Aplicación de un modelo de red neuronal para la clasificación de sistemas lacunares pampeanos

The pampean shallow lakes present different distributions in their trophic chains, the latter being cause and consequence of the state of the lacunar systems. In order to determine how each of the measured variables —climatic, edaphic, morphometric, physicochemical and biological— in contributes to the general state of the lake, an Artificial Neural Network (ANN) model is built. The ANN is capable of processing a large number of variables and returning a classification that will allow determining it’s the trophic state. The information from satellite images is one of the input variables. Hence, on a first stage, the construction of a ANN model is intended to obtain a weight for each one of the visible specter bands and near infrared bands from LANDSAT and to pick the most representative value that the image returns. This value will be used as input to the ANN that will be then trained to return a classification of the shallow lakes according to the three observed patterns in the relation between phytoplankton, zooplankton, fish and their link with to nutrient abundance and watershed management.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Instituto de Limnología "Dr. Raul A. Ringuelet" (ILPLA

Aplicación de un modelo de red neuronal para la clasificación de sistemas lacunares pampeanos

The pampean shallow lakes present different distributions in their trophic chains, the latter being cause and consequence of the state of the lacunar systems. In order to determine how each of the measured variables —climatic, edaphic, morphometric, physicochemical and biological— in contributes to the general state of the lake, an Artificial Neural Network (ANN) model is built. The ANN is capable of processing a large number of variables and returning a classification that will allow determining it’s the trophic state. The information from satellite images is one of the input variables. Hence, on a first stage, the construction of a ANN model is intended to obtain a weight for each one of the visible specter bands and near infrared bands from LANDSAT and to pick the most representative value that the image returns. This value will be used as input to the ANN that will be then trained to return a classification of the shallow lakes according to the three observed patterns in the relation between phytoplankton, zooplankton, fish and their link with to nutrient abundance and watershed management.The pampean shallow lakes present different distributions in their trophic chains, the latter being cause and consequence of the state of the lacunar systems. In order to determine how each of the measured variables —climatic, edaphic, morphometric, physicochemical and biological— in contributes to the general state of the lake, an Artificial Neural Network (ANN) model is built. The ANN is capable of processing a large number of variables and returning a classification that will allow determining it’s the trophic state. The information from satellite images is one of the input variables. Hence, on a first stage, the construction of a ANN model is intended to obtain a weight for each one of the visible specter bands and near infrared bands from LANDSAT and to pick the most representative value that the image returns. This value will be used as input to the ANN that will be then trained to return a classification of the shallow lakes according to the three observed patterns in the relation between phytoplankton, zooplankton, fish and their link with to nutrient abundance and watershed management

Investigating the phytotoxic potential of Carlina acaulis essential oil against the weed Bidens pilosa through a physiological and metabolomic approach

Essential oils (EOs) are widely studied as possible candidates for new eco-friendly herbicides for weed management due to their phytotoxicity. In this study we tested the phytotoxic potential of the EO obtained from the roots of Carlina acaulis L. (Apiaceae) against the weed Bidens pilosa L. This EO, containing 98% of the polyacetylene carlina oxide, showed strong phytotoxic effects on the plant metabolism, such as leaf necrosis, reduction of relative water content and total leaf area, and an increase in the dry weight/fresh weight ratio, suggesting a water status alteration. The EO also damaged the photosynthetic machinery, as evidenced by the significant reduction of the effective quantum yield of photosystem II (ΦII) and the maximum quantum yield of photosystem II (Fv/Fm). In addition, the non-photochemical quenching (ΦNPQ) significantly increased after spraying with C. acaulis EO. Damage to photosystem II was further demonstrated through the reduction of manganese and calcium concentrations, possibly due to an alteration in the correct functionality of the Mn4Ca cluster of the PSII. Metabolomics analysis revealed an accumulation of branched-chain amino acids, such as isoleucine and valine, which is commonly related to osmotic alterations under drought stress situations and a general reduction in sugar content (fructose, glucose, mannose, among others), suggesting reduction of the photosynthetic efficiency too. Overall, these findings suggest C. acaulis EO as a promising natural product with phytotoxic potential against weeds that deserves further investigation

