Un enfoque para la detección de enfermedades de las plantas utilizando técnicas de aprendizaje profundo

Agriculture is the backbone of Indian economy. Conventional farming systems are no longer being followed by our generation, due to lack of knowledge and expertise. Advancement of technologies pave a path that make a transition from traditional farming methods to smart agriculture by automating the processes involved. Challenges faced by today’s agriculture are depletion of soil nutrients and diseases caused by pests which lead to low productivity, irrigation problems, soil erosion, shortage of storage facilities, availability of quality seeds, lack of transportation, poor marketing etc. Among all these challenges in agriculture, prediction of diseases remains a major issue to be addressed. Identifying diseases based on visual inspection is the traditional way of farming which needs knowledge and experience to handle. Automating the process of detecting and identifying through visual inspection (cognitive) is the motivation behind this work. This is made possible with the availability of images of the plant or parts of plants, since most diseases are reflected on the leaves. A deep learning network architecture named Plant Disease Detection Network PDDNet-cv and a transfer learning approach of identifying diseases in plants were proposed. Our proposed system is compared with VGG19, ResNet50, InceptionResNetV2, the state-of-the-art methods reported in [9, 13, 5] and the results show that our method is significantly performing better than the existing systems. Our proposed PDDNet-cv has achieved average classification accuracy of 99.09% in detecting different classes of diseases. The proposed not so deep architecture is performing well compared to other deep learning architectures in terms of performance and computational time.La agricultura es la columna vertebral de la economía india. Los sistemas agrícolas convencionales ya no están siendo seguidos por nuestra generación, debido a la falta de conocimiento y experiencia. El avance de las tecnologías allana un camino que hace una transición de los métodos agrícolas tradicionales a la agricultura inteligente mediante la automatización de los procesos involucrados. Los desafíos que enfrenta la agricultura actual son el agotamiento de los nutrientes del suelo, las enfermedades causadas por plagas que conducen a una baja productividad, los problemas de riego, la erosión del suelo, la escasez de instalaciones de almacenamiento, la disponibilidad de semillas de calidad, la falta de transporte, la mala comercialización, etc. Entre todos estos desafíos en la agricultura, la predicción de enfermedades sigue siendo un tema importante que debe abordarse. La identificación de enfermedades basadas en la inspección visual es la forma tradicional de cultivo que necesita el conocimiento y la experiencia para manejarlas que obtiene un buen rendimiento. Automatizar el proceso de detección e identificación a través de la inspección visual (cognitiva) es la motivación detrás de este trabajo. Esto es posible gracias a la disponibilidad de imágenes de la planta o partes de plantas, ya que la mayoría de las enfermedades se reflejan en las hojas. Se propuso una arquitectura de red de aprendizaje profundo llamada Red de Detección de Enfermedades de las plantas por sus siglas en inglés (Plant Disease Detection Network PDDNet-cv) y un enfoque de aprendizaje por transferencia para identificar enfermedades en las plantas. Nuestro sistema propuesto se compara con VGG19, ResNet50, InceptionResNetV2, los métodos de vanguardia reportados en [9, 13, 5] y los resultados muestran que nuestro método está funcionando significativamente mejor que los sistemas existentes. Nuestra propuesta PDDNet-cv ha logrado una precisión de clasificación promedio del 99,09% en la detección de diferentes clases de enfermedades. La arquitectura no tan profunda propuesta, está funcionando bien en comparación con otras arquitecturas de aprendizaje profundo en términos de rendimiento y tiempo computacional

Inhomogeneous vortex-state-driven enhancement of superconductivity in nanoengineered ferromagnet-superconductor heterostructures

Thin film heterostructures provide a powerful means to study the antagonism between superconductivity (SC) and ferromagnetism (FM). One interesting issue in FM-SC hybrids which defies the notion of antagonistic orders is the observation of magnetic field induced superconductivity (FIS). Here we show that in systems where the FM domains/islands produce spatial inhomogeneities of the SC order parameter, the FIS can derive significant contribution from different mobilities of the magnetic flux identified by two distinct critical states in the inhomogeneous superconductor. Our experiments on nanoengineered bilayers of ferromagnetic CoPt and superconducting NbN where CoPt/NbN islands are separated by a granular NbN, lend support to this alternative explanation of FIS in certain class of FM-SC hybrids.Comment: 5 figure

Satellite- vs. verb-framing underpredicts nonverbal motion categorization: Insights from a large language sample and simulations

Is motion cognition influenced by the large-scale typological patterns proposed in Talmy’s (2000) two-way distinction between verb-framed (V) and satellite-framed (S) languages? Previous studies investigating this question have been limited to comparing two or three languages at a time and have come to conflicting results. We present the largest cross-linguistic study on this question to date, drawing on data from nineteen genealogically diverse languages, all investigated in the same behavioral paradigm and using the same stimuli. After controlling for the different dependencies in the data by means of multilevel regression models, we find no evidence that S- vs. V-framing affects nonverbal categorization of motion events. At the same time, statistical simulations suggest that our study and previous work within the same behavioral paradigm suffer from insufficient statistical power. We discuss these findings in the light of the great variability between participants, which suggests flexibility in motion representation. Furthermore, we discuss the importance of accounting for language variability, something which can only be achieved with large cross-linguistic sample

