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The rising demand for vegetables has driven the adoption of greenhouse cultivation to guarantee high yields and quality of fresh produce year-round. Consequently, this elevates the demand for fertilizers, whose costs are progressively escalating. Bacillus safensis RGM 2450 and Bacillus siamensis RGM 2529 are plant growth-promoting rhizobacteria (PGPR). The combination of these strains exhibited synergistic activity in stimulating the growth and seedling hydration of tomatoes. In this study, the effects of inoculation with a RGM 2450 plus RGM 2529 formulation were evaluated under 66% and 100% fertilization programs in tomato crops under greenhouse conditions. Fertilization programs (66% and 100%) with or without commercial biostimulants were used as control treatments. In this assay, the NPK percentage in the plant tissue, tomato average weight, tomato average weight per harvest, tomato diameter, and changes in the colonization, structure, and diversity of the bacterial rhizosphere were measured. The 100% and 66% fertilization programs supplemented with the RGM 2529 plus RGM 2450 formulation increased the average weight of tomatoes per harvest without statistical difference between them, but with the other treatments. The 66% fertilization with RGM 2450 plus RGM 2529 increased between 1.5 and 2.0 times the average weight of tomatoes per harvest compared to the 66% and 100% fertilizations with and without commercial biostimulant treatments, respectively. This study represents the first report demonstrating that the application of a formulation based on a mixture of B. siamensis and B. safensis in a fertilization program reduced by 33% is equivalent in productivity to a conventional fertilization program for tomato cultivation, achieving an increase in potential plant growth-promoting rizobacteria of the genus Flavobacterium. Therefore, the adoption of a combination of these bacterial strains within the framework of a 66% inorganic fertilization program is a sustainable approach to achieving greater tomato production and reducing the environmental risks associated with the use of inorganic fertilization.

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Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

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Fishing landings in Chile are inspected to control fisheries that are subject to catch quotas. The control process is not easy since the volumes extracted are large and the numbers of landings and artisan shipowners are high. Moreover, the number of inspectors is limited, and a non-automated method is utilized that normally requires months of training. In this work, we propose, design, and implement an automated fish landing control system. The system consists of a custom gate with a camera array and controlled illumination that performs automatic video acquisition once the fish landing starts. The imagery is sent to the cloud in real time and processed by a custom-designed detection algorithm based on deep convolutional networks. The detection algorithm identifies and classifies different pelagic species in real time, and it has been tuned to identify the specific species found in landings of two fishing industries in the Biobío region in Chile. A web-based industrial software was also developed to display a list of fish detections, record relevant statistical summaries, and create landing reports in a user interface. All the records are stored in the cloud for future analyses and possible Chilean government audits. The system can automatically, remotely, and continuously identify and classify the following species: anchovy, jack mackerel, jumbo squid, mackerel, sardine, and snoek, considerably outperforming the current manual procedure.

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The B. safensis RGM 2450 and B. siamensis RGM 2529 strains were isolated from the rhizosphere of plants presenting resilience to abiotic and biotic stress conditions. To understand the implications of bacteria in resilience, a genomic and experimental analysis was carried out on their biostimulant and phytopathogenic antagonist properties. Genome analyses of both strains indicated that they have the potential to synthesize bioactive compounds such as the battery of non-ribosomal peptides, polyketides, extracellular enzymes and phytohormones. These results were consistent with the antagonistic activities of both strains against the phytopathogens Botrytis cinerea, Colletotrichum acutatum, Fusarium oxysporum and Phytophtora cinnamomi. They also showed the capacity to solubilize phosphorus, fix nitrogen and produce indole acetic acid. This was observed in tomato seedlings grown from seeds inoculated with the mixture of strains which presented significantly greater length as well as wet and dry weight in comparison with the treatments individually inoculated with each strain and the control. Accordingly, the combination of B. safensis RGM 2450 and B. siamensis RGM 2529 showed synergistic biostimulant activity. These findings contribute new knowledge of the genomic and metabolomic properties taking part in the symbiotic interactions between these strains and the plants and uphold the combined use of both strains as a biostimulant.