Plant-Derived Epi-Nutraceuticals as Potential Broad-Spectrum Anti-Viral Agents

Although the COVID-19 pandemic appears to be diminishing, the emergence of SARS-CoV-2 variants represents a threat to humans due to their inherent transmissibility, immunological evasion, virulence, and invulnerability to existing therapies. The COVID-19 pandemic affected more than 500 million people and caused over 6 million deaths. Vaccines are essential, but in circumstances in which vaccination is not accessible or in individuals with compromised immune systems, drugs can provide additional protection. Targeting host signaling pathways is recommended due to their genomic stability and resistance barriers. Moreover, targeting host factors allows us to develop compounds that are effective against different viral variants as well as against newly emerging virus strains. In recent years, the globe has experienced climate change, which may contribute to the emergence and spread of infectious diseases through a variety of factors. Warmer temperatures and changing precipitation patterns can increase the geographic range of disease-carrying vectors, increasing the risk of diseases spreading to new areas. Climate change may also affect vector behavior, leading to a longer breeding season and more breeding sites for disease vectors. Climate change may also disrupt ecosystems, bringing humans closer to wildlife that transmits zoonotic diseases. All the above factors may accelerate the emergence of new viral epidemics. Plant-derived products, which have been used in traditional medicine for treating pathological conditions, offer structurally novel therapeutic compounds, including those with anti-viral activity. In addition, plant-derived bioactive substances might serve as the ideal basis for developing sustainable/efficient/cost-effective anti-viral alternatives. Interest in herbal antiviral products has increased. More than 50% of approved drugs originate from herbal sources. Plant-derived compounds offer diverse structures and bioactive molecules that are candidates for new drug development. Combining these therapies with conventional drugs could improve patient outcomes. Epigenetics modifications in the genome can affect gene expression without altering DNA sequences. Host cells can use epigenetic gene regulation as a mechanism to silence incoming viral DNA molecules, while viruses recruit cellular epitranscriptomic (covalent modifications of RNAs) modifiers to increase the translational efficiency and transcript stability of viral transcripts to enhance viral gene expression and replication. Moreover, viruses manipulate host cells' epigenetic machinery to ensure productive viral infections. Environmental factors, such as natural products, may influence epigenetic modifications. In this review, we explore the potential of plant-derived substances as epigenetic modifiers for broad-spectrum anti-viral activity, reviewing their modulation processes and anti-viral effects on DNA and RNA viruses, as well as addressing future research objectives in this rapidly emerging field

Insecticidal Activity of Four Essential Oils Extracted from Chilean Patagonian Plants as Potential Organic Pesticide

Patagonia is a geographical area characterized by a wide plant biodiversity. Several native plant species are traditionally used in medicine by the local population and demonstrated to be sources of biologically active compounds. Due to the massive need for green and sustainable pesticides, this study was conducted to evaluate the insecticidal activity of essential oils (EOs) from understudied plants growing in this propitious area. Ciprés (Pilgerodendron uviferum), tepa (Laureliopsis philippiana), canelo (Drimys winteri), and paramela (Adesmia boronioides) EOs were extracted through steam distillation, and their compositions were analyzed through GC–MS analysis. EO contact toxicity against Musca domestica L., Spodoptera littoralis (Boisd.), and Culex quinquefasciatus Say was then evaluated. As a general trend, EOs performed better on housefly males over females. Ciprés EO showed the highest insecticidal efficacy. The LD50(90) values were 68.6 (183.7) and 11.3 (75.1) µg adult−1 on housefly females and males, respectively. All EOs were effective against S. littoralis larvae; LD50 values were 33.2–66.7 µg larva−1, and tepa EO was the most effective in terms of LD90 (i.e., <100 µg larva−1). Canelo, tepa, and paramela EOs were highly effective on C. quinquefasciatus larvae, with LC50 values < 100 µL L−1. Again, tepa EO achieved LD90 < 100 µL L−1. This EO was characterized by safrole (43.1%), linalool (27.9%), and methyl eugenol (6.9%) as major constituents. Overall, Patagonian native plant EOs can represent a valid resource for local stakeholders, to develop effective insecticides for pest and vector management, pending a proper focus on their formulation and nontarget effects

Evaluation of Physicochemical and Microbial Properties of Extracts from Wine Lees Waste of Matelica’s Verdicchio and Their Applications in Novel Cosmetic Products

