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Evaluation of gain in statistical power for kinship analysis using sequence-based versus length-based STR genotyping
No full textIn this study, 137 pairwise relationships representing four major relationship categories involving 49 Turkish individuals from four families were analyzed to evaluate the potential gain in the statistical power associated with likelihood ratios (LR) when using sequence-based versus length-based genotyping methods over the same STR loci coverage. To this end, the MPS Precision ID GlobalFiler NGS STR panel Kit and CE GlobalFiler™ PCR Amplification Kit were used. MPS-based analysis revealed the presence of 37 STR DNA sequence variations and / or the presence of 26 STR DNA sequence flanking region SNPs compared to the 150 unique alleles obtained with CE-based genotyping. Considering that most kinship LR calculation software do not readily take into consideration STR DNA sequence variants and STR DNA sequence flanking region SNP data that becomes available during MPS-based genotyping, an alphanumeric allele re-coding system was implemented to incorporate such additional STR isoallelic data to the already available allele calls. Over all the four major relationship categories analyzed, a significant increase in the mean combined LR (cLR) was observed when going from CE-based to MPS-based typing, whereby a 78.08 to 7,864,630.60-fold increase was noted. More specifically, in 134 out of the 137 pairwise relationships analyzed, MPS-based cLR values were higher than those calculated using CE-based data. While the mean cLR was >1,000 for three out of the four major relationship categories when using CE, the only exception being the third degree relationships, the mean cLR was >1,000 for all the four major relationship categories when using MPS. Notably, the mean cLR obtained for the third degree relationships was 47.61 with CE and 3,717.31 with MPS. In comparison with CE-based genotyping, when fully taken into account as proposed in the current study, the DNA sequence variation data afforded by MPS-based genotyping led to a statistically significant gain in terms of cLR values obtained. The use of MPS for cLR calculations had the most impact for both the second and third degree relationships, the two complex / distant type analyzed, hence further underscoring the prospects for MPS in kinship analysis. While the current study demonstrated that cLR is likely to increase substantially upon going from CE to MPS genotyping over the same loci coverage for a given case, when the additional DNA sequence variances are also taken into consideration, further increases are expected due to the more diverse type of forensic markers and even wider loci coverages used by MPS kits
Postwar Embassy Competitions in Turkey: Platforms for Aesthetic Experimentation and Architectural Visibility on the International Stage
No full textCompetitive interdependence: A critical political economy of regulation during the COVID-19 pandemic
No full textThis article investigates Canada's health product regulatory authority, Health Canada (HC), and other major national regulators' responses to COVID-19. Drawing on semi-structured interviews with HC officials and secondary data on the activities of other major regulators, including the European Medicines Agency, the United Kingdom's Medicines and Healthcare Products Regulatory Agency, and the United States Food and Drug Administration, we show that during COVID-19 product evaluations, HC and other regulatory authorities adopted a strategy of increased collaboration and competition with one another. We term this strategy a pattern of 'competitive interdependence.' Using a critical political economy (CPE) approach, we argue that regulatory authorities employed the strategy to mediate increased structural tensions between capitalism and democracy engrained in health product regulation. The CPE approach, informing our analysis of competitive interdependence, highlights the dialectical nature of health product regulation. In light of our data, we demonstrate the regulators' role in upholding capitalism at both the national and global levels while also organizing popular consent by generating public trust in the safety and efficacy of medicines
Temperature-controlled porosity in glycine-derived high-entropy spinel oxides for long-life zinc-air batteries
No full textPorosity is a key factor in enhancing the electrochemical performance and durability of high entropy oxide (HEO) electrocatalysts for zinc-air batteries (ZABs) which are widely regarded as promising candidates for sustainable energy storage due to their high energy density, affordability, and environmental friendliness. The presence of an interconnected porous network within the HEO structure significantly improves oxygen diffusion, electrolyte penetration, and mass transport, while also imparting mechanical stability that assists mitigate electrode degradation during extended cycling. Although HEOs offer advantages such as tunable electronic structures and high catalytic activity, their long-term stability in harsh alkaline environments remains a challenge. By incorporating porosity, these stability concerns can be effectively addressed, enabling improved resilience and performance. In this study, we demonstrate a facile glycine-assisted sol-gel synthesis of a porous high-entropy spinel oxide, (FeCrCoMnZn)3O4-delta, which combines the catalytic benefits of HEOs with a tailored porous architecture to obtain a highly efficient and long-lasting air-cathode for ZABs. The resulting electrocatalyst exhibits remarkable durability used as an air cathode in ZAB, maintaining a stable voltage gap of over 1000 h of continuous charge-discharge cycles
Development and validation of regression model via machine learning to estimate thermal conductivity and heat flow using igneous rocks from the Dikili-Bergama geothermal region, Western Anatolia
No full textThermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling
“We Might Get What We Need, but Not What We Want”: Children's and Adolescents' Perceptions of Subjective Socioeconomic Status in Türkiye
No full textThis study examined how children and adolescents in Türkiye, a non-Western context marked by economic instability and social stratification, construct their subjective socioeconomic status (SSS). Participants included 195 children (N = 89, aged 8 to 11 years) and adolescents (N = 106, aged 12 to 17 years) from lower (N = 108) and higher (N = 87) socioeconomic backgrounds. Using both quantitative and qualitative methods, results show that SSS is a complex concept shaped by developmental, social, and contextual factors. Quantitative findings indicated that SSS ratings were moderately associated with parental education and income in adolescents but not in younger children. Moreover, adolescents and those from lower SES reported lower SSS ratings compared to their peers. Participants’ SSS ratings also correlated with their parents’ subjective status. Qualitative data revealed that participants relied on a range of cues, including purchasing power, social status symbols, values, and context-specific indicators of perceived mobility and financial strategies. The information participants used was also influenced by their socioeconomic background, with lower SES participants more likely to interpret their SSS in terms of constraints, instability, and financial strategies. Overall, these findings highlight the developmental trajectories and socio-cultural construction of SSS, offering insights into how children and adolescents navigate inequality and perceive their position within the broader social hierarchy in an underrepresented sample
