Fast and Efficient Root Phenotyping via Pose Estimation.

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Fast and efficient root phenotyping via pose estimation.

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort.

Objectives: Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity. Methods: Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection. Studied variables were poor outcome (ventilation support, intensive care unit admission or 28-days mortality) and 28-days mortality. Results: From 510 articles, 11 articles were included. Among the cut-off points proposed in the articles included, only the LUS > 15 cut-off point could be validated for its original endpoint, demonstrating also the strongest relation with poor outcome (odds ratio [OR] = 3.636, confidence interval [CI] 1.411-9.374). Regarding our cohort, 127 patients were admitted. In these patients, LUS was statistically associated with poor outcome (OR = 1.303, CI 1.137-1.493), and with 28-days mortality (OR = 1.024, CI 1.006-1.042). LUS > 15 showed the best diagnostic performance when choosing a single cut-off point in our cohort (area under the curve 0.650). LUS ≤ 7 showed high sensitivity to rule out poor outcome (0.89, CI 0.695-0.955), while LUS > 20 revealed high specificity to predict poor outcome (0.86, CI 0.776-0.917). Conclusions: LUS is a good predictor of poor outcome and 28-days mortality in COVID-19. LUS ≤ 7 cut-off point is associated with mild pneumonia, LUS 8-20 with moderate pneumonia and ≥20 with severe pneumonia. If a single cut-off point were used, LUS > 15 would be the point which better discriminates mild from severe disease.

Objetivos: Establecer diferentes puntos de corte basados en el Lung Ultrasound Score (LUS) para clasificar la gravedad de la neumonía COVID-19. Métodos: Inicialmente, realizamos una revisión sistemática entre los puntos de corte LUS propuestos previamente. Estos resultados fueron validados por una cohorte prospectiva unicéntrica de pacientes adultos con infección confirmada por SARS-CoV-2. Las variables analizadas fueron la mala evolución y la mortalidad a los 28 días. Resultados: De 510 artículos, se incluyeron 11. Entre los puntos de corte propuestos en los artículos incluidos, solo LUS > 15 pudo ser validado para su objetivo original, demostrando también la relación más fuerte con mala evolución (odds ratio [OR] = 3,636, intervalo de confianza [IC] 1,411-9,374). Respecto a nuestra cohorte, se incluyeron 127 pacientes. En estos pacientes, el LUS se asoció estadísticamente con mala evolución (OR = 1,303, IC 1,137-1,493) y con mortalidad a los 28 días (OR = 1,024, IC 1,006-1,042). LUS > 15 mostró el mejor rendimiento diagnóstico al elegir un único punto de corte en nuestra cohorte (área bajo la curva 0,650). LUS ≤ 7 mostró una alta sensibilidad para descartar mal resultado (0,89, IC 0,695-0,955), mientras que LUS > 20 reveló gran especificidad para predecir mala evolución (0,86, IC 0,776-0,917). Conclusiones: LUS es un buen predictor de mala evolución y mortalidad a 28 días en COVID-19. LUS ≤ 7 se asocia con neumonía leve, LUS 8-20 con neumonía moderada y ≥ 20 con neumonía grave. Si se utilizara un único punto de corte, LUS > 15 sería el que mejor discriminaría la enfermedad leve de la grave.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort / Puntos de corte de gravedad del Lung Ultrasound Score en la neumonía COVID-19. Revisión sistemática de la literatura y cohorte de validación

Objectives Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity. Methods Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection. Studied variables were poor outcome (ventilation support, intensive care unit admission or 28-days mortality) and 28-days mortality. Results From 510 articles, 11 articles were included. Among the cut-off points proposed in the articles included, only the LUS>15 cut-off point could be validated for its original endpoint, demonstrating also the strongest relation with poor outcome (odds ratio [OR]=3.636, confidence interval [CI] 1.411–9.374). Regarding our cohort, 127 patients were admitted. In these patients, LUS was statistically associated with poor outcome (OR=1.303, CI 1.137–1.493), and with 28-days mortality (OR=1.024, CI 1.006–1.042). LUS>15 showed the best diagnostic performance when choosing a single cut-off point in our cohort (area under the curve 0.650). LUS≤7 showed high sensitivity to rule out poor outcome (0.89, CI 0.695–0.955), while LUS>20 revealed high specificity to predict poor outcome (0.86, CI 0.776–0.917). Conclusions LUS is a good predictor of poor outcome and 28-days mortality in COVID-19. LUS≤7 cut-off point is associated with mild pneumonia, LUS 8–20 with moderate pneumonia and ≥20 with severe pneumonia. If a single cut-off point were used, LUS>15 would be the point which better discriminates mild from severe disease (AU)

