Effectiveness of Bacillus subtilis ANT01 and Rhizobium sp. 11B on the control of fusarium wilt in pineapple (Ananas comosus).

Pineapple (Ananas comosus) is commonly infected by Fusarium oxysporum, causal agent of the fusarium wilt disease. Conventionally, growers use synthetic fungicides to control the disease, which lead to environmental pollution, hazardous effects on non-target organisms and risks on human health. The aim of this work was to assess the effectiveness of Bacillus subtilis ANT01 and Rhizobium sp. 11B to control fusarium wilt on pineapple plants. Four treatments derived from a complete factorial design were tested under field conditions. Treatments composed of B. subtilis ANT01 and the combination B. subtilis ANT01-Rhizobium sp. 11B decreased disease severity by 94.4% and 86.1%, respectively. On the other hand, the treatment prepared with Rhizobium sp. 11B alone showed a reduction of 75.0%. Size of leaves and nutritional condition (SPAD units) of the biocontrol agents-treated plants showed no statistical differences. Moreover, B. subtilis ANT01 decreased by 46% the initial soil population of F. oxysporum, while Rhizobium sp. 11B, B. subtilis ANT01 plus Rhizobium sp. 11B and control, showed a population reduction of 12.5%, 24.2% and 23.0%, respectively. These results make evident the potential of B. subtilis ANT01 as biocontrol agent of the pathogen under field conditions.

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction.

Images produced by CMOS sensors may contain defective pixels due to noise, manufacturing errors, or device malfunction, which must be detected and corrected at early processing stages in order to produce images that are useful to human users and image-processing or machine-vision algorithms. This paper proposes a defective pixel detection and correction algorithm and its implementation using CMOS analog circuits, which are integrated with the image sensor at the pixel and column levels. During photocurrent integration, the circuit detects defective values in parallel at each pixel using simple arithmetic operations within a neighborhood. At the image-column level, the circuit replaces the defective pixels with the median value of their neighborhood. To validate our approach, we designed a 128×128-pixel imager in a 0.35µm CMOS process, which integrates our defective-pixel detection/correction circuits and processes images at 694 frames per second, according to post-layout simulations. Operating at that frame rate, our proposed algorithm and its CMOS implementation produce better results than current state-of-the-art algorithms: it achieves a Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) of 45 dB and 198.4, respectively, in images with 0.5% random defective pixels, and a PSNR of 44.4 dB and IEF of 194.2, respectively, in images with 1.0% random defective pixels.

Motion-Based Object Location on a Smart Image Sensor Using On-Pixel Memory.

Object location is a crucial computer vision method often used as a previous stage to object classification. Object-location algorithms require high computational and memory resources, which poses a difficult challenge for portable and low-power devices, even when the algorithm is implemented using dedicated digital hardware. Moving part of the computation to the imager may reduce the memory requirements of the digital post-processor and exploit the parallelism available in the algorithm. This paper presents the architecture of a Smart Imaging Sensor (SIS) that performs object location using pixel-level parallelism. The SIS is based on a custom smart pixel, capable of computing frame differences in the analog domain, and a digital coprocessor that performs morphological operations and connected components to determine the bounding boxes of the detected objects. The smart-pixel array implements on-pixel temporal difference computation using analog memories to detect motion between consecutive frames. Our SIS can operate in two modes: (1) as a conventional image sensor and (2) as a smart sensor which delivers a binary image that highlights the pixels in which movement is detected between consecutive frames and the object bounding boxes. In this paper, we present the design of the smart pixel and evaluate its performance using post-parasitic extraction on a 0.35 µm mixed-signal CMOS process. With a pixel-pitch of 32 µm × 32 µm, we achieved a fill factor of 28%. To evaluate the scalability of the design, we ported the layout to a 0.18 µm process, achieving a fill factor of 74%. On an array of 320×240 smart pixels, the circuit operates at a maximum frame rate of 3846 frames per second. The digital coprocessor was implemented and validated on a Xilinx Artix-7 XC7A35T field-programmable gate array that runs at 125 MHz, locates objects in a video frame in 0.614 µs, and has a power consumption of 58 mW.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision.

