Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/. Supplementary data are available at Bioinformatics online.

A classification of the behavior of smart card users is important in the field of public transit demand analysis. It provides an understanding of people’s sequence of activities within a period of time. However, classical metrics such as Euclidean distance is not appropriate when dealing with time-series classification. To solve this problem, in this article a method for the classification of public transit smart card users’ daily transactions, which are represented in time series, is presented. The chosen approach uses cross-correlation distance (CCD), hierarchical clustering, and subgroups by metric parameter to understand the users’ temporal patterns. The clustering results are compared with dynamic time warping (DTW) distance (a common method to measure time-series distance). After a brief pedagogical example to explain the DTW and CCD concepts, a program is developed in R to validate the method on a real dataset of smart card data transactions. The dataset concerns the use of the public transit system in the city of Gatineau in September 2013. The results demonstrate that CCD performs better than DTW to classify the time series, and that the classification method identifies different public transit users’ daily behaviors. The results will help transit authorities to offer better services for smart card users from diverse groups.

Smartcard automated fare collection systems (SCAFC) for transit have been considered primarily for their administrative function of controlling access to the service and for revenue management. However, it is likely that data from these systems also can be used to describe both transport supply and demand. This article illustrates the use of smartcard data to estimate various transit performance measures. Combined with well-established evaluation processes, such measures can help operators monitor their networks in greater detail. The performance of the network supply (vehicle-kilometers, vehicle-hours, commercial speed, etc.) and the statistics on passenger service (passenger-kilometers, passenger-hours, average trip length, etc.) can be calculated from these datasets for any spatial or temporal level of resolution, including route and bus stop levels.

The assemble-to-order strategy delays the final assembly operations of a product until a customer order is received. The modules used in the final assembly operation result in a large product diversity. This production strategy reduces the customer waiting time for the product. As the lead-time is short, any product rework may violate the delivery time. Since quality tests can be performed on the stocked modules without impacting the assembly schedule, the quality of the final assembly operations should be the focus. The data-mining approach presented in this paper uses the production data to determine the sequence of assemblies that minimizes the risk of producing faulty products. The extracted knowledge plays an important role in sequencing modules and forming product families that minimize the cost of production faults. The concepts introduced in the paper are illustrated with numerical examples.