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Purpose: The main aim of this paper is to study the influence of temperature on multiscale entropy (MSE) and multiscale time irreversibility (MTI) through the use of short-term measurements. Methods: A total of 12 physically active, healthy, and nonsmoker individuals ($25.6\pm 3.9$ years old; $174.2\pm 7.5$cm of height; and $68.6\pm 11.1$kg of body mass) voluntarily participated in this study. Two beat-to-beat recordings of 15min length were performed on every participant, one under hot conditions ($35^{\circ }$C) and the other assessment under cool conditions ($19^{\circ }$C). The order of these two assessments was randomly assigned. Multiscale sample entropy and MTI were assessed in every measurement through 10 scales. Results: Entropy was significantly higher under hot conditions ($p<0.05$) from the fifth scale compared to cool conditions. On the contrary, MTI values were significantly lower under hotter conditions ($p<0.05$). Conclusions: The study of MSE and time irreversibility of short RR measurements presents consistent and reliable data. Moreover, exposures to hot conditions provoke an increment of interbeat complexity throughout larger scales and a decrease in the MTI in a healthy population.

This work aims to analyze the complexity of surface electromyography (sEMG) signals under muscle fatigue conditions using Hjorth parameters and bubble entropy (BE). Signals are recorded from the biceps brachii muscle of 25 healthy males during dynamic and isometric contraction exercises. These signals are filtered and segmented into 10 equal parts. The first and tenth segments are considered as nonfatigue and fatigue conditions, respectively. Activity, mobility, complexity, and BE features are extracted from both segments and classified using support vector machine (SVM), Naïve bayes (NB), k-nearest neighbor (kNN), and random forest (RF). The results indicate a reduction in signal complexity during fatigue. The parameter activity is found to increase under fatigue for both dynamic and isometric contractions with mean values of 0.35 and 0.22, respectively. It is observed that mobility, complexity, and BE are lowest during fatigue for both contractions. Maximum accuracy of 95.00% is achieved with the kNN and Hjorth parameters for dynamic signals. It is also found that the reduction of signal complexity during fatigue is more significant in dynamic contractions. This study confirms that the extracted features are suitable for analyzing the complex nature of sEMG signals. Hence, the proposed approach can be used for analyzing the complex characteristics of sEMG signals under various myoneural conditions.

We revisit the DOUBLE DIGEST problem, which occurs in sequencing of large DNA strings and consists of reconstructing the relative positions of cut sites from two different enzymes. We first show that DOUBLE DIGEST is strongly NP-complete, improving upon previous results that only showed weak NP-completeness. Even the (experimentally more meaningful) variation in which we disallow coincident cut sites turns out to be strongly NP-complete. In the second part, we model errors in data as they occur in real-life experiments: we propose several optimization variations of DOUBLE DIGEST that model partial cleavage errors. We then show that most of these variations are hard to approximate. In the third part, we investigate variations with the additional restriction that coincident cut sites are disallowed, and we show that it is NP-hard to even find feasible solutions in this case, thus making it impossible to guarantee any approximation ratio at all.

The problem of resolving genotypes into haplotypes, under the perfect phylogeny model, has been under intensive study recently. All studies so far handled missing data entries in a heuristic manner. We prove that the perfect phylogeny haplotype problem is NP-complete when some of the data entries are missing, even when the phylogeny is rooted. We define a biologically motivated probabilistic model for genotype generation and for the way missing data occur. Under this model, we provide an algorithm, which takes an expected polynomial time. In tests on simulated data, our algorithm quickly resolves the genotypes under high rates of missing entries.

Identifying regions of DNA with extreme statistical characteristics is an important aspect of the structural analysis of complete genomes. Linguistic methods, mainly based on estimating word frequency, can be used for this as they allow for the delineation of regions of low complexity. Low complexity may be due to biased nucleotide composition, by tandem- or dispersed repeats, by palindrome-hairpin structures, as well as by a combination of all these features. We developed software tools in which various numerical measures of text complexity are implemented, including combinatorial and linguistic ones. We also added Hurst exponent estimate to the software to measure dependencies in DNA sequences. By applying these tools to various functional genomic regions, we demonstrate that the complexity of introns and regulatory regions is lower than that of coding regions, whilst Hurst exponent is larger. Further analysis of promoter sequences revealed that the lower complexity of these regions is associated with long-range correlations caused by transcription factor binding sites.

Using the laser speckle contrast imaging and wavelet-based analyses, we investigate a latent (a "hidden") stage of the development of intracranial hemorrhages (ICHs) in newborn rats. We apply two measures based on the continuous wavelet-transform of blood flow velocity in the sagittal sinus, namely, the spectral energy in distinct frequency ranges and a multiscality degree characterizing complexity of experimental data. We show that the wavelet-based multifractal formalism reveals changes in the cerebrovascular blood flow at the development of ICH.

