A peer support intervention for first-time mothers: Feasibility and preliminary efficacy of the mummy buddy program

BACKGROUND

The transition to motherhood, although joyous, can be highly stressful, and the availability of professional postpartum support for mothers is often limited. Peer volunteer support programs may offer a viable and cost-effective method to provide community-based support for new mothers.

AIM

To determine the feasibility of a peer volunteer support program-The Mummy Buddy Program-in which experienced volunteer mothers are paired with, and trained to offer social support to, first-time mothers.

METHODS

Using a single-group non-randomised feasibility trial, a total of 56 experienced mothers participated in the Mummy Buddy training program, which was focused on education and practical exercises relating to the provision of various forms of social support. Experienced mothers ('Mummy Buddies') were subsequently paired with expectant first-time mothers (n=47 pairs), and were encouraged to provide support until 24-weeks postpartum.

FINDINGS

In terms of key feasibility considerations, 95.1% of Mummy Buddies felt that they were trained sufficiently to perform their role, and 85.8% of New Mothers were satisfied with the support provided by their Buddy. Analyses of preliminary efficacy (i.e., program outcomes) revealed that the first-time mothers maintained normal levels of stress and depressive symptomology, and possessed relatively strong maternal functioning, across the program duration.

CONCLUSION

The Mummy Buddy Program appears to be a feasible and potentially valuable peer volunteer support program for first-time mothers. This study provides a foundation for program expansion and for work designed to examine program outcomes-for first-time mothers, Mummy Buddies, and entire family units-within a sufficiently-powered randomised controlled trial.

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

With recent advances in sensor and computing technology, it is now possible to use real-time machine learning techniques to monitor the state of manufacturing machines. However, making accurate predictions from raw sensor data is still a difficult challenge. In this work, a data processing pipeline is developed to predict the condition of a milling machine tool using raw sensor data. Acceleration and audio time series sensor data is aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control (CNC) milling machine. Each block of data is preprocessed using well-known and computationally efficient signal processing techniques. A novel kernel function is proposed to approximate the covariance between preprocessed blocks of time series data. Several Gaussian process regression models are trained to predict tool condition, each with a different covariance kernel function. The model with the novel covariance function outperforms the models that use more common covariance functions. The trained models are expressed using the Predictive Model Markup Language (PMML), where possible, to demonstrate how the predictive model component of the pipeline can be represented in a standardized form. The tool condition model is shown to be accurate, especially when predicting the condition of lightly worn tools.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Production of polyhydroxybutyrate by Bacillus species isolated from municipal activated sludge

Plastic wastes are considered to be severe environmental contaminants causing waste disposal problems. Widespread use of biodegradable plastics is one of the solutions, but it is limited by high production cost. Biologic wastewater treatment generates large quantities of biomass as activated sludge. Only a few reports focus on the potential of utilizing resident Bacillus species from activated sludge in polyhydroxbutyrate (PHB) production as well as the production of PHB from food wastes. They have attractive properties such as short generation time, absence of endotoxins, and secretion of both amylases and proteinases that can well utilize food wastes for nutrients, which can further reduce the cost of production of polyhydroxyalkanoates (PHAs). Two PHA-producing strains, HF-1 and HF-2, were isolated from activated sludge. HF-1 outperformed HF-2 in terms of growth and PHB production in hydrolyzed soy and malt wastes. The isolated bacteria was characterized by DNA sequence alignment. Cell extracts of HF-1 were also compared to Bacillus megaterium cell extracts on sodium dodecyl sulfate polyacrylamide gel electrophoresis. The biopolymers accumulated were analyzed by gas chromatography, nuclear magnetic resonance, and Fourier transform infrared methods.

Construction of recombinant Escherichia coli strains for polyhydroxybutyrate production using soy waste as nutrient

Construction and comparison of recombinant Escherichia coli strains harboring the polyhydroxybutyrate (PHB) operon from Ralstonia eutropha using vectors possessing different promotors, as well as the production of PHB from soy waste by the recombinant strain, are reported. The lac promotor was the most efficient on expression of the phb operon among the three promotors studied: i.e., lac promotor, T7 promotor and the normal sigma 70 promotor. The pKS/PHB was the most efficient plasmid for phb operon expression among the three plasmids used: i.e., pKS-, pAED4, and pJM9131. It was observed that isopropyl-beta-D-thiogalactopyranoside was not required for the induction of the expression of phb operon. The cell dry wt and polyhydroxyalkanoate content by E. coli XL-1 Blue (pKS/PHB) were 3.025 g/L and 27.83%, respectively.