An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil

This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through the grant 303192/2022-4 (R.O.), and Comissão de Aperfeiçoamento de Pessoal do Nível Superior (CAPES), from the Brazilian government; by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.)

Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

Case-based reasoning (CBR) is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CB

Intercambio estuario-ría de elementos traza en el sistema costero de la ría de Vigo (NO Península Ibérica)

[EN] Little research has been done on the land-sea exchange of trace elements, which is particularly applicable to ria coasts. In particular, trace metal enrichment in the inner part of the Ria of Vigo (the San Simon Inlet) has been observed from sediment studies but there is no information about Cd, Pb and Zn fluxes through the Rande Strait, which is the natural boundary of the estuary-ria water exchange. In order to assess metal exchanges in a ria-type system, six sampling cruises on board the R/V Mytilus (IIM-CSIC) were carried out. Water column profiles of salinity, temperature and tidal currents were obtained every 30 min. The water column for dissolved and particulate metals was sampled every two hours over a complete tidal cycle. Dissolved metal concentrations were 0.01–0.18 nM for Cd, 0.5-1.9 nM for Pb and 4-44 nM for Zn. Compared with Zn (16±12%) and especially with Cd (5.4±5.0%), particulate metal represented a significant fraction of the total concentration for Pb (41±21%). Net fluxes of dissolved Cd and Zn are higher than in the particulate phase, whereas for Pb an inverse situation was observed. The net metal exchange in the Vigo estuary-ria environment was not seasonally controlled. Dissolved Cd and Pb were driven by tidal ranges and particulate Pb by the Oitavén River flow. On the other hand, Zn did not show a defined trend. The budgets obtained for the Ria of Vigo, with the exception of Pb, were one or two orders of magnitude lower than those measured in other large European estuaries[ES] Intercambio estuario-ría de elementos traza en el sistema costero de la ría de Vigo (NO península Ibérica). – Existen pocos estudios sobre los elementos traza en el intercambio tierra-océano, lo que es especialmente aplicable a las zonas costeras de las rías. En particular, respecto a la ría de Vigo se ha observado un enriquecimiento de metales traza en sedimentos pero se carece de información acerca de los flujos de Cd, Pb y Zn a través del estrecho de Rande, que es la frontera natural para el intercambio estuario-ría. A fin de evaluar este tipo de intercambios se realizaron seis campañas oceanográficas a bordo del B/I Mytilus (IIM-CSIC) para cuantificar los flujos de Cd, Pb y Zn en diferentes estaciones del año. Se obtuvieron perfiles verticales de salinidad, temperatura y corrientes cada 30 min en el centro del estrecho de Rande durante un ciclo de marea. Además, se recogieron muestras de agua en cuatro niveles cada 2h. Las concentraciones de metales disueltos oscilaron entre 0.01 y 0.18 nM para Cd, 0.5 y 1.9 nM para Pb y 4 y 44 nM para Zn. Los metales particulados supusieron una fracción pequeña respecto del contenido total de metal (5.4±5.0% para Cd y 16±12% para zinc) salvo para el plomo (41±21%). Los flujos netos de Cd y Zn disuelto fueron superiores a los del particulado mientras que para Pb ocurrió lo contrario. El intercambio de metales en el entorno estuario-ría no parece depender de las estaciones del año sino de la altura de la marea en el caso de Cd y Pb disuelto y del caudal fluvial para Pb particulado mientras que Zn no presentó una tendencia definida. Excepto para Pb, los balances obtenidos son de uno a dos órdenes de magnitud inferiores a los de otros estuarios europeos.Peer reviewe

Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

Case-based reasoning (CBR) is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CB

Clasificación automática de las vocales en el lenguaje de señas colombiano

Sign language recognition is a highly-complex problem due to the amount of static and dynamic gestures needed to represent such language, especially when it changes from country to country. This article focuses on static recognition of vowels in Colombian Sign Language. A total of 151 images were acquired for each class, and an additional non-vowel class with different scenes was also considered. The object of interest was cut out of the rest of the scene in the captured image by using color information. Subsequently, features were extracted to describe the gesture that corresponds to a vowel or to the class that does not match any vowel. Next, four sets of features were selected. The first one contained all of them; from it, three new sets were generated. The second one was extracted from a subset of features given by the Principal Component Analysis (PCA) algorithm. The third set was obtained by Sequential Feature Selection (SFS) with the FISHER measure. The last set was completed with SFS based on the performance of the K-Nearest Neighbor (KNN) algorithm. Finally, multiple classifiers were tested on each set by cross-validation. Most of the classifiers achieved a performance over 90%, which led to conclude that the proposed method allows an appropriate class distinction.El reconocimiento del lenguaje de señas es un problema de alta complejidad, debido a la cantidad de gestos estáticos y dinámicos necesarios para representar dicho lenguaje, teniendo en cuenta que el mismo variará para cada país en particular. Este artículo se enfoca en el reconocimiento de las vocales del lenguaje colombiano de señas, de forma estática. Se adquirieron 151 imágenes por cada clase, teniendo en cuenta también una clase no vocal adicional con diferentes escenas. A partir de cada imagen capturada se separa el objeto de interés del resto de la escena usando información de color; luego, se extraen características para describir el gesto correspondiente a cada vocal o a la clase que no corresponde a ninguna vocal. Posteriormente, se seleccionan cuatro conjuntos de características. El primero con la totalidad de ellas; a partir de este salen tres nuevos conjuntos: el segundo extrayendo un subconjunto de características mediante el algoritmo de Análisis de Componentes Principales (PCA). El tercer conjunto, aplicando Selección Secuencial hacia Adelante (SFS), mediante la medida de FISHER y el último conjunto con SFS basado en el desempeño del clasificador de los vecinos más cercanos (KNN). Finalmente se prueban múltiples clasificadores para cada conjunto por medio de validación cruzada, obteniendo un desempeño superior al 90% para la mayoría de los clasificadores, concluyendo que la metodología propuesta permite una adecuada separación de las clases

