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Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been proposed to meet this requirement, among which encryption can be considered as one of the first and most effective solutions. The continuous increase in the computational power of computers and the rapid development of artificial intelligence techniques have made many previous encryption solutions not secure enough to protect data. Therefore, there is always a need to provide new and more efficient strategies for encrypting information. In this article, a two-way approach for information encryption based on chaos theory is presented. To this end, a new chaos model is first proposed. This model, in addition to having a larger key space and high sensitivity to slight key changes, can demonstrate a higher level of chaotic behavior compared to previous models. In the proposed method, first, the input is converted to a vector of bytes and first diffusion is applied on it. Then, the permutation order of chaotic sequence is used for diffusing bytes of data. In the next step, the chaotic sequence is used for applying second diffusion on confused data. Finally, to further reduce the data correlation, an iterative reversible rule-based model is used to apply final diffusion on data. The performance of the proposed method in encrypting image, text, and audio data was evaluated. The analysis of the test results showed that the proposed encryption strategy can demonstrate a pattern close to a random state by reducing data correlation at least 28.57% compared to previous works. Also, the data encrypted by proposed method, show at least 14.15% and 1.79% increment in terms of MSE and BER, respectively. In addition, key sensitivity of 10 −28 and average entropy of 7.9993 in the proposed model, indicate its high resistance to brute-force, statistical, plaintext and differential attacks.

Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities.

Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.

A re-analysis of intraclass correlation (ICC) theory is presented together with Monte Carlo simulations of ICC probability distributions. A partly revised and simplified theory of the single-score ICC is obtained, together with an alternative and simple recipe for its use in reliability studies. Our main, practical conclusion is that in the analysis of a reliability study it is neither necessary nor convenient to start from an initial choice of a specified statistical model. Rather, one may impartially use all three single-score ICC formulas. A near equality of the three ICC values indicates the absence of bias (systematic error), in which case the classical (one-way random) ICC may be used. A consistency ICC larger than absolute agreement ICC indicates the presence of non-negligible bias; if so, classical ICC is invalid and misleading. An F-test may be used to confirm whether biases are present. From the resulting model (without or with bias) variances and confidence intervals may then be calculated. In presence of bias, both absolute agreement ICC and consistency ICC should be reported, since they give different and complementary information about the reliability of the method. A clinical example with data from the literature is given.

Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions. We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor. We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods. We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances. The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.

This comprehensive systematic review and meta-analysis aims to quantify the association between different types of childhood maltreatment and suicidality. We searched five bibliographic databases, including Medline, PsychINFO, Embase, Web of Science and CINAHL, until January 2018. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regressions. Heterogeneity was quantified using the I2 statistic and formal publication bias tests were undertaken. The methodological quality of the studies was critically appraised and accounted in the meta-regression analyses. Data from 68 studies based on n = 261.660 adults were pooled. All different types of childhood maltreatment including sexual abuse [odds ratio (OR) 3.17, 95% confidence interval (CI) 2.76–3.64], physical abuse (OR 2.52, 95% CI 2.09–3.04) and emotional abuse (OR 2.49, 95% CI 1.64–3.77) were associated with two- to three-fold increased risk for suicide attempts. Similar results were found for the association between childhood maltreatment and suicidal ideation. Complex childhood abuse was associated with a particularly high risk for suicide attempts in adults (OR 5.18, 95% CI 2.52–10.63). Variations across the studies in terms of demographic and clinical characteristics of the participants and other core methodological factors did not affect the findings of the main analyses. We conclude that there is solid evidence that childhood maltreatment is associated with increased odds for suicidality in adults. The main outstanding challenge is to better understand the mechanisms which underpin the development of suicidality in people exposed to childhood maltreatment because current evidence is scarce.

Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon–taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.

In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI—not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index.

