Detection of sub-lattice magnetism in sigma-phase Fe-V compounds by zero-field NMR

The first successful measurements of a sub-lattice magnetism with 51V NMR techniques in the sigma-phase Fe(100-x)Vx alloys with x = 34.4, 39.9 and 47.9 are reported. Vanadium atoms present on all five crystallographic sites are magnetic. Their magnetic properties are characteristic of a given site, which strongly depend on the composition. The strongest magnetism exhibit sites A and the weakest one sites D. The estimated average magnetic moment per V atom decreases from 0.36 muB for x = 34.4 to 0.20 muB for x = 47.9. The magnetism revealed at V atoms is linearly correlated with the magnetic moment of Fe atoms, which implies that the former is induced by the latter.Comment: 13 pages, 5 figure

Cutting ages of elephant grass for chopped hay production

With the advancement of the cutting age, there is an increase in the forage yield of elephant grass (Pennisetum purpureum), but there is also a reduction of the protein levels and digestibility of this forage. This study aimed to identify the ideal cutting age of elephant grass forage (BRS Canará cultivar) to chopped hay production. The experimental design was a randomized block, with five treatments (42, 60, 76, 91 and 105 days of growing) and four replicates. Forage agronomical, morphological and chemical characteristics were evaluated. A linear positive effect of the cutting age was observed on plant height, senescent material and yield of the chopped hay. The leaf percentage and leaf:stem ratio were reduced by the advance in the cutting age. There was also a linear positive effect of the cutting ages on indigestible neutral detergent fiber, neutral detergent fiber corrected for ash and protein and neutral detergent insoluble protein, with increases of 17.13 %, 16.63 % and 20.66 %, respectively. The contents of ashes, crude protein, total digestible nutrients and net lactation energy were reduced with the advance in the forage age. From 76 days, the ashes contents reached values below 9.29 % and the crude protein below 7.16 %. In the same cutting age, the chopped hay yield was 12.91 t ha-1. To improve the hay quality and production, the BRS Canará cultivar must be harvest between 60 and 76 days, when the plants are with 1.20-1.50 m of height, leaf:stem ratio of 0.55-0.44, crude protein of 8.29-7.16 % and indigestible neutral detergent fiber of 26.21-29.06 %

Machine learning applied to data of biosensors for diagnosis of cancer and COVID-19.

Esta tese explora o conceito de sistemas computacionais semi-automatizados de diagnóstico baseados em Aprendizado de Máquina (AM), em que diferentes tipos de dados de biossensores e de outras fontes são empregados. A partir de um pipeline base de AM, foram desenvolvidas quatro aplicações e diversos métodos foram implementados para cada uma das etapas/tarefas do pipeline. Como foram selecionados problemas desafiadores, um alto desempenho na acurácia do diagnóstico em geral só foi alcançado com algoritmos de AM supervisionado. Três aplicações foram em diagnóstico de câncer, sendo a primeira a partir de imagens de microscopia eletrônica de genossensores que detectam o biomarcador de PCA3 para câncer de próstata. Essas imagens foram usadas como entrada para algoritmos de AM supervisionado. Com os atributos de textura Local Complex Features and Neural Network (LCFNN) e o algoritmo Linear Discriminant Analysis (LDA) obteve-se uma taxa de acerto de 99,9% para classificação binária (sim/não para PCA3) e 88,3% para a classificação multiclasse em que se determina a concentração do biomarcador de PCA3. As outras duas aplicações envolveram a detecção de biomarcadores de câncer a partir de medidas elétrica/eletroquímica. A concentração da proteína p53, importante marcador de diferentes tipos de câncer, em amostras de urina e saliva sintéticas, foi determinada a partir de medidas eletroquímicas com imunossensores, em que voltamogramas foram analisados com os algoritmos Logistic Regression (LR), LDA, Support Vector Machine-kernel linear (SVM- L), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN) e Decision Tree (DT). O imunossensor otimizado exibiu acurácia de 100% com todos os algoritmos na maioria dos conjuntos de atributos construídos a partir dos dados brutos. No diagnóstico de câncer de boca, a partir de medidas de impedância elétrica com uma língua eletrônica em amostras de saliva de pacientes e voluntários, a maior acurácia de 86.7% foi obtida com o algoritmo SVM-kernel radial. Nesta aplicação, a acurácia da classificação multiclasse aumentou quando foram adicionadas informações clínicas dos pacientes, indicando a importância de combinação de diferentes tipos de dados nos sistemas computacionais. A quarta aplicação foi o diagnóstico de COVID-19 com a detecção da proteína S do SARS-CoV-2 a partir de mapas hiperespectrais de Espectroscopia Raman com Amplificação de Superfície (SERS) obtidos de imunossensores. Usando algoritmo LDA obteve-se uma acurácia de 100% na distinção dos mapas para resultado positivo e negativo para SARS-CoV-2. Os resultados dessas quatro aplicações demonstram a possibilidade de se desenvolverem sistemas automatizados de diagnóstico, pois as várias etapas/tarefas dos pipelines de AM podem ser implementadas sem necessidade de intervenção humana, mesmo quando se combinam imagens, dados clínicos e de testes clínicos.This thesis explores the concept of computer-assisted diagnosis based on machine learning (ML), in which different types of data from biosensors and other sources are employed. Using a ML pipeline, we developed four applications using different methods in the steps of the pipeline. Because the diagnostic problems addressed were all challenging, a high performance in accuracy was only achieved with supervised ML algorithms. Three applications involved cancer diagnosis, the first being from electron microscopy images of genosensors that detect the PCA3 biomarker for prostate cancer. These images were used as input for the ML algorithms, with texture features from Local Complex Features and Neural Network (LCFNN) and the algorithm Linear Discriminant Analysis (LDA) leading to a 99.9% accuracy for binary classification (yes/no for PCA3) and 88.3% accuracy for the multiclass classification where the PCA3 biomarker concentration is determined. The other two applications were related to detection of cancer biomarkers using electrical or electrochemical measurements. The concentration of p53 protein, an important marker of different types of cancer, in synthetic urine and saliva samples was determined from electrochemical measurements with immunosensors, and the voltammograms were analyzed with the Logistic Regression (LR), LDA, Support Vector Machine-kernel linear (SVM-L), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN) and Decision Tree (DT) algorithms. The optimized immunosensor had 100% accuracy with all ML algorithms for most of the datasets with the raw voltammetric data. In the diagnosis of oral cancer using impedance measurements with an electronic tongue in saliva samples from volunteers and patients, the highest accuracy was 86.7% with SVM-kernel radial algorithm. In this application, the accuracy increased when patients clinical information was added, indicating the importance of combining different types of data in computer-assisted diagnosis systems. The fourth application was the diagnosis of COVID-19 with detection of the SARS-CoV-2 S protein using Surface-Enhanced Raman Spectroscopy (SERS). Using the algorithm LDA an accuracy of 100% was achieved in distinguishing spectra for positive and negative result for SARS-CoV-2. The results of these four applications demonstrate the possibility of developing automated diagnostic systems, as the various stages/tasks in the ML pipeline can be implemented without the need for human intervention, even when combining images, clinical information and data from biosensors

