dust intrusion over Europe was performed based on lidar measurements obtained by the European Aerosol Research Lidar Network (EARLINET). The dust episode was divided into two distinct periods, one in March and one in April, characterized by different dust transport paths. The dust aerosol layers were studied over 18 EARLINET stations, examining aerosol characteristics during March and April in four different regions (M-I, M-II, M-III, and M-IV and A-I, A-II, A-III, and A-IV, respectively), focusing on parameters such as aerosol layer thickness, center of mass (CoM), lidar ratio (LR), particle linear depolarization ratio (PLDR), and Ångström exponents (ÅE). In March, regions exhibited varying dust geometrical and optical properties, with mean CoM values ranging from approximately 3.5 to 4.8 km, and mean LR values typically between 36 and 54 sr. PLDR values indicated the presence of both pure and mixed dust aerosols, with values ranging from 0.20 to 0.32 at 355 nm and 0.24 to 0.31 at 532 nm. ÅE values suggested a range of particle sizes, with some regions showing a predominance of coarse particles. Aerosol Optical Depth (AOD) simulations from the NAAPS model indicated significant dust activity across Europe, with AOD values reaching up to 1.60. In April, dust aerosol layers were observed between 3.2 to 5.2 km. Mean LR values typically ranged from 35 to 51 sr at both 355 nm and 532 nm, while PLDR values confirmed the presence of dust aerosols, with mean values between 0.22 and 0.31 at 355 nm and 0.25 to 0.31 at 532 nm. The ÅE values suggested a mixture of particle sizes. The AOD values in April were generally lower, not exceeding 0.8, indicating a less intense dust presence compared to March. The findings highlight spatial and temporal variations in aerosol characteristics across the regions, during the distinctive periods. From 15 to 16 March 2022, Saharan dust significantly reduced UV-B radiation by approximately 14% over the ATZ station (Athens, GR). Backward air mass trajectories showed that the dust originated from the Western and Central Sahara when, during this specific case, the air mass trajectories passed over GRA (Granada, ES) and PAY (Payerne, CH) before reaching ATZ, maintaining high relative humidity and almost stable aerosol properties throughout its transport. Lidar data revealed elevated aerosol backscatter (baer) and PLDR values, combined with low LR and ÅE values, indicative of pure dust aerosols.

Published
2024
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42. Access to and quality of elective care: a prospective cohort study using hernia surgery as a tracer condition in 83 countries

