Micropropagation and In Vitro Inflorescence of Pentas lanceolata

In this study, different concentrations of 6-benzyladenine (BA) on in vitro shoot and inflorescence inductions of P. lanceolata were investigated. The in vivo and in vitro floral characteristics of this plant were also compared. Nodal explants of P. lanceolata were cultured vertically with the cut ends inserted into semi-solid Murashige and Skoog (MS) medium supplemented with 0, 0.5, 1, 2, 4, and 8 mg L–1 BA. The results showed that the explants formed the highest numbers of shoots even when cultured in MS basal medium without any addition of BA, while the shoots formed in the explants cultured in MS medium supplemented with 1 mg L–1 BA were the longest. No inflorescence was found in the shoots cultured in MS medium supplemented with 8 mg L–1 BA, while the highest percentage of inflorescence induction was found in the shoots cultured in the medium supplemented with 0.5 mg L–1 BA. The apperances of in vivo and in vitro flowers of P. lanceolata were the same in many aspects except that the number of flower/inflorescence formed was different. In addition, water accumulation was observed only inside the in vitro flowers. Water deposit in the long tubular structure of P. lanceolata flower could cause anther injury, suggesting that flowers developed in vitro may not always produce pollen.

Severity of clubroot in kale related to management practices and soil attributes

ABSTRACT: Clubroot disease, caused by Plasmodiophora brassicae, limits the production of Brassica spp. worldwide. Little is known about the factors related to the development of the disease in kale (Brassica oleracea var. acephala) plants and in crops in mountainous areas under tropical conditions. This study examined the severity of clubroot in kale crops as well as identify potential flaws in management and the soil and relief factors related to its occurrence. The study was conducted in 24 kale fields in the mountainous region of Rio de Janeiro (Brazil). Soil and kale growth management practices adopted in the region were identified and samples of soil and plants were collected. Subsequently, soil and relief attributes, disease severity, biomass and nutrient and Al contents and accumulation in the plants were determined. There was a high spread of the pathogen in the areas. Inappropriate and recurrent practices in the region were detected, e.g., sequential cultivation of host species, low adoption of soil fertility analysis and liming and conservation practices, and community use of agricultural machinery and implements without prior cleaning. The disease was associated with more acidic soils, subject to greater water accumulation and with high levels of Al3+ as well as with higher Al contents and accumulation in the roots. Management practices must be adopted in the region to reduce the potential inoculum of P. brassicae and to increase soil fertility.

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

Thermophysical parameters of the Kežmarok sandstone

Thermophysical parameters of building materials are required for calculating the complex heat and water transfer in building structures. It can be performed by modern simulation software such as Wufi, Delphin, Math, Comsol Multiphysics and other. This software is suitable for evaluation of water and heat transport in construction of historical buildings, because it can include the impact of water on material properties, driven rain, ground water, heat and water accumulation and other. The material properties of historical building materials are required for the use of this software. In Slovakia, the most used building material was sandstone. Sandstone from Kežmarok was chosen for this paper, which was used in the construction of historic buildings such as churches and town houses. The method of dynamic impulse transition by thermophysical tester RTB was used to determine the thermal properties of sandstone.

Deep-Water Accumulation of Volcaniclastic Detritus from a Petrographic Point of View: Beginning a Discussion from the Alpine Peripheral Basins

The interpretation of eruptive mechanisms accumulating ancient submarine volcaniclastic sequences is still extremely challenging, particularly when no spatial nor temporal constraints are identifiable. The present work reviews petrographic results gained during the last few decades on three different Paleogene Formations accumulated around the Alpine and Apennine Mountain belts, discussing how their detritus could have been formed and moved from the volcanic centers to the depo-centers, taking into account the volcanic mechanisms which are at the base of the production, transportation and accumulation of volcaniclastic detritus. In doing this, we reconsider the classical diagrams of Folk and Gazzi–Dickinson, rediscussing their significance on the basis of how orogenic volcanism delivers detritus to the environment. In addition, this work highlights the need of the scientific community for gaining new petrographic data on modern sedimentary systems to better constrain interpretative criteria for the petrographic study of ancient volcano–sedimentary sequences.

Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling

In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks.