Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor.

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Reduced CPU Workload for Human Pose Detection with the Aid of a Low-Resolution Infrared Array Sensor on Embedded Systems.

Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the scene. Combined with the still present false detections, this wastes CPU time, elevates the power consumption and overall system temperature, and generates unnecessary data. In this study, a low-cost low-resolution infrared thermal sensor array was used to control the execution of MediaPipe's pose detection algorithm using single-board computers, which only runs when the thermal camera detects a possible subject in its field of view. A lightweight algorithm with several filtering layers was developed, which allowed the effective detection and isolation of a person in the thermal image. The resulting hybrid computer vision proved effective in reducing the average CPU workload, especially in environments with low activity, almost eliminating MediaPipe's false detections, and reaching up to 30% power saving in the best-case scenario.

Construção e validação de tecnologia educativa sobre nutrição enteral em unidade de terapia intensiva adulto / Construcción y validación de tecnología educativa sobre nutrición enteral en unidad de cuidados intensivos / Construction and validation of educational technology on enteral nutrition in adult intensive care unit

Objetivo: construir e validar um vídeo educativo para o manejo de terapia de nutrição enteral em unidade de terapia intensiva adulto. Método: estudo quantitativo aplicado, dividido em duas etapas principais: construção do vídeo, seguindo o referencial para criação de vídeo e, validação por concordância de nove juízes. Resultados: o índice de validade de conteúdo geral do estudo foi de 0,96, porém de todos os itens analisados três apresentaram índice de 0,77 e dois de 0,88. Conclusão: o vídeo validado é relevante e cumpre o objetivo a que se destina. Adiciona-se que, por partir de um pacote de medidas previamente implementado, dificulta a aplicação em todas as realidades.

Objective: to develop and validate an educational video for the management of enteral nutrition therapy in the adult intensive care unit. Method:this is a quantitative applied study, divided into two main stages: construction of the video, following the framework of video creationand validation by agreement of nine judges. Results:the study's overall content validity index was 0.96, but of all the items analyzed, three had an index of 0.77 and two of 0.88. Conclusion:the validated video is relevant and fulfills its purpose. In addition, because it is based on a package of measures that has been previously implemented, it is difficult to apply in all situations.

Objetivo:construiry validar un vídeo educativo para el manejo de la terapia nutricional enteral en la unidad de cuidados intensivos paraadultos. Método:se trata de un estudio cuantitativo aplicado, dividido en dos etapas principales: construcción del vídeo, siguiendo el marco de construir videosy validación por acuerdo de nueve jueces. Resultados:el índice global de validez de contenido del estudio fue de 0,96, pero de todos los ítems analizados, tres tuvieron un índice de 0,77 y dos de 0,88. Conclusión:el vídeo validado es pertinente y cumple su propósito. Además, al estar basado en un paquete de medidas previamente implementado, es difícil de aplicar en todas las realidades.

Dimensions of emotional distress among Brazilian workers in a COVID-19 reference hospital: A factor analytical study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented challenge for public health and has caused the loss of millions of lives worldwide. Hospital workers play a key role in averting the collapse of the health system, but the mental health of many has deteriorated during the pandemic. Few studies have been devoted to identifying the needs of workers on frontline duty. AIM: To investigate dimensions of common emotional symptoms and associated predictors among Brazilian workers in a COVID-19 reference hospital. METHODS: This is an observational study of the mental health of professionals in a COVID-19 hospital in the city of São Paulo. We invited all hospital employees to respond to an online survey between July and August 2020, during the first peak of the pandemic. Data of 1000 participants who completed the survey were analyzed (83.9% were women and 34.3% were aged 30 to 40). Hospital workers self-reported the presence of symptoms of depression, anxiety, trauma-related stress, and burnout through the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised and the Mini-Z Burnout Assessment respectively. Responses were assembled and subjected to exploratory factor analysis to reveal workers' core emotional distress. Multiple linear regression models were subsequently carried out to estimate the likelihood of dimensions of distress using questions on personal motivation, threatening events, and institutional support. RESULTS: Around one in three participants in our sample scored above the threshold of depression, anxiety, post-traumatic stress disorder, and burnout. The factor analysis revealed a three-factor structure that explained 58% of the total data variance. Core distressing emotional domains were avoidance and re-experience, depression-anxiety, and sleep changes. Regression analysis revealed that institutional support was a significant protective factor for each of these dimensions (ß range = -0.41 to -0.20, P < 0.001). However, participants' personal motivation to work in healthcare service was not associated with these emotional domains. Moreover, the likelihood of presenting the avoidance and re-experience dimension was associated with having a family member or close friend be hospitalized or die due to COVID-19 and having faced an ethical conflict. CONCLUSION: Distressing emotional domains among hospital workers were avoidance and re-experience, depression and anxiety, and sleep changes. Improving working conditions through institutional support could protect hospital workers' mental health during devastating public health crises.

