Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying predator dependence in the functional response of generalist predators

A longstanding debate concerns whether functional responses are best described by prey-dependent versus ratio-dependent models. Theory suggests that ratio dependence can explain many food web patterns left unexplained by simple prey-dependent models. However, for logistical reasons, ratio dependence and predator dependence more generally have seen infrequent empirical evaluation and then only so in specialist predators, which are rare in nature. Here we develop an approach to simultaneously estimate the prey-specific attack rates and predator-specific interference rates of predators interacting with arbitrary numbers of prey and predator species. We apply the approach to field surveys and two field experiments involving two intertidal whelks and their full suite of potential prey. Our study provides strong evidence for the presence of weak predator dependence that is closer to being prey dependent than ratio dependent over manipulated and natural ranges of species abundances. It also indicates how, for generalist predators, even the qualitative nature of predator dependence can be prey-specific. Author contributions CW contributed to method development, KC and IS performed the caging experiment, and MN conceived of the study, carried out the fieldwork and analyses, and wrote the manuscript.
A computational linguistic approach to Spanish Golden Age Sonnets: metrical and semantic aspects

Several computational linguistics techniques are applied to analyze a large corpus of Spanish sonnets from the 16th and 17th centuries. The analysis is focused on metrical and semantic aspects. First, we are developing a hybrid scansion system in order to extract and analyze rhythmical or metrical patterns. The possible metrical patterns of each verse are extracted with language-based rules. Then statistical rules are used to resolve ambiguities. Second, we are applying distributional semantic models in order to, on one hand, extract semantic regularities from sonnets, and on the other hand to group together sonnets and poets according to these semantic regularities. Besides these techniques, in this position paper we will show the objectives of the project and partial results.
How present synthetic cannabinoids can help predict symptoms in the future

New synthetic cannabinoids appear regularly in the illicit drug market, often in response to legal restrictions. These compounds are characterized by increasing binding affinities (Ki) to CB1 and CB2 receptors. Increasing affinity to CB receptors can occur by substitution of a halogen on the terminal position of the pentyl chain of the classical synthetic cannabinoids. Fluorination of the aliphatic side chain of established cannabinoid agonists is a popular pathway of modifying existing active drugs and synthesizing novel drugs to increase potency. Biological impacts of these compounds that have been reported include seizures, body temperature losses that may lead to cardiac distress, as well as cases reporting delirium and severe neural incapacities. These symptoms have been reported in case reports with evidence that like their cannabinoid counterparts, these compounds distribute post-mortem into a variety of tissues, especially adipose tissue. Researchers have detected and identified certain synthetic cannabinoid compounds on botanicals using a variety of separation and detection systems such as GC-FID and GC-MS, as well as LC-MS/MS and NMR. Researchers have utilized a variety of chromatographic methods including LC MS/MS, to identify the parent compounds, their metabolites and structural analogs in biological samples such as urine and blood. The aim of this review is to spotlight the newer generations of synthetic cannabinoids with a more powerful affinity towards the cannabinoid receptors to continue to help researchers and clinicians further predict symptoms as well as future compounds and treatments.
Automatic layout generation of static CMOS circuits targeting delay and power

The evolution of integrated circuits technologies demands the development of new CAD tools. The traditional development of digital circuits at physical level is based in library of cells. These libraries of cells offer certain predictability of the electrical behavior of the design due to the previous characterization of the cells. Besides, different versions of each cell are required in such a way that delay and power consumption characteristics are taken into account, increasing the number of cells in a library. The automatic full custom layout generation is an alternative each time more important to cell based generation approaches. This strategy implements transistors and connections according patterns defined by algorithms. So, it is possible to implement any logic function avoiding the limitations of the library of cells. Tools of analysis and estimate must offer the predictability in automatic full custom layouts. These tools must be able to work with layout estimates and to generate information related to delay, power consumption and area occupation. This work includes the research of new methods of physical synthesis and the implementation of an automatic layout generation in which the cells are generated at the moment of the layout synthesis. The research investigates different strategies of elements disposition (transistors, contacts and connections) in a layout and their effects in the area occupation and circuit delay. The presented layout strategy applies delay optimization by the integration with a gate sizing technique. This is performed in such a way the folding method allows individual discrete sizing to transistors. The main characteristics of the proposed strategy are: power supply lines between rows, over the layout routing (channel routing is not used), circuit routing performed before layout generation and layout generation targeting delay reduction by the application of the sizing technique. The possibility to implement any logic function, without restrictions imposed by a library of cells, allows the circuit synthesis with optimization in the number of the transistors. This reduction in the number of transistors decreases the delay and power consumption, mainly the static power consumption in submicrometer circuits. Comparisons between the proposed strategy and other well-known methods are presented in such a way the proposed method is validated.
Response to nitrogen addition reveals metabolic and ecological strategies of soil bacteria

