Low Vaccine Coverage and Factors Associated with Incomplete Childhood Immunization in Racial/Ethnic Minorities and Rural Groups, Central Brazil.

Discrimination and limited access to healthcare services in remote areas can affect vaccination coverage. Therefore, this study aimed to estimate vaccination coverage for children living in quilombola communities and rural settlements in the central region of Brazil during their first year of life and to analyze the factors associated with incomplete vaccination. An analytical cross-sectional study was conducted on children born between 2015 and 2017. The percentage of children who received all vaccines recommended by the National Immunization Program in Brazil by 11 months and 29 days was used to calculate immunization coverage. Children who received the following vaccines were considered as having a complete basic vaccination schedule: one dose of BCG; three doses of Hepatitis B, of Diphtheria-Tetanus-Pertussis (DPT), of Haemophilus influenzae type b (Hib), and of Poliovirus (Polio); two doses of Rotavirus, of 10-valent pneumococcal (PCV10), and of Serogroup C meningococcal conjugate (MenC); and one dose of Yellow Fever (YF). Measles-mumps-rubella (MMR) and other doses recommended at or after 12 months were not included. Consolidated logistic regression was used to identify factors associated with incomplete vaccination coverage. Overall vaccination coverage was 52.8% (95% CI: 45.5-59.9%) and ranged from 70.4% for the Yellow Fever vaccine to 78.3% for the Rotavirus vaccine, with no significant differences between the quilombola and settler groups. Notably, the likelihood of incomplete general vaccination coverage was higher among children who did not receive a visit from a healthcare professional. Urgent strategies are required to achieve and ensure health equity for this unique and traditionally distinct group with low vaccination coverage.

Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.

Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature selection algorithm. In order to validate our study, we used eight benchmark data sets which are commonly used among researchers who developed machine learning methods for medical data classification. The experiment has shown that the performance of our proposed new feature selection method combined with twin-bounded support vector machine (FSTBSVM) is very efficient. The robustness of the FSTBSVM is examined using classification accuracy, analysis of sensitivity, and specificity. The proposed FSTBSVM is a very promising technique for classification, and the results show that the proposed method is capable of producing good results with fewer features than the original data sets. Graphical abstract Model using a new feature selection and grid search with 10-fold CV to optimize model parameters in our FSTBSVM.

Efficient, Absorption-Powered Artificial Muscles Based on Carbon Nanotube Hybrid Yarns.

A new type of absorption-powered artificial muscle provides high performance without needing a temperature change. These muscles, comprising coiled carbon nanotube fibers infiltrated with silicone rubber, can contract up to 50% to generate up to 1.2 kJ kg(-1) . The drive mechanism for actuation is the rubber swelling during exposure to a nonpolar solvent. Theoretical energy efficiency conversion can be as high as 16%.

Three-dimensionally bonded spongy graphene material with super compressive elasticity and near-zero Poisson's ratio.

It is a challenge to fabricate graphene bulk materials with properties arising from the nature of individual graphene sheets, and which assemble into monolithic three-dimensional structures. Here we report the scalable self-assembly of randomly oriented graphene sheets into additive-free, essentially homogenous graphene sponge materials that provide a combination of both cork-like and rubber-like properties. These graphene sponges, with densities similar to air, display Poisson's ratios in all directions that are near-zero and largely strain-independent during reversible compression to giant strains. And at the same time, they function as enthalpic rubbers, which can recover up to 98% compression in air and 90% in liquids, and operate between -196 and 900 °C. Furthermore, these sponges provide reversible liquid absorption for hundreds of cycles and then discharge it within seconds, while still providing an effective near-zero Poisson's ratio.

Flexible, ultralight, porous superconducting yarns containing shell-core magnesium diboride-carbon nanotube nanofibers.

Magnesium-diboride-coated carbon nanotube arrays are synthesized by templating carbon-nanotube aerogel sheets with boron and then converting the boron to MgB2. The resultant MgB2-CNT sheets are twisted into flexible, light-weight yarns that have a superconducting transition around 37.8 K and critical current and critical field comparable with those of existing MgB2 wires, but have about 20 times lower density than bulk MgB2.