Author

Dönmez, A Eylül, Goswami, Aakansha Giri, Raheja, Aashna, Bhadani, Aayush, El Kady, Abd Elrahman Safwat, Alniemi, Abdalaziz, Awad, Abdalkarim, Aladl, Abdalla, Younis, Abdalla, Alwali, Abdallah, Khalil, Abdallah, Mansour, Abdallah, Elkhouly, Abdallah Mohamed, Rashad Temerik, Abdallah, Hasan, Abdallatef, Chied, Abdelhafid, Abdraba, Abdelhamid, Hrora, Abdelmalek, Abdelshafi, Abdelrahman, Alwali, Abdelrahman, Maraqa, Abdelrahman, Ghaffar, Abdul, Chekfa, Abdul Jabar, Al Balushi, Abdul Majeed, Quddus, Abdul, Hammadieh, Abdul Rahman, Muntaka, Abdul-Jalilu Mohammed, Asekun, Abdulazeez, Alshahrani, Abdulaziz, Jowharji, Abdulaziz, Alshukre, Abdulbaset, Khair Etareig, Abdulhafid, Abubakar Abdulkarim, Abdulhafiz, Ashiru, Abdulhafiz, Alailesh, Abdulhamid, Al-Mallah, Abdullah, Baydoun, Abdullah, Chaudhry, Abdullah, Askin, Abdullah Emre, Almutairi, Abdulmjeed, Belkhair, Abdulmunem, Abdllah, Abdulrahman, Alhammali, Abdulrahman, Basalim, Abdulrahman, Abouelnagah, Abdulrahman Galal Mohamed Mostafa, Ghazal, Abdulrahman, Mohammed, Abdulrahman, Qasem, Abdulrahman, Anadani, Abdulrazak, Jimoh, Abdulrazaq, Lawal, Abdulrazzaq Oluwagbemiga, Khaity, Abdulrhman, Lawal, Abdulwahab, Elzoubi, Abdurrahim Esseid, Sheshe, Abdurrahman Abba, Cetişli, Abdurrahman Furkan, Oyedele, Abeeb, Aljahdali, Abeer, Aboelnasr, Abeer Gamal Elsyed, Demessie, Abel, Bhargava, Abhilasha, Sonkar, Abhinav Arun, Bavaharan, Abhirame, Khan, Abid, Campbell, Abigail, Oyelekan, Abimbola Adeola Akintunde, Okunlola, Abiodun Idowu, Ojewuyi, Abiodun, Ekerin, Abiola, Saleh, Abobakr, Khairy, Abouelatta, Muthunayagam, Abraham Benjamin, Botha, Abraham, Teshome Sahilemariam, Abraham, Nawawi, Abrar, Muhammad, Abubakar Bala, Dahiru, Abubakar, Yahaya, Abubakar, Abdelmalik, Abubaker, Anuar, Abul Akmal Arif, Ousseini, Adakal, Leon -Del- Angel, Adalberto, Frankel, Adam, Gyedu, Adam, Lim, Adam, Lukáč, Adam, Peckham-Cooper, Adam, Sabbah, Adam, Varga, Adam, Sanou, Adama, Issaka, Adamu, Ningi, Adamu, Yeshitila, Addis, Yingess, Addisu, Olugbami, Adebayo, Moses, Damola-Okesiji, Adebola, Lawal, Adedayo, Ojo, Adedoyin, Hashmi, Adeel, Fakoya, Adegbolahan, Ishola, Adegoke, Abiodun, Adekunle, Ajayi, Adekunle, Mouffokes, Adel, Bonelli, Adele, Adekoya, Adeleke, Akeem Aderogba, Adeleke, Özcan, Adem, Ayoade, Ademidun, Adeyeye, Ademola, Agbaje, Ademola, Talabi, Ademola, Ademuyiwa, Adesoji, Adewumiya, Adesoji, Ademuyiwa, Adesoji O, Aderounmu, Adewale, Adisa, Adewale, Oke, Adewale, Ragab, Adham, Alzadjali, Adil, Baksi, Aditya, Jayapalan, Aditya, Md Yunos, Adnan, Pisanu, Adolfo, Salazar, Adolfo, Salinas Barragan, Adrian Alberto, Fox, Adrian, Stollberg, Adrian, Franco, Adriana, Neves, Adriana, Vricheva, Adriana, Hodonou, Adrien Montcho, Aghayeva, Afag, Liaquat, Affifa, Alsultan, Afnan, Morad, Afnan, Kalidis, Afroditi Angella, Gkouniaroudi, Agathi, Karakosta, Agathi, Lovi, Agbenya, Yevide, Agossou Barthélémy, Duro, Agustin, Rodriguez Gonzalez, Agustin, Tariq, Ahbala, Abaidalla, Ahmad, Abuhammad, Ahmad, Abuhantash, Ahmad, Mansour, Ahmad Adel, Ahmad Zaidi, Ahmad Al Hafeez, Alahmad Alismael, Ahmad, Alayed, Ahmad, Alfarwan, Ahmad, Alhaj, Ahmad, Ali, Ahmad, Alroobi, Ahmad, Alsheikh, Ahmad, Alusef, Ahmad, Alzu'Bi, Ahmad, Kayali, Ahmad Amir, Bouhuwaish, Ahmad, Doghaim, Ahmad, El-Sherbiney, Ahmad, Ghazal, Ahmad, Ramly, Ahmad Nasir, Raza, Ahmad, Qureshi, Ahmad Uzair, Arnaout, Ahmad Yamen, Abdelhamid, Ahmed, Abuthaher, Ahmed, Adel, Ahmed, Ahmayda, Ahmed, Al-Mouahhed, Ahmed, Alagha, Ahmed, Albalawi, Ahmed, Albhaisi, Ahmed, Alrifaee, Ahmed, Azam, Ahmed, Barakat, Ahmed, Al-Hajjaj, Ahmed Basim Abed, Dawoud, Ahmed, El Shamarka, Ahmed, El-Bastwesy, Ahmed, Elghrieb, Ahmed, Elkhalifa, Ahmed, Elmosalamy, Ahmed, Elshazli Mahmoud, Ahmed, Montaser, Ahmed G., Gbeadese, Ahmed, Haty, Ahmed, K. 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Daniel, Montwedi, Daniel, Muñoz, Daniel, Patterson, Daniel, Reim, Daniel, Agbley, Daniel Yao Dodzie, Patiño García, Daniela Cristina, Di Pietrantonio, Daniela, Pais, Daniela, Rosado, Daniela, Tavares, Daniela, Alberti, Daniele, Fusario, Daniele, Massaro, Daniele, Paglione, Daniele, Pironi, Daniele, Kurochka, Daniil, Centonze, Danilo Corrado, Hackner, Danilo, Herrera, Danilo, Amangaliyev, Daniyar, Silva-Cano, Danny Wladimir, Chaudhry, Daoud, Palmisano, Dario, Ribero, Dario, Vukosav, Dario, Misca, Darius, Lowen, Darren, Rattray, Darren, Walia, Darshanjit Singh, Maan, Darvesh, Muyenzi, Daryle, Verdi, Daunia, Bardsley, David, Reyes Rodríguez, David Estuardo, Fra Corral, David, Garcia López De Goicoechea, David, Gavit, David, Gómez, David, Idowu, David, Konkin, David, Labadah, David Kplorla Kekeli, Liu, David, Merlini, David, Mitchell, David, Moro-Valdezate, David, Osifo, David, Proud, David, Ridder, David, Gobatti, Davide, Muschitiello, Davide, Pertile, Davide, Zulian, Davide, Mengiste, Dawit, Mikami, Dean, Mohanty, Debajyoti, Chatterjee, Debarghya, Komolafe, Deborah Olaitan, Scotto Di Carlo, Deborah, Wright, Deborah, Rahme, Deema, Talreja, Deep Parkash, Jain, Deepak, Rajput, Deepak, Singh, Deepak, Anbu, Deepika, Ibrahim, Dena, Phom, Denchu, Hili, Denise, Osei, Dennis Broni, Daary, Dennis, Kyeremeh, Dennis, Shen, Dennis, Uymaz, Derya