Potential Action Mechanism and Inhibition Efficacy of Morinda citrifolia Essential Oil and Octanoic Acid against Stagonosporopsis cucurbitacearum Infestations.

The use of plant-based products has been shown to efficiently inhibit fungi-mediated diseases in agricultural crops. Here, we extracted and evaluated the composition of noni, Morinda citrifolia L., essential oil and assessed its activities against Stagonosporopsis cucurbitacearum in Cucumis melo L. Using in silico molecular approaches, potential interactions between the essential oil major components and S. cucurbitacearum tyrosine-tRNA ligase were predicted. Finally, we also measured the potential interference of plant physiology (the stomatal conductance and net photosynthesis) mediated by the application of the M. citrifolia essential oil. Chromatographic analysis revealed that octanoic acid (75.8%), hexanoic acid (12.8%), and isobutyl pent-4-enyl carbonate (3.1%) were the major essential oil compounds. Octanoic acid and noni essential oil, when used as preventive measures, reduce fungal mycelial growth at a concentration of 5 mg/mL without causing significant damage to the treated leaves, which reinforces their efficacies as preventive tools against S. cucurbitacearum. Molecular docking analyses predicted very stable interactions between the major essential oil constituents and S. cucurbitacearum tyrosine-tRNA ligase, suggesting the interference of these plant-based molecules upon enzyme activation. Octanoic acid and M. citrifolia essential oil at concentrations of 20 mg/mL decreased the stomatal conductance and net photosynthesis rate of melon plants, resulting in robust phytotoxicity. Collectively, our findings indicated that despite the phytotoxicity risks at higher concentrations, M. citrifolia essential oil and octanoic acid, have potential as alternative tools for the integrative management of S. cucurbitacearum.

COMVC-19: A Program to protect healthcare workers' mental health during the COVID-19 Pandemic. What we have learned.

OBJECTIVE: In 2020, the COVID-19 pandemic brought a work and stress overload to healthcare workers, increasing their vulnerability to mental health impairments. In response, the authors created the COMVC-19 program. The program offered preventive actions and mental health treatment for the 22,000 workers of The Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP). This paper aims to describe its implementation and share what we have learned from this experience. METHODS: Workers were able to easily access the program through a 24/7 hotline. Additionally, a mobile phone app that screened for signs and symptoms of emotional distress and offered psychoeducation and/or referral to treatment was made available. Data from both these sources as well as any subsequent psychiatric evaluations were collected. RESULTS: The first 20 weeks of our project revealed that most participants were female, and part of the nursing staff working directly with COVID-19 patients. The most frequently reported symptoms were: anxiety, depression and sleep disturbances. The most common diagnoses were Adjustment, Anxiety, and Mood disorders. CONCLUSIONS: Implementing a mental health program in a multimodal intervention was feasible in a major quaternary public hospital. Our data also suggests that preventive actions should primarily be aimed at anxiety and depression symptoms, with a particular focus on the nursing staff.

Dosimetry in Digital Breast Tomosynthesis Evaluated by Monte Carlo Technique.

