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Salicylic acid and Azospirillum brasilense stimulate plant growth and productivity. In some environments, plant physiology similarly responds to both bioactive products. Considering this, a field experiment was conducted to study the physiological effect of Salicilic acid and A. brasilense on growth and grain yield of maize plants. The experiment involved three treatments consisting of imbibed seeds in an aqueous solution of SA (0.01 mM), inoculated seeds with A. brasilense and a control treatment. Seed imbibition in SA and inoculation with A. brasilense improved vegetative growth in the early stages of crop ontogeny, increasing leaf growth, plant height, stem diameter and biomass accumulation. Spikelet length and weight were greater in plants first inoculated with A. brasilense and then treated with SA. Results indicated that SA stimulated biomass partitioning towards leaves, root and stem, while A. brasilense mainly affected leaf growth, plant height, ear dimensions and grain yield. Such results turn crucial for biological fertilization strategies aimed at reducing pollutant loads that accompany chemical fertilizers. Both products can be part of maize management practices given competitive economic advantages and sustainability. Highlights: • Seed imbibition in SA and inoculation with brasilense improved vegetative growth in the early stages of crop ontogeny, increasing leaf growth, plant height, stem diameter and biomass accumulation. • Salicylic Acid stimulated biomass partitioning towards leaves, root and stem in maize plants. • Azospirillum brasilense stimulates leaf growth, plant height, as well as ear dimensions and grain yield in corn plants.

La tercera parte de los suelos del mundo son ácidos, en estas condiciones muchos cultivos se afectan, sobre todo cuando las concentraciones de aluminio alcanzan niveles tóxicos. Con el objetivo de establecer las relaciones entre indicadores de acidez y la capacidad de intercambio catiónico, el pH y el contenido de materia orgánica, se seleccionaron 13 sitios en las regiones Frailesca y Valles Zoque. Se tomaron cinco sub-muestras a 0.30 m de profundidad, para luego conformar una muestra homogénea por sitio experimental. Se determinaron el pH, cationes intercambiables, capacidad de intercambio catiónico, contenido de materia orgánica, saturación de aluminio (Al3+) y de hidrógeno (H+). El porcentaje de saturación del Al3+ se correlacionó exponencialmente con incrementos en la CIC, el pH y el % de saturación del calcio (Ca2+); sin embargo, respecto a la materia orgánica, la saturación de Al3+ se ajustó significativamente a un modelo polinomial de segundo grado, indicando un máximo de saturación a valores de 4.14% de materia orgánica. El porcentaje de saturación de H+ se ajustó negativamente a una función exponencial, disminuyendo a valores de materia orgánica superiores a 2%. Se concluye que en los suelos de la región Frailesca se está produciendo un proceso de acidificación que pudiera conducir a incrementos tóxicos del aluminio para el cultivo de maíz, sobre todo a valores de pH inferiores a cinco. Tales incrementos en la acidificación de los suelos pudieran estar asociados al excesivo uso de fertilizantes amoniacales producto de una agricultura mucho más intensiva que en la región Valles Zoques.

Objective: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age., Approach: A convolutional neural network is developed to perform 2- and 4-class sleep classification in neonates. The network takes as input an 8-channel 30 s EEG segment and outputs the sleep state probabilities. Apart from simple downsampling of the input and smoothing of the output, the suggested network is an end-to-end algorithm that avoids the need for hand-crafted feature selection or complex pre/post processing steps. To train and test this method, 113 EEG recordings from 42 infants are used., Main Results: For quiet sleep detection (the 2-class problem), mean kappa between the network estimate and the ground truth annotated by EEG human experts is 0.76. The sensitivity and specificity are 90% and 88%, respectively. For 4-class classification, mean kappa is 0.64. The averaged sensitivity and specificity (1 versus all) respectively equal 72% and 91%. The results outperform current state-of-the-art methods for which kappa ranges from 0.66 to 0.70 in preterm and from 0.51 to 0.61 in term infants, based on training and testing using the same database., Significance: The proposed method has the highest reported accuracy for EEG sleep state classification for both preterm and term age neonates.

In this paper we explore the use of updated tensor decompositions for the monitoring of brain hemodynamics in neonates. For this study, we used concomitant measurements of heart rate, mean arterial blood pressure, arterial oxygen saturation, EEG, and brain oxygenation - measured using near-infrared spectroscopy. These measurements were obtained from 22 neonates undergoing an INSURE procedure (INtubation, SURfactant and Extubation) and sedation using propofol. To develop the monitoring framework using tensors, we used radial basis kernel function (RBF) to construct a similarity matrix for consecutive segments of the signals. These matrices were concatenated forming a tensor. Updating canonical polyadic decomposition was used to evaluate the impact of propofol in the coupling between the different signals. Results indicate, as previously reported, a drop in the interaction between signals due to propofol administration. This shows that tensor decompositions can be useful in order to monitor the coupling between different physiological signals.

Early life stress in the neonatal intensive care unit (NICU) predisposes premature infants to adverse health outcomes. Although those patients experience frequent apneas and sleep-wake disturbances during their hospital stay, clinicians still rely on clinical scales to assess pain and stress burden. This study addresses the relationship between stress and apneic spells in NICU patients to implement an automatic stress detector. EEG, ECG and SpO 2 were recorded from 40 patients for at least 3 hours and the stress burden was assessed using the Leuven Pain Scale. Different logistic regression models were designed to detect the presence or the absence of stress based on the signals reactivity to each apneic spell. The classification shows that stress can be detected with an area under the curve of 0.94 and a misclassification error of 19.23%. These results were obtained via SpO 2 dips and EEG regularity. These findings suggest that stress deepens the physiological reaction to apneas, which could ultimately impact the neurological and behavioral development.

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments.

Background: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome., Methods: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1 h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6 h-intervals., Results: Dynamic IBI values reached the best prognostic value between 18 and 24 h (AUC of 0.93). EEGs with dIBI amplitude ≥15 μV and duration <10 s had a specificity of 100% at 6-12 h for favorable outcome but decreased subsequently to 67% at 25-42 h. Suppressed EEGs with dIBI amplitude <15 μV and duration >10 s were specific for adverse outcome (89-100%) at 18-24 h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points., Conclusions: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset., (Copyright © 2018. Published by Elsevier B.V.)

Objective: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation., Approach: In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age., Main Results: The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively., Significance: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.

The Diagnosis Related Group classification has provided an excellent basis for enhancing the equity of resource allocation between public acute hospitals. However, it underestimates the higher levels of severity and consequent costliness of referral hospitals. This paper describes a practical way of measuring within-DRG variations in severity, which can be used to increase the precision of casemix-based funding. It involves the regression of length of stay against the numbers of significant diagnoses and procedures, and bence the prediction of additional justified costs. An example is given of its application to data from South Australian public hospitals.

The DRG patient classification system is widely used in Australia, and its use is likely to increase. However, it has some weakness, including the failure to adequately take account of variations in resource use among patients in the same classes. A new version, called the Refined DRG (or RDRG) system became available in January 1989. Inter alia, it makes better use of data on secondary diagnoses; and defines three or four levels of resource use intensity for all major types of conditions, rather than just two levels for fewer types of conditions. The new system is briefly described. Some preliminary results of its use on Australian data are presented, which suggest that it does have advantages in terms of increased homogeneity of resource use in classes. If the RDRG system is to replace the DRG system as the international standard, there are some important implications for Australia. In particular, it would seem to be advisable for health agencies to plan for its imlementation on a national basis.