Evaluating the legibility of urban parks based on neuroscience and the FVFL model

PurposeThe purpose of the study is to quantitatively evaluate the role of landscape values and factors in urban parks experimentally based on neuroscience.Design/methodology/approachIn the first step, ten major parks were selected out of 59 regional and trans-regional parks in Tehran for field study analysis. Next, considering the diversity and abundance of landscape elements in the selected parks, Mellat Park was chosen for the case study.FindingsThe fixation duration of the factors has an average correlation coefficient of 0.5865, −0.5035 and −0.5125 for the overall sketch map, quality and accuracy, respectively. The results indicated that the “quality of people's cognitive maps” has a direct relation to fixation duration on “human-made factors” and an inverse relation to fixation duration on “natural factors” and “human activities and behavioral factors” in the park.Practical implicationsThe results can pave the way for further research in the interdisciplinary fields of landscape architecture and neuroscience.Originality/valueLegibility is a superior quality of urban spaces that profoundly affect how people perceive and behave.

CHANGES IN SOME FRACTIONS OF CYTOKINES IN CHILDREN WITH IRON DEFICIENCY ANEMIA

The aim of the study was to investigate the effect of iron deficiency anaemia on certain cytokine fractions (IL-2, INF-γ, TNF-α) in children aged 6 months to 5 years in Azerbaijan. Methods. The study involved 123 children. According to the hematological and biochemical blood parameters of children aged 6 months to 5 years, 95 children (58 boys, 37 girls) were diagnosed as having anaemia of varying degrees: 32 children had mild iron deficiency anaemia, 37 children had moderate iron deficiency anaemia, and 26 children had severe iron deficiency anaemia. Results. The study demonstrates the level of INF-γ is lower in the general group of children with iron deficiency anaemia compared to the control group (2.5±1.2 pg/ml and 3.9±1.6 pg/ml, respectively). In the general group of children with iron deficiency anaemia, the average correlation coefficient (r=+0.7) between INF-γ with haemoglobin and with serum ferritin (r=+0.6) has been shown. Taking into account the weakening of INF-γ in children with iron deficiency anaemia during the study period, we investigated the annual incidence of acute respiratory viral infections among the general group of children with iron deficiency anaemia and the control group (63.2% and 25%, respectively). There were no significant changes in TNF-α value ​​in children of the general group with iron deficiency anaemia compared to the control group (2.7±1.5 pg / ml, 2.7±1.1 pg / ml, respectively). The IL-2 index in the general group was lower than in the control group (1.6±1.0 pg / ml, 2.9±1.6 pg / ml, respectively). Children in the general group with iron deficiency anaemia had an average correlation coefficient (r=+0.65) IL-2 with haemoglobin and a high correlation coefficient with serum ferritin (r=+0.8). The incidence of pneumonia in the group of children with iron deficiency anaemia was 17.9%. Conclusion. The obtained lowered results of İNF-γ and İL-2 in comparison with the control group and an increased incidence of respiratory diseases indicate a possible reduction in cellular immunity.

Trends of CO and NO2 Pollutants Change in Iran during Covid-19 Pandemic using Time-Series Sentinel-5 Images in Google Earth Engine

The end of the Persian year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in Iran. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satellite data were in agreement with the in-situ data (average correlation coefficient =0.7). Moreover, the concentration of NO 2 and CO pollutants in 2020 was 5% lower than in 2019, indicating the observance of quarantine rules as well as people’s initial fear of the Coronavirus. Contrarily, these pollutants in 2021 were higher than those in 2020 by 5%, which could be due to high vehicle traffic and the lack of serious policy by the government to ban urban and interurban traffic. Furthermore, the increase of these pollutants in 2021 was followed by an increase in the deaths caused by Covid-19 and triggering the fourth peak in the Covid-19 cases, signifying a link between exposure to air pollution and Covid-19 mortality in Iran.

Changes in pulmonary microcirculation after COVID-19

The endothelium is a tissue most vulnerable to the SARS-CoV-2 virus. Systemic endothelial dysfunction leads to the development of endothelitis which causes the main manifestations of the disease and systemic disturbance of microcirculation in various organs. Pulmonary microcirculatory damage, the most striking clinical manifestation, was the reason to perform SPECT to detect microcirculation disorders.Aim. To assess microcirculatory changes in the lungs of patients who had no previous respiratory diseases and had a COVID-19 infection at different times from the onset of the disease.Methods. SPECT data were analyzed in 136 patients who had a proven coronavirus infection of varying severity from May 2020 to June 2021.Results. All patients showed changes in microcirculation in the lungs in the post-COVID period. The severity of microcirculation disorders had a significant correlation (rs = 0.76; p = 0.01) with the degree of damage to the pulmonary parenchyma and an average correlation (rs = 0.48; p = 0.05) with the timing of the post-COVID period and the degree of residual lesions on CT (rs = 0.49; p = 0.01). The examined patients with persistent clinical complaints had pulmonary microcirculatory lesions, which may indicate the development of vasculitis, at all stages of the post-COVID period. Despite regression of the lesions confirmed by CT in 3 to 6 months after the acute COVID-19 infection, specialists from Russian and other countries report that 30–36% of patients develop pulmonary fibrosis. Similar changes were identified in 19.1% of the examined patients in our study.Conclusion. Microcirculation disorders are detected in all patients in the post-COVID period, irrespective of the severity according to CT. Progressive decrease in microcirculation in the lower parts of the lungs, local zones of hypoperfusion with the critically low accumulation of radiopharmaceuticals, persistent areas of compaction of the lung tissue (so-called “ground glass”), reticular changes, and the development of traction bronchiectasis, a decrease in the diffusion capacity of the lungs and alveolar volume may indicate fibrotic lesions with subsequent development of virus-associated interstitial lung disease.

