A recipe for scavenging in vertebrates - the natural history of a behaviour

Despite its prevalence, the importance of scavenging to carnivores is difficult to ascertain in modern day forms and impossible to study directly in extinct species. Yet, there are certain intrinsic and environmental features of a species that push it towards a scavenging lifestyle. These can be thought of as some of the principal parameters in optimal foraging theory namely, encounter rate and handling time. We use these components to highlight the morphologies and environments that would have been conducive to scavenging over geological time by focusing on the dominant vertebrate groups of the land, sea and air. The result is a synthesis on the natural history of scavenging. The features that make up our qualitative scale of scavenging can be applied to any given species and allow us to judge the likely importance of this foraging behaviour.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

Are deep-sea cephalopods really common preys for oceanic seabirds?

Uma análise de dados publicados sobre dietas de aves marinhas oceânicas mostra a predominância de cefalópodes musculares e de distribuição mais superficial nas camadas oceânicas, mas também são importantes as espécies gelatinosas e amoniacais restritas a camadas abaixo dos 300 m da superfície. A princípio, não deveria se esperar que cefalópodes de profundidade fossem considerados presas comuns de aves marinhas oceânicas como reportados por muitos autores. É proposto neste estudo que uma fonte indireta, importante e de fácil obtenção, surgiu com o início das atividades dos barcos atuneiros que operam com espinhel. O hábito de ingerir restos de vísceras de peixes capturados em barcos espinheleiros pode explicar as prováveis conclusões equivocadas de que cefalópodes de profundidade são presas naturais de aves marinhas oceânicas.

Antifungal activity of extracts from Atacama Desert fungi against Paracoccidioides brasiliensis and identification of Aspergillus felis as a promising source of natural bioactive compounds

Fungi of the genus Paracoccidioides are responsible for paracoccidioidomycosis. The occurrence of drug toxicity and relapse in this disease justify the development of new antifungal agents. Compounds extracted from fungal extract have showing antifungal activity. Extracts of 78 fungi isolated from rocks of the Atacama Desert were tested in a microdilution assay against Paracoccidioides brasiliensis Pb18. Approximately 18% (5) of the extracts showed minimum inhibitory concentration (MIC) values ≤ 125.0 μg/mL. Among these, extract from the fungus UFMGCB 8030 demonstrated the best results, with an MIC of 15.6 μg/mL. This isolate was identified as Aspergillus felis (by macro and micromorphologies, and internal transcribed spacer, β-tubulin, and ribosomal polymerase II gene analyses) and was grown in five different culture media and extracted with various solvents to optimise its antifungal activity. Potato dextrose agar culture and dichloromethane extraction resulted in an MIC of 1.9 μg/mL against P. brasiliensis and did not show cytotoxicity at the concentrations tested in normal mammalian cell (Vero). This extract was subjected to bioassay-guided fractionation using analytical C18RP-high-performance liquid chromatography (HPLC) and an antifungal assay using P. brasiliensis. Analysis of the active fractions by HPLC-high resolution mass spectrometry allowed us to identify the antifungal agents present in the A. felis extracts cytochalasins. These results reveal the potential of A. felis as a producer of bioactive compounds with antifungal activity.

Development of an ecotoxicological protocol for the deep-sea fauna using the hydrothermal vent shrimp Rimicaris exoculata

In light of deep-sea mining industry development, particularly interested in massive-sulphide deposits enriched in metals with high commercial value, efforts are increasing to better understand potential environmental impacts to local fauna. The aim of this study was to assess the natural background levels of biomarkers in the hydrothermal vent shrimp Rimicaris exoculata and their responses to copper exposure at in situ pressure (30MPa) as well as the effects of depressurization and pressurization of the high-pressure aquarium IPOCAMP. R. exoculata were collected from the chimney walls of the hydrothermal vent site TAG (Mid Atlantic Ridge) at 3630m depth during the BICOSE cruise in 2014. Tissue metal accumulation was quantified in different tissues (gills, hepatopancreas and muscle) and a battery of biomarkers was measured: metal exposure (metallothioneins), oxidative stress (catalase, superoxide dismutase, glutathione-S-transferase and glutathione peroxidase) and oxidative damage (lipid peroxidation). Data show a higher concentration of Cu in the hepatopancreas and a slight increase in the gills after incubations (for both exposed groups). Significant induction of metallothioneins was observed in the gills of shrimps exposed to 4μM of Cu compared to the control group. Moreover, activities of enzymes were detected for the in situ group, showing a background protection against metal toxicity. Results suggest that the proposed method, including a physiologically critical step of pressurizing and depressurizing the test chamber to enable the seawater exchange during exposure to contaminants, is not affecting metal accumulation and biomarkers response and may prove a useful method to assess toxicity of contaminants in deep-sea species.