The nitrogen (N) cycle represents one of the most well studied systems yet the taxonomic diversity of the organisms that contribute to it is mostly unknown, or linked to poorly characterized microbial groups. While progress has allowed functional groups to be refined, they still rely on a priori knowledge of enzymes involved, and the assumption of functional conservation, with little connection to the role the transformation plays for specific organisms. Here, we use soil microcosms to test the impact of N deposition on prokaryotic communities. By combining chemical, genomic and transcriptomic analysis we are able to identify and link changes in community structure to specific organisms catalyzing given chemical reactions. Urea deposition led to a decrease in prokaryotic richness, and a shift in community composition. This was driven by replacement of stable native populations, which utilize energy from N-linked redox reactions for maintenance, with fast responding populations that use this energy for growth. This model can be used to predict response to N disturbances and allows us to identify putative life strategies of different functional, and taxonomic, groups thus providing insights into how they persist in ecosystems by niche differentiation.
GENTLE/A - Adaptive robotic assistance for upper-limb rehabilitation

Advanced devices that can assist the therapists to offer rehabilitation are in high demand with the growing rehabilitation needs. The primary requirement from such rehabilitative devices is to reduce the therapist monitoring time. If the training device can autonomously adapt to the performance of the user, it can make the rehabilitation partly self-manageable. Therefore the main goal of our research is to investigate how to make a rehabilitation system more adaptable. The strategy we followed to augment the adaptability of the GENTLE/A robotic system was to (i) identify the parameters that inform about the contribution of the user/robot during a human-robot interaction session and (ii) use these parameters as performance indicators to adapt the system. Three main studies were conducted with healthy participants during the course of this PhD. The first study identified that the difference between the position coordinates recorded by the robot and the reference trajectory position coordinates indicated the leading/lagging status of the user with respect to the robot. Using the leadlag model we proposed two strategies to enhance the adaptability of the system. The first adaptability strategy tuned the performance time to suit the user’s requirements (second study). The second adaptability strategy tuned the task difficulty level based on the user’s leading or lagging status (third study). In summary the research undertaken during this PhD successfully enhanced the adaptability of the GENTLE/A system. The adaptability strategies evaluated were designed to suit various stages of recovery. Apart from potential use for remote assessment of patients, the work presented in this thesis is applicable in many areas of human-robot interaction research where a robot and human are involved in physical interaction.
Modelling and aggregation of loads in flexible power networks

Abstract High penetration of renewable energies presents a new challenge for conventional power system. Such penetration of renewables resulted in renewed interest in load modeling which may include small renewable generation as a portion of loads. A new CIGRE working group was established to provide guidance with respect to load modeling of new types of load including renewables using measurement data and historical data. This paper summarizes major results of the work of this WG after two years' activities. Although the deliverables are not fully completed, beneficial recommendations of the load modelling such as selection of load model structure, picking up suitable data, proper approach for deriving load model parameters, requirement of measurement data, etc. are introduced in this paper identifying clearly advantage and disadvantage of various load modelling approaches.
M-Learning Sentiment Analysis with Data Mining

The development of mobile communication and hand-held device offers new and innovative pedagogical approach in the learning system. The M-learning approach offers an anytime, anywhere learning scenario to the learners through mobile devices. Sentiment Analysis is the area where the user's reviews or opinions about the products are analyzed. Analyzing the opinion or sentiment available online gives significant amount of information about the product, through which a person or a company can gauge the product quality and its status in the market. This paper analyzes the sentiments about the M-learning system and also investigates the classification accuracy of Naive Bayes algorithm. The accuracy of the Naive Bayes algorithm is compared with random forest data mining algorithm and K nearest neighbor (KNN) algorithm for opinion mining and in specific for M learning systems.
DYNAMIC PET DATA FOR GENERATING PARAMETRIC IMAGESl

