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Supplemental material, sj-docx-1-asm-10.1177_10731911231176274 for The Chinese Parenting Stress Scale for Preschoolers’ Parents: Development and Initial Validation by Jinghui Zhao, Min Xie, Jingyi He and Meng-Cheng Wang in Assessment

Platelet transfusion refractoriness (PTR) is an intractable issue in hematological patients, which increases bleeding risks and hospitalization costs to a great extent. We reviewed 108 patients with hematological diseases including acute leukemia, myelodysplastic syndrome, aplastic anemia, and others who received allogeneic hematopoietic stem cell transplantation (HSCT) from January 2019 through December 2020. After multivariable logistic regression, we found that splenomegaly (odds ratio [OR] = 26.98, p < .001) and JAK mutation (OR = 17.32, p = .024) were independent risk factors for PTR. During the period of transplantation, patients in the PTR group had a significantly higher platelet transfusion demand, which was reflected in the increased number of platelet transfusions (10.23 ± 6.696 vs. 5.06 ± 1.904, p < .001). After multivariate adjustment, PTR turned out to be independently associated with worse overall survival (hazard ratio = 2.794, 95% confidence interval = 1.083–7.207, p = .034). In conclusion, we found that splenomegaly and JAK gene mutation were independent risk factors for PTR in patients with hematological diseases. A history of PTR prior to allo-HSCT indicates a poor prognosis. What is the context?Platelet transfusion refractoriness is a critical issue, and it greatly increases bleeding risks and hospitalization costs.Patients with hematological diseases tend to develop PTR.PTR results from immune and nonimmune factors and the latter account for 80–90%.At present, there are few studies focused on the inducing factors of PTR, and the specific mechanism is not clear. Platelet transfusion refractoriness is a critical issue, and it greatly increases bleeding risks and hospitalization costs. Patients with hematological diseases tend to develop PTR. PTR results from immune and nonimmune factors and the latter account for 80–90%. At present, there are few studies focused on the inducing factors of PTR, and the specific mechanism is not clear. What is new?In this study, we investigated 108 patients with hematological disorders who received allogeneic HSCT from January 2019 to December 2020.We found that splenomegaly and JAK gene mutation were independent risk factors for PTR in patients with hematological diseases.PTR had a passive effect on the prognosis of patients after HSCT, as indicated by worse OS and a trend toward lower platelets after transplantation.PTR might affect megakaryocyte reconstitution after transplantation. In this study, we investigated 108 patients with hematological disorders who received allogeneic HSCT from January 2019 to December 2020. We found that splenomegaly and JAK gene mutation were independent risk factors for PTR in patients with hematological diseases. PTR had a passive effect on the prognosis of patients after HSCT, as indicated by worse OS and a trend toward lower platelets after transplantation. PTR might affect megakaryocyte reconstitution after transplantation. What is the impact?This study provides evidence that hematological patients with splenomegaly should be alert to the occurrence of PTR, which often indicates a worse prognosis of transplantation.Spleen reduction and JAK inhibitors in the treatment of PTR are worth exploring. This study provides evidence that hematological patients with splenomegaly should be alert to the occurrence of PTR, which often indicates a worse prognosis of transplantation. Spleen reduction and JAK inhibitors in the treatment of PTR are worth exploring. Abbreviations PLT: platelets; PTR: platelet transfusion refractoriness; HSCT: hematopoietic stem cell transplantation; OR: odds ratio; HR: hazard ratio; CI: confidence interval; IQR: interquartile range; SD: standard deviation; HLA: human leukocyte antigen; HPA: human platelet antigen; OS: overall survival; RFS: relapse free survival; PI: post-transfusion increment; PPR: percentage platelet recovery; CCI: corrected count increment; ICU: intensive care unit; AA: aplastic anemia; MDS: myelodysplastic syndrome; AML: acute myeloid leukemia; ALL: acute lymphocytic leukemia; CML: chronic myeloid leukemia; CMML: chronic myelomonocytic leukemia; MPN: myeloproliferative neoplasm; SI: splenic irradiation; Abs: antibodies; CR: complete remission; DAC: decitabine; GVHD: graft-versus-host disease; BM: bone marrow; PB: peripheral blood

Dataset and code used in a journal paper entitled Geographically weighted regression based on a network weight matrix: a case study using urbanization driving force data in China , published in the International Journal of Geographical Information Science. Abstract: Geographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China’s urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly.

The embryo stage is critical for chicken development. Numerous studies have been conducted to clarify the dynamic changes and functions of various proteins and the composition of amino acids during embryo development. However, the physiological characteristics of extraembryonic fluid (allantoic and amniotic), especially allantoic fluid (AF), remain largely unexplored; furthermore, how information is transmitted from embryonic fluid is unknown. In this study, AF-derived exosomes ranging from 60 to 160 nm in diameter from fertilized eggs at 13 d of incubation of fast-growth chickens (WG chicken), medium-growth chickens (Silky N4 chicken), and slow-growth chickens (Huiyang Beard chicken) were isolated and purified by different ultra-centrifugations and further verified by transmission electron microscopy and a flow nano-analyzer. Expression of the exosomal positive biomarkers of ALIX and HSP70 as well as lack of the epithelium marker GRP78 was observed by Western blotting. In addition, small RNA sequencing revealed that AF-derived exosomes at 13 d of incubation contained a large number of known miRNAs (32.62%–65.83%). The top 10 most abundant and co-expressed miRNAs were primarily related to development, growth, and immunity. In addition, AF-derived exosomes promoted DF-1 cell migration. These findings broadened our understanding of the characteristic of AF-derived exosomes.

As technology evolves, big data bring us great opportunities to identify patterns which were infeasible to identify from observations before. At the same time, it also brings challenges to Statisticians in analyzing massive data and transforming them into knowledge. Many existing implementations of traditional statistical methods can not cope with the volume of big data. Our research is motivated by the need to fit Linear Mixed Effect (LME) models to big data.Subsampling and divide and conquer (D\&C) methods have been proposed to analyze the big data. In this thesis, we focus on sampling and D\&C methods for fitting LME models with big data. We start with one-way random effect model in Chapter 2 and consider different subsampling methods such as sampling of subjects, sampling of both subjects and repeated measurements, and D\&C methods to estimate the parameters. Estimation procedures, statistical properties, and simulation results are presented. After comparing the estimators from different methods for one-way random effect model, we consider subsampling of subjects and D\&C method for random intercepts model and general linear mixed effects model in Chapters 3 and 4, respectively. Comparisons for different methods are provided at the end of each chapter. Overall we find that the D\&C method has better performance. Finally, we apply subsampling and D\&C method to investigate the relationship between ultraviolet radiation and blood pressure in Chapter 5.