The design of cooperative advanced driver assistance systems (C-ADAS) involves a holistic and systemic vision that considers the bidirectional interaction among three main elements: the driver, the vehicle, and the surrounding environment. The evolution of these systems reflects this need. In this work, we present a survey of C-ADAS and describe a conceptual architecture that includes the driver, vehicle, and environment and their bidirectional interactions. We address the remote operation of this C-ADAS based on the Internet of vehicles (IoV) paradigm, as well as the involved enabling technologies. We describe the state of the art and the research challenges present in the development of C-ADAS. Finally, to quantify the performance of C-ADAS, we describe the principal evaluation mechanisms and performance metrics employed in these systems.

Absolute configuration assignment of stigmasterol oxiranes.

Diastereoisomeric stigmasterol oxiranes 4, 5, 8, and 9 are known phytosterol oxidation products (POPs) that have been evaluated for their cytotoxicity, although the results are of limited significance since, in most cases, they were evaluated as mixtures. Consequently, to establish biological activity hierarchy of these oxides, it is critical to evaluate individual pure POPs. Therefore, we now describe the obtention of individual molecules and their absolute configuration (AC) determination. The two acetylated C-5-C-6 oxiranes 6 and 7; the two acetylated C-22-C-23 oxides 10 and 11, obtained by means of Δ5 double bond protection-deprotection; and the four C-5-C-6, C-22-C-23 diepoxystigmasteryl acetates 19-22 were now individually gained and their AC determined by vibrational circular dichroism. Vibrational modes associated with the C-5-C-6 and the C-22-C-23 bonds were identified in dioxiranes 19-22 and used to assign the AC of monoepoxides 6, 7, 10, and 11. The AC of biological active non-acetylated molecules follows immediately. Due to the scarce spectroscopic information available for these POPs, the 1 H and 13 C NMR chemical shifts of 3-22 were assigned using 1D- and 2D-NMR experiments.

A new germacranolide from Ageratina vernalis.

The aerial parts of Ageratina vernalis provided the new germacranolide 1,10-epoxydeltoidin A (3), together with the known pentacyclic triterpenoid hopane-6α,22-diol (1), and the also known germacranolides deltoidin A (2) and 15-hydroxydeltoidin A (4). In addition, pTsOH catalyzed cyclization of 2 afforded the new guaianolide 5. The absolute configuration of 2, 4, and 5 was assigned by vibrational circular dichroism spectroscopy, while the complete 1H and 13C NMR data assignments of 2-5 followed from 1 D- and 2 D-NMR experiments.

A Heterogeneous Hardware Accelerator for Image Classification in Embedded Systems.

Convolutional neural networks (CNN) have been extensively employed for image classification due to their high accuracy. However, inference is a computationally-intensive process that often requires hardware acceleration to operate in real time. For mobile devices, the power consumption of graphics processors (GPUs) is frequently prohibitive, and field-programmable gate arrays (FPGA) become a solution to perform inference at high speed. Although previous works have implemented CNN inference on FPGAs, their high utilization of on-chip memory and arithmetic resources complicate their application on resource-constrained edge devices. In this paper, we present a scalable, low power, low resource-utilization accelerator architecture for inference on the MobileNet V2 CNN. The architecture uses a heterogeneous system with an embedded processor as the main controller, external memory to store network data, and dedicated hardware implemented on reconfigurable logic with a scalable number of processing elements (PE). Implemented on a XCZU7EV FPGA running at 200 MHz and using four PEs, the accelerator infers with 87% top-5 accuracy and processes an image of 224×224 pixels in 220 ms. It consumes 7.35 W of power and uses less than 30% of the logic and arithmetic resources used by other MobileNet FPGA accelerators.

Face Recognition on a Smart Image Sensor Using Local Gradients.