ABSTRACT: The influence of the angular deviation of the tube during digital breast tomosynthesis (DBT) acquisition to the dose in the examined breast and in other organs and tissues is not well known. In this work, the Monte Carlo method was used with an adult female virtual anthropomorphic phantom to investigate the impact of this angular variation on the breast dose. The absorbed dose in the examined breast was normalized by the air kerma, which resulted in an absorbed dose coefficient (DT/Kair) for the breast. The absorbed dose in each organ was normalized by the glandular dose in the breast, resulting in the relative organ dose (ROD). An adult female virtual anthropomorphic phantom (FSTA_M50_H50) was incorporated into a scenario containing tomosynthesis equipment with Mo/Mo, Mo/Rh, and W/Rh target/filter combinations and tube voltages of 28 kV. The comparison between the results of the simulations considering digital mammography (DM) and DBT data showed that the DT/Kair values for the examined breast obtained with the DBT parameters were up to 24 times higher than with the DT/Kair obtained with DM parameters. A DT/Kair of 0.97 × 10-1 mGy mGy-1 was obtained in a DBT exam of the right breast. Considering the other organs, the highest ROD values were observed in the thyroid (6.45 × 10-4), eyes (3.87 × 10-4), liver (1.95 × 10-5), and eye lenses (3.21 × 10-3). A variation in the absorbed dose values for the breast and other organs was observed for all projections different from 0°.

Stevia rebaudiana germplasm characterization using microsatellite markers and steviol glycosides quantification by HPLC.

Stevia rebaudiana Bertoni, Asteraceae, is an herbaceous perennial plant native to Paraguay. This species is considered since ancient times a medicinal plant with important bioactive compounds and pharmacologic and food properties, namely diterpenes glycosides. The high natural sweetener potential stevioside and rebaudioside A produced by S. rebaudiana plants are suitable sucrose substitutes, and their obtention is influenced by environmental, phytosociological, and genetic factors. The plants' genetic profile and sweet potential depiction are needed for suitable plant selection for improvement and deployment. Thirty-one S. rebaudiana accessions grown in the same plot where leaves samples were collected in early 2019, were genotyped using six microsatellite markers, including two steviol glycosides biosynthesis functionally involved markers. Additionally, an aqueous extract of each sample was obtained in a water bath and purified by SPE for stevioside and rebaudioside A quantification by normal phase HPLC. Stevioside and rebaudioside A contents varied between 0.53-7.36% (w w-1) and 0.37-3.60% (w w-1), respectively. Two genotypes displayed interesting ratios of rebaudioside A/stevioside (number 3 and 33). The level of genetic similarity between genotypes was tested through a pairwise similarity coefficient, and two groups of individuals had the same fingerprinting. Strong relatedness was found within genotypes, possibly due to cloning, thus, influx of new germplasm ought to be made to prevent mating between relatives, and for further selection and genetic improvement.

Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1-score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.

Botanical and chemical fungicides in the treatment of commercial seeds of Brachiaria brizantha and Panicum maximum.

Brazil is the largest producer, consumer, and exporter of forage seeds in the world. However, the high incidence of pathogens threatens the seed production system, resulting in decreases in production and quality. Currently, the emphasis has been placed on the use of alternative products as a way of maintaining productivity and environmental sustainability. Thus, the objective of this study was to evaluate the mycoflora associated with the commercial seeds of Brachiaria brizantha and Panicum maximum treated with chemical and alternative fungicide. To test with chemical and alternative treatments, seeds were subjected to fluazinam + thiophanate-methyl, fludioxonil + metalaxyl-M, noni essential oil, rosemary pepper essential oil, sulfuric acid. There was also an untreated control. The most efficient products in the control of incident fungi were, in decreasing order, fluazinam + thiophanate-methyl, fludioxonil + metalaxyl-M, noni essential oil, rosemary pepper essential oil, and sulfuric acid. Fungicides mainly controlled the genera Alternaria, Aspergillus, Bipolaris, Chaetomium, Cladosporium, Colletotrichum, Curvularia, Fusarium, Penicillium, Phoma and Rhizopus. Essential oils showed positive effects in controlling Bipolaris, Curvularia, and Fusarium. Sulfuric acid reduced the incidence of Bipolaris, Colletotrichum, Curvularia, Fusarium, Phoma, and Rhizopus. Seed treatment is one of the promising strategies for obtaining seeds of a healthy quality. As a result, the fungicides and botanical components listed above were promising in reducing fungi associated with forage seeds and can be used for seed treatments and in the development of new products with a natural base through essential oils.