Salim, Cianflocca, Desiree, Ashitey, Desmond, Evripidou, Despo, Papageorgouli, Despoina, Kaushal, Devesh, Abeysirigunawardana, Dewmi, Dugar, Dharmendra, Mallik, Dhiraj, Ghosh, Dhruva, Houmran, Diaa, Abdulrahman, Diala, Pantoja Pachajoa, Diana Alejandra, Cardona Gomez, Diana Catalina, Fenner, Diana, Gonzalez, Diana Samantha, Siriwardena, Diana, Garcés Palacios, Diana Sofía, Stoian, Diana, Zuikyte, Diana, Sosa Méndez, Dianne Alitzá, Wickramarathna, Dickson, Mutter, Didier, Palma Portillo, Diego Andrés, Chavez Fernandez, Diego, Córdova García, Diego, Enjuto, Diego, Giulitti, Diego, Sasia, Diego, Visconti, Diego, Yiğit, Dilara, George, Dilip, Rabaia, Dima, Georgiev, Dimitar, Hadzhiev, Dimitar, Alexandrou, Dimitrios, Balalis, Dimitrios, Giakoustidis, Dimitrios, Manatakis, Dimitrios K., Korkolis, Dimitrios, Lytras, Dimitrios, Massaras, Dimitrios, Moris, Dimitrios, Politis, Dimitrios, Sampanis, Dimitrios, Schizas, Dimitrios, Tsiardas, Dimitrios, Vardakostas, Dimitrios, Kotsaris, Dimitris, Khattab, Dina Hesham, M. Awad, Dina, Alrokh, Dina Sami, Schippers, Dina, Waleed, Dina, Salinovic, Dinko, Gonçalves Múrias Gomes, Diogo Miguel, Silva, Diogo, Vaz Acosta, Diogo, Morton, Dion, Panagopoulos, Dionisios, Kelgiorgi, Dionysia, Dellaportas, Dionysios, Prevezanos, Dionysios, Joshi, Dipti, Ozal, Direm, Yigit, Direnc, Chimkaomasiri, Divine, Jabbar, Diya, Nepogodiev, Dmitri, Belekar, Dnyanesh, Abdalaziz, Doaa, Abunemer, Doaa, Attia, Doaa, Elkafrawy, Doaa, Dimitrov, Dobromir, Pignatelli, Domenica, Merlicco, Domenico, Soriero, Domenico, Zerbo, Domenico, Atouguia, Domingas, Fairbrass, Dominic, T. Enti, Donald, Venskutonis, Donatas, Rukavina, Dora, Caruso, Dorena, Herappe, Dorihela, Lee, Dorinda Norbetha, Ofosuhene, Doris, Hajela, Apoorva, Badwan, Basem, Kulkarni, Datta, Dhawan, Deep, Dhillon, Jeevanjit, Mehmood, Manzer, Mahmood, Namrah, Nikam, Narendra, Ramchandani, Radhakrishna, Thind, Ravinder Singh, Riazhussain, D Hafiz, Maqsood, Saliha, Alalawi, Yousef, Tyagi, Harshdeep, Kshirsagar, Shivani, Ahmed, Hisham, Juan Carlos, Luna Cydejko, Kone, Dramane, Omar, Duaa, Alves, Duarte Gil, Subasinghe, Duminda, Ommi, Durgesh, Joshiba, Durshney, Chakarov, Dzhevdet, Arkoh, Ebenezer Ameyaw, Frimpong-Manso, Ebenezer, Ada, Ece, Lincango, Eddy P., Lozada Hernandez, Edgard Efren, Hodžić, Edin, Di Marco, Edith, Baccalini, Edoardo Andrea, Baldini, Edoardo, González Marín, Eduardo, Gutierrez, Eduardo, Sepúlveda, Eduardo, Dainius, Edvinas, Zhang, Edward, Yenli, Edwin Mwintiereh Ta-Ang, Antoniou, Efstathios, Kapasakis, Efstathios, Baili, Efstratia, Poulios, Efthymios, Kubiliute, Egle, Osaze, Ehioghae, Afeikhena, Ehis, Elshennawy, Eiad, Athanasopoulou, Eirini, Synekidou, Eirini, Ekaladze, Eka, Tampaki, Ekaterini Christina, Adam, Elaf, Adel Hamdoun Aziz, Elaf, Yousuf, Elamin, Gialamas, Eleftherios, Spartalis, Eleftherios, Bedzhanyan, Elen, Toma, Elena Adelina, Daukšaitė, Elena, Desiato, Elena, Dvivedi, Elena, Hadzhieva, Elena, Martí Cuñat, Elena, Montalbán Martínez, Elena, Montanari, Elena, Signaroli, Elenasofia, Efremidou, Eleni, Gavriil, Eleni, Karakeke, Eleni, Mavrodimitraki, Eleni, Papamattheou, Eleni, Jose, Elenza, Cianci, Eleonora, Ciccioli, Eleonora, Daleva, Eleonora, Locci, Eleonora, Lori, Eleonora, Silanos, Eleonora, Ayala, Elí, Bankart, Eliana, Brolese, Eliane, Efstathiou, Elias, Salloum, Elias, Colak, Elif, Terzi, Elif Didem, Ametefe, Elikem, Cagigal Ortega, Elima Pilar, Donnarumma, Elio, Mina, Elisa, Reitano, Elisa, Wallner, Elisabeth, Moggia, Elisabetta, Betz, Elise, Kontis, Elissaios, Anestiadou, Elissavet, Gimson, Eliza, Lun, Elizabeth, Fraser, Elizabeth, Li, Elizabeth, Torres De Anda, Elizabeth Magaly, Murphy, Elizabeth Mary Ann, O'Connell, Elizabeth, Redondo, Elizabeth, Lun, Elizabeth Weng Yan, Francis, Ella, Haiden, Elmar, Hamed, Elmonzir, Alkhalifa, Elmustafa, Dexter, Eloise, Daketsey, Elorm, Setsoafia, Elorm, Mohamed, Elsagad, Mohammed, Elsamoual, Hegab, Elsayed, Ay, Elvin, Adinolfi, Elvira, Mbanzabugabo, Elvis, Chin, Elynn, Ahmed, Emad Ali, Younes, Eman, Basile, Emanuela, Gessa, Emanuela, Pontecorvi, Emanuele, Romairone, Emanuele, Hatangimana, Eméry, Ndizeye, Emile, Ciofic, Emilia, Dijan, Emilio, López-Negrete Cueto, Emilio, Baker, Emily, Reid, Emily, Schmidt, Emily, Alić, Emir, Bičakčić, Emir, Agbowada, Emma, Colton, Emma, Downing, Emma, Haege, Emma, O'Neill, Emma, Roussos, Emmanouel, Nachelleh, Emmanuel Aadereyir, Adebunmi, Emmanuel, Obiesie, Emmanuel Ahuizechukwu, Akakpo, Emmanuel Bismark, Konlan, Emmanuel Dammigu Francis, Nwangwu, Emmanuel Ifeanyi, Kafui Ayodeji, Emmanuel, Ofori, Emmanuel Owusu, Spanoudakis, Emmanuel, Abate, Emmanuele, Balik, Emre, Bozdağ, Emre, Bozkurt, Emre, Karabulut, Emre Cagri, Ozoran, Emre, Tunçcan, Emre, Tuzuner, Emre, Halilović, Emsad, Alabed, Enaam, Zidan, Enas, Hodžić, Enes, Aybar, Engin, Yildirim, Engin Okan, Wong, Enoch, Yhoshu, Enono, Bisogno, Enrica, Andolfi, Enrico, Ferri, Enrico, Potenza, Enrico, Spalice, Enrico, Volpin, Enrico, González, Enrique, Downes, Eoin, Gallo, Erdachew, Kamer, Erdinc, Naidoo, Eressa, Erginöz, Ergin, Bara, Eric, Irungu, Eric, García-Villegas, Eric Yusif, Mazzotta, Erica, Monati, Erica, Chan, Erick, Gómez Mejía, Erika, Dylja, Eris, Somuncu, Erkan, Yavuz, Erkan, Begunić, Ernes, Margelis, Ernest, Osei, Ernest Owusu, Saitoglou, Ertzan, Agastra, Ervis, Mazumdar, Eshan, Soyer