Based on the laser speckle contrast imaging (LSCI) and the multiscale entropy (MSE), we study in this work the blood flow dynamics at the levels of cerebral veins and the surrounding network of microcerebral vessels. We discuss how the phenylephrine-related acute peripheral hypertension is reflected in the cerebral circulation and show that the observed changes are scale-dependent, and they are significantly more pronounced in microcerebral vessels, while the macrocerebral dynamics does not demonstrate authentic inter-group distinctions. We also consider the permeability of blood–brain barrier (BBB) and study its opening caused by sound exposure. We show that alterations associated with the BBB opening can be revealed by the analysis of blood flow at the level of macrocerebral vessels.

All complex life on Earth is composed of ‘eukaryotic’ cells. Eukaryotes arose just once in 4 billion years, via an endosymbiosis — bacteria entered a simple host cell, evolving into mitochondria, the ‘powerhouses’ of complex cells. Mitochondria lost most of their genes, retaining only those needed for respiration, giving eukaryotes ‘multi-bacterial’ power without the costs of maintaining thousands of complete bacterial genomes. These energy savings supported a substantial expansion in nuclear genome size, and far more protein synthesis from each gene.

The efficiency of complex industrialized farming systems are compared to that of natural environmental systems while taking into account economic and environmental benefit as well as the needs of farmers and cattle.

Prolonged standing is related to various health problems such as lower back pain and lower extremity discomfort. This study was to investigate the effects of prolonged standing on posture control and whether the sloped surface is beneficial to adults who are required to stand for a long period of time. Twenty young healthy adults (age: $20.5\pm 0.8$ years, height: $165.2\pm 8.5$ cm, weight: $56.6\pm 9.6$ kg, 6 males and 14 females) participated in this study. They were asked to perform a sixty-second quiet-standing evaluation first (i.e. the pre-test condition), then the thirty-minute standing test, and followed by the sixty-second standing test again (i.e. the post-test condition). They stood barefoot quietly on a force plate watching a video on television located 2 m ahead. Three sloped conditions, i.e. the level ground, inclined (with the ankle dorsiflexed), and declined (with the ankle plantarflexed), were randomly examined on separate days. The trajectory, maximal anteroposterior/mediolateral displacement, sway area, and complexity index (CI) of the center of pressure (CoP) during the standing tests were analyzed. Ten-point visual analogue scale (VAS) for perceived fatigue was also recorded. One-way ANOVA and paired t-test were used to analyze postural changes among sloped conditions before and after the prolonged standing.

Signs of fatigue (VAS were $4.1\pm 1.9$, $4.6\pm 1.3$ and $3.5\pm 1.5$ for the level, inclined, and declined conditions, respectively) and significant increases in all CoP measures for the three slope conditions after thirty minutes of standing (all $p<0.05$) were noted. Trajectory was greatest under inclined, followed by the declined and level conditions ($p<0.05$). The CI was generally greater under the declined surface than the level and inclined surfaces along with the thirty-minute standing.

These findings indicated that prolonged standing resulted in fatigue and increased postural changes, particularly on the inclined surface. A greater complexity on the declined surface implied that participants had better adaptability while standing on a declined surface than a flat or inclined surface. Current findings suggested that a declined surface could be a suitable choice for a prolonged standing and further studies are warranted to evaluate its efficacy on different career workers.

Detection of mental stress has been receiving great attention from the researchers for many years. Many studies have analyzed electroencephalogram signals in order to estimate mental stress using linear methods. In this paper, a novel nonlinear stress assessment method based on multivariate multiscale entropy has been introduced. Since the multivariate multiscale entropy method characterizes the complexity of nonlinear time series, this research determines the mental stress of human during cognitive workload using complexity of electroencephalogram (EEG) signals. To perform this work, 36 subjects including 9 men and 27 women were participated in the cognitive workload experiment. Multivariate multiscale entropy method has been applied to electroencephalogram data collected from those subjects for estimating mental stress in terms of complexity. The complexity feature of brain electroencephalogram signals collected during resting and cognitive workload has shown statistically significant ($p<0.01$) differences across brain regions and mental tasks which can be implemented practically for building stress detection system. In addition, the complexity profile of electroencephalogram signals has shown that higher stress is reflected in good counting compared to bad counting. Moreover, the support vector machine (SVM) has shown promising classification between resting and mental counting states by providing 80% sensitivity, 100% specificity and 90% classification accuracy.