Statistical analysis plan for a cluster randomised trial in Madhya Pradesh, India: community health promotion and medical provision and impact on neonates (CHAMPION2).

BACKGROUND: Neonatal mortality in India has fallen steadily and was estimated to be 24 per 1000 live births in the year 2017. However, neonatal mortality remains high in rural parts of the country. The Community Health Promotion and Medical Provision and Impact On Neonates (CHAMPION2) trial investigates the effect of a complex health intervention on neonatal mortality in the Satna District of Madhya Pradesh. METHODS/DESIGN: The CHAMPION2 trial forms one part of a cluster-randomised controlled trial with villages (clusters) randomised to receive either a health (CHAMPION2) or education (STRIPES2) intervention. Villages receiving the health intervention are controls for the education intervention and vice versa. The primary outcome is neonatal mortality. The effect of the active intervention on the primary outcome (compared to usual care) will be expressed as a risk ratio, estimated using a generalised estimating equation approach with robust standard errors that take account of clustering at village level. Secondary outcomes include maternal mortality, stillbirths, perinatal deaths, causes of death, health care and knowledge, hospital admissions of enrolled women during pregnancy or in the immediate post-natal care period or of their babies (during the neonatal period), maternal blood transfusions, and the cost effectiveness of the intervention. A total of 196 villages have been randomised and over 34,000 women have been recruited in CHAMPION2. DISCUSSION: This update to the published trial protocol gives a detailed plan for the statistical analysis of the CHAMPION2 trial. TRIAL REGISTRATION: Registry of India: CTRI/2019/05/019296. Registered on 23 May 2019. https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MzExOTg=&Enc=&userName=champion2.

Recent developments in enzymatic and microbial biosynthesis of flavor and fragrance molecules.

Flavors and fragrances are an important class of specialty chemicals for which interest in biomanufacturing has risen during recent years. These naturally occurring compounds are often amenable to biosynthesis using purified enzyme catalysts or metabolically engineered microbial cells in fermentation processes. In this review, we provide a brief overview of the categories of molecules that have received the greatest interest, both academically and industrially, by examining scholarly publications as well as patent literature. Overall, we seek to highlight innovations in the key reaction steps and microbial hosts used in flavor and fragrance manufacturing.

Harnessing biocatalysis to achieve selective functional group interconversion of monomers.

Polymeric materials are ubiquitous to modern life. However, reliance of petroleum for polymeric building blocks is not sustainable. The synthesis of macromolecules from recalcitrant polymer waste feedstocks, such as plastic waste and lignocellulosic biomass, presents an opportunity to bypass the use of petroleum-based feedstocks. However, the deconstruction and transformation of these alternative feedstocks remained limited until recently. Herein, we highlight examples of monomers liberated from the deconstruction of recalcitrant polymers, and more extensively, we showcase the state-of-the-art in biocatalytic technologies that are enabling synthesis of diverse upcycled monomeric starting materials for a wide variety of macromolecules. Overall, this review emphasizes the importance of functional group interconversion as a promising strategy by which biocatalysis can aid the diversification and upcycling of monomers.

Protocol for a cluster randomised trial in Madhya Pradesh, India: community health promotion and medical provision and impact on neonates (CHAMPION2); and support to rural India's public education system and impact on numeracy and literacy scores (STRIPES2).