Estimated risk of radiation-induced cancer following breast screening employing tomosynthesis and digital mammography.

The objective of this study was to estimate the risk of radiation-induced injury to the organs due to ionizing radiation following breast screening recommendations employing digital breast tomosynthesis (DBT) and digital mammography (DM). Using the Monte Carlo method, absorbed doses in the tissues and organs were calculated on an adult female phantom, considering two-view craniocaudal (CC) and mediolateral oblique (MLO) projections for each breast. The results showed differences in the total effective risk due to DM (CC + MLO) and DBT (CC + MLO) examinations in Brazil, ranging from 20.73 cases 10-5(DM) to 27.19 cases 10-5(DBT). Significant differences were also observed in the total effective risk of cancer incidence in the lungs due to DM (CC + MLO) and DBT (CC + MLO), ranging from 1.75×10-01cases 10-5(DM) to 1.76×10-01cases 10-5(DBT). The results indicate that the total effective risk of incidence should be considered as an additional parameter for the evaluation of DBT or DBT + DM program performance.

COMVC-19: A Program to protect healthcare workers' mental health during the COVID-19 Pandemic. What we have learned

OBJECTIVE: In 2020, the COVID-19 pandemic brought a work and stress overload to healthcare workers, increasing their vulnerability to mental health impairments. In response, the authors created the COMVC-19 program. The program offered preventive actions and mental health treatment for the 22,000 workers of The Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP). This paper aims to describe its implementation and share what we have learned from this experience. METHODS: Workers were able to easily access the program through a 24/7 hotline. Additionally, a mobile phone app that screened for signs and symptoms of emotional distress and offered psychoeducation and/or referral to treatment was made available. Data from both these sources as well as any subsequent psychiatric evaluations were collected. RESULTS: The first 20 weeks of our project revealed that most participants were female, and part of the nursing staff working directly with COVID-19 patients. The most frequently reported symptoms were: anxiety, depression and sleep disturbances. The most common diagnoses were Adjustment, Anxiety, and Mood disorders. CONCLUSIONS: Implementing a mental health program in a multimodal intervention was feasible in a major quaternary public hospital. Our data also suggests that preventive actions should primarily be aimed at anxiety and depression symptoms, with a particular focus on the nursing staff.

Evidence of association between the use of drugs and community-dwelling older people frailty: a cross-sectional study

ABSTRACT BACKGROUND: The scientific literature has shown that an association between polypharmacy and frailty exists. However, few studies have also considered drug interactions and the use of potentially inappropriate medications. OBJECTIVE: To evaluate the association between the use of drugs and frailty among community-dwelling older people. DESIGN AND SETTING: Cross-sectional study carried out among 580 older people in Uberaba (MG). METHODS: Data were collected at these older people's homes using instruments validated in Brazil. Descriptive, bivariate and binary logistic regression analyses were performed (P < 0.05). RESULTS: Most of these individuals were classified as pre-frail (55.7%), while 13.1% were frail. It was found that 31.7% of them presented polypharmacy, 41.7% had drug interactions and 43.8% were using potentially inappropriate medications. In the initial model, polypharmacy (odds ratio, OR = 1.91; confidence interval, CI = 1.27-2.86) and use of potentially inappropriate medications (OR = 2.45; CI = 1.68-3.57) increased the chance that these older people would be pre-frail or frail. In the final adjusted model, use of potentially inappropriate drugs remained associated with the outcome (OR = 2.26; CI = 1.43-3.57). CONCLUSION: Use of potentially inappropriate medications was the independent variable that explained the occurrence of frailty in a representative sample of community-dwelling older adults.