In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen's algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 µm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 µm process and 76% on a 0.18 µm process with 32 µm × 32 µm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150×80-pixel image in 94 µs, and consumes 71 mW of power.

An eight-year multicenter study on short-term peripheral intravenous catheter-related bloodstream infection rates in 100 intensive care units of 9 countries in Latin America: Argentina, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Venezuela. Findings of the International Nosocomial Infection Control Consortium (INICC).

BACKGROUND: Data on short-term peripheral intravenous catheter-related bloodstream infections per 1,000 peripheral venous catheter days (PIVCR BSIs per 1,000 PVC days) rates from Latin America are not available, so they have not been thoroughly studied. METHODS: International Nosocomial Infection Control Consortium (INICC) members conducted a prospective, surveillance study on PIVCR BSIs from January 2010 to March 2018 in 100 intensive care units (ICUs) among 41 hospitals, in 26 cities of 9 countries in Latin America (Argentina, Brazil, Colombia, Costa Rica, Dominican-Republic, Ecuador, Mexico, Panama, and Venezuela). The Centers for Disease Control and Prevention (CDC) National Health Safety Network (NHSN) definitions were applied, and INICC methodology and INICC Surveillance Online System software were used. RESULTS: In total, 10,120 ICU patients were followed for 40,078 bed days and 38,262 PVC days. In addition, 79 PIVCR BSIs were identified, with a rate of 2.06 per 1,000 PVC days (95% confidence interval [CI], 1.635-2.257). The average length of stay (ALOS) of patients without a PIVCR BSI was 3.95 days, and the ALOS was 5.29 days for patients with a PIVCR BSI. The crude extra ALOS was 1.34 days (RR, 1.33; 95% CI, 1.0975-1.6351; P = .040).The mortality rate in patients without PIVCR BSI was 3.67%, and this rate was 6.33% in patients with a PIVCR BSI. The crude extra mortality was 1.70 times higher. The microorganism profile showed 48.5% gram-positive bacteria (coagulase-negative Staphylococci 25.7%) and 48.5% gram-negative bacteria: Acinetobacter spp, Escherichia coli, and Klebsiella spp (8.5% each one), Pseudomonas aeruginosa (5.7%), and Candida spp (2.8%). The resistances of Pseudomonas aeruginosa were 0% to amikacin and 50% to meropenem. The resistance of Acinetobacter baumanii to amikacin was 0%, and the resistance of coagulase-negative Staphylococcus to oxacillin was 75%. CONCLUSIONS: Our PIVCR BSI rates were higher than rates from more economically developed countries and were similar to those of countries with limited resources.

Isolation and identification of marine strains of Stenotrophomona maltophilia with high chitinolytic activity.

Chitin is the second most abundant organic compound in nature and represents a rich carbon and nitrogen source that is primarily transformed by bacterial communities. Bacteria capable of gradually hydrolyzing chitin into N-acetylglucosamine monomers can have applications in the transformation of residues from shrimp and other crustaceans. The objective of the present study was to isolate, characterize and identify microorganisms with high chitinolytic activity. These microorganisms were isolated and characterized based on macro- and microscopic morphological traits. Strains were selected on colloidal chitin agar medium primarily based on a hydrolysis halo larger than 2 mm and a growing phase no longer than 6 days. Secondary selection consisted of semi-quantitative evaluation of chitinolytic activity with a drop dilution assay. From the above, ten strains were selected. Then, strain-specific activity was evaluated. The B4 strain showed the highest specific activity, which was 6,677.07 U/mg protein. Molecular identification indicated that the isolated strains belong to the species Stenotrophomonas maltophilia.

Association Between SSR Molecular Markers and Chemical and Sensory Traits of Cacao Samples Using Multiple Regression Analysis.