Güldoğan, Esra, Abu Siam, Esra'A, Abdelbaset, Esraa, Abdu, Esraa, Adel Mahmod Sultan, Esraa, Nubi Mohamed, Esraa Gamal, Abuobaida Banaga Hag El Tayeb, Essam Eldien, Ali, Essraa, Page-Taylor, Estelle, Marra, Ester, Garcia Santos, Esther Pilar, Elsheikh, Ethar, Unal, Ethem, Tan, Eugene, De Leo, Eugenio, Borges, Eva, Gáspár, Éva, Ruiz-Daum, Eva, Apostolopoulos, Evaggelos, Samara, Evangelia, Barkolias, Evangelos, Fradelos, Evangelos, Hopping, Eve, Romo Ascencio, Evelia Veronica, Sosa Ferreira, Ever David, Shestakov, Evgenii, Tyurina, Evgenya, Hristova, Evguenia, Efthymiou, Evripidis, Tokidis, Evripidis, Harrison, Ewen, Dontoh, Ewurama Akroma, Mansour, Eyad, Yeshialem, Eyosiyas, Dabbagh, Ezeddin, Amer, Ezzeldin, Manasci, Fabiana, Gomes, Fabio, Medas, Fabio, d'Acapito, Fabrizio, Vassallo, Fabrizio, Haddad, Fadi, Elhabishi, Fadwa, Akhtar, Fahad, Tauheed, Fahad, Al Abbood, Fahd, Gareb, Fahed, Ghazali, Fairuz, Alnazawi, Faisal, Ondago, Faith, Rasoaherinomenjanahary, Fanjandrainy, Wiese, Fanny, González Ponce, Fanny Yesenia, Georgiades, Fanourios, Ali, Farah, Galley, Fareeda, Huda, Farhanul, Amir, Farhat Naaz, Ashraf, Fariha, Alfarsi, Faris, Ayasra, Faris, Ali, Farman, Mahmoud, Faten, Aliskander, Fater, Tahir Lwdie, Fathea, Buyuker, Fatih, Aguilar Del Castillo, Fátima, Mohammed Daoud, Fatima Ahmed, Alowjaly, Fatima, Oni, Fatima, Rauf, Fatima, Elkhafeefi, Fatimah, Hourri, Fatine, Monib, Fatma A., Alshreef, Fatma, Benghalbon, Fatma, Ntirenganya, Faustin, Ashraf, Fauziyah, Chahrour, Fayez, Ahmad, Fazal, Parray, Fazlul Qadir, Philips, Febi, Campus, Federica, Di Marco, Federica, Ferraina, Federica, Frongia, Federica, Maffeis, Federica, Natali, Federica, Pederiva, Federica, Scolari, Federica, Tofani, Federica, Marin, Federico Alberto Niccolò, Biolchini, Federico, Cappellacci, Federico, Corronca, Federico, Mazzola, Federico, Suldrup, Federico, Nyarko, Felicia, Casas J, Felipe, Zapata, Felipe, Aigner, Felix, Alakaloko, Felix, Amoako, Felix, Camacho, Felix, Hammett, Felix, Kuubetersob B. N, Felix, Alahmad, Feras, Alnimri, Feras, Haddad, Feras, Gool, Ferhana, Castro, Fernanda, Diaz, Fernanda, Lucero, Fernanda, Vovola, Fernanda, Mahnic, Fernando, Mercado Sanchez, Fernando, Prieto La Noire, Fernando, Soggiu, Fiammetta, Tsige, Fikreab, Brzeszczyński, Filip, Rudisch, Filip, Valente Costa Pinto, Filipa, Ramalho De Almeida, Filipe, Banchini, Filippo, Palomino Escalante, Fiorella, Fadzlullah, Fitri, Terefe, Fitsum, Sulo, Fjorenta, Ciccarone, Flavia, Tirelli, Flavio, Krämer, Florian, Grama, Florin, Spanos, Foivos, Solimene, Folco, Akinwande, Foluso, Osman, Forzia, Stefou, Foteini, Kyramargios, Fotios, Stavratis, Fotios, Alassani, Fousseni, Ascari, Francesca, Cammelli, Francesca, D'Agostino, Francesca, Tropeano, Francesca Paola, Peters, Francesca, Ré, Francesca, Brucchi, Francesco, Casti, Francesco, Coratti, Francesco, Ferrara, Francesco, Floris, Francesco, Izzo, Francesco, Maffei, Francesco, Palmieri, Francesco, Pata, Francesco, Porcelli, Francesco, Selvaggi, Francesco, Velluti, Francesco, Mulita, Francesk, Bello, Francis, Caiquo, Francis, Dossou, Francis, Eshun, Francis Junior, Opoku Twene, Francis, Tierenye, Francis, Quayson, Francis William, Romo Rosales, Francisco David, Ibañez Ortiz, Francisco, León Frutos, Francisco Javier, Redondo Calvo, Francisco Javier, Silva Rivera, Francisco Javier, Barbosa Camacho, Francisco José, Bujalance Cabrera, Francisco Manuel, Ruescas, Francisco, Irakiza, Franck Fanon, Corvatta, Franco, Pascual, Franco Raúl, Roviello, Franco, Roodt, Francois, Gyamfi, Frank Enoch, Olaniru, Frank, Owusu, Frank, Bader, Franz G., Anzinger, Franz-Xaver, Agbodo, Fred David Anku, Ris, Frederic, Agbemafoh, Frederick Mawuli, Afonso, Frederico, Nazareth, Frederico, Berrevoet, Frederik, Hounde, Fréjus, Demiral, Furkan, Hassooni, Furqan Ridha Hassooni, Kafa, Gabi, Cárdenas Rivera, Gabriel, Ntwari, Gabriel, Szydlo Shein, Gabriel, Ambriz González, Gabriela, Nita, Gabriela Elisa, Bellio, Gabriele, Moitzi, Gabriele, Urdang, Gabriella, Marom, Gad, Gallo, Gaetano, Luglio, Gaetano, Poillucci, Gaetano, Singh, Gagandeep, Saletta, Gaia, Abouelnagah, Galal, Yarovenko, Galina, Kazobinka, Gallina, Ahmed, Gamal Mutwakil Gamal, Rahman, Ganiyu Adebisi, Smith, Garett, Pillai, Gargi, Saxena, Garima, Gbessi, Gaspard, Nkunguzi, Gaston, Jalal, Gaurav, Chillarge, Gauri, Singh, Gautam, Mevognon, Gedeon, Getachew, Gedion, George, Gejoe, De La Peña González, Gema, Martínez Izquierdo, Gema, Grande, Gennaro, Birikorang, Geoffrey, Weber, Georg F., Singer, Georg, Ansong, George, Bashour, George, Brown, George Darko, Hneino, George, Kavalieratos, George, Kenchadze, George, Nagy, George, Pappas Gogos, George, Skroubis, George, Waiyaki, George, Cox, Georgia, Zeringa, Georgia, De Wee, Georgin, Hart, Georgina, Bekakos, Georgios, Fragulidis, Georgios, Kapogiannatos, Georgios, Karakaidos, Georgios, Kokkinos, Georgios, Kostoulas, Georgios, Koukoulis, Georgios, Peros, Georgios, Rados, Georgios, Tsoulfas, Georgios, Verras, Georgios-Ioannis, Berger, Geraint, Adoro, Gerard Ayuka, Chamoso Mialdea, Gerardo, Palmieri, Gerardo, Procaccini, Gerardo, Steenkamp, Gerhard, Viscido, Germán Rodrigo, Dajti, Gerti, Alemayehu, Getachew, Seyfu, Gezachew, Birqeeq, Ghada, Alsheikh, Ghadeer, Alrayyes, Ghaith, El