Evidence of association between the use of drugs and community-dwelling older people frailty: a cross-sectional study.

BACKGROUND: The scientific literature has shown that an association between polypharmacy and frailty exists. However, few studies have also considered drug interactions and the use of potentially inappropriate medications. OBJECTIVE: To evaluate the association between the use of drugs and frailty among community-dwelling older people. DESIGN AND SETTING: Cross-sectional study carried out among 580 older people in Uberaba (MG). METHODS: Data were collected at these older people's homes using instruments validated in Brazil. Descriptive, bivariate and binary logistic regression analyses were performed (P < 0.05). RESULTS: Most of these individuals were classified as pre-frail (55.7%), while 13.1% were frail. It was found that 31.7% of them presented polypharmacy, 41.7% had drug interactions and 43.8% were using potentially inappropriate medications. In the initial model, polypharmacy (odds ratio, OR = 1.91; confidence interval, CI = 1.27-2.86) and use of potentially inappropriate medications (OR = 2.45; CI = 1.68-3.57) increased the chance that these older people would be pre-frail or frail. In the final adjusted model, use of potentially inappropriate drugs remained associated with the outcome (OR = 2.26; CI = 1.43-3.57). CONCLUSION: Use of potentially inappropriate medications was the independent variable that explained the occurrence of frailty in a representative sample of community-dwelling older adults.

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions.

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

Multiple Plant Growth-Promotion Traits in Endophytic Bacteria Retrieved in the Vegetative Stage From Passionflower.

Bacteria exhibiting beneficial traits like increasing the bioavailability of essential nutrients and modulating hormone levels in plants are known as plant growth promoting (PGP) bacteria. The occurrence of this specific group of bacteria in the endophytic environment may reflect the decisive role they play in a particular condition. This study aimed to determine the taxonomical diversity of the culturable bacterial endophytes, isolated in the vegetative stage of passionflower (Passiflora incarnata), and assess its potential to promote plant growth by phenotypic and genotypic approaches. The sequencing and phylogenetic analysis of the 16S rRNA gene allowed us to classify 58 bacterial endophytes into nine genera. Bacillus (70.7%) was the most dominant genus, followed by Pseudomonas (8.6%) and Pantoea (6.9%). A few isolates belonged to Rhodococcus and Paenibacillus, whereas the genera Lysinibacillus, Microvirga, Xanthomonas, and Leclercia were represented by only one isolate. The strains were tested for nitrogen fixation, phosphate solubilization, indole-acetic-acid synthesis, and siderophore production. Moreover, PGP related genes (nifH, ipdC, asb, and AcPho) were detected by PCR-based screening. Most of the isolates (94.8%) displayed a potential for at least one of the PGP traits tested by biochemical assays or PCR-based screening. Nine strains were selected based on results from both approaches and were evaluated for boosting the Cape gooseberry (Physalis peruviana) germination and growth. All tested isolates improved germination in vitro, and the majority (78%) increased growth parameters in vivo. The results suggested that most of culturable bacteria inhabiting P. incarnata in the vegetative stage could be used as probiotics for agricultural systems. Besides, their occurrence may be associated with specific physiological needs typical of this development stage.

The geographical and seasonal mosaic in a plant-herbivore interaction: patterns of defences and herbivory by a specialist and a non-specialist.

In order to evaluate the geographic mosaic theory of coevolution, it is crucial to investigate geographical variation on the outcome of ecological interactions and the functional traits which dictate these outcomes. Plant populations are attacked by specialist and non-specialist herbivores and may have different types of chemical and biotic defences. We investigated geographical and seasonal variation in the interaction between the plant Crotalaria pallida and its two major herbivores (the specialist Utetheisa ornatrix and the non-specialist Etiella zinckenella). We first showed that attack by the two herbivores and a chemical and a biotic defence vary greatly in time and space. Second, we performed a common garden experiment that revealed genetic variation among populations in herbivore resistance and a chemical defence, but no genetic variation in a biotic defence. Third, we sampled 20 populations on a much larger geographical scale and showed great variation in attack rates by the two herbivores and a chemical defence. Finally, we showed that herbivory is not correlated with a chemical defence in the 20 field populations. Our study shows that to understand the evolution of ecological interactions it is crucial to investigate how the outcome of the interaction and the important species traits vary geographically and seasonally.

