Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting.

With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.

SNT: a unifying toolbox for quantification of neuronal anatomy.

SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ.

Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain.

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

Electronic nose dataset for detection of wine spoilage thresholds.

In this data article, we provide a time series dataset obtained for an application of wine quality detection focused on spoilage thresholds. The database contains 235 recorded measurements of wines divided into three groups and labeled as high quality (HQ), average quality (AQ) and low quality (LQ), in addition to 65 ethanol measurements. This dataset was collected using an electronic nose system (E-Nose) based on Metal Oxide Semiconductor (MOS) gas sensors, self-developed at the Universidade Federal Rural de Pernambuco (Brazil). The dataset is related to the research article entitled "Wine quality rapid detection using a compact electronic nose system: application focused on spoilage thresholds by acetic acid" by Rodriguez Gamboa et al., 2019. The dataset can be accessed publicly at the repository: https://data.mendeley.com/datasets/vpc887d53s/.

Single-neuron axonal reconstruction: The search for a wiring diagram of the brain.

Reconstruction of the axonal projection patterns of single neurons has been an important tool for understanding both the diversity of cell types in the brain and the logic of information flow between brain regions. Innovative approaches now enable the complete reconstruction of axonal projection patterns of individual neurons with vastly increased throughput. Here, we review how advances in genetic, imaging, and computational techniques have been exploited for axonal reconstruction. We also discuss how new innovations could enable the integration of genetic and physiological information with axonal morphology for producing a census of cell types in the mammalian brain at scale.

A perturbative approach for enhancing the performance of time series forecasting.

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published.

An Approach to Improve the Performance of PM Forecasters.

The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth's atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs) have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series), obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS) composed by genetic algorithm (GA) and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM2.5 and PM10 concentration series in Kallio and Vallila stations in Helsinki and evaluated from six metrics. Experimental results show that the proposed approach improves the accuracy of the forecasting method in terms of fitness function for all cases, when compared with the method without correction. The correction via HS obtained a superior performance, reaching the best results in terms of fitness function and in five out of six metrics. These results also were found when a sensitivity analysis was performed varying the proportions of the sets of training, validation and test. The proposed approach reached consistent results when compared with the forecasting method without correction, showing that it can be an interesting tool for correction of PM forecasters.

Defective microglial development in the hippocampus of Cx3cr1 deficient mice.

Microglial cells participate in brain development and influence neuronal loss and synaptic maturation. Fractalkine is an important neuronal chemokine whose expression increases during development and that can influence microglia function via the fractalkine receptor, CX3CR1. Mice lacking Cx3cr1 show a variety of neuronal defects thought to be the result of deficient microglia function. Activation of CX3CR1 is important for the proper migration of microglia to sites of injury and into the brain during development. However, little is known about how fractalkine modulates microglial properties during development. Here we examined microglial morphology, response to ATP, and K(+) current properties in acute brain slices from Cx3cr1 knockout mice across postnatal hippocampal development. We found that fractalkine signaling is necessary for the development of several morphological and physiological features of microglia. Specifically, we found that the occurrence of an outward rectifying K(+) current, typical of activated microglia, that peaked during the second and third postnatal week, was reduced in Cx3cr1 knockout mice. Fractalkine signaling also influenced microglial morphology and ability to extend processes in response to ATP following its focal application to the slice. Our results reveal the developmental profile of several morphological and physiological properties of microglia and demonstrate that these processes are modulated by fractalkine signaling.

Correcting and combining time series forecasters.

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. The present paper introduces a two-step method for correcting and combining forecasting models. Firstly, the stochastic process underlying the bias of each predictive model is built according to a recursive ARIMA algorithm in order to achieve a white noise behavior. At each iteration of the algorithm the best ARIMA adjustment is determined according to a given information criterion (e.g. Akaike). Then, in the light of the corrected predictions, it is considered a maximum likelihood combined estimator. Applications involving single ARIMA and artificial neural networks models for Dow Jones Industrial Average Index, S&P500 Index, Google Stock Value, and Nasdaq Index series illustrate the usefulness of the proposed framework.

Degradation of newly synthesized polypeptides by ribosome-associated RACK1/c-Jun N-terminal kinase/eukaryotic elongation factor 1A2 complex.

Folding of newly synthesized polypeptides (NSPs) into functional proteins is a highly regulated process. Rigorous quality control ensures that NSPs attain their native fold during or shortly after completion of translation. Nonetheless, signaling pathways that govern the degradation of NSPs in mammals remain elusive. We demonstrate that the stress-induced c-Jun N-terminal kinase (JNK) is recruited to ribosomes by the receptor for activated protein C kinase 1 (RACK1). RACK1 is an integral component of the 40S ribosome and an adaptor for protein kinases. Ribosome-associated JNK phosphorylates the eukaryotic translation elongation factor 1A isoform 2 (eEF1A2) on serines 205 and 358 to promote degradation of NSPs by the proteasome. These findings establish a role for a RACK1/JNK/eEF1A2 complex in the quality control of NSPs in response to stress.

An entropy-based approach for the identification of phylogenetically informative genomic regions of Papillomavirus.

The papillomaviruses form a highly diverse group that infect mammals, birds and reptiles. We know little about their genetic diversity and therefore the evolutionary mechanisms that drive the diversity of these viruses. Genomic sequences of papillomaviruses are highly divergent and so it is important to develop methods that select the most phylogenetic informative sites. This study aimed at making use of a novel approach based on entropy to select suitable genomic regions from which to infer the phylogeny of papillomavirus. Comparative genomic analyzes were performed to assess the genetic variability of each gene of Papillomaviridae family members. Regions with low entropy were selected to reconstruct papillomavirus phylogenetic trees based on four different methods. This methodology allowed us to identify regions that are conserved among papillomaviruses that infect different hosts. This is important because, despite the huge variation among all papillomaviruses genomes, we were able to find regions that are clearly shared among them, presenting low complexity levels of information from which phylogenetic predictions can be made. This approach allowed us to obtain robust topologies from relatively small datasets. The results indicate that the entropy approach can successfully select regions of the genome that are good markers from which to infer phylogenetic relationships, using less computational time, making the estimation of large phylogenies more accessible.

Serotonin receptor 1A modulates actin dynamics and restricts dendritic growth in hippocampal neurons.

Mice lacking serotonin receptor 1A (Htr1a) display increased anxiety behavior that depends on the expression of the receptor in the forebrain during the third to fifth postnatal weeks. Within the forebrain, Htr1a is prominently expressed in the soma and dendrites of CA1 pyramidal neurons of the hippocampus and these cells undergo rapid dendritic growth and synapse formation during this period. Consistent with a possible role of Htr1a in synaptic maturation, CA1 pyramidal neurons in the knockout mice show increased ramification of oblique dendrites. These findings suggest that Htr1a may shape hippocampal circuits by directly modulating dendritic growth. Here we show that pharmacological blockade of the receptor during the third to fifth postnatal weeks is sufficient to reproduce the increased branching of oblique dendrites seen in knockout mice. Using dissociated hippocampal cultures we demonstrate that serotonin functions through Htr1a to attenuate the motility of dendritic growth cones, reduce their content of filamentous actin and alter their morphology. These findings suggest that serotonin modulates actin cytoskeletal dynamics in hippocampal neurons during a limited developmental period to restrict dendritic growth and achieve a long-term adjustment of neural connectivity.