Objetivos Establecer diferentes puntos de corte basados en el Lung Ultrasound Score (LUS) para clasificar la gravedad de la neumonía COVID-19. Métodos Inicialmente, realizamos una revisión sistemática entre los puntos de corte LUS propuestos previamente. Estos resultados fueron validados por una cohorte prospectiva unicéntrica de pacientes adultos con infección confirmada por SARS-CoV-2. Las variables analizadas fueron la mala evolución y la mortalidad a los 28 días. Resultados De 510 artículos, se incluyeron 11. Entre los puntos de corte propuestos en los artículos incluidos, solo LUS>15 pudo ser validado para su objetivo original, demostrando también la relación más fuerte con mala evolución (odds ratio [OR]=3,636, intervalo de confianza [IC] 1,411-9,374). Respecto a nuestra cohorte, se incluyeron 127 pacientes. En estos pacientes, el LUS se asoció estadísticamente con mala evolución (OR=1,303, IC 1,137-1,493) y con mortalidad a los 28 días (OR=1,024, IC 1,006-1,042). LUS>15 mostró el mejor rendimiento diagnóstico al elegir un único punto de corte en nuestra cohorte (área bajo la curva 0,650). LUS≤7 mostró una alta sensibilidad para descartar mal resultado (0,89, IC 0,695-0,955), mientras que LUS>20 reveló gran especificidad para predecir mala evolución (0,86, IC 0,776-0,917). Conclusiones LUS es un buen predictor de mala evolución y mortalidad a 28 días en COVID-19. LUS≤7 se asocia con neumonía leve, LUS 8-20 con neumonía moderada y ≥20 con neumonía grave. Si se utilizara un único punto de corte, LUS>15 sería el que mejor discriminaría la enfermedad leve de la grave (AU)

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort.

OBJECTIVES: Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity. METHODS: Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection. Studied variables were poor outcome (ventilation support, intensive care unit admission or 28-days mortality) and 28-days mortality. RESULTS: From 510 articles, 11 articles were included. Among the cut-off points proposed in the articles included, only the LUS>15 cut-off point could be validated for its original endpoint, demonstrating also the strongest relation with poor outcome (odds ratio [OR]=3.636, confidence interval [CI] 1.411-9.374). Regarding our cohort, 127 patients were admitted. In these patients, LUS was statistically associated with poor outcome (OR=1.303, CI 1.137-1.493), and with 28-days mortality (OR=1.024, CI 1.006-1.042). LUS>15 showed the best diagnostic performance when choosing a single cut-off point in our cohort (area under the curve 0.650). LUS≤7 showed high sensitivity to rule out poor outcome (0.89, CI 0.695-0.955), while LUS>20 revealed high specificity to predict poor outcome (0.86, CI 0.776-0.917). CONCLUSIONS: LUS is a good predictor of poor outcome and 28-days mortality in COVID-19. LUS≤7 cut-off point is associated with mild pneumonia, LUS 8-20 with moderate pneumonia and ≥20 with severe pneumonia. If a single cut-off point were used, LUS>15 would be the point which better discriminates mild from severe disease.

Ultrasound findings of lung ultrasonography in COVID-19: A systematic review.