Smart NFC-enabled point-of-care biosensor utilizing liquid metal Bi–Sn/Au nanostructures for cardiac troponin T detection
No full textA straightforward label-free assay for detecting cardiac biomarker troponin T (cTnT) was developed using a smartphone-controlled electrochemical sensor fully controlled via Near Field Communication (NFC). The integrated system consisted of a smartphone, an electrode sensor modified with a cTnT aptamer, and a card-sized electrochemical NFC tag sensor. Screen-printed electrode (SPE) surfaces were modified by co-immobilizing liquid metal BiSn core–shell particles and gold nanoparticles. This modified surface was subsequently functionalized with cTnT aptamer using mercaptohexanol (MCH) as a linker. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to confirm the immobilization and alteration procedures. The performance of the developed biosensor was evaluated by detecting cTnT in human serum samples using an Android smartphone's data display and a small NFC reader, demonstrating its potential for clinical diagnostics. The electrochemical signal was quantified by chronoamperometric detection using the NFC-based electrochemical aptasensor, by measuring the current response of the (Fe (CN)6)3−/4− redox pair before and after target addition. The sensor exhibited a limit of detection of 1.27 ng/mL for cTnT, with a linear calibration range from 0.075 to 10 ng/mL. Using a low-cost, smartphone-controlled sensor system, this electrochemical aptasensor offers a portable, straightforward, sensitive, and selective platform capable of evaluating a wide range of health biomarkers
Individual and collective epistemic virtue in science
No full textWe investigate the explanatory role of epistemic virtue in accounting for the success of science as a social institution that is characterized by predominantly epistemic ends. We explore several structural explanations of the epistemic success of science that commonly rule out virtue attributions to scientists. These accounts underline the economic structure of science as the chief explanatory factor in its collective success, and endorse a common conclusion, namely that collective epistemic virtue can obtain in the absence of individual epistemic virtue. We call this position virtue radicalism. We analyze the credibility crisis in the social and behavioral sciences as an anomaly for the virtue radicalist position, as the same incentive structure is shared across all scientific fields but leads to different collective outcomes. We then argue for a stronger claim against virtue radicalism, namely that the collective success of science cannot be reduced to any social structure, because the presence of a significant proportion of epistemically virtuous scientists in a scientific community is a necessary condition for the establishment, maintenance, and reform of any social structure with a view to reliable and sustainable knowledge-production
Semiautomated Delay Analysis Method Selection for Construction Projects: A Rule-Based Approach
No full textGiven the availability of various delay analysis methods, each yielding different results, the proper selection of an appropriate methodology is of paramount importance. Despite the necessity for automation to resolve delay conflicts, the current literature lacks an automated approach to assist contractors and project owners in reaching a consensus on selecting the most suitable delay analysis method without requiring a third party. Hence, to bridge the gap between theoretical research and practical application in achieving an automated delay analysis method, a novel rule-based expert system has been proposed. A structured, multiphase methodology that includes a review of existing methods, identification of key facts, determination of facts and expert rules, development of a forward chaining inference engine, and validation stages is used. Five real-world case examples and the decisions of experts for 15 hypothetical case examples are used for validation. The case examples demonstrate that the system can successfully automate the selection of the most appropriate delay analysis method and support a transparent, systematic approach to managing delays in construction projects. Furthermore, the system can foster consensus among project stakeholders during the selection of a delay analysis method and has the potential to contribute to the resolution of delay disputes
Toward sustainable metal additive manufacturing: Environmental hotspots and circularity pathways
No full textMetal Additive Manufacturing (MAM) is the method of producing three-dimensional objects based on a digital model by layer-by-layer material addition. MAM is important for the transition to a Circular Economy because of its various advantages over conventional production. It improves performance by optimizing complicated parts design, which was previously hard to fabricate. This study aims to assess the environmental and circularity potential of the MAM process, including ingot and powder production steps. Data collection was carried out through MAM prototyping, ingot, and powder production facilities. MAM production is evaluated for Laser-Based Powder Bed Fusion (PBF-LB) and Electron Beam Powder Bed Fusion (PBF-EB) techniques by focusing on their energy and material flow. This integrated evaluation process includes Carbon footprint (CF) analysis and Material Circularity Indicator (MCI) calculations. It was found that total carbon emissions are 124.86 kg CO2-eq/kg for PBF-EB, 281.18 kgCO2-eq/kg and 435.58 kg CO2-eq/kg for two different PBF-LB machines. Energy use was mainly from MAM machines, followed by post process and auxiliary equipment which highlights that energy efficiency in this stage is quite important in environmental performance. Moreover, material circularity was found to be a complementary indicator to be 0.33 for this facility. It was identified that circularity performance could be increased by 118 % with existing methods in the literature, highlighting that the effects of small improvements in material use can have a high impact. These findings contribute to a better understanding of environmental and circularity performance and can be utilized for other additive manufacturing methods