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Context The conversion of natural environments into agricultural land has profound effects on the composition of the landscape, often resulting in a mosaic of human-altered and natural habitats. The response to these changes may however vary among organisms. Bats are highly vagile, and their requirements often imply the use of distinct habitats, which they select responding to both landscape and local features. Objectives We aimed to identify which features influence bat richness and activity within Baixo Vouga Lagunar, a heterogeneous landscape located on the Central-North Portuguese coast, and to investigate if that influence varies across a gradient of focal scales. Methods We sampled bats acoustically, while simultaneously sampling insects with light traps. We assessed the relationships between species richness, bat activity, and activity of eco-morphological guilds with landscape and local features, across four scales. Results Our results revealed both scale- and guild-dependent responses of bats to landscape and local features. At broader scales we found positive associations between open-space foraging bats and habitat heterogeneity and between edge-space foraging bats and greater edge lengths. Woodland cover and water availability at an intermediate scale and weather conditions and insect abundance at a local scale were the factors that mostly influenced the response variables. Conclusions Globally, our results suggest that bats are sensitive to local resource availability and distribution, while simultaneously reacting to landscape features acting at coarser scales. Finally, our results suggest that the responses given by bats are guild-dependent, and some habitats act as keystone structures for bats within this mosaic.

The meat and meat product industry has evolved according to the needs of the market. Consumers are increasingly seeking quality in food. Thus, the concern regarding the excessive use of additives such as preservatives and antioxidants has driven research towards natural, healthy and safe substitutes. Essential oils and plant extracts have been shown to be a good option for resolving this problem. They are completely natural with biological activity, which mainly includes prevention of oxidation and the proliferation of microorganisms, thus arousing the interest of the industry and consumers. This review will present studies published in the last five years regarding the potential of essential oils and plant extracts to act as preservatives and antioxidants in meat and meat products. The forms of application, innovations in the area, alternatives to the incorporation of essential oils and extracts in meat products, effects caused in food, and limitations of applications will be detailed and discussed.

Although loneliness is a human experience, it can be estimated in laboratory animals deprived from physical contact with conspecifics. Rodents under social isolation (SI) tend to develop emotional distress and cognitive impairment. However, it is still to be determined whether those conditions present a common neural mechanism. Here, we conducted a series of behavioral, morphological, and neurochemical analyses in adult mice that underwent to 1 week of SI. We observed that SI mice display a depressive-like state that can be prevented by enriched environment, and the antidepressants fluoxetine (FLX) and desipramine (DES). Interestingly, chronic administration of FLX, but not DES, was able to counteract the deleterious effect of SI on social memory. We also analyzed cell proliferation, neurogenesis, and astrogenesis after the treatment with antidepressants. Our results showed that the olfactory bulb (OB) was the neurogenic niche with the highest increase in neurogenesis after the treatment with FLX. Considering that after FLX treatment social memory was rescued and depressive-like behavior decreased, we propose neurogenesis in the OB as a possible mechanism to unify the FLX ability to counteract the deleterious effect of SI.

Thermal transitions of polyisocyanide single molecules to polymer bundles and finally networks lead to hydrogels mimicking the properties of biopolymer intermediate-filament networks; their analysis shows that bundling and chain stiffness are crucial design parameters for hydrogels. Biomimetic polymer networks This paper describes a new class of water-soluble, relatively stiff polymers that bundle in a controlled manner on heating to produce very stiff fibres. These fibres, in turn, form hydrogels that very closely mimic components of the cell cytoskeleton, intermediate filaments. Synthesis involves the thermal transition of polyisocyanide polymers from single molecules to bundles of polymer chains. Networks made with this material demonstrate a stress-stiffening behaviour that is usually absent in synthetic polymer gels, and their mechanical properties can be modified by altering the chemical structure of the polymer, offering greater versatility than biopolymer networks. Mechanical responsiveness is essential to all biological systems down to the level of tissues and cells 1 , 2 . The intra- and extracellular mechanics of such systems are governed by a series of proteins, such as microtubules, actin, intermediate filaments and collagen 3 , 4 . As a general design motif, these proteins self-assemble into helical structures and superstructures that differ in diameter and persistence length to cover the full mechanical spectrum 1 . Gels of cytoskeletal proteins display particular mechanical responses (stress stiffening) that until now have been absent in synthetic polymeric and low-molar-mass gels. Here we present synthetic gels that mimic in nearly all aspects gels prepared from intermediate filaments. They are prepared from polyisocyanopeptides 5 , 6 , 7 grafted with oligo(ethylene glycol) side chains. These responsive polymers possess a stiff and helical architecture, and show a tunable thermal transition where the chains bundle together to generate transparent gels at extremely low concentrations. Using characterization techniques operating at different length scales (for example, macroscopic rheology, atomic force microscopy and molecular force spectroscopy) combined with an appropriate theoretical network model 8 , 9 , 10 , we establish the hierarchical relationship between the bulk mechanical properties and the single-molecule parameters. Our results show that to develop artificial cytoskeletal or extracellular matrix mimics, the essential design parameters are not only the molecular stiffness, but also the extent of bundling. In contrast to the peptidic materials, our polyisocyanide polymers are readily modified, giving a starting point for functional biomimetic hydrogels with potentially a wide variety of applications 11 , 12 , 13 , 14 , in particular in the biomedical field.