Hunjul, Ghalib Nashaat, Zaza, Ghayath, Matroud, Ghazlah, Alhadwah, Ghena, Maltinti, Gherardo, Awais, Ghina, Shamsi, Ghina, Calini, Giacomo, Carganico, Giacomo, Zanus, Giacomo, Canu, Gian Luigi, Mendiola, Gian, Ibero Casadiego, Giana Pamela, Cocorullo, Gianfranco, Baronio, Gianluca, Pellino, Gianluca, Zanni, Gianluca, Poto, Gianmario Edoardo, Gravante, Gianpiero, Aduroja, Gideon, Almogy, Gidon, Bond-Smith, Giles, Natchagande, Gilles, Bechara, Gio, Brachini, Gioia, Riccioli, Giordana, Ercolani, Giorgio, Farris, Giorgio, Giraudo, Giorgio, Mammolo, Giovanna, Pavone, Giovanna, Sgarzini, Giovanna, Aprea, Giovanni, Fonsi, Giovanni Battista, Boroni, Giovanni, De Angeli, Giovanni, De Palma, Giovanni Domenico, Emiliani, Giovanni, Grava, Giovanni, Guercio, Giovanni, De Carlo, Giulia, Del Corpo, Giulia, Duranti, Giulia, Galatioto, Giulia, Grassi, Giulia, Graziano, Giulia, Marini, Giulia Maria Francesca, Munzi, Giulia, Tarantino, Giulia, Borda-Luque, Giuliano, Pellicano', Giuseppe Antonino, Canonico, Giuseppe, Carollo, Giuseppe, Ietto, Giuseppe, Palomba, Giuseppe, Pascarella, Giuseppe, Salamone, Giuseppe, Sica, Giuseppe, Sorrentino, Giuseppe, Orlando, Giuseppina, Pignata, Giusto, Pisarevi, Givi, Akcakoca, Gizem, Owusu, Godfred, Yohanna Abrak, Godwin, Kurtoglu, Gokalp Kagan, Kurtoglugk, Gokalp Kagan, Alıcı, Gökhan, Kurtoglu, Goktug Mert, Delgado Hernandez, Gonzalo, Augustin, Goran, Bhat, Gowhar Aziz, Sundaram Venkatesan, Gowtham, Chang, Grace, Eke, Grace, Lee, Grace, Massarra, Grace, Travaglini, Grazia, Scialandrone, Grazisa, Stavrou, Gregor A., Di Franco, Gregorio, Charalambous, Gregory, Christodoulidis, Gregory, Kouraklis, Gregory, Goodwin, Gregory Richard, Devidze, Grigol, Fialho, Guilherme, Santos, Guilherme, Cabezudo, Guillermo, García-Santos, Guillermo, Pou, Guillermo, Romero Reyna, Guillermo, Yanowsky-Reyes, Guillermo, Ozyuksel, Gul, Karadeniz Cakmak, Guldeniz, Kucuk, Gultekin Ozan, Bulbuloglu, Guney Cem, Sarp, Guray, Kaur, Gurleen, Nibogora, Guy Darcy, Hamdan, Gymar, Ogundipe, Habeeb, Yunes, Habib, Ayesh, Hadeel, Al Atassi, Hadi, Jalilehvand, Hadis, Masalma, Hafez, Jafarkhan, Hafis, Shahid, Hafiz Huzaifa, Younus, Hafsa, Al-Derume, Hail, Shames, Haitam, Mimouni, Haithem Ahmed, Aldare, Hajar, Odah Bashi, Hajar Omar, Abusnina, Hajer, Altounsi, Hajir, Qayum, Hajra, Baysal, Hakan, Bolukbasi, Hakan, Yigitbas, Hakan, Alkhatib, Hala, Afzal, Haleema, Mftah, Halima, Oyinlola, Halimah, Tümer, Haluk, Dolo, Hamadoun, Braham, Hamdi, Ozsahin, Hamdi, Abu-Arish, Hamdoon, Al-Aamri, Hamed, Fadlalmola, Hammad Ali, Abu Obead, Hamza, Al-Fahel, Hamza, Al-Zoubi, Hamza, Asaad, Hamza, Ejaz, Hamza, Jaber, Hamza, Bhatti, Hamza Waqar, Mohammadi Sardoo, Hamzeh, Farhat, Hana, Idheiraj, Hanadi, Zwaraa, Hanady, Al Sohabi, Hanan, Basharat, Hanan, Said, Hanan, Al-Abdallat, Haneen, Foad, Haneen, Daaboul, Hanen, Al Qadhi, Hani, Elhallaq, Hani, Alhusaini, Hanin, Lidbetter, Hannah, Reilly, Hannah, Moya- Ambriz, Hannia Betzabeth, Lederhuber, Hans, Fehrer, Hans-Jörg, Xiao, Haotian, Iswariah, Harish, Kaur, Harmeena, Cheema, Harnoor Kaur, Foda, Haroun, Chauhan, Harpreet, Kaur, Harpreet, Sagar, Harry, Unwin, Harry, Kaur, Harsharan, Panga, Harshavardhan, Gupta, Harshit, Prabhakar, Harshit Kumar, Anand, Harshitha, Pahwa, Harvinder Singh, Abbadi, Hasan, Amoudi, Hasan, Zaini, Hasan Hadi Zaini, Škiljo, Hasan, Miyasika, Hasangi, Altabbaa, Hashem, Haj Freej, Hasn, Abdelazim, Hassan, Habiba, Hassan, Kamal, Hassan, Sabir, Hassan, Zaigham, Hassan, Pehlivan, Hatice, Telci, Hatice, Abuobaida, Hayat, Tetley, Heather, Taher, Heba, Aguado López, Héctor, Askarpour, Hedyeh, Sherif Farouk Ahmed Hassan, Heidi, Devesa, Helena, Arbogast, Helmut, Nuñez Del Barrio, Henar, Abiyere, Henry, Delacave, Henry, Rice, Henry E., Pleass, Henry, Bradly, Hera, 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Rugambwa, Jean Paul, Jordaan, Jean- Jacques, Razafindrahita, Jeannie Ba, Do, Jee Eun, Senavirathna, Jeewantha, Kalyanapu, Jeffrey A, Chan, Jeffrey, Ofori, Jeffrey, Otote, Jeffrey, Akiely, Jehad, Cassiano Neves, Jelena, Gillingham, Jemima, Herrera Batres, Jenifer Olga María, Ezeh, Jennifer, Okei, Jennifer, Vu, Jennifer, Guevara, Jenny, Siu, Jenny, Rolinger, Jens, Skaff, Jeolle, Uys, Jernelle, Makama, Jerry Godfrey, Barklimore, Jessica, Dei-Asamoa, Jessica, Jahjah, Jessica, Jones, Jessica, Ng, Jessica, Ricardo, Jéssica, Schembri Higgans, Jessica, You, Jessica, Annan, Jessie, Aguilar, Jesus Antonio, Pizarro Lozano, Jesús, Lim, Jia Hui, Zhu, Jiang, Wang, Jianxue, Abbasy, Jibran, Ugwu, Jideofor, Rathod, Jignesh, Albalushi, Jihan, Najajra, Jinan, Fu, Jing Hui, Tefay, Joan Sia Tan, Domingues, Joana Cristina, Simoes, Joana FF, Figueiredo, Joana, Fontaínhas, Joana, Frazão, Joana, Marques Antunes, Joana, Simoes, Joana, Marx, Joanna Naemi, Dias-Ferreira, João, Fernandes, João, Mendes, João, Pinho, João, Ribeiro, João, Vaz, João, Dominguez, Joaquina, Abutu John, Job, Chen, Joe Jun Qiao, Enaholo, Joel, Alaji, Joelle, Miller, Johanna, Binder, Johannes, Daleku, John, Katogiritis, John, Khoury, John, Anyorigiya, John Noah, Reilly, John, Abebrese, John Tabiri, Ahale, Johnny Lordson, Tabora-Zepeda, Johnny, Barrutia Leonardo, Jon, Demma, Jonathan Abraham, Hong, Jonathan, Chejfec-Ciociano, Jonathan