Identification of electroporation sites in the complex lipid organization of the plasma membrane.

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrhythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study, we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.

Data Collection Practices of Mobile Applications Played by Preschool-Aged Children.

Importance: Child-directed mobile applications (apps) have been found to collect digital identifiers and transmit them to third-party companies, a potential violation of federal privacy rules. This study seeks to examine the differences in app data collection and sharing practices by evaluating the sociodemographic characteristics of the children who play them. Objective: To examine data collection and sharing practices of 451 apps played by young children and to test associations with child sociodemographic characteristics. Design, Setting, and Participants: This study used data from the baseline phase of the Preschooler Tablet Study, a prospective cohort study conducted from August 2018 to January 2020. This study used a population-based sample. A convenience sample of the parents of preschool-aged children was recruited from pediatric offices, childcare centers, social media posts, and an online participant registry. Eligibility criteria included (1) parent or guardian of a child aged 3 to 5 years, (2) parent or guardian who lived with the child at least 5 days per week, (3) participants who spoke English, and (4) a child who used an Android (Google LLC) device. All interactions with participants were through email, online surveys, and mobile device sampling. Exposures: Sociodemographic characteristics were assessed by parental report. Main Outcomes and Measures: This study tested the hypothesis that data transmissions to third-party domains are more common in apps played by children from low-socioeconomic-status homes. Child app usage was assessed via a mobile sampling app for an average of 9 days. Persistent identifier data transmissions to third-party domains were quantified for each app using an instrumented Android environment with monitoring of network traffic; for each child, the counts of total data transmissions were calculated, and the total third-party domains were detected for the apps they played. Results: Our sample comprised 124 children who used Android devices (35 tablets, 89 smartphones; 65 girls [52%]; mean [SD] age, 3.85 [0.57] years; 87 non-Hispanic White [71%]). One hundred twenty of participating parents (97%) were women. Of 451 apps tested, 303 (67%) transmitted persistent identifiers to 1 to 33 third-party domains. Child data transmission counts ranged from 0 to 614 (median [interquartile range], 5.0 [1-17.5]) and third-party domain counts from 0 to 399 (4.0 [1-12.5]). In multivariable negative binomial regression models, higher transmission and third-party domain rates per app were positively associated with older age (rate ratio, 1.67 [95% CI, 1.20-2.33]; P = .002 and 1.69 [95% CI, 1.26-2.27]; P < .001, respectively) and lower parent educational attainment (eg, high school or General Educational Development or less rate ratio, 2.29 [95% CI, 1.20-4.39]; P = .003 and 2.05 [95% CI, 1.13-3.70]; P < .02, respectively), but not with household income. Conclusions and Relevance: This study found that apps used by young children had a high frequency of persistent identifier transmissions to third-party companies, suggesting that federal privacy rules are not being enforced. Older children, those with their own devices, or those from lower-education households may be at higher risk of potential privacy violations.

[Design of a half solenoid coil for optimization of magnetic focusing in trans-cranial magnetic stimulation].

Trans-cranial magnetic stimulation (TMS) is the process that excitable human brain tissue is activated with the electric field induced from a changing magnetic field. Magnetic focusing characteristic is one of the most important technical considerations of coil design in TMS. In this paper, a half solenoid coil was proposed to be used in TMS and the magnitude profile of the induced electric fields in different depth was studied based on the induced electric field theory of magnetic stimulating coil. The magnitude profile of the induced electric fields produced by half solenoid coils was compared with that of butterfly-shaped coils. The result shows that half solenoid coils retain the good focusing characteristics of the main lobe of the butterfly-shaped coils. At the same time side effect of the side lobes on notargeted tissue is mitigated, which would otherwise lead to undesirable stimulation. Hence magnetic focusing is optimized, which is expected to give a more accurate delivery of the focal point for more effective stimulation.