PURPOSE: To identify the defining lung ultrasound (LUS) findings of COVID-19, and establish its association to the initial severity of the disease and prognostic outcomes. METHOD: Systematic review was conducted according to the PRISMA guidelines. We queried PubMed, Embase, Web of Science, Cochrane Database and Scopus using the terms ((coronavirus) OR (covid-19) OR (sars AND cov AND 2) OR (2019-nCoV)) AND (("lung ultrasound") OR (LUS)), from 31st of December 2019 to 31st of January 2021. PCR-confirmed cases of SARS-CoV-2 infection, obtained from original studies with at least 10 participants 18 years old or older, were included. Risk of bias and applicability was evaluated with QUADAS-2. RESULTS: We found 1333 articles, from which 66 articles were included, with a pooled population of 4687 patients. The most examined findings were at least 3 B-lines, confluent B-lines, subpleural consolidation, pleural effusion and bilateral or unilateral distribution. B-lines, its confluent presentation and pleural abnormalities are the most frequent findings. LUS score was higher in intensive care unit (ICU) patients and emergency department (ED), and it was associated with a higher risk of developing unfavorable outcomes (death, ICU admission or need for mechanical ventilation). LUS findings and/or the LUS score had a good negative predictive value in the diagnosis of COVID-19 compared to RT-PCR. CONCLUSIONS: The most frequent ultrasound findings of COVID-19 are B-lines and pleural abnormalities. High LUS score is associated with developing unfavorable outcomes. The inclusion of pleural effusion in the LUS score and the standardisation of the imaging protocol in COVID-19 LUS remains to be defined.

Lung Ultrasound, Clinical and Analytic Scoring Systems as Prognostic Tools in SARS-CoV-2 Pneumonia: A Validating Cohort.

At the moment, several COVID-19 scoring systems have been developed. It is necessary to determine which one better predicts a poor outcome of the disease. We conducted a single-center prospective cohort study to validate four COVID-19 prognosis scores in adult patients with confirmed infection at ward. These are National Early Warning Score (NEWS) 2, Lung Ultrasound Score (LUS), COVID-19 Worsening Score (COWS), and Spanish Society of Infectious Diseases and Clinical Microbiology score (SEIMC Score). Our outcomes were the combined variable "poor outcome" (non-invasive mechanical ventilation, intubation, intensive care unit admission, and death at 28 days) and death at 28 days. Scores were analysed using univariate logistic regression models, receiver operating characteristic curves, and areas under the curve. Eighty-one patients were included, from which 21 had a poor outcome, and 9 died. We found a statistically significant correlation between poor outcome and NEWS2, LUS > 15, and COWS. Death at 28 days was statistically correlated with NEWS2 and SEIMC Score although COWS also performs well. NEWS2, LUS, and COWS accurately predict poor outcome; and NEWS2, SEIMC Score, and COWS are useful for anticipating death at 28 days. Lung ultrasound is a diagnostic tool that should be included in COVID-19 patients evaluation.

Relación entre consumo de alcohol y violencia de pareja en estudiantes de educación superior / Relationship between alcohol consumption and partner violence in students of higher education

Introducción: la Organización Mundial de la Salud reporta que la violencia se considera un problema de salud pública, siendo las mujeres con una relación de pareja uno de los grupos más vulnerables, generando en ellas consecuencias físicas, psicológicas y sociales, dicho problema se relaciona con el consumo de drogas, particularmente el alcohol. Objetivo: establecer la relación entre el consumo de alcohol y la violencia de pareja en estudiantes de una escuela de educación superior. Metodología: estudio observacional, prospectivo, transversal de nivel relacional. El universo estuvo conformado por 418 estudiantes de sexo femenino, muestreo aleatorio simple estratificado por grado escolar y grupo de 118 estudiantes. Resultados: no existe correlación entre el consumo de alcohol y la violencia de pareja, encontrando un nivel de significancia mayor a lo esperado. Sin embargo, al realizar el cruce de variables sobre el número de consumiciones con los tipos de violencia, se encuentra correlación, evidenciando que las estudiantes que sufren violencia psicológica consumen más bebidas alcohólicas. Conclusión: el tipo de violencia más frecuente es la psicológica, siendo de mayor impacto el aumento de la violencia sexual. No existe correlación entre el consumo de alcohol y la violencia de pareja, contrario a lo que socialmente se espera, siendo este resultado la pauta para futuras investigaciones.

Introduction: The World Health Organization reported violence as a public health problem, being women with a relationship one of the most vulnerable groups, generating in them physical, psychological and social consequences, this problem is related to the consumption of drugs particularly alcohol. Objective: To establish the relationship between alcohol consumption and partner violence in students of a higher education school. Methodology: Observational, prospective, cross-sectional study of relational level. Universe conformed by 418 students, stratified simple random samplingof 118 students. Results: There is no correlation between alcohol consumption and partner violence, finding a level of significance higher than expected, however, when Crossing the variables of the number of drinks with the types of violence, correlation is found showing that the students who suffer psychological violence consume more alcoholic beverages. Conclusión: The most frequent type of violence is psychological, with the greatest impact being the increase in sexual violence. There is no correlation between alcohol consumption and partner violence, contrary to what is socially expected, this result being the guideline for future research.

