Content Analysis of Digital Media Coverage of the Human Papillomavirus Vaccine School-Entry Requirement Policy in Puerto Rico

BACKGROUND: In August 2018, Puerto Rico (PR) became the 4th state or territory in the United States to adopt a human papillomavirus (HPV) vaccine school-entry requirement, for students 11-12 years old. Evidence suggests that the content of media coverage may impact people\u27s perception of HPV vaccine and their willingness to vaccinate. This study aimed to analyze the content of digital news coverage related to the implementation of the policy in PR. METHODS: A content review was conducted of digital media published from January 2017 through December 2018. The content reviewed was carried out in two steps: 1) creating a matrix to summarize each article\u27s content about the policy and 2) qualitative analysis using a grounded theory approach. RESULTS: The search resulted in 34 articles obtained from 17 online local and international news outlets that reported the policy\u27s implementation. Analyses showed that 61% of the news articles did not mention the number of required doses, and 79% discussed the new policy concerning cancer prevention. In 2017, news coverage focused mostly on describing the policy, while 2018 coverage focused on controversies surrounding the implementation. Neutral emergent codes included: 1) Description of the policy; 2) Information about HPV related cancers; and 3) General information about HPV vaccine. Negative emergent codes included: 1) infringement to patient and parental autonomy; 2) Hesitancy from the political sector, and 3) Hesitancy from groups and coalitions. Positive content included: 1) knowledge and acceptance of HPV vaccine for cancer prevention; 2) importance of education and protective sexual behaviors; and 3) new vaccination law proposal. CONCLUSIONS: Most of the media coverage in PR was neutral and included limited information related to the vaccine, HPV, and HPV-related cancers. Neutral and negative themes could influence public concerns regarding the new policy, as well as HPV vaccination rates in PR

The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area