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Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims' decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work's predictive model, the accuracy to detect withdrawal improved by 7.5%., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Escobar-Linero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Baladron et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Accurate measurement of cortical bone parameters may improve fracture risk assessment and help clinicians on the best treatment strategy. Patients at risk of fracture are currently detected using the current X-Ray gold standard DXA (Dual XRay Absorptiometry). Different alternatives, such as 3D X-Rays, Magnetic Resonance Imaging or Quantitative Ultrasound (QUS) devices, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. The objective of this cross-sectional study was to evaluate the performance of a Bi-Directional Axial Transmission (BDAT) device used by trained operators in a clinical environment with older subjects. The device, positioned at one-third distal radius, provides two velocities: VFAS (first arriving signal) and VA0 (first anti-symmetrical guided mode). Moreover, two parameters are obtained from an inverse approach: Ct.Th (cortical thickness) and Ct.Po (cortical porosity), along with their ratio Ct.Po/Ct.Th. The areal bone mineral density (aBMD) was obtained using DXA at the femur and spine. One hundred and six patients (81 women, 25 men) from Marien Hospital and St. Anna Hospital (Herne, Germany) were included in this study. Age ranged from 41 to 95 years, while body mass index (BMI) ranged from 16 to 47 kg.m-2. Three groups were considered: 79 non-fractured patients (NF, 75±13years), 27 with non-traumatic fractures (F, 80±9years) including 14 patients with non-vertebral fractures (NVF, 84±7years). Weak to moderate significant Spearman correlations (R ranging from 0.23 to 0.53, p < 0.05) were found between ultrasound parameters and age, BMI. Using multivariate Partial Least Square discrimination analyses with Leave-One-Out Cross-Validation (PLS-LOOCV), we found the combination of VFAS and the ratio Ct.Po/Ct.Th to be predictive for all non traumatic fractures (F) with the odds ratio (OR) equals to 2.5 [1.6-3.4] and the area under the ROC curve (AUC) equal to 0.63 [0.62-0.65]. For the group NVF, combination of four parameters VA0. Ct.Th, Ct.Po and Ct.Po/Ct.Po, along with age provides a discrimination model with OR and AUC equals to 7.5 [6.0-9.1] and 0.75 [0.73-0.76]. When restricted to a smaller population (87 patients) common to both BDAT and DXA, BDAT ORs and AUCs are comparable or slightly higher to values obtained with DXA. The fracture risk assessment by BDAT method in older patients, in a clinical setting, suggests the benefit of the affordable and transportable device for the routine use., Competing Interests: The authors state that they have no conflicts of interest., (Copyright: © 2022 Minonzio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant., Patients and Methods: We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other., Results: Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases., Conclusion: The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy., Competing Interests: The authors have declared that no competing interests exist.

This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times., Competing Interests: The authors have declared that no competing interests exist.

Childhood obesity is an undeniable reality that has rapidly increased in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that is defined by a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid in healthy nutritional menu planning that may prevent childhood obesity. This model provides a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake). In addition, this approach considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. Test instances associated with children aged 4-18 years were created with the support of health professionals to complete this numerical study. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in a shorter computational time. These results are submitted to statistical hypothesis tests to be validated. The numerical results indicate proper guidelines for personalized plans for individual children., Competing Interests: The authors have declared that no competing interests exist.

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased., Competing Interests: The authors have declared that no competing interests exist.

Modern bioinformatics and computational biology are fields of study driven by the availability of effective software required for conducting appropriate research tasks. Apart from providing reliable and fast implementations of different data analysis algorithms, these software applications should also be clear and easy to use through proper user interfaces, providing appropriate data management and visualization capabilities. In this regard, the user experience obtained by interacting with these applications via their Graphical User Interfaces (GUI) is a key factor for their final success and real utility for researchers. Despite the existence of different packages and applications focused on advanced data visualization, there is a lack of specific libraries providing pertinent GUI components able to help scientific bioinformatics software developers. To that end, this paper introduces GC4S, a bioinformatics-oriented collection of high-level, extensible, and reusable Java GUI elements specifically designed to speed up bioinformatics software development. Within GC4S, developers of new applications can focus on the specific GUI requirements of their projects, relying on GC4S for generalities and abstractions. GC4S is free software distributed under the terms of GNU Lesser General Public License and both source code and documentation are publicly available at http://www.sing-group.org/gc4s., Competing Interests: The authors have declared that no competing interests exist.

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Antimicrobial research is being pressured to look for more effective therapeutics for the ever-growing antibiotic-resistant infections, and antimicrobial peptides (AMP) and antimicrobial combinations are promising solutions. This work evaluates colistin-AMP combinations against two major pathogens, Pseudomonas aeruginosa and Staphylococcus aureus, encompassing non- and resistant strains. Colistin (CST) combined with the AMP temporin A (TEMP-A), citropin 1.1 (CIT-1.1) and tachyplesin I linear analogue (TP-I-L) was tested against planktonic, single- and double-species biofilm cultures. Overall synergy for planktonic P. aeruginosa and synergy/additiveness for planktonic S. aureus were observed. Biofilm growth prevention was achieved with synergy and additiveness. Pre-established 24 h-old biofilms were harder to eradicate, especially for S. aureus and double-species biofilms; still, some synergy and addictiveness was observed for higher concentrations, including for the biofilms of resistant strains. Different treatment times and growth media did not greatly influence AMP activity. CST revealed low toxicity compared with the other AMP but its combinations were toxic for high concentrations. Overall, combinations reduced effective AMP concentrations, mainly in prevention scenarios. Improvement of effectiveness and toxicity of therapeutic strategies will be further investigated.

The polymicrobial nature of ventilator-associated pneumonia (VAP) is now evident, with mixed bacterial-fungal biofilms colonizing the VAP endotracheal tube (ETT) surface. The microbial interplay within this infection may contribute for enhanced pathogenesis and exert impact towards antimicrobial therapy. Consequently, the high mortality/morbidity rates associated to VAP and the worldwide increase in antibiotic resistance has promoted the search for novel therapeutic strategies to fight VAP polymicrobial infections. Under this scope, this work aimed to assess the activity of mono- vs combinational antimicrobial therapy using one antibiotic (Polymyxin B; PolyB) and one antifungal (Amphotericin B; AmB) agent against polymicrobial biofilms of Pseudomonas aeruginosa and Candida albicans. The action of isolated antimicrobials was firstly evaluated in single- and polymicrobial cultures, with AmB being more effective against C. albicans and PolyB against P. aeruginosa. Mixed planktonic cultures required equal or higher antimicrobial concentrations. In biofilms, only PolyB at relatively high concentrations could reduce P. aeruginosa in both monospecies and polymicrobial populations, with C. albicans displaying only punctual disturbances. PolyB and AmB exhibited a synergistic effect against P. aeruginosa and C. albicans mixed planktonic cultures, but only high doses (256 mg L-1) of PolyB were able to eradicate polymicrobial biofilms, with P. aeruginosa showing loss of cultivability (but not viability) at 2 h post-treatment, whilst C. albicans only started to be inhibited after 14 h. In conclusion, combination therapy involving an antibiotic and an antifungal agent holds an attractive therapeutic option to treat severe bacterial-fungal polymicrobial infections. Nevertheless, optimization of antimicrobial doses and further clinical pharmacokinetics/pharmacodynamics and toxicodynamics studies underpinning the optimal use of these drugs are urgently required to improve therapy effectiveness and avoid reinfection., Competing Interests: The authors have declared that no competing interests exist.

In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron's resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron's response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.

In this paper, we apply a real time activity-dependent protocol to study how freely swimming weakly electric fish produce and process the timing of their own electric signals. Specifically, we address this study in the elephant fish, Gnathonemus petersii, an animal that uses weak discharges to locate obstacles or food while navigating, as well as for electro-communication with conspecifics. To investigate how the inter pulse intervals vary in response to external stimuli, we compare the response to a simple closed-loop stimulation protocol and the signals generated without electrical stimulation. The activity-dependent stimulation protocol explores different stimulus delivery delays relative to the fish's own electric discharges. We show that there is a critical time delay in this closed-loop interaction, as the largest changes in inter pulse intervals occur when the stimulation delay is below 100 ms. We also discuss the implications of these findings in the context of information processing in weakly electric fish.

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.

When computing the trajectory of an autonomous vehicle, inevitable collision states must be avoided at all costs, so no harm comes to the device or pedestrians around it. In dynamic environments, considering collisions as binary events is risky and inefficient, as the future position of moving obstacles is unknown. We introduce a time-dependent probabilistic collision state checker system, which traces a safe route with a minimum collision probability for a robot. We apply a sequential Bayesian model to calculate approximate predictions of the movement patterns of the obstacles, and define a time-dependent variation of the Dijkstra algorithm to compute statistically safe trajectories through a crowded area. We prove the efficiency of our methods through experimentation, using a self-guided robotic device.