Engaging communication experts in a Delphi process to identify patient behaviors that could enhance communication in medical encounters

<p>Abstract</p> <p>Background</p> <p>The communication literature currently focuses primarily on improving physicians' verbal and non-verbal behaviors during the medical interview. The Four Habits Model is a teaching and research framework for physician communication that is based on evidence linking specific communication behaviors with processes and outcomes of care. The Model conceptualizes basic communication tasks as "Habits" and describes the sequence of physician communication behaviors during the clinical encounter associated with improved outcomes. Using the Four Habits Model as a starting point, we asked communication experts to identify the verbal communication behaviors of patients that are important in outpatient encounters.</p> <p>Methods</p> <p>We conducted a 4-round Delphi process with 17 international experts in communication research, medical education, and health care delivery. All rounds were conducted via the internet. In round 1, experts reviewed a list of proposed patient verbal communication behaviors within the Four Habits Model framework. The proposed patient verbal communication behaviors were identified based on a review of the communication literature. The experts could: approve the proposed list; add new behaviors; or modify behaviors. In rounds 2, 3, and 4, they rated each behavior for its fit (agree or disagree) with a particular habit. After each round, we calculated the percent agreement for each behavior and provided these data in the next round. Behaviors receiving more than 70% of experts' votes (either agree or disagree) were considered as achieving consensus.</p> <p>Results</p> <p>Of the 14 originally-proposed patient verbal communication behaviors, the experts modified all but 2, and they added 20 behaviors to the Model in round 1. In round 2, they were presented with 59 behaviors and 14 options to remove specific behaviors for rating. After 3 rounds of rating, the experts retained 22 behaviors. This set included behaviors such as asking questions, expressing preferences, and summarizing information.</p> <p>Conclusion</p> <p>The process identified communication tasks and verbal communication behaviors for patients similar to those outlined for physicians in the Four Habits Model. This represents an important step in building a single model that can be applied to teaching patients and physicians the communication skills associated with improved satisfaction and positive outcomes of care.</p

Changing Directions: Steering science, technology and innovation towards the Sustainable Development Goals

Science, technology and innovation are failing to address the world’s most urgent sustainability challenges, according to a major new report from the STRINGS project. ‘Changing Directions: Steering science, technology and innovation towards the Sustainable Development Goals’ is the final report of an in-depth study involving collaborators from across the globe. It highlights a glaring mismatch between the priorities of the world’s scientific communities and the United Nations’ Sustainable Development Goals, which were set up to drive change across all areas of social justice and environmental issues

Combinatorial polymeric conjugated micelles with dual cytotoxic and antiangiogenic effects for the treatment of ovarian cancer

Emerging treatment paradigms like targeting the tumor microenvironment and/or dosing as part of a metronomic regimen are anticipated to produce better outcomes in ovarian cancer, but current drug delivery systems are lacking. We have designed and evaluated paclitaxel (PTX) and rapamycin (RAP) micellar systems that can be tailored for various dosing regimens and target tumor microenvironment. Individual and mixed PTX/RAP (MIX-M) micelles are prepared by conjugating drugs to a poly­(ethylene glycol)-<i>block</i>-poly­(β-benzyl l-aspartate) using a pH-sensitive linker. The micelles release the drug(s) at pH 5.5 indicating preferential release in the acidic endosomal/lysosomal environment. Micelles exhibit antiproliferative effects in ovarian cell cancer lines (SKOV-3 (human caucasian ovarian adenocarcinoma) and ES2 (human ovarian clear cell carcinoma)) and an endothelial cell line (HUVEC; human umbilical vein endothelial cells) with the MIX-M being synergistic. The micelles also inhibited endothelial migration and tube formation. In healthy mice, micelles at 60 mg/kg/drug demonstrated no acute toxicity over 21 days. ES2 xenograft model efficacy studies at 20 mg/kg/drug dosed every 4 days and evaluated at 21 days indicate that the individual micelles exhibit antiangiogenic effects, while the MIX-M exhibited both antiangiogenic and apoptotic induction that results in significant tumor volume reduction. On the basis of our results, MIX-M micelles can be utilized to achieve synergistic apoptotic and antiangiogenic effects when treated at frequent low doses

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Ultrafine ZnO nanowires grown on patternable Pd catalyst and their source-energy dependent photoluminescence

Patterned arrays of ZnO nanostructures have been obtained from a solution based method using Pd nanoparticles as catalyst. The catalyst nanoparticles were patterned using a direct-write e-beam lithography process. Within 10 minutes in an aqueous solution of zinc nitrate and triethyamine borane at 80&#176;C, fine nanowires of ZnO with diameter around 10 nm were produced on patterned Pd nanoparticles. Longer duration in the precursor solution led to formation of ZnO fibril-like structures. Photoluminescence measurements revealed that the nanostructures exhibited a blue shift for the emission peak at 390 nm, when the excitation energy was increased, unlike bulk ZnO. This is attributed to the discrete defect states present in confined nanostructures