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The entomopathogenic fungus Beauveria pseudobassiana strain RGM 2184 can reach a maximum efficacy of 80% against the quarantine pest Lobesia botrana in field assays. In this study, the RGM 2184 genome was sequenced, and genome mining analyses were performed to predict the factors involved in its insecticidal activity. Additionally, the metabolic profiling of the RMG 2184 culture's supernatants was analyzed by mass spectrometry, and the insecticidal activity from one of these extracts was evaluated in Galleria mellonella larvae. The genome analysis resulted in 114 genes encoding for extracellular enzymes, four biosynthetic gene clusters reported as producers of insecticidal and bactericidal factors (oosporein, beauvericin, desmethylbassianin, and beauveriolide), 20 toxins, and at least 40 undescribed potential biocontrol factors (polyketides and nonribosomal peptides). Comparative genomic analysis revealed that 65-95% of these genes are Beauveria genus-specific. Metabolic profiling of supernatant extracts from RGM 2184 cultures exhibited secondary metabolites such as beauveriolide, oosporein, inflatin C, and bassiatin. However, a number of detected metabolites still remain undescribed. The metabolite extract caused 79% mortality of Galleria mellonella larvae at 28 days. The results of this research lay the groundwork for the study of new insecticidal molecules.

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Lobesia botrana (Denis and Shiffermüller) (Lepidoptera: Tortricidae) is one of the main pests that affect the production and export of table grapes in Chile. Because this pest has quarantine status, the fruit must be fumigated with methyl bromide, which reduces the fruit's export competitiveness in the destination market. In the present study, to help resolve this issue, six native entomopathogenic fungi were identified through multilocus analysis, including three Beauveria pseudobassiana and three Metarhizium robertsii. These fungi were evaluated in the laboratory to control L. botrana in its pupal stage in a silk cocoon and compared against a biological control product. Formulations with additional carbon sources improved the performance of the fungi. The treatments with outstanding performance contained the fungal strains B. pseudobassiana RGM 2184 and M. robertsii RGM 678. These strains were evaluated in the field during the winter season in two different regions of the country; the strains reached maximum efficacies of 80% and 88%, respectively, at 21 days post first application. Therefore, entomopathogenic fungi can contribute to reducing pupal populations in winter, thereby decreasing the moth population in spring-summer.

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In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were designed and synthesized by chemically bonding gold nanoparticles (AuNPs) with aptamer bioreceptors highly selective for four widely used antibiotics in the field of veterinary medicine, namely, Kanamycin, Ampicillin, Oxytetracycline and Sulfadimethoxine. When molecules of the antibiotics are present in the milk sample, the interaction with the aptamers induces random AuNP aggregation. This phenomenon modifies the initial absorption spectrum of the milk sample without antibiotics, producing spectral features that indicate both the presence of antibiotics and, to some extent, its concentration. Secondly, we designed and constructed an electro-opto-mechanic device that performs automatic high-resolution spectral data acquisition in a wavelength range of 400 to 800 nm. Thirdly, the acquired spectra were processed by a machine-learning algorithm that is embedded into the acquisition hardware to determine the presence and concentration ranges of the antibiotics. Our approach outperformed state-of-the-art standardized techniques (based on the 520/620 nm ratio) for antibiotic detection, both in speed and in sensitivity.

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Objectives: We aimed to determine the prevalence of Obstructive Sleep Apnea (OSA) in children and adolescents from four districts of Santiago, Chile by using a six-question subscale from the Sleep-Related Breathing Disorders (SRBD) scale, which measures respiratory symptoms while sleeping. Material and Methods: Cross-sectional observational study. The six-question subscale of the SRBD scale was applied to the parents or guardians of the children and adolescents from four educational establishments in different districts of Santiago. Convenience sampling was used. This subscale allowed to divide the sample into two groups: one group with OSA and one at low risk of OSA. In addition, statistical tests were applied to evaluate the variation between gender and age range. Results: Of the total number of subjects (n=838, 4-18 years, mean: 11.3±4.2), 681 were included. According to the six-question subscale, 2.2% (CI 95% 1.64-2.76%) of the sample had OSA. There is a slight predominance in males, without statistically significant difference (p=0.083). In relation to the age of the participants, there was no statistically significant difference (p=0.512).Conclusion: The prevalence of OSA in Chilean children and adolescents was similar to previous reports. The results obtained by the analysis of the six-question subscale of the SRBD scale allow a more accurate detection of OSA. Future research should promote the translation of this questionnaire into the Chilean context and its use throughout the country.