AbstractObjectiveTo consolidate evidence from systematic reviews and meta-analyses investigating the association between reproductive factors in women of reproductive age and their subsequent risk of cardiovascular disease.DesignUmbrella review.Data sourcesMedline, Embase, and Cochrane databases for systematic reviews and meta-analyses from inception until 31 August 2019.Review methodsTwo independent reviewers undertook screening, data extraction, and quality appraisal. The population was women of reproductive age. Exposures were fertility related factors and adverse pregnancy outcomes. Outcome was cardiovascular diseases in women, including ischaemic heart disease, heart failure, peripheral arterial disease, and stroke.Results32 reviews were included, evaluating multiple risk factors over an average follow-up period of 7-10 years. All except three reviews were of moderate quality. A narrative evidence synthesis with forest plots and tabular presentations was performed. Associations for composite cardiovascular disease were: twofold for pre-eclampsia, stillbirth, and preterm birth; 1.5-1.9-fold for gestational hypertension, placental abruption, gestational diabetes, and premature ovarian insufficiency; and less than 1.5-fold for early menarche, polycystic ovary syndrome, ever parity, and early menopause. A longer length of breastfeeding was associated with a reduced risk of cardiovascular disease. The associations for ischaemic heart disease were twofold or greater for pre-eclampsia, recurrent pre-eclampsia, gestational diabetes, and preterm birth; 1.5-1.9-fold for current use of combined oral contraceptives (oestrogen and progesterone), recurrent miscarriage, premature ovarian insufficiency, and early menopause; and less than 1.5-fold for miscarriage, polycystic ovary syndrome, and menopausal symptoms. For stroke outcomes, the associations were twofold or more for current use of any oral contraceptive (combined oral contraceptives or progesterone only pill), pre-eclampsia, and recurrent pre-eclampsia; 1.5-1.9-fold for current use of combined oral contraceptives, gestational diabetes, and preterm birth; and less than 1.5-fold for polycystic ovary syndrome. The association for heart failure was fourfold for pre-eclampsia. No association was found between cardiovascular disease outcomes and current use of progesterone only contraceptives, use of non-oral hormonal contraceptive agents, or fertility treatment.ConclusionsFrom menarche to menopause, reproductive factors were associated with cardiovascular disease in women. In this review, presenting absolute numbers on the scale of the problem was not feasible; however, if these associations are causal, they could account for a large proportion of unexplained risk of cardiovascular disease in women, and the risk might be modifiable. Identifying reproductive risk factors at an early stage in the life of women might facilitate the initiation of strategies to modify potential risks. Policy makers should consider incorporating reproductive risk factors as part of the assessment of cardiovascular risk in clinical guidelines.Systematic review registrationPROSPERO CRD42019120076.

AbstractObjectiveTo evaluate the association between consumption of ultra-processed foods and all cause mortality.DesignProspective cohort study.SettingSeguimiento Universidad de Navarra (SUN) cohort of university graduates, Spain 1999-2018.Participants19 899 participants (12 113 women and 7786 men) aged 20-91 years followed-up every two years between December 1999 and February 2014 for food and drink consumption, classified according to the degree of processing by the NOVA classification, and evaluated through a validated 136 item food frequency questionnaire.Main outcome measureAssociation between consumption of energy adjusted ultra-processed foods categorised into quarters (low, low-medium, medium-high, and high consumption) and all cause mortality, using multivariable Cox proportional hazard models.Results335 deaths occurred during 200 432 persons years of follow-up. Participants in the highest quarter (high consumption) of ultra-processed foods consumption had a higher hazard for all cause mortality compared with those in the lowest quarter (multivariable adjusted hazard ratio 1.62, 95% confidence interval 1.13 to 2.33) with a significant dose-response relation (P for linear trend=0.005). For each additional serving of ultra-processed foods, all cause mortality relatively increased by 18% (adjusted hazard ratio 1.18, 95% confidence interval 1.05 to 1.33).ConclusionsA higher consumption of ultra-processed foods (>4 servings daily) was independently associated with a 62% relatively increased hazard for all cause mortality. For each additional serving of ultra-processed food, all cause mortality increased by 18%.Study registrationClinicalTrials.gov NCT02669602.