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Introduction: Coffee is one of the leading tropical crops produced worldwide. Colombia ranks third in coffee production in the world. Microorganisms associated with coffee plants can have many biotechnological applications, such as plant growth promotion and biological control. Objective: To describe the bacteria present in the soil associated with coffee cultivation. This was done using a bulk sequencing or 16S rRNA metabarcoding approach. Methodology: Soil samples were collected to analyze their microbiome from three different departments of Colombia (Cauca, Risaralda and Magdalena). The plants were of different ages, and the crop management was different (conventional and organic). Subsequently, an analysis was carried out using Qiime2 to describe the communities associated with coffee cultivation and soil chemical properties. Results: Some important genera were identified, such as Janthinobacterium, Bacillus, Actinomadura and Actinoallomurus. These genera can be used as plant growth promoters, organic matter transformers, producers of antibiotics and metabolites with potential biotechnological applications. Implications: The study presented in this manuscript describes the communities associated with the soil in coffee cultivation. However, a much more complex approach could be through metagenomics, where the bacterial communities and the functions of; however, this vision is up to ten times higher in cost. Conclusion: Through the results obtained, it is concluded that the studies carried out through metabarcoding help to understand the composition of the microorganisms associated with coffee and, with this, try to elucidate the functions of the associated microorganisms.

The pulmonary valve’s anomalies can lead to the obstruction of the right ventricle’s outflow tract. This leads to the right ventricle’s pressure overload, dilation, hypertrophy and eventually his malfunction. Balloon angioplasty or valve replacement surgery is indicated in cases of severe pulmonary stenosis. Often, the insertion of bioprosthetic pulmonary valves is required; however, after several years these patients often require a second surgery due to the valve’s malcfunction. We report the case of a pregnant patient with a bioprosthetic pulmonary valve who required a ballon angioplasty due to this valve’s stenosis.

Given that health is so relevant for global productivity and competitiveness, and that the Information and Communications Technology (ICTs) play an important role in all of the productivity factors, this work makes use of the ICTs in health matters proposing the to use a WiFi oximeter. This article describes the operating principles of a Pulse Oximeter (PO) which is an opto-electronic non-invasive medical instrument capable of measuring changes in heart rate (HR) and SpO2 at the fingertip and its upgrade to the standards; HL7 and IEEE 11073-10404:2008, its design, and its validation against the three existing devices. Variables (SpO2%, Ppm and Temperature) were compared, and its performance and impact were discussed the addition of the WiFi technology allows a better communication between devices, causing a greater impact in global competiveness.

Abstract Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.