Wine lees are sediments deposited on the walls and bottom of barrels resulting from wine fermentation and mainly consist of yeasts. Saccharomyces cerevisiae extracts, rich in beneficial components for the skin, have already been used in cosmesis, while wine lees have not been well exploited by the cosmetics industry yet. The aim of this work was the full characterization of the wine lees from Verdicchio's wine, with the aim to exploit it as a beneficial ingredient in new cosmetic products. After mapping the microbial composition of the sample waste, the parameters for the sonication extraction process were optimized and the physicochemical properties of the extract were analyzed. The efficiency of the aqueous extraction-and in particular the yeast cell lysis necessary for the release of proteins from the cell-was assessed by evaluating cell shape and size, and protein release, under scanning electron microscopy (SEM), dynamic light scattering (DLS) and Bradford's protein assays. Thus, the total phenol content and antioxidant capacity of the supernatant recovered from native and sonicated lees were determined by Folin-Ciocalteu's and spectrophotometric assays, respectively. To quantify the heavy metals and highlight the presence of microelements beneficial for the skin, inductively coupled plasma-mass spectrometry (ICP-MS) was applied. In vitro metabolic activity and cytotoxicity were tested on both HaCat keratinocytes and human gingival fibroblasts, showing that wine lees are safe for skin's cells. The results show that sonicated lees appear to be more interesting than native ones as a consequence of the release of the active ingredients from the cells. Due to the high antioxidant capacity, content of beneficial elements for skin and an appropriate microbiologic profile, wine lees were included in five new solid cosmetic products and tested for challenge test, compatibility with human skin, sensory analysis, trans epidermal water loss (TEWL) and sebometry

Entrepreneurial finance: Emerging approaches using machine learning and big data

For equity investors the identification of ventures that most likely will achieve the expected return on investment is an extremely complex task. To select early-stage companies, venture capitalists and business angels traditionally rely on a mix of assessment criteria and their own experience. However, given the high level of risk with new, innovative companies, the number of financially successful startups within an investment portfolio is generally very low. In this context of uncertainty, a data-driven approach to investment decision-making can provide more effective results. Specifically, the application of machine learning techniques can provide equity investors and scholars in entrepreneurial finance with new insights on patterns common to successful startups. This study presents a comprehensive overview of the applications of machine learning algorithms to the Crunchbase database. We highlight the main research goals that can be addressed and then we review all the variables and algorithms used for each goal. For each machine learning algorithm, we analyze the respective performance metrics to identify a baseline model. This study aims to be a reference for researchers and practitioners on the use of machine learning as an effective tool to support decision-making processes in equity investments

Reviewing equity investors\u2019 funding criteria: a comprehensive classification and research agenda

Venture capitalists and angel investors usually apply a set of assessment criteria to evaluate the key elements of entrepreneurial projects. However, since each investor considers different criteria, previous researchers who analysed investors\u2019 decision making, ended up analysing a variety of divergent aspects. In this paper, a systematic literature review on the assessment criteria applied by equity investors was carried out. The purpose of this study was to identify and classify all the criteria considered by previous researchers to determine whether some aspects were investigated more extensively than others and to understand the reasons for this type of approach. After screening the abstracts of 894 journal publications, 53 articles were selected for a detailed analysis. In total, 208 unique criteria were identified and were subsequently classified into 35 specific categories, 11 generic classes and 4 main domains of analysis. The high level of detail and granularity of this work is one of its added values and can provide a knowledge base for future researchers who intend to apply new methodologies for the analysis of investors\u2019 decision-making. Starting from the results obtained so far, a new agenda for future research is suggested to encourage a more data-driven approach leveraging data science techniques

ASCARI: a component based simulator for distributed mobile robot systems

ASCARI is a simulator dedicated to distributed and cooperative mobile robotics systems. ASCARI has been designed to be a generic framework for implementing and testing multi-agent collaborative algorithms, especially suited to evaluate algorithms performances with a non-perfect communication channel (e.g. delayed, limited bandwidth, limited range). The design process has taken into account state-of-art robotics simulators but was mainly driven by a complex new requirement: inter-agent communication has to be integrated in the simulation loop. The core of the project is a server with a simple dynamic engine, a synchronization facility and an API (Application Programming Interface) to control simulated communication which is used by server plugins to provide implementations for the characteristics of the communication channel. Different channel requirements can be added by users through the dedicated plugin. Beside the server, there are the agents involved in the simulation.The only custom code required from the user is the agent control law. The control law has to be written as a plugin which receives sensors data and control actuators using an API as an abstraction layer from hardware. As in every mature simulator, the abstraction layer quickly enables the developer to use the desired control law directly on a real robot by changing only hardware drivers. Finally, the inter-agent communication is provided in a transparent way to the user by some template communication classes. The user can exchange information between agents by simply creating senders and receivers classes with their custom data type. Simulated communications and filters are all handled automatically. The simulator is completed by a 2D viewer and a simple GUI (Graphical User Interface) that allow the user to intuitively follow the simulation evolution and to start the various processes (simulator, agents, viewer), controlling the number of agents involved in and the simulation speed. In this paper we first describe the ASCARI simulator and we then validate it on a distributed traffic control and a distributed task assignment algorithm