Due to the high noise level of pixel-wise kinetic data in dynamic studies, direct model fitting of the kinetic data on a pixel-by-pixel basis requires many iterations and has high variability, making it unsuitable for generating parametric images of the micro-parameters of tracer kinetic models. In this study, we propose and investigate a spatially-coordi nated method for image-wise nonlinear regression that allows good quality parametric images to be generated by direct model fitting. Instead of model fitting the kinetics of each pixel independently, the new method performs regression simultaneously in parallel for all pixels in the image with the intermediate results at each iteration step coordinated among neighboring pixels. Marquardt algorithm is used to minimize the sum of square differences and a simple smoothing is employed for coordination of estimates at each iteration. The method has been implemented using MA TLAB and has been applied to botl1 computer simulated data and real FDG dynamic images.
Immediate early gene activation throughout the brain is associated with dynamic changes in social context

Social competence is dependent on successful processing of social context information. The social opportunity paradigm is a methodology in which dynamic shifts in social context are induced through removal of the alpha male in a dominance hierarchy, leading to rapid ascent in the hierarchy of the beta male and of other subordinate males in the social group. In the current study, we use the social opportunity paradigm to determine what brain regions respond to this dynamic change in social context, allowing an individual to recognize the absence of the alpha male and subsequently perform status-appropriate social behaviors. Replicating our previous work, we show that following removal of the alpha male, beta males rapidly ascend the social hierarchy and attain dominant status by increasing aggression towards more subordinate individuals. Analysis of patterns of Fos immunoreactivity throughout the brain indicates that in individuals undergoing social ascent, there is increased activity in regions of the social behavior network, as well as the infralimbic and prelimbic regions of the prefrontal cortex and areas of the hippocampus. Our findings demonstrate that male mice are able to respond to changes in social context and provide insight into the how the brain processes these complex behavioral changes.
Modeling and control of a 6-control-area interconnected power system to protect the network frequency applying different controllers

Modern and large power systems, which are normally composed of several interconnected generating stations (multiarea), have complex and diverse structures in practice. It is necessary to deal with the automatic generation control to ensure the stability, continuity, and economy of the generation schedule in a power grid. For the automatic generation control strategy, the most important goal is to protect the network frequency from load variations, which can appear randomly in any area. As a result, it is essential to design efficient controllers applied to multiarea interconnected power systems in order to maintain the network frequency at the nominal values (50 Hz or 60 Hz), and keep the tie-line power ow at the scheduled MW. In this paper, werst analyze and build a model of the 6-control-area interconnected power system as the typical case study. Subsequently, different frequency controllers based on tie-line bias control strategy, namely integral, proportional{int proportional{int , and PI-based fuzzy logic, will be investigated and applied to the power system model. The most important control performances, such as overshoots and settling times, are considered in this study to evaluate the stability of the power system and choose the most suitable controller for maintaining the network frequency. The simulation results have been achieved by using MATLAB/Simulink package version 2013 in this work. According to these results, the maximum overshoots of the PI-based fuzzy logic controller are from 36.56% to 90.13%, and its settling times are from 19.00% to 98.26% in comparison with the other regulators in the given control power system. Therefore, the PI-based fuzzy logic controller has been chosen as the best control solution to bring the grid frequency back to its nominal value as quickly as possible after occurrence of load variations.
STEADY-STATE VISUAL EVOKED POTENTIAL RESPONSE - IMPACT OF THE TIME SEGMENT LENGTH

When a visual stimulus with a constant frequency is presented to a person, it is possible to observe a continuous brain response in the visual cortical area. This response is called a steady-state visual evoked potential (SSVEP). The detection of this brain response can be analyzed as a command for tailoring a Brain-Computer interface (BCI). Its goal is to provide a new output channel for the brain that requires voluntary control. The efficiency of a BCI depends on the commands accuracy and their latency: the time between the order from the subject and the moment when the command is detected. Given one particular SSVEP response classification method, the BCI control is variable among the users. This variance is translated into different performance between subjects. We propose a robust classification method of SSVEP responses, which does not require any training. A performance comparison on 8 different time segment lengths over 10 subjects is proposed. The average time segment length for obtaining a SSVEP response recognition of 95% is determined to be 2.8s.
A high-throughput technique to quantify pathogens’ virulence on the insect model Galleria mellonella

