Maladaptive offspring emotion regulation has been identified as one pathway linking maternal and child psychological well-being in school-aged children. Whether such a pathway is present earlier in life still remains unclear. The present study investigated the role of preschoolers’ emotion reactivity and regulation in the association between maternal psychological distress and child internalizing and externalizing problems. Children’s emotion reactivity and regulation were assessed through both observed behavior and physiology. At 42 months of age, children (n = 251; 128 girls) completed a fear induction task during which their heart-rate variability was assessed and their behavior was monitored, and maternal self-reports on depressive mood and anxiety were collected. At 48 months mothers and fathers reported on their children’s internalizing and externalizing problems. Higher maternal depressive mood was associated with lower child fear-related reactivity and regulation, as indexed by heart-rate variability. The latter mediated the association between higher maternal depressive mood and higher preschoolers’ externalizing problems. Overall, our findings support the role of preschoolers’ emotion reactivity and regulation in the relationship between maternal psychological distress and children’s socio-emotional difficulties. This role may also depend on the discrete emotion to which children react or seek to regulate as, here, we only assessed fear-related reactivity and regulation.

Pneumatic launch systems for Unmanned Aerial Vehicles (UAVs), including mechanical and pneumatic systems, are complex and non-linear. They are subjected to system parameters during launch, which leads to difficulty in engineering research analysis. For example, the mismatch between the UAV parameters and the parameter design indices of the launch system as well as the unclear design indices of the launching speed and overload of UAVs have a great impact on launch safety. Considering this situation, some studies are presented in this paper. Taking the pneumatic launch system as a research object, a pneumatic launcher dynamic simulation model is built based on co-simulation considering the coupling characteristics of the mechanical structure and transmission system. Its accuracy was verified by laboratory test results. Based on this model, the paper shows the effects of the key parameters, including the mass of the UAV, cylinder volume, pressure and moment of inertia of the pulley block, on the performance of the dynamic characteristics of the launch process. Then, a method for matching the parameter characteristics between the UAV and launch system based on batch simulation is proposed. The set of matching parameter values of the UAV and launch system that satisfy the launch take-off safety criteria are calculated. Finally, the influence of the system parameters of the launch process on the launch performance was analysed in detail, and the design optimised. Meaningful conclusions were obtained. The analysis method and its results can provide a reference for engineering and theoretical research and development of pneumatic launch systems.

A study of 1,558 US households in June 2020 evaluated utilization of online grocery shopping during the COVID-19 pandemic, influences on utilization, and plans for future online grocery shopping. Nearly 55 percent of respondents shopped online in June 2020; 20 percent were first-timers. Cragg model estimates showed influences on online shopping likelihood and frequency included demographics, employment, and prior online shopping. Illness concerns increased likelihood, while food shortage concerns increased frequency of online shopping. A multinomial probit suggested 58 percent respondents planned to continue online grocery shopping regardless of pandemic conditions.

New Zealand has a strategy of eliminating SARS-CoV-2 that has resulted in a low incidence of reported coronavirus-19 disease (COVID-19). The aim of this study was to describe the spread of SARS-CoV-2 in New Zealand via a nationwide serosurvey of blood donors. Samples (n = 9806) were collected over a month-long period (3 December 2020–6 January 2021) from donors aged 16–88 years. The sample population was geographically spread, covering 16 of 20 district health board regions. A series of Spike-based immunoassays were utilised, and the serological testing algorithm was optimised for specificity given New Zealand is a low prevalence setting. Eighteen samples were seropositive for SARS-CoV-2 antibodies, six of which were retrospectively matched to previously confirmed COVID-19 cases. A further four were from donors that travelled to settings with a high risk of SARS-CoV-2 exposure, suggesting likely infection outside New Zealand. The remaining eight seropositive samples were from seven different district health regions for a true seroprevalence estimate, adjusted for test sensitivity and specificity, of 0.103% (95% confidence interval, 0.09–0.12%). The very low seroprevalence is consistent with limited undetected community transmission and provides robust, serological evidence to support New Zealand's successful elimination strategy for COVID-19.