Matias, Oppong, Jonathan, Quansah, Jonathan, Sutcliffe, Jonathan, Taiwo, Jones, Nyamekye - Baidoo, Joojo, Prat-Ortells, Jordi, Dongmo, Jordy, Kleeff, Jorg, Jimenez Flores, Jorge Alberto, Pellecer Cano, Jorge Bernardo, Chóliz, Jorge, Giubi Bobeda, Jorge Eduardo, Escartin, Jorge, Febré, Jorge, Gass, Jörn-Markus, Calvache, Jose A, Guzman Barba, Jose Aldo, Gutiérrez Gómez, José Alonso, Calvache, Jose Andres, Acevedo Parrales, José Danilo, Orozco Navarro, José Esteban, Viñas, José Federico, Gomez, José, Goncalves-Nobre, Jose Guilherme, Valenzuela, José Ignacio, Garcia Galocha, Jose Luis, 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Marcella, Pisano, Marcello, Doniquian, Marcelo Alejandro, Martínez Bareiro, Marcelo Nicolás, Pagani, Marcelo, Scortechini, Marcelo, Carvalho, Márcia, Sánchez Audelo, Marco Antonio, Brizzolari, Marco, Ceresoli, Marco, De Prizio, Marco, Amisano, Marco Francesco, Gramellini, Marco, Marcianò, Marco, Mariani, Marco, Milone, Marco, Montagna, Marco, Piccino, Marco, Santos, Marco, Dyer, Marcus, Kang, Marcus, Mahadi, Mardhiana, Zawadzki, Marek, Yinusa, Margaret, Bernardo, Margarida, Nunes Luís, Margarida, Gambelli, Margherita, Flint, Margot, Aggelidou, Maria, Bequis, Maria Agustina, Sotiriou, Maria Aikaterini, Ñañez, Maria Alejandra, Tahir, Maria Ambreen, Gascón Domínguez, María Ángeles, Asimakidou, Maria, Mourato, Maria Beatriz, Blanco, Maria, Christou, Maria, Coronas Soucheiron, Maria, Pignanelli, Maria Costanza, Kalaydjian, María Cristina, Martí-Ejarque, Maria del Mar, Olmedo Reinoso, María Del Mar, Cebollero, María Del Pilar, Derattani, Maria, Gama Caldeira, Maria Do Carmo, Drogouti, Maria, Gonzalez-Gonzalez, Maria Elena, Muñoz Fernández, María Elena, Muriel, María Emilia, Duran, María Eugenia, Zarate Casas, María Fernanda, Florou, Maria, Piacentini, Maria Gaia, Seixo, Maria Inês, Bellini, Maria Irene, Peña Soria, Maria Jesús, Martínez, María José, Medina, Maria José, Rueda Medécigo, María José, Karakeke, Maria, Kausar, Maria, Kyriazi, Maria, Marqueta De Salas, Maria, Modolo, Maria Marta, Mateu, Maria, Mavri, Maria, El Ghoul Miliotou, Maria Melina, Moreno Gijon, Maria, Papadoliopoulou, Maria, Proclamà, Maria Pia, Picciochi, Maria, Reis, Maria, Rennis, Maria, D'Anna, Maria Rosa, Ruffoli, Maria, Ruiz Soriano, María, Sánchez Ramirez, Maria, Sánchez-Rubio, María, Schüler, Maria, Sotiropoulou, Maria, Barrio Renteria, Maria Teresa, Tsopozidi, Maria, Vasconcelos, Maria, Facchino, Maria Vittoria, Wikar, Maria, Aguilera-Arevalo, Maria-Lorena, Abdu, Mariam, Asarbakhsh, Mariam, Elmiesiry, Mariam, Ashong, Marian Esi, Gomes, Mariana, Capuano, Marianna, Lill, Marianne, Nieto Galvan, 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Nathan, Michael, Maclean, Michael, Nortey, Michael, Pellen, Michael, Pham, Michael, Pitiakoudis, Michael, Singh, Michael Prodip, Spartalis, Michael, Van Der Colf, Michael, Wilson, Michael, Billis, Michail, Karanikas, Michail, Papamichail, Michail, Vailas, Michail, Kisielewski, Michał, Kusiński, Michał, Nycz, Michał, Święch, Michał, Fagbayimu, Micheal, Adamina, Michel, Fiogbe, Michel, Prudhomme, Michel, Angelucci, Michela, Campanelli, Michela, Cricrì, Michele, Grande, Michele, Manigrasso, Michele, Scopelliti, Michele, George, Michelle, Mozel, Michelle, Galasyuk, Mick, Chevallay, Mickael, Giuliano, Micol, Dobón Rascón, Miguel Ángel, Fernandez Romero, Miguel Angel, García Ureña, Miguel Ángel, Zaragoza Mendieta, Miguel Ángel, Bejarano Serrano, Miguel, Brito, Miguel, Calderon, Miguel, Jurado Román, Miguel, Palas, Miguel, Li Valencia, Miguel Roberto, Sebastián-Mendoza, Miguel-Julián, Muresan, Mihai-Stefan, Slavchev, Mihail, Agapov, Mikhail, Milentijević, Milica, Catterall, Millie, 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Abosedra, Mohammed, Sarker, Mohammed Abu Kawsar, Abudabbous, Mohammed, Abuwarda, Mohammed, Elgak, Mohammed Ahmed, Al Hinai, Mohammed, Al Zebda, Mohammed, Al-Shehari, Mohammed, Alharthi, Mohammed, Ali, Mohammed, Ismael Alamin, Mohammed Ali, Alqadasi, Mohammed, Alshanwani, Mohammed, Alshehri, Mohammed, Asaad, Mohammed, Bashir, Mohammed, Bohlala, Mohammed, Meghaizerou, Mohammed El Amine, Albanna, Mohammed Emad Hashem, Ghunaim, Mohammed, Goga, Mohammed, Hajhamad, Mohammed, Hamada Takrouney, Mohammed, Hammoda, Mohammed, Hamoud Almahly, Mohammed, Muragi, Mohammed, Mohammed, Mohammed Mustafa Hassan, Obaid, Mohammed, Omar, Mohammed, Said, Mohammed, Salah, Mohammed, Saleh Khatab, Mohammed, Aliyu, Mohammed Salele, Shakhshir, Mohammed, Sheriff, Mohammed, Tageldin, Mohammed, Abdelfattah, Mohanad, Daher, Mohanad, Lotfy, Mohanad, Alqedrh, Mohannad, Nasani, Mohannad, Mohd Shah, Mohd Shahrulsalam, Madany, Mohie El-Din Mostafa, Mobolaji-Ojibara, Mojisola, Nkogatse, Moleseng, Alazabi, Mona, Gupta, 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Abubasheer, Tareq, Alhusban, Tariq, Yılmaz, Tarık Emre, Aloqaili, Tasneem, Aldirawi, Tayef, Karahasanoğlu, Tayfun, Gülşen, Taygun, Jemal, Tebarek, Nadeem, Tehmina, Singh, Tejinder Paul, Alinger, Temsula, Longkumer, Temsutoshi, Lo, Terence, Gimenez Maurel, Teresa, Uprak, Tevfik Kıvılcım, Petropoulou, Thalia, Rawther, Thampi, Techapongsatorn, Thawin, Manickam, Thendral, Padioti, Theodora, Kozonis, Theodoros, Sidiropoulos, Theodoros, Tsirlis, Theodoros, Theodosopoulos, Theodosios, Hessou, Thierry