The sensory properties of cacao beans are linked to the chemical composition of the seeds, and both characteristics are the partial results of its allelic composition. Therefore, it is useful to search for molecular markers associated with these traits. We perform multiple regression analysis to associate previously generated data of alleles generated with 12 SSR (of cultivated cacao trees) with data obtained from chemical and sensory characterization (of beans) of plants grown in the southern region from Mexico. When the association was significant, the mathematical models for predictive purposes were proposed. All phenotypic traits evaluated showed equations with setting values R2 > 0.5. All chemical characters tested have a significant association with at least two alleles (P < 0.05). In addition, the fat content was associated with six molecular markers (mTcCIR03209, mTcCIR12188, mTcCIR19286, mTcCIR07150, mTcCIR19310). The most common allele was mTcCIR12188, which was associated with the contents of eicosanoic acid, moisture, fat and total polyphenols content. The mTcCIR28362 allele is associated with sensory characters bitterness, musty odor, and roasted odor. These alleles could be useful as molecular markers of chemical and sensory characteristics of cacao samples.

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.

Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.

Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.

Production of cytotoxic compounds in dedifferentiated cells of Jatropha curcas L. (Euphorbiaceae).

This study addresses the in vitro culture as an alternative to obtain compounds with cytotoxic activity from the medicinal plant Jatropha curcas (Euphorbiaceae). We determined the presence of cytotoxic compounds in both whole plants and dedifferentiated cells. We evaluated the effect of auxin, cytokinins and light on callus induction in cotyledon explants. We found that the most effective combination to induce callus was the auxin 2,4-D (5 mM) with the cytokinin 6-BAP (2.5 mM), on Murashige-Skoog medium in darkness. We compared the callogenic potential among accessions from different geographic origins, finding that ARR-251107-MFG7 is most prone to form callus. The roots of J. curcas grown in field produced a compound chromatographically similar to the cytotoxic diterpene jatrophone. The profile of compounds extracted from the dedifferentiated cells was similar to that of the whole plant, including a relatively abundant stilbene-like compound. This study contributes to the future establishment of protocols to produce anti-cancer compounds from J. curcas cultivated in vitro.

Alcaloides y polifenoles del cacao, mecanismos que regulan su biosíntesis y sus implicaciones en el sabor y aroma / Cacao alkaloids and polyphenols: Mechanisms that regulate their biosynthesis and its implications on the taste and aroma

El sabor y aroma de los granos de cacao (Theobroma cacao L.) fueron las principales razones que promovieron su domesticación y uso alimentario por los pueblos precolombinos de Mesoamérica. Incluso hoy día, la calidad sensorial determina la clasificación entre cacaos finos y a granel. Muchos compuestos químicos de las almendras son responsables de la calidad sensorial, pero sobresalen los polifenoles y los alcaloides, compuestos que de manera directa inciden en el sabor y palatabilidad de las almendras y de manera indirecta sobre los precursores de aroma. Los alcaloides están asociados con el amargor. Su concentración está relacionada con la variedad y se modifica con el procesamiento. Los polifenoles son responsables, junto con otras moléculas de la astringencia (poco deseable en chocolates), pero también de propiedades antioxidantes deseables por los consumidores. En esta revisión se abordan aspectos de la biosíntesis de estas importantes moléculas en las almendras de cacao, de las implicaciones en el sabor y aroma, así como los cambios que ocurren durante el procesamiento de las mismas(AU)

The flavor and aroma of cacao (Theobroma cacao) beans were the main reasons that promoted its domestication and food-use by pre-Columbian peoples of Mesoamerica. Polyphenols and alkaloids are compounds that directly affect the flavor of the cocoa beans and indirectly on the flavor precursors. The alkaloids are associated with bitterness; its concentration is related to the cultivar and its modifying through the processing. Polyphenols molecules are responsible together with other molecules of the astringency (not desirable in chocolate), but also are responsible for antioxidant properties, very desirable by consumers. This review focuses on aspects of the biosynthesis of these important molecules in cocoa beans as well as implications in taste and flavor. The changes of these molecules that occur during processing are also approached(AU)

Modeling and Compensating Temperature-Dependent Non-Uniformity Noise in IR Microbolometer Cameras.

Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ∘ C, when the array's temperature varies by approximately 15 ∘ C.

Sex expression and floral diversity in Jatropha curcas: a population study in its center of origin.

Sex expression and floral morphology studies are central to understand breeding behavior and to define the productive potential of plant genotypes. In particular, the new bioenergy crop Jatropha curcas L. has been classified as a monoecious species. Nonetheless, there is no information about its reproductive diversity in the Mesoamerican region, which is considered its center of origin and diversification. Thus, we determined sex expression and floral morphology in J. curcas populations from southern Mexico and Guatemala. Our results showed that most of J. curcas specimens had typical inflorescences with separate sexes (monoecious); meanwhile, the rest were atypical (gynoecious, androecious, andromonoecious, androgynomonoecious). The most important variables to group these populations, based on a discriminant analysis, were: male flower diameter, female petal length and male nectary length. From southern Mexico "Guerrero" was the most diverse population, and "Centro" had the highest variability among the populations from Chiapas. A cluster analysis showed that the accessions from southern Mexico were grouped without showing any correlation with the geographical origin, while those accessions with atypical sexuality were grouped together. To answer the question of how informative are floral morphological traits compared to molecular markers, we perform a Mantel correlation test between the distance matrix generated in this study and the genetic distance matrix (AFLP) previously reported for the same accessions. We found significant correlation between data at the level of accessions. Our results contribute to design genetic improvement programs by using sexually and morphologically contrasting plants from the center of origin.

Unique haplotypes of cacao trees as revealed by trnH-psbA chloroplast DNA.

Cacao trees have been cultivated in Mesoamerica for at least 4,000 years. In this study, we analyzed sequence variation in the chloroplast DNA trnH-psbA intergenic spacer from 28 cacao trees from different farms in the Soconusco region in southern Mexico. Genetic relationships were established by two analysis approaches based on geographic origin (five populations) and genetic origin (based on a previous study). We identified six polymorphic sites, including five insertion/deletion (indels) types and one transversion. The overall nucleotide diversity was low for both approaches (geographic = 0.0032 and genetic = 0.0038). Conversely, we obtained moderate to high haplotype diversity (0.66 and 0.80) with 10 and 12 haplotypes, respectively. The common haplotype (H1) for both networks included cacao trees from all geographic locations (geographic approach) and four genetic groups (genetic approach). This common haplotype (ancient) derived a set of intermediate haplotypes and singletons interconnected by one or two mutational steps, which suggested directional selection and event purification from the expansion of narrow populations. Cacao trees from Soconusco region were grouped into one cluster without any evidence of subclustering based on AMOVA (F ST = 0) and SAMOVA (F ST = 0.04393) results. One population (Mazatán) showed a high haplotype frequency; thus, this population could be considered an important reservoir of genetic material. The indels located in the trnH-psbA intergenic spacer of cacao trees could be useful as markers for the development of DNA barcoding.

Reproductive biology of the biofuel plant Jatropha curcas in its center of origin.

In this work, we studied the main characteristics of flowering, reproductive system and diversity of pollinators for the biofuel plant Jatropha curcas (L.) in a site of tropical southeastern Mexico, within its center of origin. The plants were monoecious with inflorescences of unisexual flowers. The male flowers produced from 3062-5016 pollen grains (266-647 per anther). The plants produced fruits with both geitonogamy and xenogamy, although insect pollination significantly increased the number and quality of fruits. A high diversity of flower visiting insects (36 species) was found, of which nine were classified as efficient pollinators. The native stingless bees Scaptotrigona mexicana (Guérin-Meneville) and Trigona (Tetragonisca) angustula (Latreille) were the most frequent visitors and their presence coincided with the hours when the stigma was receptive. It is noteworthy that the female flowers open before the male flowers, favoring xenogamy, which may explain the high genetic variability reported in J. curcas for this region of the world.