Stratospheric airships are lighter-than-air vehicles that work at an altitude of 20km in the lower calm portion of the stratosphere. They can be used as real-time surveillance platforms for environment monitoring and civil communication. Solar energy is the ideal power choice for long-endurance stratospheric airships. Attitude control is important for airships so that they can point at a target for observation or adjust the attitude to improve the output performance of solar panels. Stratospheric airships have a large volume and semi-flexible structure. The typical actuators used are aerodynamic surfaces, vectored thrust and ballonets. However, not all these actuators can work well under special working conditions, such as low density and low speed. In this study, moving-mass control is introduced to stratospheric airships because its control efficiency is independent of airspeed and atmospheric density. A nonlinear feedback controller based on generalised inverse with a nonlinear mapping module is designed to implement moving-mass control. Such a new station keeping scheme with moving masses is proposed for airships with different working situations.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) led to a significant disease burden and disruptions in health systems. We describe the epidemiology and transmission characteristics of early coronavirus disease 2019 (COVID-19) cases in Bavaria, Germany. Cases were reverse transcription polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infections, reported from 20 January−19 March 2020. The incubation period was estimated using travel history and date of symptom onset. To estimate the serial interval, we identified pairs of index and secondary cases. By 19 March, 3546 cases were reported. A large proportion was exposed abroad (38%), causing further local transmission. Median incubation period of 256 cases with exposure abroad was 3.8 days (95%CI: 3.5–4.2). For 95% of infected individuals, symptom onset occurred within 10.3 days (95%CI: 9.1–11.8) after exposure. The median serial interval, using 53 pairs, was 3.5 days (95%CI: 3.0–4.2; mean: 3.9, s.d.: 2.2). Travellers returning to Germany had an important influence on the spread of SARS-CoV-2 infections in Bavaria in early 2020. Especially in times of low incidence, public health agencies should identify holiday destinations, and areas with ongoing local transmission, to monitor potential importation of SARS-CoV-2 infections. Travellers returning from areas with ongoing community transmission should be advised to quarantine to prevent re-introductions of COVID-19.

ABSTRACT IMPACT: Screening the effect of thousands of non-coding genetic variants will help identify variants important in the etiology of diseases OBJECTIVES/GOALS: Massively parallel reporter assays (MPRAs) can experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders and use those results to help understand their contributions to disease etiology. METHODS/STUDY POPULATION: To choose variants to evaluate with the MPRA, we first gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value < 10-5. For each SNP, we identified the region that was in linkage disequilibrium (r2 > 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We used an MPRA to measure the impact of these 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. RESULTS/ANTICIPATED RESULTS: Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. Of the 84 SNPs significantly impacted in both cells, the direction of impact was the same in 81. The direction of eQTL in GTEx tissues agreed with the assay SNP effect in SH-SY5Y cells but not microglial cells. The deep-learning model predicted sequence activity level correlated with the experimental activity level (Spearman’s corr = 0.45). The deep-learning model identified several predictive motifs similar to motifs of RNA-binding proteins. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that MPRAs can be used to evaluate the effect of non-coding variants, and the results can be used to train a machine learning model and interpret its predictions. Together, these can help identify causal variants and further understand the etiology of diseases.

ABSTRACT IMPACT: Urine tumor DNA non-invasively detects minimal residual disease and infers tumor mutational burden in locally advanced bladder cancer prior to radical cystectomy, which may potentially enable the selection of patients for bladder-sparing treatment or facilitate personalized adjuvant immunotherapy. OBJECTIVES/GOALS: Standard-of-care treatment for muscle-invasive bladder cancer (MIBC) is radical cystectomy. The inability to assess minimal residual disease (MRD) non-invasively limits our ability to offer bladder-sparing treatment. We sought to develop a liquid biopsy solution via urine tumor DNA (utDNA) analysis. METHODS/STUDY POPULATION: We applied uCAPP-Seq, a targeted sequencing method for detecting utDNA, to urine cell-free DNA samples acquired on the day of radical cystectomy from 42 patients with bladder cancer. utDNA variant-calling was performed non-invasively without prior tumor mutational knowledge. The overall utDNA level for each patient was represented by the non-silent mutation with the highest variant allele fraction after removing germline variants. Urine was similarly analyzed from 15 healthy adults. Tumor mutational burden (TMB) was inferred from the number of non-silent mutations detected in urine cell-free DNA by applying a linear relationship derived from TCGA whole exome sequencing of 409 MIBC tumors. RESULTS/ANTICIPATED RESULTS: utDNA levels were significantly higher in patients with residual disease detected in their surgical pathology compared to those who achieved a pathologic complete response (P = 0.002). Using an optimal utDNA threshold to define MRD detection, positive utDNA MRD significantly predicted the absence of pathologic complete response with a sensitivity of 81% and specificity of 81%. Positive utDNA MRD also portended significantly worse progression-free survival (HR = 7.4; P = 0.03) compared to negative utDNA MRD. Furthermore, we applied a linear relationship (Pearson r = 0.84; P < 0.0001) to identify patients with high inferred TMB who may have been candidates for early immune checkpoint blockade. DISCUSSION/SIGNIFICANCE OF FINDINGS: utDNA MRD analysis prior to surgery correlated significantly with pathologic response and progression-free survival, which may help select patients for bladder-sparing treatment. utDNA can also non-invasively infer TMB, which could facilitate personalized adjuvant therapy for patients in the future.