Kinnongni, Uiyapat, Thitapon, Agyen, Thomas, Harris, Thomas, Hugh, Thomas J, Jorgensen, Thomas W., Theivendrampillai, Thrshegan, Pham, Thuy Vy, Branco, Tiago, Nagwamutse, Tijjani Nasiru, Tony V, Tina, Devabalan Koil, Titus, Pilia, Tiziana, Abdulmola, Tmador Mahmed Ali, Oyeyemi, Tobiloba, Kebede, Tolera, Ibarra-Hurtado, Tomás Ramón, Reichelt, Tomas, Gach, Tomasz, Stefura, Tomasz, Wojewoda, Tomasz, Soric, Tomislav, Risteski, Toni, Tridip, Tridip Dutta Baruah, Yotsov, Tsanko, Doma Bhutia, Tshering, Girma, Tsion, Kwan, Tsun Yu, Ivanov, Tsvetomir, Iahmo, TT, Ashkar, Tuleen, Mabogoane, Tumi, Olobatoke, Tunde, Sholadoye, Tunde T., Mishra, Tushar Subhadarshan, Parker, Tyla, Ezomike, Uchechukwu, Dilibe, Uchenna, Kumbhar, Uday, Una, Uduak-Abasi, Grossi, Ugo, Ezidiegwu, Ugochukwu, Edith, Ugwuoke, Daspal, Ujjwal, Kalu, Ukoha Agwu, Dietz, Ulrich, Ronellenfitsch, Ulrich, Wirth, Ulrich, Abdullah, Umar, Rivolta, Umberto, Jayarajah, Umesh, Kanyan Kassim, Umu-Hera, Govindan, Unnikrishnan, Fernandes, Urânia, Akram, Usman, Mohammed Bello, Usman, Omara, Usra, Naidoo, Uvarasen, Mahendravarman, Vaishnaivy, Negoita, Valentina Madalina, Mariottini, Valentina, Murzi, Valentina, Zucchini, Valentina, Agbakwuru, Valentine, Armenta Tapia, Valeria Estefania, Gentilino, Valerio, Borgogni, Vanessa, Ng, Vanessa, Wolfschluckner, Vanessa, Silvestri, Vania, Žufić, Vanja, Gunasaegaram, Varman, Kamalathevan, Vasan, Pillay, Vasanthan, Neykov, Vasil, Kalles, Vasileios, Mousafeiris, Vasileios, Papadopoulos, Vasileios, Tsaousis, Vasileios, Tselepidis, Vasileios, Vougas, Vasileios, Georgilaki, Vasiliki, Nikolaou, Vasiliki, Themelidi, Vasiliki, Manchev, Vassil, Ioannou, Veatriki, Jha, Vedant Shekhar, Dragisic, Vedran, Sutharshan, Vengadasalam, Alonso, Veronica, Brocco, Veronica, Ongil Rodríguez, Verónica, Schirinzi, Veronica, Budyakova, Veronika, Naunova, Vesna, Panduro-Correa, Vicky, Ruiz López, Víctor Andrés, Bill, Victor, Dassah, Victor, Gonzabay, Víctor Eduardo, Etwire, Victor, Modekwe, Victor, Serna-Alarcon, Victor, De Donato, Victor Ugo, Duque Mallén, Victoria, Liu, Victoria, Sharma, Vidushi, Kumar, Vijaya, Muralidharan, Vijayaragavan, Kakotkin, Viktor, Enemuo, Vincent, Kudoh, Vincent, Osoka, Vincent, Siepaal, Vincent, Granata, Vincenza, Dinuzzi, Vincenza Paola, Lizzi, Vincenzo, Ricchiuti, Vincenzo, Mosca, Vinicio, Kanna D, Vinoth, Nair, Vipin Venugopal, Ledda, Virginia, Michael, Vishal, Singh, Vishavdeep, Govindasamy, Vishendran, Kollengode, Viswanathan V, Giordano, Vittoria, Morinelli, Vittoria, Lin, Vivian, Cuk, Vladica, Cijan, Vladimir, Çalik, Volkan, Bakshi, Vrushank, Ten, Vyacheslav, Ratheesh, Vysakh, Alsado, Wael, Mayo, Wafik, Yang, Wah, Rehman, Wajeeh Ur, Abbas, Walaa, Al-Khyatt, Waleed, Mabood, Waleed, Santucci, Walter, Yew, Wan Ting, Tahir, Warda, De Silva, Warusha Dhammika Dulantha, Wijenayake, Wasantha, Almadhoun, Waseem J.N., Alhroub, Wasef, Ebrahim, Wesam, Jabłoński, Wiktor, Krawczyk, Wiktor, Bhatti, William, Lai, William Kwan Ho, Petrushnko, Wilson, Afedo, Wisdom, Kasetsermwiriya, Wisit, Serednicki, Wojciech, Wysocki, Wojciech, Zerefa, Wondwossen Amtataw, Vagena, Xanthoula, Tarrado, Xavier, Papazarkadas, Xenofon, Po, Xiang Yuen, Ouadi, Yacine, Myla, Yacob, Haido, Yacoub, Hasan, Yafea, Karataş, Yağmur, Carpio Colmenares, Yahaira, Galvañ Félix, Yaiza, Balogun, Yakub Lekan, Maktabi, Yaman, Nerabani, Yaman, Fulgence, Yamuremye, De Silva, Yannick, Asar, Yara, Samer Morsy, Yara, Ghaleb, Yasameen Saif Saeed Mohammed, Vashishth, Yash, M S, Yashas, Manickchund, Yashoda, Sakaray, Yashwant, Kara, Yasin, Tosun, Yasin, Zwain, Yasir, Farag, Yasmeen, Jedidi, Yasmine, Seada, Yasmine, Aldebasi, Yasser, Ashour, Yasser, Badr, Yassin, Sarpong, Yaw, Alawneh, Yazan, Aung, Yee Lay, Orabi, Yehia, Lam, Yick Ho, Şen, Yigit Kaan, Koyluoglu, Yilmaz Onat, Huang, Yining, Mintz, Yoav, Perez, Yocarli, Chamara, Yohan, Melkamu, Yohannes, Maestre González, Yolanda, Dean, Yomna, Metaferia, Yonas Yilma, Malki, Yosra, Mohamed, Younis, A. Omar, Yousef, Alhammoud, Yousef, Allaoua, Yousef, Tanas, Yousef, El Okazy, Youssef, Kerolous, Youssef, Mohamed, Youssef, Al-Junaidi, Youssuf, Lakdawala, Yousuf, Lu, Yuann, Asano, Yukio, Altinel, Yuksel, Aktimur, Yunus Emre, Fishman, Yuri, Kudryavcev, Yurii, Tandoğan, Yusuf Iskender, Razafimandimby, Yves Michaël, Adofo-Asamoah, Yvonne, Şenol, Zafer, Odeh, Zahra, Benamrouche, Zahra Rayane, Alkhaier, Zahraa, Al-Sheikh Ali, Zaid, Zaher, Zain Aldin, Alnajjar, Zaina, Al-Balushi, Zainab, Alkhuzaie, Zainab, Khraim, Zakaria, Toutounji, Zakaria, Djama, Zakarya, Farraj, Zamil, Shah, Zamir Ahmad, Khan, Zara, Khan, Zarnigar, Abo Alarous, Zayn Alabden, Asaad, Zaynab, Demetrashvili, Zaza, Lorenc, Zbigniew, Brekalo, Zdrinko, Hannouneh, Zein Alabdin, Robertson, Zelda, Bady, Zeyad, Durna, Zeynep, Wong, Zhen Yu, Fozo, Zien Alabdin, Klib, Zienab, Chin, Ziling, Tamou, Zime Farid, Johnson, Zina, Shabello, Ziyad, Orzeszko, Zofia, Aljalabi, Zohal, Al-Azher El-Hamel, Zohour, Karampotaki, Zoi Rafaila, Valera Sanchez, Zoraida, Matkovic, Zoran, Azhar, Zubair, Qureshi, Zubaria, Correa López, Zulema Margarita, Balciscueta, Zutoia, Musilová, Zuzana, Katusic, Zvonimir, Osama, Zyad, and Dauksa, Zygimantas