BACKGROUND: Rural areas of India exhibit high neonatal mortality, and low literacy and numeracy. We assess the effect of a complex package of health interventions on neonatal survival and the effect of out-of-school-hours teaching on children's literacy and numeracy in rural Madhya Pradesh. METHODS/DESIGN: This is a cluster-randomised controlled trial with villages (clusters) receiving either a health (CHAMPION2) or education (STRIPES2) intervention. Building on the design of the earlier CHAMPION/STRIPES trial, villages receiving the health intervention are controls for the education intervention and vice versa. The clusters are 196 villages in Satna district, Madhya Pradesh, India: each is at least 5 km from a Community Health Centre, has a population below 2500, and has at least 15 children eligible for the education intervention. The participants in CHAMPION2 are resident married women younger than 50 years of age who had not undergone a family planning operation, provided they are enumerated pre-randomisation or marry a man enumerated pre-randomisation. The participants in STRIPES2 are resident children born 16 June 2010 to 15 June 2013, not in school before the 2018-2019 school year and intending to enrol in first grade in 2018-2019 or 2019-2020. DISCUSSION: In CHAMPION2, the NICE Foundation will deliver a 3.5-year programme comprising Accredited Social Health Activists or village health workers and midwives promoting health knowledge and providing antenatal, postnatal, and neonatal healthcare; community mobilisation; referrals to appropriate government health facilities; and a health education campaign. In STRIPES2, the Pratham Education Foundation will deliver a programme of village-based, before/after school support focusing on literacy and numeracy. As controls, the CHAMPION2 control villages will receive the usual health services (plus the STRIPES2 intervention). STRIPES2 control villages will receive the usual education services (plus the CHAMPION2 intervention). The primary outcome in CHAMPION2 is neonatal mortality. Secondary outcomes include antenatal, delivery, immediate neonatal and postnatal care practices, maternal mortality, stillbirths, early neonatal deaths, perinatal deaths, health knowledge, hospital admissions, maternal blood transfusions, and cost effectiveness. The primary outcome in STRIPES2 is a composite literacy and numeracy test score. Secondary outcomes include separate literacy and numeracy scores, reported school enrolment and attendance, parents' engagement with children's learning, and cost effectiveness. Independent research and implementation teams will conduct the trial. Trial Steering and Data Monitoring Committees, with independent members, will supervise the trial. TRIAL REGISTRATION: Clinical Trial Registry of India: CTRI/2019/05/019296. Registered on 23 May 2019. http://www.ctri.nic.in/Clinicaltrials/pdf_generate.php?trialid=31198&EncHid=&modid=&compid=%27,%2731198det%27.

T7 Polymerase Expression of Guide RNAs in vivo Allows Exportable CRISPR-Cas9 Editing in Multiple Yeast Hosts.

Efficient guide RNA expression often limits CRISPR-Cas9 implementation in new hosts. To address this limitation in fungal systems, we demonstrate the utility of a T7 polymerase system to effectively express sgRNAs. Initially, we developed a methodology in Saccharomyces cerevisiae using a modified version of the T7 P266L mutant polymerase with an SV40 nuclear localization signal to allow guide RNA expression immediately downstream of a T7 promoter. To improve targeting efficiency, guide RNA design was found to be tolerant to three mismatches or up to three additional bases appended to the 5' end. The addition of three guanines to a T7-based guide RNA improved guide RNA expression 80-fold and achieved transcriptional output similar to the strong Pol III snr52 promoter. Resulting gene editing and dCas9-guided gene regulation with a T7-based guide RNA was on par with the commonly used snr52 system in S. cerevisiae. Finally, 96% and 60% genome editing efficiencies were achieved in Kluyveromyces lactis and Yarrowia lipolytica respectively with minimal optimization of this system. Thus, T7-based expression of sgRNAs offers an orthogonal method for implementing CRISPR systems in fungal systems.

Yeast Terminator Function Can Be Modulated and Designed on the Basis of Predictions of Nucleosome Occupancy.

The design of improved synthetic parts is a major goal of synthetic biology. Mechanistically, nucleosome occupancy in the 3' terminator region of a gene has been found to correlate with transcriptional expression. Here, we seek to establish a predictive relationship between terminator function and predicted nucleosome positioning to design synthetic terminators in the yeast Saccharomyces cerevisiae. In doing so, terminators improved net protein output from these expression cassettes nearly 4-fold over their original sequence with observed increases in termination efficiency to 96%. The resulting terminators were indeed depleted of nucleosomes on the basis of mapping experiments. This approach was successfully applied to synthetic, de novo, and native terminators. The mode of action of these modifications was mainly through increased termination efficiency, rather than half-life increases, perhaps suggesting a role in improved mRNA maturation. Collectively, these results suggest that predicted nucleosome depletion can be used as a heuristic approach for improving terminator function, though the underlying mechanism remains to be shown.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.