Selective phase separation with polyelectrolytes can be used to separate a mixture of proteins. The efficiency of separation was examined using the cationic polyelectrolyte poly(diallyldimethylammonium chloride) and the model proteins bovine serum albumin, -lactoglobulin, γ-globulin, and ribonuclease A. The coacervation yield for individual proteins and the degree of separation, for selected protein pairs, were studied as a function of polymer molecular weight, ionic strength, and pH.

Negatively charged polyelectrolytes such as carboxymethylcellulose, pectin, and alginate are commonly present in food products. These polyelectrolytes serve a variety of functions such as controlling viscosity and stabilizing emulsions. Proteins are also present in many food formulations. Because of their high charge density, polyelectrolytes can be expected to interact with these proteins. Hence, an understanding of the parameters controlling protein‐polyelectrolyte interactions is useful.

Dimension reduction (DR) algorithms project data from high dimensions to lower dimensions to enable visualization of interesting high-dimensional structure. DR algorithms are widely used for analysis of single-cell transcriptomic data. Despite widespread use of DR algorithms such as t-SNE and UMAP, these algorithms have characteristics that lead to lack of trust: they do not preserve important aspects of high-dimensional structure and are sensitive to arbitrary user choices. Given the importance of gaining insights from DR, DR methods should be evaluated carefully before trusting their results. In this paper, we introduce and perform a systematic evaluation of popular DR methods, including t-SNE, art-SNE, UMAP, PaCMAP, TriMap and ForceAtlas2. Our evaluation considers five components: preservation of local structure, preservation of global structure, sensitivity to parameter choices, sensitivity to preprocessing choices, and computational efficiency. This evaluation can help us to choose DR tools that align with the scientific goals of the user.

BackgroundFear of childbirth (FOC) is one of the most common psychological symptoms among pregnant women and significantly relates to cesarean section, anxiety, and depression. However, it is not clear the prevalence and risk factors of FOC among Chinese pregnant women since the outbreak of the COVID-19 pandemic.AimsThe objective of this study was to examine the associations between coping styles, intolerance of uncertainty, and FOC.MethodFrom December 2021 to April 2022, a cross-sectional survey was conducted in two hospitals in China through convenient sampling. The cross-sectional survey was conducted among 969 pregnant women, which included the Childbirth Attitude Questionnaire (CAQ), Intolerance of Uncertainty Scale-12 (IUS-12), and Simplified Coping Style Questionnaire (SCSQ).ResultsThe total prevalence of FOC was 67.8%. The percentages of women with mild (a score of 28–39), moderate (40–51), and severe FOC (52–64) were 43.6, 20.2, and 4.0%, respectively. The regression results indicated that primiparas, unplanned pregnancy, few spousal support, intolerance of uncertainty, and negative coping styles were significant risk factors of FOC. Women who adopt positive coping strategies experienced a lower level of childbirth fear.ConclusionThese findings suggest that cultivating positive coping styles and obtaining sufficient childbirth information may be helpful for mothers' mental health. Regular screening assessment of perinatal psychological symptoms, such as the high level of intolerance of uncertainty and negative coping styles, should be adopted to reduce the risk of fear of childbirth.

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