Demography of a deep-sea lantern shark (Etmopterus spinax) caught in trawl fisheries of the northeastern Atlantic: Application of Leslie matrices with incorporated uncertainties

The deep-sea lantern shark Etmopterus spinax occurs in the northeast Atlantic on or near the bottoms of the outer continental shelves and slopes, and is regularly captured as bycatch in deep-water commercial fisheries. Given the lack of knowledge on the impacts of fisheries on this species, a demographic analysis using age-based Leslie matrices was carried out. Given the uncertainties in the mortality estimates and in the available life history parameters, several different scenarios, some incorporating stochasticity in the life history parameters (using Monte Carlo simulation), were analyzed. If only natural mortality were considered, even after introducing uncertainties in all parameters, the estimated population growth rate (A) suggested an increasing population. However, if fishing mortality from trawl fisheries is considered, the estimates of A either indicated increasing or declining populations. In these latter cases, the uncertainties in the species reproductive cycle seemed to be particularly relevant, as a 2-year reproductive cycle indicated a stable population, while a longer (3-year cycle) indicated a declining population. The estimated matrix elasticities were in general higher for the survivorship parameters of the younger age classes and tended to decrease for the older ages. This highlights the susceptibility of this deep-sea squaloid to increasing fishing mortality, emphasizing that even though this is a small-sized species, it shows population dynamics patterns more typical of the larger-sized and in general more vulnerable species. (C) 2014 Elsevier Ltd. All rights reserved.

Propuesta de creación de la Reserva Natural Municipal El Chuval en el Municipio de El Retén, Magdalena, Colombia

Colombia se ha comprometido a nivel internacional a realizar acciones que conduzcan al país hacia el desarrollo sostenible, específicamente a proteger los recursos naturales. En línea con esta apuesta, la presente investigación propone la construcción de la Reserva Natural El Chuval, en el municipio El Retén, Magdalena. Para la construcción de dicha Reserva se realiza un diagnóstico que indica las principales características del Chuval, se identifican posibles riesgos para la conservación de sus valores ambientales, y se complementa el análisis con una propuesta de manejo que se enmarca en la metodología de Agendas Ambientales Locales desarrollada por la ONU. Pese a que el municipio es consciente de la relevancia ambiental del Chuval, aún no se han tomado decisiones definitivas para proteger esta zona, razón por la cual, la presente investigación busca llenar un vacío en el ordenamiento territorial del municipio, que fortalezca la protección de la Ciénaga Grande de Santa Marta.

Cellulose: A review as natural, modified and activated carbon adsorbent

Cellulose is a biodegradable, renewable, non-meltable polymer which is insoluble in most solvents due to hydrogen bonding and crystallinity. Natural cellulose shows lower adsorption capacity as compared to modified cellulose and its capacity can be enhanced by modification usually by chemicals. This review focuses on the utilization of cellulose as an adsorbent in natural/modified form or as a precursor for activated carbon (AC) for adsorbing substances from water. The literature revealed that cellulose can be a promising precursor for production of activated carbon with appreciable surface area (∼1300 m2 g−1) and total pore volume (∼0.6 cm3 g−1) and the surface area and pore volume varies with the cellulose content. Finally, the purpose of review is to report a few controversies and unresolved questions concerning the preparation/properties of ACs from cellulose and to make aware to readers that there is still considerable scope for future development, characterization and utilization of ACs from cellulose.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Cellulose: A review as natural, modified and activated carbon adsorbent

Cellulose is a biodegradable, renewable, non-meltable polymer which is insoluble in most solvents due to hydrogen bonding and crystallinity. Natural cellulose shows lower adsorption capacity as compared to modified cellulose and its capacity can be enhanced by modification usually by chemicals. This review focuses on the utilization of cellulose as an adsorbent in natural/modified form or as a precursor for activated carbon (AC) for adsorbing substances from water. The literature revealed that cellulose can be a promising precursor for production of activated carbon with appreciable surface area ( 1300 m2 g 1) and total pore volume ( 0.6 cm3 g 1) and the surface area and pore volume varies with the cellulose content. Finally, the purpose of review is to report a few controversies and unresolved questions concerning the preparation/properties of ACs from cellulose and to make aware to readers that there is still considerable scope for future development, characterization and utilization of ACs from cellulose.

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation

Driven by recent deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we suggest NLG-Metricverse—an end-to-end open-source library for NLG evaluation based on Python. This framework provides a living collection of NLG metrics in a unified and easy- to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support of heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area.

Brazilian Natural Fiber (jute) As Raw Material For Activated Carbon Production.

Jute fiber is the second most common natural cellulose fiber worldwide, especially in recent years, due to its excellent physical, chemical and structural properties. The objective of this paper was to investigate: the thermal degradation of in natura jute fiber, and the production and characterization of the generated activated carbon. The production consisted of carbonization of the jute fiber and activation with steam. During the activation step the amorphous carbon produced in the initial carbonization step reacted with oxidizing gas, forming new pores and opening closed pores, which enhanced the adsorptive capacity of the activated carbon. N2 gas adsorption at 77K was used in order to evaluate the effect of the carbonization and activation steps. The results of the adsorption indicate the possibility of producing a porous material with a combination of microporous and mesoporous structure, depending on the parameters used in the processes, with resulting specific surface area around 470 m2.g-1. The thermal analysis indicates that above 600°C there is no significant mass loss.

Comparing Near-infrared Conventional Diffuse Reflectance Spectroscopy And Hyperspectral Imaging For Determination Of The Bulk Properties Of Solid Samples By Multivariate Regression: Determination Of Mooney Viscosity And Plasticity Indices Of Natural Rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.