Instrumentation for zero-field NMR with application to study of FeV alloys in the ordered magnetic state

Esse trabalho foi motivado pela conjugação das necessidades do Laboratório de Ressonância Magnética Nuclear com as características técnicas do autor dessa dissertação. Havia a necessidade urgente no grupo da documentação e melhoria da instrumentação e do software de processamento dos dados já desenvolvidos para a realização de experimentos de Ressonância Magnética Nuclear em campo externo nulo. Ao longo do desenvolvimento desse trabalho, verificouse a necessidade de novos desenvolvimentos, a saber: o desenvolvimento de um software para processamento dos dados e o desenvolvimento da automação do aparato de controle da temperatura dos experimentos (4,2 a 400 K). A partir do software para processamento dos dados desenvolvido em Matlab, é possível processar facilmente o sinal para reconstrução do espectro. O processamento dos dados pode ser feito por duas vias: integrando o sinal no domínio do tempo ou integrando sua Transformada Fourier (FT). Deste modo, o tempo de processamento dos dados foi consideravelmente reduzido. A automação do aparato para o controle da temperatura permite uma interação mais amigável com o sistema do criostato. Finalmente, o espectrômetro de RMN foi utilizado para o estudo de ligas de FeV na fase sigma. Esses experimentos foram realizados de modo a se observar os espectros dos núcleos de 51V (10 - 100 MHz) e suas oscilações quadrupolares em função da concentração de Fe e da temperatura. Com esse estudo, podese confirmar que os espectros são relativos aos núcleos de 51V. As intensidades das cinco linhas observadas são coerentes com as populações de átomos de V em cada um dos cinco sítios (A, B, C, D e E) cristalográficos, assim como determinado por Difração de Nêutrons. O acoplamento quadrupolar medido foi de aproximadamente 312 KHz para todos os sítios estudados.This work was motivated by the combination of the needs of the Nuclear Magnetic Resonance Laboratory with the technical features of the author of this dissertation. There was an urgent need in the group to document and improve the instrumentation and data processing software already developed to carry out the zero external magnetic field Nuclear Magnetic Resonance experiments. During the development of this work, it was verified the need for new developments, namely: development of software for data processing and development of automation of the apparatus used for the NMR experiments versus temperature. From the software developed for data processing, written in Matlab, it is easily possible processing the signal to reconstruct the spectrum. The data processing can be done in two ways: integrating the signal in the time domain or integrating its Fourier Transform. The processing time of the data was fast. The automation of the apparatus for controlling the temperature allows a more friendly interaction with the cryostat system. Finally, the NMR spectrometer was used to the study of FeV alloys in sigma phase. The experiments were performed to observe the 51V spectra (10 MHz to 100 MHz) and their quadrupolar oscillations, versus of the Fe concentration and temperature. With this study, it was possible to confirm that the spectra are relative to the 51V nuclei. The intensities of the five lines observed are coherent with the population of the V atoms in each of the Five crystallographic site (A, B, C, D e E), as determined by Neutron Diffraction. The quadrupolar couplings measured were found to be approximately 312 kHz for all sites studied

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data