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free. This new index uses two parameters that are obtained directly from the response signal of the cerebral blood flow velocity to a transient decrease in arterial blood pressure provoked by the sudden release of bilateral thigh cuffs, and a third parameter measuring the difference in slope of this response and the change in arterial blood pressure achieved. With the values of these parameters, a corresponding classic autoregulatory index value could be calculated by using a linear regression model built from theoretical curves generated with the Aaslid-Tiecks model. In 16 healthy subjects who underwent repeated thigh-cuff manoeuvres, the model-free approach exhibited significantly lower intra-subject variability, as measured by the unbiased coefficient of variation, than the classic autoregulatory index (p = 0.032) and the Rate of Return (p<0.001), another measure of cerebral autoregulation used for this type of systemic pressure stimulus, from 39.23%±41.91% and 55.31%±31.27%, respectively, to 15.98%±7.75%.

In microbiology, identification of all isolates by sequencing is still unfeasible in small research laboratories. Therefore, many yeast diversity studies follow a screening procedure consisting of clustering the yeast isolates using MSP-PCR fingerprinting, followed by identification of one or a few selected representatives of each cluster by sequencing. Although this procedure has been widely applied in the literature, it has not been properly validated. We evaluated a standardized protocol using MSP-PCR fingerprinting with the primers (GTG)5 and M13 for the discrimination of wine associated yeasts in South Brazil. Two datasets were used: yeasts isolated from bottled wines and vineyard environments. We compared the discriminatory power of both primers in a subset of 16 strains, choosing the primer (GTG)5 for further evaluation. Afterwards, we applied this technique to 245 strains, and compared the results with the identification obtained by partial sequencing of the LSU rRNA gene, considered as the gold standard. An array matrix was constructed for each dataset and used as input for clustering with two methods (hierarchical dendrograms and QAPGrid layout). For both yeast datasets, unrelated species were clustered in the same group. The sensitivity score of (GTG)5 MSP-PCR fingerprinting was high, but specificity was low. As a conclusion, the yeast diversity inferred in several previous studies may have been underestimated and some isolates were probably misidentified due to the compliance to this screening procedure.

Early olfactory deprivation in rodents is accompanied by an homeostatic regulation of the synaptic connectivity in the olfactory bulb (OB). However, its consequences in the neural sensitivity and discrimination have not been elucidated. We compared the odorant sensitivity and discrimination in early sensory deprived and normal OBs in anesthetized rats. We show that the deprived OB exhibits an increased sensitivity to different odorants when compared to the normal OB. Our results indicate that early olfactory stimulation enhances discriminability of the olfactory stimuli. We found that deprived olfactory bulbs adjusts the overall excitatory and inhibitory mitral cells (MCs) responses to odorants but the receptive fields become wider than in the normal olfactory bulbs. Taken together, these results suggest that an early natural sensory stimulation sharpens the receptor fields resulting in a larger discrimination capability. These results are consistent with previous evidence that a varied experience with odorants modulates the OB's synaptic connections and increases MCs selectivity.

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark.

The intrinsic dynamics of sensory networks play an important role in the sensory-motor transformation. In this paper we use conductance based models and electrophysiological recordings to address the study of the dual role of a sensory network to organize two behavioral context-dependent motor programs in the mollusk Clione limacina. We show that: (i) a winner take-all dynamics in the gravimetric sensory network model drives the typical repetitive rhythm in the wing central pattern generator (CPG) during routine swimming; (ii) the winnerless competition dynamics of the same sensory network organizes the irregular pattern observed in the wing CPG during hunting behavior. Our model also shows that although the timing of the activity is irregular, the sequence of the switching among the sensory cells is preserved whenever the same set of neurons are activated in a given time window. These activation phase locks in the sensory signals are transformed into specific events in the motor activity. The activation phase locks can play an important role in motor coordination driven by the intrinsic dynamics of a multifunctional sensory organ.

The idea of closed-loop interaction in in vitro and in vivo electrophysiology has been successfully implemented in the dynamic clamp concept strongly impacting the research of membrane and synaptic properties of neurons. In this paper we show that this concept can be easily generalized to build other kinds of closed-loop protocols beyond (or in addition to) electrical stimulation and recording in neurophysiology and behavioral studies for neuroethology. In particular, we illustrate three different examples of goal-driven real-time closed-loop interactions with drug microinjectors, mechanical devices and video event driven stimulation. Modern activity-dependent stimulation protocols can be used to reveal dynamics (otherwise hidden under traditional stimulation techniques), achieve control of natural and pathological states, induce learning, bridge between disparate levels of analysis and for a further automation of experiments. We argue that closed-loop interaction calls for novel real time analysis, prediction and control tools and a new perspective for designing stimulus-response experiments, which can have a large impact in neuroscience research.

Background: The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain "hidden regularities" and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects., Methodology/principal Findings: We present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets., Conclusions/significance: Overall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method as a novel alternative tool for functional genomics.