Are People-Centered Intelligences Psychometrically Distinct from Thing-Centered Intelligences? A Meta-Analysis

The Cattell–Horn–Carroll (CHC) or three-stratum model of intelligence envisions human intelligence as a hierarchy. General intelligence (g) is situated at the top, under which are a group of broad intelligences such as verbal, visuospatial processing, and quantitative knowledge that pertain to more specific areas of reasoning. Some broad intelligences are people-centered, including personal, emotional, and social intelligences; others concern reasoning about things more generally, such as visuospatial and quantitative knowledge. In the present research, we conducted a meta-analysis of 87 studies, including 2322 effect sizes, to examine the average correlation between people-to-people intelligences relative to the average correlation between people-to-thing-centered intelligences (and similar comparisons). Results clearly support the psychometric distinction between people-centered and thing-centered mental abilities. Coupled with evidence for incremental predictions from people-centered intelligences, our findings provide a secure foundation for continued research focused on people-centered mental abilities.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

Objective We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. Design We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist’s segmentations (Reader 1) and the model’s segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. Results Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model’s estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model’s estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. Conclusions Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

The Influence of Mass Media on Italian Web Users during COVID-19: an Infodemiological Analysis.

Background: Alongside the COVID-19 pandemic, the world has had to face a growing infodemic, which has caused severe damage to economic and health systems and has often compromised the effectiveness of infection containment regulations. Although this has spread mainly through social media, there are numerous occasions in which the mass media have shared dangerous information, giving resonance to statements without a scientific basis. For these reasons, infoveillance and infodemiology methods are increasingly exploited to monitor online information traffic. The same tools have also been used to make epidemiological predictions. Among these, Google Trends - a service by GoogleTM that quantifies the web interest of users in the form of relative search volume - has often been adopted by the scientific community. Objective: The purpose of this paper is to use Google Trends to estimate the impact of Italian mass media on users' web searches in order to understand the role of press and television channels in both the infodemic and the interest of Italian netizens on COVID-19.Methods: First, from January 2020 to March 2021, we collected the headlines containing specific COVID-19-related keywords published on PubMed, Google, the Ministry of Health, and the most read newspapers in Italy. These keywords were selected based on previous literature and the related queries of Google Trends. Second, we evaluated the percentage of infodemic terms on these platforms. Third, through Google Trends, we looked for correlations between newspaper headlines and Google searches related to COVID-19. We assessed the significance and intensity of changes in user web searches through Welch's t-test and percentage differences or increases. We also highlighted the presence of trends via the Mann-Kendall test. Finally, we analyzed the web interest in infodemic content posted on YouTube. In particular, we counted the number of views of videos containing disinformation for each channel considered.Results: During the first COVID-19 wave, the Italian press preferred to draw on infodemic terms (from 1.6% to 6.3%) and moderately infodemic terms (from 88% to 94%), while scientific sources favored the correct names (from 65% to 88%). The correlational analysis showed that the press heavily influenced users in adopting the terms to identify the novel coronavirus (best average correlation = 0.91, P-value <.001). The use of scientific denominations by the press reached acceptable values only during the third wave (about 80% except for Rai and Mediaset). Web queries about COVID-19 symptoms also appeared to be influenced by the press (best average correlation = .92, P<.007). Furthermore, users have shown a pronounced web interest in YouTube videos of an infodemic nature. Finally, the press gave resonance to serious fake news on COVID-19 that caused pronounced spikes of interest from web users.Conclusions: Our results suggest that the Italian mass media have played a decisive role both in the spread of the infodemic and in addressing netizens' web interest, thus favoring the adoption of terms unsuitable for identifying the novel coronavirus (COVID- 19 disease). Therefore, it is highly advisable that the directors of news channels and newspapers be more cautious and government dissemination agencies exert more control over such news.

Selection of Sampling Interval and Size of Random Sample for Radar Detection in the Moments Space

Recently a novel method for radar detection was conceived to process the scattered signal parameters and detect through its statistical moments. Among the advantages of detection in the moments space stands the opportunity of considering the moments like Gaussian random variables, decreasing the uncertainty about the distribution of the variables used by traditional methods. Therefore, it is very important to study the conditions for assuming the above within certain level of confidence. This work uses real radar signals in order to study the influence of two essential variables for detection in the moments space: the sampling interval and the size of the random sample. Average correlation coefficient, hypothesis testing and numerical goodness-of-fit coefficients are used to estimate the values of the previous variables that allow to take the joint distribution of moments as close to the multivariate Gaussian. The guidelines presented should be taken into account for the proper configuration of detectors in the moments space.

The Influence of Mass Media on Italian Web Users during COVID-19: an Infodemiological Analysis. (Preprint)

BACKGROUND Alongside the COVID-19 pandemic, the world has had to face a growing infodemic, which has caused severe damage to economic and health systems and has often compromised the effectiveness of infection containment regulations. Although this has spread mainly through social media, there are numerous occasions in which the mass media have shared dangerous information, giving resonance to statements without a scientific basis. For these reasons, infoveillance and infodemiology methods are increasingly exploited to monitor online information traffic. The same tools have also been used to make epidemiological predictions. Among these, Google Trends - a service by GoogleTM that quantifies the web interest of users in the form of relative search volume - has often been adopted by the scientific community. OBJECTIVE The purpose of this paper is to use Google Trends to estimate the impact of Italian mass media on users' web searches in order to understand the role of press and television channels in both the infodemic and the interest of Italian netizens on COVID-19. METHODS First, from January 2020 to March 2021, we collected the headlines containing specific COVID-19-related keywords published on PubMed, Google, the Ministry of Health, and the most read newspapers in Italy. These keywords were selected based on previous literature and the related queries of Google Trends. Second, we evaluated the percentage of infodemic terms on these platforms. Third, through Google Trends, we looked for correlations between newspaper headlines and Google searches related to COVID-19. We assessed the significance and intensity of changes in user web searches through Welch's t-test and percentage differences or increases. We also highlighted the presence of trends via the Mann-Kendall test. Finally, we analyzed the web interest in infodemic content posted on YouTube. In particular, we counted the number of views of videos containing disinformation for each channel considered. RESULTS During the first COVID-19 wave, the Italian press preferred to draw on infodemic terms (from 1.6% to 6.3%) and moderately infodemic terms (from 88% to 94%), while scientific sources favored the correct names (from 65% to 88%). The correlational analysis showed that the press heavily influenced users in adopting the terms to identify the novel coronavirus (best average correlation = 0.91, P-value <.001). The use of scientific denominations by the press reached acceptable values only during the third wave (about 80% except for Rai and Mediaset). Web queries about COVID-19 symptoms also appeared to be influenced by the press (best average correlation = .92, P<.007). Furthermore, users have shown a pronounced web interest in YouTube videos of an infodemic nature. Finally, the press gave resonance to serious fake news on COVID-19 that caused pronounced spikes of interest from web users. CONCLUSIONS Our results suggest that the Italian mass media have played a decisive role both in the spread of the infodemic and in addressing netizens' web interest, thus favoring the adoption of terms unsuitable for identifying the novel coronavirus (COVID- 19 disease). Therefore, it is highly advisable that the directors of news channels and newspapers be more cautious and government dissemination agencies exert more control over such news.

Flex-Printed Ear-EEG Sensors for Adequate Sleep Staging at Home

A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep. Here, 10 participants slept at home and were equipped with a cEEGrid and a portable amplifier (mBrainTrain, Serbia). In addition, the EEG of Fpz, EOG_L and EOG_R was recorded. All signals were recorded wirelessly with a smartphone. On average, each participant provided data for M = 7.48 h. An expert sleep scorer created hypnograms and annotated grapho-elements according to AASM based on the EEG of Fpz, EOG_L and EOG_R twice, which served as the baseline agreement for further comparisons. The expert scorer also created hypnograms using bipolar channels based on combinations of cEEGrid channels only, and bipolar cEEGrid channels complemented by EOG channels. A comparison of the hypnograms based on frontal electrodes with the ones based on cEEGrid electrodes (κ = 0.67) and the ones based on cEEGrid complemented by EOG channels (κ = 0.75) both showed a substantial agreement, with the combination including EOG channels showing a significantly better outcome than the one without (p = 0.006). Moreover, signal excerpts of the conventional channels containing grapho-elements were correlated with those of the cEEGrid in order to determine the cEEGrid channel combination that optimally represents the annotated grapho-elements. The results show that the grapho-elements were well-represented by the front-facing electrode combinations. The correlation analysis of the grapho-elements resulted in an average correlation coefficient of 0.65 for the most suitable electrode configuration of the cEEGrid. The results confirm that sleep stages can be identified with electrodes placement around the ear. This opens up opportunities for miniaturized ear-EEG systems that may be self-applied by users.