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Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast real-world behaviors. These tasks typically ask a series of similarly structured questions. The authors posit that every time a respondent answers an additional elicitation question, two things happen: (1) they provide information about some parameter(s) of interest, such as their time preference or the partworth for a product attribute, and (2) the respondent increasingly “adapts” to the task—that is, using task-specific decision processes specialized for this task that may or may not apply to other tasks. Importantly, adaptation comes at the cost of potential mismatch between the task-specific decision process and real-world processes that generate the target behaviors, such that asking more questions can reduce external validity. The authors used mouse and eye tracking to trace decision processes in time preference measurement and conjoint choice tasks. Respondents increasingly relied on task-specific decision processes as more questions were asked, leading to reduced external validity for both related tasks and real-world behaviors. Importantly, the external validity of measured preferences peaked after as few as seven questions in both types of tasks. When measuring preferences, less can be more.

 

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge’s data compatibility. 

Using meteorological observation data and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data, the impacts of the atmospheric circulation pattern on the interannual variability of haze-fog in northern China in January are studied by means of statistical methods. The results showed that the Eurasian teleconnection (EU) at the 500 hPa isostatic surface is the most important pattern affecting the haze-fog frequency in northern China. However, the existing EU index cannot perfectly describe this pattern. To this end, this study selects three main activity centers to define a new EU index, which are located in the Europe (10 °E, 55 °N), Siberia (80 °E, 60 °N), and Shandong, China (120 °E, 40 °N). The difference between the existing EU index and the new EU index is mainly the position of the anomaly center of the 500 hPa geopotential height. The EU is in a negative phase in higher haze-fog years but is in a positive phase in lower haze-fog years. The 500 hPa geopotential height shows negative anomalies in Europe and East Asian and a positive anomaly in Siberia in the negative EU phase. Using Plumb wave activity flux analysis, it was found that the cold wave affecting northern China is less in the negative EU phase than that in the positive EU phase, which resulted in more haze-fog days. In addition, the results also showed that the EU pattern goes through a considerable development and decay within 13 days. The visibility starts to significantly decrease at a lag of −1 to 2 days in the negative EU peak phase and is influenced by the weak north wind that is caused by the high pressure. 

ABSTRACT The analogy of the host galaxy of the repeating fast radio burst (FRB) source FRB 121102 and those of long gamma-ray bursts (GRBs) and superluminous supernovae (SLSNe) has led to the suggestion that young magnetars born in GRBs and SLSNe could be the central engine of repeating FRBs. We test such a hypothesis by performing dedicated observations of the remnants of six GRBs with evidence of having a magnetar central engine using the Arecibo telescope and the Robert C. Byrd Green Bank Telescope (GBT). A total of ∼20 h of observations of these sources did not detect any FRB from these remnants. Under the assumptions that all these GRBs left behind a long-lived magnetar and that the bursting rate of FRB 121102 is typical for a magnetar FRB engine, we estimate a non-detection probability of 8.9 × 10−6. Even though these non-detections cannot exclude the young magnetar model of FRBs, we place constraints on the burst rate and luminosity function of FRBs from these GRB targets. 

This note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains.

nTracer was written as a plug-in for the open source image processing software ImageJ. The software, instructional documentation, tutorial videos, sample image and sample tracing results are available at https://www.cai-lab.org/ntracer-tutorial.

Supplementary data are available at Bioinformatics online.