Published
2024
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44. Open Problems in Computational Historical Linguistics [version 2; peer review: 3 approved, 2 approved with reservations]

Author

Johann-Mattis List

Subjects
historical linguistics, computational linguistics, open problems, scientific problem solving, eng, Science, Social Sciences
Abstract

Problems constitute the starting point of all scientific research. The essay reflects on the different kinds of problems that scientists address in their research and discusses a list of 10 problems for the field of computational historical linguistics, that was proposed throughout 2019 in a series of blog posts (see http://phylonetworks.blogspot.com/). In contrast to problems identified in different contexts, these problems were considered to be solvable, but no solution could be proposed back then. By discussing the problems in the light of developments that have been made in the field during the past five years, a modified list is proposed that takes new insights into account but also finds that the majority of the problems has not yet been solved.

Published
2024
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45. Carbon footprint of healthcare systems: a systematic review of evidence and methods

Author

Mattis Keil, Leonie Frehse, Marco Hagemeister, Mona Knieß, Oliver Lange, Tobias Kronenberg, and Wolf Rogowski

Subjects
Medicine
Abstract

Objective Given the demand for net-zero healthcare, the carbon footprint (CF) of healthcare systems has attracted increasing interest in research in recent years. This systematic review investigates the results and methodological transparency of CF calculations of healthcare systems. The methodological emphasis lies specifically on input–output based calculations.Design Systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.Data sources PubMed, Web of Science, EconBiz, Scopus and Google Scholar were initially searched on 25 November 2019. Search updates in PubMed and Web of Science were considered until December 2023. The search was complemented by reference tracking within all the included studies.Eligibility criteria We included original studies that calculated and reported the CF of one or more healthcare systems. Studies were excluded if the specific systems were not named or no information on the calculation method was provided.Data extraction and synthesis Within the initial search, two independent reviewers searched, screened and extracted information from the included studies. A checklist was developed to extract information on results and methodology and assess the included studies’ transparency.Results 15 studies were included. The mean ratio of healthcare system emissions to total national emissions was 4.9% (minimum 1.5%; maximum 9.8%), and CFs were growing in most countries. Hospital care led to the largest relative share of the total CF. At least 71% of the methodological items were reported by each study.Conclusions The results of this review show that healthcare systems contribute substantially to national carbon emissions, and hospitals are one of the main contributors in this regard. They also show that mitigation measures can help reduce emissions over time. The checklist developed here can serve as a reference point to help make methodological decisions in future research reports as well as report homogeneous results.

Published
2024
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46. Explaining the Mechanism Behind mRNA Vaccines Influences Perceived Vaccine Effectiveness but not Vaccination Intentions

Author

Lisa Felgendreff, Felix G. Rebitschek, Mattis Geiger, Parichehr Shamsrizi, Mirjam A. Jenny, and Cornelia Betsch

Subjects
vaccine hesitancy, educational intervention, analogy, SARS-CoV-2-pandemic, informed decision-making, fact box, Public aspects of medicine, RA1-1270, Social sciences (General), H1-99
Abstract

Vaccine effectiveness and safety concerns can prevent people from receiving their first Covid-19 vaccines or boosters. Understanding the vaccine mechanism may lead people to perceive vaccine effectiveness appropriately. This study tested whether helping people understand the vaccine’s mechanism could improve their perceived vaccine safety and effectiveness. In a preregistered study, N = 1,548 unvaccinated or non-boosted participants were randomly presented with one of three communication formats: a fact box (a benefit-risk profile in tabular format; control condition), an expository text (i.e., a purely factual explanation) plus fact box, or an analogy plus fact box. Participants rated the vaccine’s effectiveness in preventing a Covid-19 disease, their perceived risk of getting vaccinated, and their intention to get vaccinated or boosted (depending on their vaccination status). Reading either additional text about the vaccines’ mechanism increased participants’ effectiveness ratings for the vaccine to prevent Covid-19 but did not affect risk ratings or vaccination intentions. The participants’ vaccine-related perceptions and intentions did not differ between the two text types. Elaborating on the vaccine’s mechanism of protection, in addition to presenting the benefit-risk profile of a vaccine, can lead people to perceive the vaccine effectiveness as slightly higher, yet it is insufficient to increase vaccination intentions.

Published
2024
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47. A Do-It-Yourself Czerny-Turner Spectrometer: Atomic Emission, Absorption, Reflection and Fluorescence Spectroscopy in Natural Sciences

Author

Osterheider, Mattis, Bourdon, Bjoern, Boettcher, Rasmus, Beeken, Marco, and Imlau, Mirco

Abstract

Spectrometers play a decisive role in the education of A-level and/or under-graduate students in the field of natural sciences. They are capable to demonstrate atomic emission, transmission, reflection and fluorescence spectra, but can be used for wavelength filtering, as well. Here, we present a do-it-yourself spectrometer setup based on a Czerny--Turner design that (a) can be used for all measurement types with one and the same setup and (b) is capable to spectrally resolve atomic emission lines. We use LEGO® bricks for the optomechanical construction and combine it with affordable, but high-quality optical components and linear array based on a charge-coupled device (CCD). A modular design is implemented and allows for a quick change between the various measurement techniques, but also fosters the technological understanding of the underlying systems' optical design.

Published
2022
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48. Communicating climate change and health to specific target groups

Author

Lena Lehrer, Lennart Hellmann, Hellen Temme, Leonie Otten, Johanna Hübenthal, Mattis Geiger, Mirjam A. Jenny, and Cornelia Betsch

Subjects
planetary health, communication, climate protection, information behaviour, readiness to act, Medicine
Abstract

Background: The German status report on climate change and health 2023 identifies numerous health risks that are caused or exacerbated by climate change. One recommendation arising from the report is to strengthen education, information, and communication in the field. This article aims to serve as a basis for this. Methods: Based on four survey waves (2022/2023) of the PACE study (Planetary Health Action Survey, n=3,845, online), the status of risk perception as well as the Readiness to Act against climate change in the adult population in Germany is examined and a target group analysis is carried out. Results: Some health risks due to the climate crisis are perceived as comparatively low (e.g. mental health problems). People with higher risk perception show a higher Readiness to Act. Younger people, men, people with low education, and those living in smaller communities are identified as relevant target groups as they have a lower Readiness to Act. One third state that they never or hardly ever seek out specific information on climate change. Media use differs depending on target group. Conclusions: Target group-specific communication can help to educate people about the health impacts of the climate crisis. In the discussion of this article, implications from existing literature are discussed in detail, which offer practical guidance for effective climate change communication.

Published
2023
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