Systematic assessment of fluid responsiveness during early septic shock resuscitation: secondary analysis of the ANDROMEDA-SHOCK trial

BACKGROUND: Fluid boluses are administered to septic shock patients with the purpose of increasing cardiac output as a means to restore tissue perfusion. Unfortunately, fluid therapy has a narrow therapeutic index, and therefore, several approaches to increase safety have been proposed. Fluid responsiveness (FR) assessment might predict which patients will effectively increase cardiac output after a fluid bolus (FR+), thus preventing potentially harmful fluid administration in non-fluid responsive (FR-) patients. However, there are scarce data on the impact of assessing FR on major outcomes. The recent ANDROMEDA-SHOCK trial included systematic per-protocol assessment of FR. We performed a post hoc analysis of the study dataset with the aim of exploring the relationship between FR status at baseline, attainment of specific targets, and clinically relevant outcomes. METHODS: ANDROMEDA-SHOCK compared the effect of peripheral perfusion- vs. lactate-targeted resuscitation on 28-day mortality. FR was assessed before each fluid bolus and periodically thereafter. FR+ and FR- subgroups, independent of the original randomization, were compared for fluid administration, achievement of resuscitation targets, vasoactive agents use, and major outcomes such as organ dysfunction and support, length of stay, and 28-day mortality. RESULTS: FR could be determined in 348 patients at baseline. Two hundred and forty-two patients (70%) were categorized as fluid responders

Mid-Infrared laser spectroscopy detection and quantification of explosives in soils using multivariate analysis and artificial intelligence

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils

BRSMG Curinga: cultivar de arroz de terras altas de ampla adaptação para o Brasil.

O objetivo deste trabalho é a apresentação das características da BRSMG Curinga, a sétima cultivar de arroz de terras altas originária da colaboração da Embrapa com o programa CIAT/CIRAD, lançada em 2005 para cultivo em condições de terras altas nos Estados de Minas Gerais, Goiás, Mato Grosso, Rondônia, Pará, Roraima, Maranhão, Piauí e Tocantins.bitstream/CNPAF/23577/1/comt_114.pd

Coexistence of a fluid responsive state and venous congestion signals in critically ill patients: a multicenter observational proof-of-concept study

Background Current recommendations support guiding fluid resuscitation through the assessment of fluid responsiveness. Recently, the concept of fluid tolerance and the prevention of venous congestion (VC) have emerged as relevant aspects to be considered to avoid potentially deleterious side effects of fluid resuscitation. However, there is paucity of data on the relationship of fluid responsiveness and VC. This study aims to compare the prevalence of venous congestion in fluid responsive and fluid unresponsive critically ill patients after intensive care (ICU) admission. Methods Multicenter, prospective cross-sectional observational study conducted in three medical–surgical ICUs in Chile. Consecutive mechanically ventilated patients that required vasopressors and admitted < 24 h to ICU were included between November 2022 and June 2023. Patients were assessed simultaneously for fluid responsiveness and VC at a single timepoint. Fluid responsiveness status, VC signals such as central venous pressure, estimation of left ventricular filling pressures, lung, and abdominal ultrasound congestion indexes and relevant clinical data were collected. Results Ninety patients were included. Median age was 63 [45–71] years old, and median SOFA score was 9 [7–11]. Thirty-eight percent of the patients were fluid responsive (FR+), while 62% were fluid unresponsive (FR−). The most prevalent diagnosis was sepsis (41%) followed by respiratory failure (22%). The prevalence of at least one VC signal was not significantly different between FR+ and FR− groups (53% vs. 57%, p = 0.69), as well as the proportion of patients with 2 or 3 VC signals (15% vs. 21%, p = 0.4). We found no association between fluid balance, CRT status, or diagnostic group and the presence of VC signals. Conclusions Venous congestion signals were prevalent in both fluid responsive and unresponsive critically ill patients. The presence of venous congestion was not associated with fluid balance or diagnostic group. Further studies should assess the clinical relevance of these results and their potential impact on resuscitation and monitoring practices

Graphene oxide-silver nanoparticle hybrid material: an integrated nanosafety study in zebrafish embryos.

Abstract: This work reports an integrated nanosafety study including the synthesis and characterization of the graphene oxide-silver nanoparticle hybrid material (GO-AgNPs) and its nano-ecotoxicity evaluation in the zebrafish embryo model. The influences of natural organic matter (NOM) and a chorion embryo membrane were considered in this study, looking towards more environmentally realistic scenarios and standardized nanotoxicity testing. The nanohybrid was successfully synthesized using the NaBH4 aqueous method, and AgNPs (~ 5.8 nm) were evenly distributed over the GO surface. GO-AgNPs showed a dose-response acute toxicity: the LC50 was 1.5 mg L-1 for chorionated embryos. The removal of chorion, however, increased this toxic effect by 50%. Furthermore, the presence of NOM mitigated mortality, and LC50 for GO-AgNPs changed respectively from 2.3 to 1.2 mg L-1 for chorionated and de-chorionated embryos. Raman spectroscopy confirmed the ingestion of GO by embryos; but without displaying acute toxicity up to 100 mg L-1, indicating that the silver drove toxicity down. Additionally, it was observed that silver nanoparticle dissolution has a minimal effect on these observed toxicity results. Finally, understanding the influence of chorion membranes and NOM is a critical step towards the standardization of testing for zebrafish embryo toxicity in safety assessments and regulatory issues