Improving the timeliness of birth registration in Fiji through a financial incentive.

Background: Fiji is a Pacific Island nation with the predominant ethnic groups indigenous Fijians (iTaukei) (62 %) and Fijians of Indian descent (31 %). This study reports on the effect of a Parental Assistance Payment Program (PAPP) tied to on-time birth registration, available in Fiji from August 2018 to July 2020.

Methods: Unit record birth registration data ( = 117,829) for children born during 2016-22 were used to calculate mean birth-to-registration intervals and the likelihood of on-time birth registration (within 365 days) before the PAPP (January 2016-July 2018) compared to during the PAPP (August 2018-July 2020), by population disaggregations (sex, ethnicity, age, marital status).

Extended Sentinel Monitoring of Resistance to Cry and Vip3Aa Toxins in Bt Sweet Corn: Assessing Changes in Phenotypic and Allele Frequencies of Resistance.

Transgenic corn and cotton that produce Cry and Vip3Aa toxins derived from (Bt) are widely planted in the United States to control lepidopteran pests. The sustainability of these Bt crops is threatened because the corn earworm/bollworm, (Boddie), is evolving a resistance to these toxins. Using Bt sweet corn as a sentinel plant to monitor the evolution of resistance, collaborators established 146 trials in twenty-five states and five Canadian provinces during 2020-2022.

A qualitative study exploring nursing home care environments where nurse practitioners work.

Research is needed to support the growing nurse practitioner workforce to assure higher quality care for older adults in nursing homes. Nursing homes with optimal care environments that support nurse practitioner roles, increased visibility, independence, and relationships are better positioned to support care of older adults. This study reports findings of thirteen qualitative interviews with nurse practitioners to explore facets of nursing home care environments and adapt a tool to measure care environments.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs.