Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machine learning (ML)-based classification methods with supervised learning support vector machine (SVM) algorithms, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Trees (DTs). Also, there are frequent approaches based on the analysis of EEG data as time series and their classification with Recurrent Neural Networks (RNNs), as well as with improved algorithms such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM), and Gated Recurrent Units (GRUs). In the present work, we evaluate the classification method based on the comparison of domain limits for two specific characteristics of EEG signals: the statistical correlation of pairs of signals and the size of the spectral peak detected in theta, alpha, and beta bands. This study provides some interpretations regarding the electrical activity of the brain, consolidating and complementing the results of similar research. The classification method used is simple and easy to apply and interpret. The analysis of EEG data showed that the theta and beta frequency bands were the only discriminators between the relaxation and arithmetic calculation states. Notably, the F7 signal, which used the spectral peak criterion, achieved the best classification accuracy (100%) in both theta and beta bands for the subjects with the best results in performing calculations. Also, our study found the Fz signal to be a good sensor in the theta band for mental task discrimination for all subjects in the group with 90% accuracy.

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Factores de riesgo asociados a la agresividad en adolescentes: diferenciación por sexo

La agresividad se ha reconocido como prevalente en la población adolescente, debido a su carácter impulsivo y de inestabilidad emocional, que también caracteriza a la adolescencia. Ello lleva a la necesidad de conocer la frecuencia y la relación de los factores de riesgo de la agresividad en población adolescente, explorando sus diferencias según sexo. Así, esta investigación de enfoque cuantitativo, diseño no experimental, transversal de alcance correlacional, estudió la presencia y las relaciones entre la agresividad y sus factores de riesgo en 212 mujeres y 188 hombres adolescentes, entre 12 y 17 años de edad. Para lo cual, se aplicó una ficha de valoración de factores de riesgo y el cuestionario de agresividad premeditada e impulsiva (CAPI-A). Los hallazgos muestran mayor prevalencia de agresividad impulsiva, y la presencia de más relaciones con factores de riesgo en mujeres respecto a los hombres. Los factores de riesgos relevantes en las mujeres son las actitudes hacia la norma, la percepción sobre la agresividad y los sentimientos; en los hombres, las conductas de riesgo y los sentimientos. Esto resulta de utilidad para la comprensión de la agresividad como pauta comportamental, y el diseño de intervenciones preventivas de la agresividad y sus consecuencias.

The aggressiveness has been recognized as prevalent in teenage population due to their impulsive character, with that emotional instability which characterizes adolescence. This conducts to the need of knowing the frequency and the relationship of risk factors of aggressiveness in teenage population, exploring their differences according to sex. Thus, this research of quantitative approach, non-experimental design, cross-sectional of correlational scope, studied the presence and the relations between aggressiveness and its risk factors in 212 teenage women and 188 teenage men between 12 and 17 years of age. For this, a record card of risk factor evaluation was implemented along with the questionnaire of premediated / impulsive aggressiveness (CAPI-A in Spanish). The findings show a higher prevalence of impulsive aggressiveness and the presence of more relations to risk factors in women than in men. The relevant risk factors in women are the attitudes towards norms, the perception about aggressiveness and the feelings; in men, risk conducts and feelings are the ones to mention. These results useful for the comprehension of aggressiveness as a behavioral pattern, and the design of preventive interventions towards aggressiveness and their consequences.

Meaningful Secret Image Sharing with Saliency Detection.

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.

Transfer Entropy of West Pacific Earthquakes to Inner Van Allen Belt Electron Bursts.

Lithosphere-ionosphere non-linear interactions create a complex system where links between different phenomena can remain hidden. The statistical correlation between West Pacific strong earthquakes and high-energy electron bursts escaping trapped conditions was demonstrated in past works. Here, it is investigated from the point of view of information. Starting from the conditional probability statistical model, which was deduced from the correlation, the Shannon entropy, the joint entropy, and the conditional entropy are calculated. Time-delayed mutual information and transfer entropy have also been calculated analytically here for binary events: by including correlations between consecutive earthquake events, and between consecutive earthquakes and electron bursts. These quantities have been evaluated for the complex dynamical system of lithosphere-ionosphere; although the expressions calculated by probabilities resulted in being valid for each pair of binary events. Peaks occurred for the same time delay as in the correlations, Δt = 1.5-3.5 h, and as well as for a new time delay, Δt = -58.5--56.5 h, for the transfer entropy; this last is linked to EQ self-correlations from the analysis. Even if the low number of self-correlated EQs makes this second peak insignificant in this case, it is of interest to separate the non-linear contribution of the transfer entropy of binary events in the study of a complex system.

Correlation between phenotypic virulence traits and antibiotic resistance in Pseudomonas aeruginosa clinical isolates.

Pseudomonas aeruginosa is a ubiquitous pathogen capable of infecting virtually all tissues and its one of the standout amongst the most hazardous microorganisms of high morbidity and mortality rates especially in debilitated patients with few successful antibiotic choices available. This pathogen regulating most virulence traits by that so-called quorum sensing (QS), a cell to cell communication system. the present study was intended to phenotypically evaluate the activity of specific virulence traits (including swarming and swimming motility, protease, pyocyanin, and biofilm production) in Pseudomonas aeruginosa clinical isolates and assess the statistical correlation between these traits and antibiotic resistance. One hundred and thirteen bacterial isolates were obtained from different clinical samples and identified as P. aeruginosa, among them, 73.4% have the ability to forming biofilm with different degrees; 59.2% were able to produce pyocyanin pigment while all isolates having the ability to make swarming and swimming motility and able to produce protease enzyme with different degrees. The isolates that produce the higher levels of the virulence traits were identified by both biochemical using Vitek2 automated system and genetically via 16s rRNA gene analysis. The statistical analysis results indicate that a positive significant correlation was found between biofilm formation and other studied virulence traits except for protease (r = 0.584: 0.324, P < 0.05) while a non-significant correlation was found between biofilm formation and protease activity (r = 0.105, P ˃ 0.05). Swimming and swarming motility have a positive significant correlation with other studied virulence traits (r = 0.613: 0.297, P < 0.05) except for protease. Pyocyanin pigment production have a positive significant correlation with other studied virulence traits (r = 0.33: 0.297, P < 0.05) except for protease. on the other hand, negative significant correlations were found between biofilm formation, swimming; and swarming motility, Pyocyanin pigment production, and the susceptibility of antibiotics (r = -0.512: -0.281, P < 0.05). Detection of such correlations in P. aeruginosa is useful for study the behavior of this pathogen and may be provide a new target for the treatment of MDR infections.

Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain.

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

Brain-wide functional diffuse optical tomography of resting state networks.

Objective.Diffuse optical tomography (DOT) has the potential in reconstructing resting state networks (RSNs) in human brains with high spatio-temporal resolutions and multiple contrasts. While several RSNs have been reported and successfully reconstructed using DOT, its full potential in recovering a collective set of distributed brain-wide networks with the number of RSNs close to those reported using functional magnetic resonance imaging (fMRI) has not been demonstrated.Approach.The present study developed a novel brain-wide DOT (BW-DOT) framework that integrates a cap-based whole-head optode placement system with multiple computational approaches, i.e. finite-element modeling, inverse source reconstruction, data-driven pattern recognition, and statistical correlation tomography, to reconstruct RSNs in dual contrasts of oxygenated (HbO) and deoxygenated hemoglobins (HbR).Main results.Our results from the proposed framework revealed a comprehensive set of RSNs and their subnetworks, which collectively cover almost the entire neocortical surface of the human brain, both at the group level and individual participants. The spatial patterns of these DOT RSNs suggest statistically significant similarities to fMRI RSN templates. Our results also reported the networks involving the medial prefrontal cortex and precuneus that had been missed in previous DOT studies. Furthermore, RSNs obtained from HbO and HbR suggest similarity in terms of both the number of RSN types reconstructed and their corresponding spatial patterns, while HbR RSNs show statistically more similarity to fMRI RSN templates and HbO RSNs indicate more bilateral patterns over two hemispheres. In addition, the BW-DOT framework allowed consistent reconstructions of RSNs across individuals and across recording sessions, indicating its high robustness and reproducibility, respectively.Significance.Our present results suggest the feasibility of using the BW-DOT, as a neuroimaging tool, in simultaneously mapping multiple RSNs and its potential values in studying RSNs, particularly in patient populations under diverse conditions and needs, due to its advantages in accessibility over fMRI.

Safety effectiveness and performance of lane support systems for driving assistance and automation - Experimental test and logistic regression for rare events.

As of 2022, lane-keeping assistance systems and other selected safety technologies will become mandatory in new European vehicles to increase safety for passengers, pedestrians and cyclists. Lane support systems (LSS) are based on advanced computer vision technologies and they are expected to give safety benefits in reducing Ran of Road and head-on crashes comparable to already consolidated physical countermeasures like rumble strips. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for "reading" the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and sections characterized by variable maintenance conditions for pavement and markings. LSS faults were detected in 2.6 % of the sections, running the roads in day light and dry pavement conditions. Logit models were developed to better understand road characteristics and conditions that can affect the system performance. The Firth penalized maximum-likelihood method was applied to estimate the logistic regression coefficients and standard errors to account for the rareness of the events. Results show that luminance coefficient of marking in diffuse lighting conditions (Qd) and horizontal curvature radius (1/R) are the main predictors of system fault. Based on the case study and test conditions, other marking characteristics, longitudinal cracking, verge width and running speed resulted not significant in explaining the probability of fault. Thresholds values for Qd and 1/R are suggested and remarks on road maintenance and design standards presented.

Experimental Identification of Statistical Correlation Between Mechanical Properties of FRP Composite.

Recent prediction on the heavy statistical correlation between the mechanical properties of fiber reinforced composite (FRP) raises new concerns on the accurate reliability evaluation of composite structures, but such statistical correlation still lacks experimental verification. In this work, an experimental methodology is proposed to determine the statistical correlation between mechanical properties of unidirectional FRP composite. A rectangular shaped carbon fiber reinforced plastic (CFRP) specimen with a circular hole is loaded by tension, and 3D digital image correlation (DIC) is employed to characterize the heterogeneous strain field around the hole. Virtual field method (VFM) is used to derive E11, E22, ν12, and G12 of specimens. Specimen configuration considering fiber angle and hole diameter is optimized to achieve accurate determination of correlation coefficients. Experimental results on the linear correlation coefficients between E11, E22, ν12, and G12 agree well with previous theoretical predictions.

Groundwater quality evaluation for different uses in the lower Ketar Watershed, Ethiopia.

Groundwater quality samples from 33 wells were collected in the lower Ketar watershed (Ethiopia) to study its suitability for domestic and irrigation purposes. Samples were evaluated for major ions and physicochemical properties. In 58% of the samples analyzed, Ca2+ is the dominant cation and Na+ dominates the remaining 42% of the samples. Among the anions found during analyzation, HCO3- is the solo dominant ion in all the wells sampled. The order of the concentration of the major ions was Ca2+ > Na+ > Mg2+ > K+ for the cations and HCO3- > SO42- > Cl > NO3- for the anions. AquaChem analysis shows that Ca-HCO3 and Na-HCO3 are the major water types in the area. The analyses indicated that the dissolution of fluorite or fluorapatite is the possible source of the high fluoride concentration in the area. And, the interactions between water and rock and cation exchanges mainly determine the water quality. The suitability of the groundwater for use in irrigation was evaluated based on the salinity (EC), SAR, %Na, RSC, PI, KR, and the USSL Salinity diagram. The groundwater from most of the wells can be used for irrigation without any significant restriction except for a few of the wells downstream. Its suitability for domestic use was evaluated by comparing with the WHO standard limits. The parameters limiting the use of this groundwater for drinking purposes are F- (94%), HCO3- (45%), and Ca2+ (33%). All the remaining major cations and anions complied with the WHO standard limits for drinking.

[Correlation: not all correlation entails causality]. / Correlación: no toda correlación implica causalidad.

The concept of correlation entails having a couple of observations (X and Y), that is to say, the value that Y acquires for a determined value of X; the correlation makes it possible to examine the trend of two variables to be grouped together. We know that, with increasing age, blood pressure figures also increase, therefore, if we want to answer a research question like "what is the connection between age and blood pressure?" the relevant statistical test is a correlation test. This test makes it possible to quantify the magnitude of the correlation between two variables, but it is also helpful for predicting values. If these variables had a perfect correlation, the value of the variable Y could be deduced by knowing the value of X. Because of these advantages, the correlation is one of the most frequently used tests in the clinical setting since, in addition to measuring the direction and magnitude of the association of two variables, it is one of the foundations for prediction models, such as linear regression model, logistic regression model and Cox proportional hazards model.

El concepto de correlación implica contar con un par de observaciones (X y Y), es decir, el valor que toma Y para determinado valor de X; la correlación permite examinar la tendencia de dos variables a ir juntas, por ejemplo, sabemos que al incrementar la edad también aumentan las cifras de presión arterial, por lo tanto, si queremos responder una pregunta de investigación como ¿cuál es la relación entre edad y presión arterial?, la prueba estadística pertinente es una prueba de correlación. Esta prueba permite cuantificar la magnitud de la correlación entre dos variables y ayuda a predecir valores. Si estas variables tuvieran una correlación perfecta se podría inferir el valor de la variable Y conociendo el valor de X. Debido a estas ventajas, la correlación es una de las pruebas más usadas en el ámbito clínico, ya que además de medir la dirección y magnitud de la asociación de dos variables, es uno de los fundamentos de los modelos de predicción, como los modelos de regresión lineal, logística y riesgos proporcionales de Cox.

Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures.

NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D 13C, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers.

Comparative in vitro/in situ approaches to three biomarker responses of Myriophyllum alterniflorum exposed to metal stress.

Surface water pollution by trace metal elements constitutes problems for both public and terrestrial/aquatic ecosystem health. Myriophyllum alterniflorum (alternate watermilfoil), an aquatic macrophyte known for bioaccumulating this type of pollutant, is an attractive species for plant biomonitoring within the scope of environmental research. The two metal elements copper (Cu) and cadmium (Cd) are considered in the present study. Cu is essential for plant development at low concentrations, while very high Cu concentrations are detrimental or even lethal to most plants. On the other hand, Cd is usually toxic even at low concentrations since it adversely affects the physiological plant functions. In order to check whether watermilfoil could be used for the in situ biomonitoring of Cu or Cd pollution in rivers, the plant biomarker sensitivity is first tested during long-term in vitro assays. Three markers specific to oxidative stress (glucose-6-phosphate dehydrogenase, malondialdehyde and α-tocopherol) are evaluated by varying the pollutant concentration levels. Given the absence of effective correlations between Cu and all biomarkers, the response profiles actually reveal a dependency between Cd concentration and malondialdehyde or α-tocopherol biomarkers. Conversely, preliminary in situ assays performed at 14 different localities demonstrate some clear correlations between all biomarkers and Cu, whereas the scarcity of Cd-contaminated rivers prevents using the statistical data. Consequently, the three indicated biomarkers appear to be effective for purposes of metal exposure analyses; moreover, the in situ approach, although preliminary, proves to be paramount in developing water biomonitoring bases.

Correlation of Atherosclerotic Risk Factors with the Size of Abdominal Aortic Aneurysm (AAA).

