The Rubiaceae family in the Carrancas Mountain Complex, state of Minas Gerais, southeastern Brazil

Rubiaceae is the fourth largest family of the angiosperms in terms of species diversity worldwide. It is present in all plant physiognomies and occupies various vegetation strata, being one of the most important components of tropical vegetation. It is easily recognized by the opposite leaves, interpetiolar stipules, gamopetalous corolla, and inferior ovary. The main objective of this study was to investigate the diversity of the Rubiaceae in the Carrancas Mountain Complex, Minas Gerais, Brazil. This region encompasses approximately 17,609 km2, extending from the southern border of the state of Minas Gerais, approaching the Itatiaia Plateau, to the Sao Joao del-Rei and Barbacena region, where the Sao Francisco River basin begins. It includes the municipalities of Lavras, Itumirim, Ingaí, Itutinga, Carrancas, and Minduri. Situated in an ecotone between the Atlantic Forest and Cerrado domains, it presents a surface covered by various vegetation types, such as campo rupestre, savannas, open fields, scrublands, seasonal semideciduous forests, riparian or gallery forests, and cloud forests at the higher elevations. A total of 681 herbarium specimens were analysed, most of them deposited at the ESAL herbarium, which holds most of the collections made in the region. In this study, 26 genera and 51 species were recorded. These represent noteworthy 37.7% of the genera and 13.5% of the species of Rubiaceae recorded in Minas Gerais, including 14 endemic species to Brazil. Borreria and Palicourea were the most diverse genera (five species each), followed by Cordiera, Galianthe, and Psychotria (four species each). Therefore, this work enriches the taxonomic knowledge of the Rubiaceae family in the state of Minas Gerais, particularly for the flora of the Carrancas Mountain Complex

A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force

[EN] The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper we propose a weighting based on the Newton's gravitational force, so that a mass (or relevance) has to be assigned to each instance. We evaluated this idea in the kNN context over 13 benchmark data sets used for binary and multi-class classification experiments. Results in F1 score, statistically validated, suggest that our proposal outperforms the original version of kNN and is statistically competitive with the distance weighted kNN version as well.This research was partially supported by CONACYT-Mexico (project FC-2410). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Aguilera, J.; González, LC.; Montes-Y-Gómez, M.; Rosso, P. (2019). A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force. Lecture Notes in Computer Science. 11401:305-313. https://doi.org/10.1007/978-3-030-13469-3_36S3053131140

Ozone affects plant, insect, and soil microbial communities. A threat to terrestrial ecosystems and biodiversity

Elevated tropospheric ozone concentrations induce adverse effects in plants. We reviewed how ozone affects (i) the composition and diversity of plant communities by affecting key physiological traits; (ii) foliar chemistry and the emission of volatiles, thereby affecting plant-plant competition, plant-insect interactions, and the composition of insect communities; and (iii) plant-soil-microbe interactions and the composition of soil communities by disrupting plant litterfall and altering root exudation, soil enzymatic activities, decomposition, and nutrient cycling. The community composition of soil microbes is consequently changed, and alpha diversity is often reduced. The effects depend on the environment and vary across space and time. We suggest that Atlantic islands in the Northern Hemisphere, the Mediterranean Basin, equatorial Africa, Ethiopia, the Indian coastline, the Himalayan region, southern Asia, and Japan have high endemic richness at high ozone risk by 2100

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Deep Learning Techniques for Geospatial Data Analysis

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher Springer, Cham reproduced with permission of publisher Springer, Cha

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Zebrafish Her8a Is Activated by Su(H)-Dependent Notch Signaling and Is Essential for the Inhibition of Neurogenesis

Understanding how diversity of neural cells is generated is one of the main tasks of developmental biology. The Hairy/E(spl) family members are potential targets of Notch signaling, which has been shown to be fundamental to neural cell maintenance, cell fate decisions, and compartment boundary formation. However, their response to Notch signaling and their roles in neurogenesis are still not fully understood. In the present study, we isolated a zebrafish homologue of hairy/E(spl), her8a, and showed this gene is specifically expressed in the developing nervous system. her8a is positively regulated by Su(H)-dependent Notch signaling as revealed by a Notch-defective mutant and injection of variants of the Notch intracellular regulator, Su(H). Morpholino knockdown of Her8a resulted in upregulation of proneural and post-mitotic neuronal markers, indicating that Her8a is essential for the inhibition of neurogenesis. In addition, markers for glial precursors and mature glial cells were down-regulated in Her8a morphants, suggesting Her8a is required for gliogenesis. The role of Her8a and its response to Notch signaling is thus similar to mammalian HES1, however this is the converse of what is seen for the more closely related mammalian family member, HES6. This study not only provides further understanding of how the fundamental signaling pathway, Notch signaling, and its downstream genes mediate neural development and differentiation, but also reveals evolutionary diversity in the role of H/E(spl) genes

Extensive Transcriptional Regulation of Chromatin Modifiers during Human Neurodevelopment

Epigenetic changes, including histone modifications or chromatin remodeling are regulated by a large number of human genes. We developed a strategy to study the coordinate regulation of such genes, and to compare different cell populations or tissues. A set of 150 genes, comprising different classes of epigenetic modifiers was compiled. This new tool was used initially to characterize changes during the differentiation of human embryonic stem cells (hESC) to central nervous system neuroectoderm progenitors (NEP). qPCR analysis showed that more than 60% of the examined transcripts were regulated, and >10% of them had a >5-fold increased expression. For comparison, we differentiated hESC to neural crest progenitors (NCP), a distinct peripheral nervous system progenitor population. Some epigenetic modifiers were regulated into the same direction in NEP and NCP, but also distinct differences were observed. For instance, the remodeling ATPase SMARCA2 was up-regulated >30-fold in NCP, while it remained unchanged in NEP; up-regulation of the ATP-dependent chromatin remodeler CHD7 was increased in NEP, while it was down-regulated in NCP. To compare the neural precursor profiles with those of mature neurons, we analyzed the epigenetic modifiers in human cortical tissue. This resulted in the identification of 30 regulations shared between all cell types, such as the histone methyltransferase SETD7. We also identified new markers for post-mitotic neurons, like the arginine methyl transferase PRMT8 and the methyl transferase EZH1. Our findings suggest a hitherto unexpected extent of regulation, and a cell type-dependent specificity of epigenetic modifiers in neurodifferentiation