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70–80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20–50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.

The multichannel objective response detection (MORD) techniques are statistical methods, which use information from more than one electroencephalography (EEG) channel, to infer the presence of evoked potential. However, the correlation level between the channels can lead to a decrease in MORD performance, such as an increase in the false positive (FP) rate and/or a decrease in the detection rate (DR). The present study aims to propose a method to deal with the correlations in the multichannel EEG. The method consists of making an adjustment in the Monte Carlo simulation, considering the information between channels. The MORD techniques with and without the new method were applied to an auditory steady-state response (ASSR) database, composed of the EEG multichannel of eleven volunteers during multifrequency stimulation. The proposed method kept the FP rate at values equal to or less than the significance level of the test and led to an increase of 8.51% in the DR in relation to non-application of the method. Results of this study indicate that the proposed method is an alternative to deal with the effect of the correlation between channels in situations where MORD techniques are applied.

Fault detection in computer networks is a difficult task, since most faults, attacks or abnormalities may not be observed in advance and modeled prior to the event. In addition, network attacks tend to disguise broken rules by minimizing their resemblance to previous trials, which makes it even more difficult to use past observed data to obtain detection models. High dimensionality in relation to the number of network elements and observable variables is also a major concern. Instead of modeling the fault itself, the work presented in this paper describes a detection model that is based on detecting drifts from normality. This problem is treated in the present paper by weekly updating the estimation of density functions that represent normality or as close as possible to this rate. Abnormalities are detected by thresholding drifts from the estimated densities. Root cause analysis can also be accomplished by tracing back combined variables output from principal component analysis projections. The method is scalable for large networks and was tested on real data from a large company with the developed online monitoring platform.

Nitrite and nitrate salts are preservatives that act as antimicrobial (bacteriostatic and bactericidal activity) and antioxidant agents in the processing of meat products and confer sensory properties to meat (by creating and preserving colours and flavours). Nitrite is mainly used as a preservative to prevent the growth of Clostridium botulinum and the production of its toxins. However, nitrite and nitrate are also associated with the production of N-nitroso compounds, such as carcinogenic N-nitrosamines, which can have adverse health effects. Therefore, the health risks of these preservatives must be weighed against the need to prevent foodborne pathogens, especially spores of C. botulinum, from infecting food. In this review, we discuss the advantages and disadvantages of using nonthermal technologies as a strategy to partially or totally replace nitrite in meat products, particularly regarding antimicrobial efficacy and N-nitrosamine formation. Methods such as high-pressure processing, pulsed electric fields and cold plasma have been studied for these purposes, but these technologies can alter the sensory properties and stability of foods. Nevertheless, irradiation at lower doses has great potential as a tool for reformulation of cured meat products. It contributes to the reduction of the residual nitrite and consequently to the production of N-nitrosamines while ensuring microbiological safety without significant changes in the product quality.

The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems’ resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.

The quality of public transport is essential when considering urban mobility in large cities. Several factors, such as the increase in urban population, rain, and traffic events, can impact mobility, causing congestion. Addressing this issue is essential for the population and is part of the UN’s 2030 Agenda for Sustainable Development goals. Integrating data from different sources is crucial to understanding and planning urban traffic. This work aims to provide a dataset with spatiotemporal information on the mobility of municipal buses, including the estimated emission of polluting gases and the rainfall volume in Rio de Janeiro from 2014 to 2023. Its format facilitates integration with other Rio de Janeiro City Hall datasets, enabling the increase and deepening of the analyses. This work is the first to combine data from bus observation with positional information on neighborhoods and rainfall regions, rainfall volumes, and pollutant gas emissions. Thus, its availability opens opportunities for research topics involving public transport associated with environmental indicators and data science with time series studies and positional data.