INTRODUCTION: Abdominal aortic aneurysm represents a local pathological dilatation of the abdominal aorta. It is caused by structural weakness of aortic wall but there are many other risk factors that may positively correlate with incidence of AAA like hypertension, smoking, male gender, older age, family history etc. AIM: The purpose of the study was to evaluate the correlation of atherosclerotic risk factors and the size of aortic aneurysm in patients that were admitted for the surgical treatment at the Clinic for cardiovascular surgery in Sarajevo during period 2016-2019. METHODS: The study was designed as a retrospective study with one group of patients that was conducted at the Clinical Center of the University of Sarajevo at the Clinic for Cardiovascular Surgery. It included 150 patients, 126 males and 24 females, all of them with infrarenal localization of AAA. From medical records we have collected relevant anamnestic data (age, gender, positive family history, diabetes mellitus, hypertension, hyperlipidemia, smoking, alcohol consumption and obesity). The size of aneurysm was determined by both ultrasound and CT arteriography. The data are processed in the Statistical Package for Social Sciences Ver. 22.0. The results are tabulated or graphically showed, and level of statistical significance was set at p <0.05. RESULTS: Total amount of 129 of patients (86%) had hypertension, 57.3% (n=86) of them were smokers, 18.7% (n = 28) were former smokers, and 24% (n = 36) were non-smokers Blood lipid level analysis have shown that 44% (n = 66) of patients were normolipemic, while elevated blood lipid levels were found in 56% (n = 84) of patients. Diabetes mellitus was present in 17.3% (n = 26) of patients, 2.7% (n = 4) of them had an insulin-dependent form, while 14.7% (n = 22) of the analyzed patients had insulin independent DM. Almost half of total number of patients (46%, n = 69) were obese. 19.3% (n = 29) of patients consumed alcohol while the 80.7% (n = 121) denied alcohol consumption. Ratio of males in comparison to females was 5:1. The average age in males was 69.79 ± 8.16 years and 72.13 ± 9.11 years in females. Significant statistical correlation of AAA size and risk of atherosclerosis factor has not been established. We have found that there is a significant positive correlation between size of aneurysm and risk of rupture (p= 0,000<0,05). CONCLUSION: Although risk factors of atherosclerosis were present, statistically positive correlation was not confirmed between the size of AAA and analyzed risk factors.

Cortical Statistical Correlation Tomography of EEG Resting State Networks.

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. A new computational framework for reconstructing RSNs with human EEG data has been developed in the present study. The proposed framework utilizes independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was evaluated on three sets of resting-state EEG data obtained in the comparison of two conditions: (1) healthy controls with eyes closed and eyes open; (2) healthy controls and individuals with a balance disorder; (3) individuals with a balance disorder before and after receiving repetitive transcranial magnetic stimulation (rTMS) treatment. In these analyses, the same group of five RSNs with similar spatial and spectral patterns were successfully reconstructed by the proposed framework from each individual EEG dataset. These EEG RSN tomographic maps showed significant similarity with RSN templates derived from functional magnetic resonance imaging (fMRI). Furthermore, significant spatial and spectral differences of RSNs among compared conditions were observed in tomographic maps as well as their spectra, which were consistent with findings reported in the literature. Beyond the success of reconstructing EEG RSNs spatially on the cortical surface as in fMRI studies, this novel approach defines RSNs further with spectra, providing a new dimension in understanding and probing basic neural mechanisms of RSNs. The findings in patients' data further demonstrate its potential in identifying biomarkers for the diagnosis and treatment evaluation of neuropsychiatric disorders.

Relationship between the curve of Spee and craniofacial variables: A regression analysis.

INTRODUCTION: The aim of this regression analysis was to identify the determining factors, which impact the curve of Spee during its genesis, its therapeutic reconstruction, and its stability, within a continuously evolving craniofacial morphology throughout life. MATERIAL AND METHODS: We selected a total of 107 patients, according to the inclusion criteria. A morphological and functional clinical examination was performed for each patient: plaster models, tracing of the curve of Spee, crowding, Angle's classification, overjet and overbite were thus recorded. Then, we made a cephalometric analysis based on the standardized lateral cephalograms. In the sagittal dimension, we measured the values of angles ANB, SNA, SNB, SND, I/i; and the following distances: AoBo, I/NA, i/NB, SE and SL. In the vertical dimension, we measured the values of angles FMA, GoGn/SN, the occlusal plane, and the following distances: SAr, ArD, Ar/Con, Con/Gn, GoPo, HFP, HFA and IF. The statistical analysis was performed using the SPSS software with a significance level of 0.05. RESULTS: Our sample including 107 subjects was composed of 77 female patients (71.3%) and 30 male patients (27.8%) 7 hypodivergent patients (6.5%), 56 hyperdivergent patients (52.3%) and 44 normodivergent patients (41.1%). Patients' mean age was 19.35±5.95 years. The hypodivergent patients presented more pronounced curves of Spee compared to the normodivergent and the hyperdivergent populations; patients in skeletal Class I presented less pronounced curves of Spee compared to patients in skeletal Class II and Class III. These differences were non significant (P>0.05). The curve of Spee was positively and moderately correlated with Angle's classification, overjet, overbite, sellion-articulare distance, and breathing type (P<0.05). We found no correlation between age, gender and the other parameters included in the study with the curve of Spee (P>0.05). Seventy five percent (75%) of the hyperdivergent patients with an oral breathing presented an overbite of 3mm, which is quite excessive given the characteristics often admitted for this typology; this parameter could explain the overbite observed in the hyperdivergent population included in this study. For the multivariate analysis, the overbite and the sellion-articulare distance remained independently related to the curve of Spee according to the breathing type, Angle's classification, and overjet. This regression model explains 21.4% of the changes in the curve of Spee.

Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends.

BACKGROUND: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. OBJECTIVE: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. METHODS: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. RESULTS: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. CONCLUSIONS: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy.

Relationships between facial divergence and DMD parameters.

INTRODUCTION: The aim of this study was to examine the relationship between facial divergence and the parameters of dentomaxillary discrepancy (DMD), in particular crowding, the curve of Spee and the position of the incisors in the sagittal dimension. MATERIAL AND METHODS: A total of 90 young adult patients was selected from among the Moroccan orthodontic population attending the dentofacial orthopedic department and satisfying the following inclusion criteria: complete permanent dentition and a skeletal class I pattern with no previous orthodontic treatment, no crossbite, no periodontal disease, no mandibular asymmetry and no condylodiscal disunion. On cephalometric tracings, measurements were made of angles FMA, Go-Gn/SN, Occ/SN in the vertical direction, and of the values I/NA et i/NB in the sagittal direction. The curve of Spee and dental crowding were assessed using the one-way ANOVA test and the Bonferroni post-hoc test. Correlation analysis was performed between divergence and the different variables measured, using SPSS software with a 0.05 significance threshold. RESULTS: Patients recruited for the study had a mean age of 19.8±0.5 and were distributed as follows: 28 normodivergent, 31 hypodivergent and 31 hyperdivergent, 42 females and 48 males. Comparison showed that hypodivergent subjects had less crowding than hypo- or normodivergent individuals (P<0.05). Hypodivergent subjects had a more pronounced curve of Spee than the other two groups. This difference was not significant (P>0.05). Hyperdivergent subjects presented more labioversion and vestibular positioning of the incisors compared with the hypodivergent (P<0.05) and normodivergent (P<0.05) groups. Correlation analysis showed that crowding and the incisor positions in millimeters and in degrees were positively correlated to a moderate extent with facial divergence (r=0.3, r=0.5, r=0.4; P<0.05), while the curve of Spee was not (P>0.05). No correlation was found between age or sex and the DMD parameters (P>0.05).

Chemical Structure-Biological Activity Models for Pharmacophores' 3D-Interactions.

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners' (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.