PLD1 and ERK2 regulate cytosolic lipid droplet formation.

We have previously uncovered roles for phospholipase D (PLD) and an unknown cytosolic protein in the formation of cytosolic lipid droplets using a cell-free system. In this report, PLD1 has been identified as the relevant isoform, and extracellular signal-regulated kinase 2 (ERK2) as the cytosolic protein. Increased expression of PLD1 increased lipid droplet formation whereas knockdown of PLD1 using siRNA was inhibitory. A role for ERK2 in basal lipid droplet formation was revealed by overexpression or microinjection, and ablation by siRNA knockdown or pharmacological inhibition. Similar manipulations of other Map kinases such as ERK1, JNK1 or JNK2 and p38alpha or p38beta were without effect. Insulin stimulated the formation of lipid droplets and this stimulation was inhibited by knockdown of PLD1 (by siRNA) and by inhibition or knockdown (by siRNA) of ERK2. Inhibition of ERK2 eliminated the effect of PLD1 on lipid droplet formation without affecting PLD1 activity, suggesting that PLD1 functions upstream of ERK2. ERK2 increased the phosphorylation of dynein which increased the amount of the protein on ADRP-containing lipid droplets. Microinjection of antibodies to dynein strongly inhibited the formation of lipid droplets, demonstrating that dynein has a central role in this formation. Thus dynein is a possible target for ERK2.

Adipocyte differentiation-related protein promotes fatty acid storage in cytosolic triglycerides and inhibits secretion of very low-density lipoproteins.

OBJECTIVE: We investigated the role of adipocyte differentiation-related protein (ADRP) in triglyceride turnover and in the secretion of very low-density lipoprotein (VLDL) from McA-RH7777 cells and primary rat hepatocytes. METHODS AND RESULTS: An increase in the expression of ADRP increased triglyceride accumulation in cytosolic lipid droplets and prevented the incorporation of fatty acids into secretable triglycerides, thereby reducing the secretion of triglycerides as well as of apolipoprotein B-100 (apoB-100) and apoB-48 VLDL. The ability of ADRP to block the secretion of apoB-100 VLDL1 decreased with increasing quantities of fatty acids in the medium, indicating a saturable process and emphasizing the importance of sequestering of fatty acids for the effect of ADRP on VLDL secretion. Knockdown (small interfering RNA) of ADRP decreased the pool of cytosolic lipid droplets but increased only the secretion of apoB-48 VLDL1. Additionally, there was an increased flow of fatty acids into beta-oxidation. CONCLUSIONS: ADRP is essential for the accumulation of triglycerides in cytosolic lipid droplets. An increase in ADRP prevents the formation of VLDL by diverting fatty acids from the VLDL assembly pathway into cytosolic triglycerides, whereas a decrease of the protein increases the sorting of fatty acids to beta-oxidation and promotes the secretion of apoB-48 VLDL1.

Cross-resistance to pyrethroid and organophosphorus insecticides induced by selection with temephos in Aedes aegypti (Diptera: Culicidae) from Cuba.

A sample of Aedes aegypti (L.) from Santiago de Cuba, Cuba, with a high level of temephos resistance (19.58x at the 50% lethal concentration [LC50]), was subjected to temephos selection to evaluate the utility of this organophosphate insecticide for mosquito control. High resistance developed after six generations of selection (200.00x). Little or no cross-resistance was observed to the organophosphates, malathion and fenitrothion, but high cross-resistance was observed for the pyrethroid deltamethrin (337.5x) and the organophosphate fenthion (12.74x). Synergism tests implicated detoxifying esterases in temephos and fenthion resistance and deltamethrin resistance was associated with a cytochrome P450 monooxygenasa. Biochemical tests, polyacrylamide gel electrophoresis (PAGE), and inhibition studies confirmed the presence of elevated esterase activity associated with temephos resistance. Pyrethroid resistance could be associated with a glutathione-S-transferase mechanism but not the esterases. The cross-resistance to deltamethrin from temephos selection could limit the use of both insecticides for Ae. aegypti control.