We combined spectrophotometry and an original statistical approach to infer bacteria virulence, using the lepidoptera Galleria mellonella as a host model. With this method, it is possible to use a microplate reader to automatize data collection on both pathogens multiplication and host survival on batches of 96 samples.
Spiking Neural Networks As Continuous-Time Dynamical Systems: Fundamentals, Elementary Structures And Simple Applications

In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier.
Stability analysis of adaptively controlled flexible joint manipulators

The composite slow/fast control strategy, consisting of a slow adaptive controller designed for a rigid robot together with a fast control to damp the elastic oscillations of the joints, was previously derived by the authors. In this paper, mathematical details of the algorithm are presented. By using the composite Lyapunov theory for singularly perturbed systems, sufficient conditions are obtained for adaptive trajectory tracking. For point-to-point motion it is shown that there is always a range of joint stiffness for which convergence is achieved, and the region of convergence is quantified. For tracking (smooth and bounded) reference trajectories, sufficient conditions for closed-loop stability and uniform boundedness of the tracking error are given. A residual set to which the tracking error converges is quantified. It is also shown that for special classes of trajectories, which include step responses generated from reference models and certain joint interpolated trajectories, asymptotic tracking can be achieved. <<ETX>>
Stress response of Chironomus riparius to changes in water temperature and oxygen concentration in a lowland stream

The increasing impairment of lotic ecosystems has promoted a growing effort into assessing their ecological status by means of biological indicators. While community-based approaches have proven valuable to assess ecosystem integrity, they mostly reflect long-term changes and might not be suitable for tracking and monitoring short-term events. Responses to rapid changes in environmental conditions have been rarely studied under natural conditions. Biomarkers offer the benefit of integrating biological responses at different time scales. Here we used a field experiment to test how the synthesis of heat shock protein 70 (HSP70) and Haemoglobin (Hb) in laboratory-reared larvae of Chironomus riparius (Diptera, Chironomidae) were influenced by short-term changes to water temperature and oxygen concentration in a lowland stream. Our aim was to determine whether HSP70 mRNA expression and Hb content could be used as an in situ “early warning system” for freshwater habitats undergoing environmental change. HSP70 exhibited a clear response to changes in temperature measured over a one-day period, confirming its suitability as an indicator of environmental stress. Hb concentration was related to oxygen concentration, but not to temperature. Our findings support the hypothesis that depletion in oxygen induces Hb synthesis in C. riparius larvae. Because tolerance to low oxygen is not only related to total Hb, but also to a more efficient uptake (binding to Hb, e.g. Bohr effect) and release of oxygen to the cell (Root effect), we cannot discern from our data whether increased efficiency played a role. We suggest that C. riparius is a suitable model organism for monitoring sub-lethal stress in the field and that the approach could be applied to other species as more genomic data are available for non-model organisms.
The polar localization hub protein PopZ restrains adaptor dependent ClpXP proteolysis in Caulobacter crescentus

In Caulobacter crescentus, timely degradation of several proteins by the ClpXP protease is critical for proper cell cycle progression. During the cell cycle, the ClpXP protease, the substrate CtrA and many other proteins are localized to the stalked pole dependent on a polar interaction hub composed of PopZ protein oligomers. Prior work suggests that the localization of ClpXP, protease substrates, and cofactors is needed for recognition of substrates such as CtrA by ClpXP. Here, we formally test this hypothesis by examining the role of PopZ in ClpXP activity and find surprisingly that CtrA degradation is enhanced in cells lacking polar localization due to loss of PopZ. The ClpXP adaptor CpdR is required for this enhanced degradation of CtrA and other adaptor-dependent substrates, but adaptor-independent substrate degradation is not affected upon loss of PopZ. We find that overexpression of PopZ also leads to faster degradation of CtrA, but is likely due to nonphysiologically relevant recognition of CtrA by ClpXP alone as loss of CpdR does not affect this enhancement. Our main conclusion is that loss of PopZ, and therefore loss of polar localization, does not result in the loss of ClpXP regulated proteolysis, as would be predicted from a model which requires polar localization of ClpXP for its activation. Rather, our data point to a model where PopZ normally restrains ClpXP proteolysis by promoting the inactivation of the CpdR adaptor, likely through the phosphorylation activity of the CckA kinase.