From a Monolingual Mind to a Multilingual Heart: An Autoethnography through Dominant Language Constellation

This article narrates and analyzes the author’s life experiences as a learner, teacher, and researcher of diverse languages across three contexts: mainland China, Hong Kong, and Norway. Deconstructing the influential episodes in the writer’s life trajectory, this autoethnography explores the author’s transformation from a monolingually-minded individual to a border-crossing, multilingually hearted scholar. The analysis is undertaken through the theoretical lens of language ideology and dominant language constellation (DLC) and epitomizes the profound influence of sociocultural structures on an individual’s identity search and development. Confronting the multilingual turn in education and echoing the call to centralize identity in language teaching, this self-study exemplifies autoethnography as an empowering method for an ideological shift from perceiving “language(s)-as-problem” to advocating “language(s)-as-resource.” The study also illustrates how the construct of DLC can be deployed as a tangible model of multilinguals’ ever-evolving linguistic identities

Conceptual planning of urban–rural green space from a multidimensional perspective: A case study of Zhengzhou, China

The structure and function of green-space system is an eternal subject of landscape architecture, especially due to limited land and a need for the coordinated development of PLEs (production, living, and ecological spaces). To make planning more scientific, this paper explored green-space structure planning via multidimensional perspectives and methods using a case study of Zhengzhou. The paper applies theories (from landscape architecture and landscape ecology) and technologies (like remote sensing, GIS—geographic information system, graph theory, and aerography) from different disciplines to analyze current green-space structure and relevant physical factors to identify and exemplify different green-space planning strategies. Overall, our analysis reveals that multiple green-space structures should be considered together and that planners and designers should have multidisciplinary knowledge. For specific strategies, the analysis finds (i) that green complexes enhance various public spaces and guide comprehensive development of urban spaces; (ii) that green ecological corridors play a critical role in regional ecological stability through maintaining good connectivity and high node degree (Dg) and betweenness centrality index (BC) green spaces; (iii) that greenway networks can integrate all landscape resources to provide more secured spaces for animals and beautiful public spaces for humans; (iv) that blue-green ecological networks can help rainwater and urban flooding disaster management; and (v) that green ventilation corridors provide air cleaning and urban cooling benefits, which can help ensure healthy and comfortable urban–rural environments. In our view, this integrated framework for planning and design green-space structure helps make the process scientific and relevant for guiding future regional green-space structure

A multi-subgroup predictive model based on clinical parameters and laboratory biomarkers to predict in-hospital outcomes of plasma exchange-centered artificial liver treatment in patients with hepatitis B virus-related acute-on-chronic liver failure

BackgroundPostoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients’ clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.MethodsWe enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.ResultsWe built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.ConclusionsThe multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS

Nitrogen Removal in a Horizontal Subsurface Flow Constructed Wetland Estimated Using the First-Order Kinetic Model

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH4 + -N) and nitrate (NO3 −-N) concentrations were significantly lower than the influent concentrations (p \u3c 0.01, n = 38). The NO3 −-N load was significantly correlated with the removal rate (R 2 = 0.96, p \u3c 0.01), but the NH4 + -N load was not correlated with the removal rate (R 2 = 0.02, p \u3e 0.01). The area-based constants of NO3 −-N and NH4 + -N at 20 ◦C were 27 ± 26 (mean ± SD) and 14 ± 10 m·year−1 , respectively. The temperature coefficients for NO3 −-N and NH4 + -N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO3 −-N and NH4 + -N were not correlated with temperature (p \u3e 0.01). The NO3 −-N area-based constant was correlated with the corresponding load (R 2 = 0.96, p \u3c 0.01). The NH4 + -N area rate was correlated with DO (R 2 = 0.69, p \u3c 0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

The Simons Observatory: Magnetic Shielding Measurements for the Universal Multiplexing Module

The Simons Observatory (SO) includes four telescopes that will measure the temperature and polarization of the cosmic microwave background using over 60,000 highly sensitive transition-edge bolometers (TES). These multichroic TES bolometers are read out by a microwave RF SQUID multiplexing system with a multiplexing factor of 910. Given that both TESes and SQUIDs are susceptible to magnetic field pickup and that it is hard to predict how they will respond to such fields, it is important to characterize the magnetic response of these systems empirically. This information can then be used to limit spurious signals by informing magnetic shielding designs for the detectors and readout. This paper focuses on measurements of magnetic pickup with different magnetic shielding configurations for the SO universal multiplexing module (UMM), which contains the SQUIDs, associated resonators, and TES bias circuit. The magnetic pickup of a prototype UMM was tested under three shielding configurations: no shielding (copper packaging), aluminum packaging for the UMM, and a tin/lead-plated shield surrounding the entire dilution refrigerator 100 mK cold stage. The measurements show that the aluminum packaging outperforms the copper packaging by a shielding factor of 8-10, and adding the tin/lead-plated 1K shield further increases the relative shielding factor in the aluminum configuration by 1-2 orders of magnitude.Comment: 7 pages, 4 figure, conference proceedings submitted to the Journal of Low Temperature Physic

Proband-independent haplotyping based on NGS-based long-read sequencing for detecting pathogenic variant carrier status in preimplantation genetic testing for monogenic diseases

Preimplantation genetic testing for monogenic diseases (PGT-M) can be used to select embryos that do not develop disease phenotypes or carry disease-causing genes for implantation into the mother’s uterus, to block disease transmission to the offspring, and to increase the birth rate of healthy newborns. However, the traditional PGT-M technique has some limitations, such as its time consumption, experimental procedural complexity, and the need for a complete family or reference embryo to construct the haplotype. In this study, proband-independent haplotyping based on NGS-based long-read sequencing (Phbol-seq) was used to effectively construct haplotypes. By targeting the mutation sites of single gene disease point mutations and small fragment deletion carriers, embryos carrying parental disease-causing mutations were successfully identified by linkage analysis. The efficiency of embryo resolution was then verified by classical Sanger sequencing, and it was confirmed that the construction of haplotype and SNP linkage analysis by Phbol-seq could accurately and effectively detect whether embryos carried parental pathogenic mutations. After the embryos confirmed to be nonpathogenic by Phbol-seq-based PGT-M and confirmed to have normal copy number variation by Phbol-seq-based PGT-A were transplanted into the uterus, gene detection in amniotic fluid of the implanted embryos was performed, and the results confirmed that Phbol-seq technology could accurately distinguish normal genotype embryos from genetically modified carrier embryos. Our results suggest that Phbol-seq is an effective strategy for accurately locating mutation sites and accurately distinguishing between embryos that inherit disease-causing genes and normal embryos that do not. This is critical for Phbol-seq-based PGT-M and could help more single-gene disease carriers with incomplete families, de novo mutations or suspected germline mosaicism to have healthy babies with normal phenotypes. It also helps to reduce the transmission of monogenic genetic diseases in the population