Objetivos: Nuestro objetivo fue determinar la prevalencia de la apnea obstructiva del sueño (AOS) en niños y adolescentes de cuatro distritos de Santiago, Chile mediante el uso de una subescala de seis preguntas de la escala de trastornos respiratorios relacionados con el sueño (Sleep-Related Breathing Disorders, SRBD), que mide los síntomas respiratorios mientras dormido. Material y Métodos: estudio observacional transversal. La subescala de seis preguntas de la escala SRBD se aplicó a los padres o tutores de los niños y adolescentes de cuatro establecimientos educativos en diferentes distritos de Santiago. Se utilizó muestreo de conveniencia. Esta subescala permitió dividir la muestra en dos grupos: un grupo con OSA y otro con bajo riesgo de OSA. Además, se aplicaron pruebas estadísticas para evaluar la variación entre el género y el rango de edad. Resultados: Del número total de sujetos (n=838, 4-18 años, media: 11,3±4,2), se incluyeron 681. Según la subescala de seis preguntas, el 2,2% (IC 95% 1,64-2,76%) de la muestra tenía AOS. Hay un ligero predominio en varones, sin diferencia estadísticamente significativa (p=0,083). En relación con la edad de los participantes, no hubo diferencias estadísticamente significativas (p=0,512). Conclusión: La prevalencia de AOS en niños y adolescentes chilenos fue similar a informes anteriores. Los resultados obtenidos mediante el análisis de la subescala de seis preguntas de la escala SRBD permiten una detección más precisa de OSA. Investigaciones futuras deberían promover la adaptación de este cuestionario al contexto chileno y su uso en todo el país

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Gold nanoparticles (AuNPs) are currently under intense investigation for biomedical and biotechnology applications, thanks to their ease in preparation, stability, biocompatibility, multiple surface functionalities, and size-dependent optical properties. The most commonly used method for AuNP synthesis in aqueous solution is the reduction of tetrachloroauric acid (HAuCl4) with trisodium citrate. We have observed variations in the pH and in the concentration of the gold colloidal suspension synthesized under standard conditions, verifying a reduction in the reaction yield by around 46% from pH 5.3 (2.4 nM) to pH 4.7 (1.29 nM). Citrate-capped AuNPs were characterized by UV-visible spectroscopy, TEM, EDS, and zeta-potential measurements, revealing a linear correlation between pH and the concentration of the generated AuNPs. This result can be attributed to the adverse effect of protons both on citrate oxidation and on citrate adsorption onto the gold surface, which is required to form the stabilization layer. Overall, this study provides insight into the effect of the pH over the synthesis performance of the method, which would be of particular interest from the point of view of large-scale manufacturing processes.

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We present a heterogeneous architecture for image registration and multimodal segmentation on an embedded system for noninvasive skin cancer screening. The architecture combines Otsu thresholding and the random walker algorithm to perform image segmentation, and features a hardware implementation of the Harris corner detection algorithm to perform region-of-interest detection and image registration. Running on a Xilinx XC7Z020 reconfigurable system-on-a-chip, our prototype computes the initial segmentation of a 400×400-pixel region of interest in the visible spectrum in 12.1 seconds, and registers infrared images against this region at 540 frames per second, while consuming 1.9W.

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Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method.

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Plume characteristics, such as temperature and velocity, emitted from pMDIs could significantly affect the dose delivered to the lung. Currently, high speed cameras and thermocouples are used separately to evaluate these parameters. We used a low-noise infrared camera to evaluate both the temperature and velocity of the emitted plume from pMDIs. Additionally, we investigated whether the fine particle fraction (FPF) is affected when time between actuations is varied. We tested three different albuterol sulfate pMDIs: ProAir® HFA, Proventil® HFA, and Ventolin® HFA. The plume and aerodynamic characteristics from these pMDIs were evaluated, after varying the time between actuations (15, 30, 60, and 120s), using the infrared camera and a next generation impactor, respectively. The aerodynamic characteristics were evaluated with and without a valved holding chamber (VHC). ProAir HFA had the softest plume followed by Proventil HFA and Ventolin HFA. Further, Ventolin HFA was slightly cooler and had significantly lower FPF than ProAir HFA and Proventil HFA. All inhalers had higher FPF when used with VHC. Further, we observed that the time between actuations affected the FPF across pMDIs. Moreover, generalized guidelines suggesting one-minute interval between actuations for pMDIs should be reconsidered, with and without a VHC.

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Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.

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While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL's bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL's non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated.

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The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.

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The amphibian oocyte cell model is widely used for heterologous expression of ionic channels and receptors. Little is known, however, about the physiology of oocyte cell models other than Xenopus laevis. In this study, the two-electrode voltage clamp technique was used to assess the most common electrical patterns of oocytes of the South American toad Bufo arenarum. Basal membrane resistance, resting potential, and ionic currents were determined in this cell model. The oocyte transmembrane resistance was 0.35 M(Omega), and the resting potential in normal saline was about -33 mV with a range between -20 mV and -50 mV. This is, to our knowledge, the first attempt to begin an understanding of the ion transport mechanisms of Bufo arenarum oocytes. This cell model may provide a viable alternative to the expression of ion channels, in particular those endogenously observed in Xenopus laevis oocytes.

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