A comparative study of analytic representations of potential energy curves for O2, N2, and SO in their ground electronic states.

In this work, a review of six functional forms used to represent potential energy curves (PECs) is presented. The starting point is the Rydberg potential, followed by functions by Hulburt-Hirschfelder, Murrell-Sorbie, Thakkar, Hua and finalizing with the potential for diatomic systems by Aguado and Paniagua. The mathematical behavior of these functions for the short- and long-range regions is discussed. A comparison highlighting the positive and negative aspects of each representation is also presented. As study cases, three diatomic systems O2, N2 and SO in their respective ground electronic states were selected. To obtain spectroscopic parameters, ab initio energies were first calculated at multi-reference configuration interaction (MRCI) with the Davidson modification (MRCI+Q) level of theory, using aug-cc-pVXZ (X = T,Q,5,6) Dunning basis sets. Such energies were then fitted to respective functional forms. The so-obtained spectroscopic constants are compared also with available literature data.

Morphology and infection process of Olivea neotectonae in teak leaves / Morfologia e processo de infecção de Olivea neotectonae em folhas de teca

Rust, caused by the biotrophic fungus Olivea neotectonae, stands out as an important disease in teak plantations in Brazil. Considering the current importance of this rust and the need to obtain additional basic information about its causal agent to better disease management, the present study aimed to analyze the morphology of O. neotectonae using light microscopy and demonstrate the infection process in teak leaves using scanning electron microscopy techniques (SEM). The morphological characteristics observed using light microscopy were similar to those described in the literature for this species. SEM revealed that the infection structures of the pathogen penetrated the teak leaf through the stomata complex. This study provides the first microscopic evidence of the infection process of Olivea neotectonae in leaves of teak plants.(AU)

A ferrugem, causada pelo fungo biotrófico Olivea neotectonae, destaca-se como uma doença importante nas plantações de teca no Brasil. Considerando a importância atual dessa ferrugem e a necessidade de obter informações básicas adicionais sobre seu agente causal para melhor manejo da doença, o presente estudo teve como objetivo analisar a morfologia da O. neotectonae utilizando microscopia de luz e demonstrar o processo de infecção em folhas de teca por meio de técnicas de microscopia eletrônica de varredura (MEV). As características morfológicas observadas em microscopia de luz foram semelhantes às descritas na literatura para essa espécie. A MEV revelou que as estruturas de infecção do patógeno penetraram na folha de teca através do complexo estomático. Este estudo fornece a primeira evidência microscópica do processo de infecção de Olivea neotectonae em folhas de plantas de teca.(AU)

Neglected Mediterranean plant species are valuable resources: the example of Cistus ladanifer.

MAIN CONCLUSION: The combination of genotypic selection, targeted and improved cultivation, and processing techniques for specific applications gives C. ladanifer the potential to be used as a valuable resource in Mediterranean areas with poor agronomic advantages. Cistus ladanifer (rockrose) is a perennial shrub, well adapted to the Mediterranean climate and possibly to upcoming environmental changes. As a sequence to a thorough review on taxonomic, morphological, chemical and competitive aspects of C. ladanifer, the research team focuses here on the economic potential of C. ladanifer: from production to applications, highlighting also known biological activities of extracts and their compounds. The use of this natural resource may be a viable solution for poor and contaminated soils with no need for large agricultural techniques, because this species is highly resistant to pests, diseases and extreme environmental factors. In addition, this species reveals interesting aptitudes that can be applied to food, pharmaceutical, phytochemical and biofuel industries. The final synthesis highlights research lines toward the exploitation of this neglected resource, such as selection of plant lines for specific applications and development of agronomic and processing techniques.