Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographical range overlap networks and the macroecology of species co-occurrence

Direct interactions among species are only possible if there is some overlap in their geographical distributions. However, despite intense focus of macroecological research on species geographical ranges, relatively little theoretical and empirical work has been done on the evolution of range overlap. In this study we explore a simple model of range overlap based on a log-normal distribution of species range sizes along a one-dimensional domain, with or without absorbing boundary conditions. In particular, we focus on the mean and variance of range overlap distributions, as well as the topology of the resulting overlap networks with respect to their degree distribution, evenness, and betweenness scores. According to the model, there is an approximately linear relationship between many aspects of the distribution of range overlaps and their underlying species distributions, such as their mean and variance. However, the expected mean number of non-zero range overlaps for a given species varied from linear to convex depending on the variance of the underlying geographical range distribution. The expected topology of range overlap networks varied substantially depending on the mean and variance in the corresponding geographical distributions, particularly in the case of the degree and closeness distributions. Finally, we test the expectations of our model against five datasets of altitudinal distributions of Neotropical birds. We found strong departures from the expectations based on our model, which could potentially result from phylogenetic niche conservatism related to altitudinal gradients in environmental conditions, or from the asymmetric colonization of mountains by species from lowlands. Potential applications of range overlap networks to a variety of ecological and evolutionary phenomena are discussed.
Thermal remote sensing over heterogeneous urban and suburban landscapes using sensor-driven super-resolution

Thermal remote sensing is an important tool for monitoring regional climate and environment, including urban heat islands. However, it suffers from a relatively lower spatial resolution compared to optical remote sensing. To improve the spatial resolution, various “data-driven” image processing techniques (pan-sharpening, kernel-driven methods, and machine learning) have been developed in the previous decades. Such empirical super-resolution methods create visually appealing thermal images; however, they may sacrifice radiometric consistency because they are not necessarily sensitive to specific sensor features. In this paper, we evaluated a “sensor-driven” super-resolution approach that explicitly considers the sensor blurring process, to ensure radiometric consistency with the original thermal image during high-resolution thermal image retrieval. The sensor-driven algorithm was applied to a cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) scene of heterogeneous urban and suburban landscape that included built-up areas, low mountains with a forest, a lake, croplands, and river channels. Validation against the reference high-resolution thermal image obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shows that the sensor-driven algorithm can downscale the MODIS image to 250-m resolution, while maintaining a high statistical consistency with the original MODIS and ASTER images. Part of our algorithm, such as radiometric offset correction based on the Mahalanobis distance, may be integrated with other existing approaches in the future.
A potent HNF4α agonist reveals that HNF4α controls genes important in inflammatory bowel disease and Paneth cells

HNF4α has been implicated in IBD through a number of genome-wide association studies. Recently, we developed potent HNF4α agonists, including N-trans caffeoyltyramine (NCT). NCT was identified by structural similarity to previously the previously identified but weak HNF4α agonists alverine and benfluorex. Here, we administered NCT to mice fed a high fat diet, with the goal of studying the role of HNF4α in obesity-related diseases. Intestines from NCT-treated mice were examined by RNA-seq to determine the role of HNF4α in that organ. Surprisingly, the major classes of genes altered by HNF4α were involved in IBD and Paneth cell biology. Multiple genes downregulated in IBD were induced by NCT. Paneth cells identified by lysozyme expression were reduced in high fat fed mice. NCT reversed the effect of high fat diet on Paneth cells, with multiple markers being induced, including a number of defensins, which are critical for Paneth cell function and intestinal barrier integrity. NCT upregulated genes that play important role in IBD and that are downregulated in that disease. It reversed the loss of Paneth cell markers that occurred in high fat diet fed mice. These data suggest that HNF4α could be a therapeutic target for IBD and that the agonists that we have identified could be candidate therapeutics.
Health risk assessment on tunnel workers’ exposure to PM10 based on triangular fuzzy numbers

The triangular fuzzy numbers were introduced to environmental assessment system, health risk assessment model based on triangular fuzzy numbers was built to calculate exposure dose and characterize the health risk of tunnel workers’ exposure to PM10. PM10 was measured on the site at 7 sampling spots of the tunnel. In order to ensure the accuracy of the assessment, the exposure factors of tunnel workers were obtained through questionnaires instead of handbooks. 176 workers including 5 types of workers were selected as samples. The results showed that PM10 exposure concentrations of different types of workers from high to low were excavation workers, blasting workers, secondary-lining workers, slag-out workers, and supporting workers. According to health risk assessment, all of the five types of tunnel workers had health risk. Excavation workers’ highest hazard quotient was mainly due to extremely high concentration of PM10. For secondary-lining workers, their hazard quotient was second only to excavation w.
Deep learning to enable color vision in the dark

Humans perceive light in the visible spectrum (400-700 nm). Some night vision systems use infrared light that is not perceptible to humans and the images rendered are transposed to a digital display presenting a monochromatic image in the visible spectrum. We sought to develop an imaging algorithm powered by optimized deep learning architectures whereby infrared spectral illumination of a scene could be used to predict a visible spectrum rendering of the scene as if it were perceived by a human with visible spectrum light. This would make it possible to digitally render a visible spectrum scene to humans when they are otherwise in complete “darkness” and only illuminated with infrared light. To achieve this goal, we used a monochromatic camera sensitive to visible and near infrared light to acquire an image dataset of printed images of faces under multispectral illumination spanning standard visible red (604 nm), green (529 nm) and blue (447 nm) as well as infrared wavelengths (718, 777, and 807 nm). We then optimized a convolutional neural network with a U-Net-like architecture to predict visible spectrum images from only near-infrared images. This study serves as a first step towards predicting human visible spectrum scenes from imperceptible near-infrared illumination. Further work can profoundly contribute to a variety of applications including night vision and studies of biological samples sensitive to visible light.
An Observability-based Approach to Extract Spatiotemporal Patterns from Power System Koopman Mode Analysis

Abstract Koopman mode analysis has shown considerable promise for the analysis and characterization of global behavior of power system transient processes recorded using wide-area sensors. In this paper, a framework for feature extraction and mode decomposition of spatiotemporal data based on the Koopman operator is presented. A physical interpretation of the Koopman modes as columns of a matrix of observability measures is derived, and criteria for selecting a reduced set of Koopman modes are proposed. This approach allows to selectively isolate and quantify the dominant physical mechanisms underlying the recorded power system data, and it can be used for wide-area monitoring and assessment. Simulation results on both simulated and measured data show that the method can accurately identify the dominant spatial patterns or shapes and temporal patterns associated with specific system behavior.
E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes

Background Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. Methods and findings Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system. Conclusions Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.
Development of a professional learning programme for tourism in higher education

Tourism education has matured over the past fifty years, and several authors have questioned and presented studies on what should be in the content of tourism programmes offered in higher education. This Doctorate in Professional Studies has also questioned what should be included in the content of the programme to be offered at higher education level; and further, developed a new teaching pedagogy for Girne American University, School of Tourism and Hospitality Management, in North Cyprus. The selected research method has been action research, with two main cycles. The first cycle applied in-depth face-to-face interviews with twenty-two representatives from accrediting bodies, the tourism sector and students. Thematic analysis was used to find common themes and conclusions. After the first cycle, the new tourism programme was developed. A three- way structured professional learning programme which included: class room pedagogies, online pedagogies, and on-the-job training (compared to apprenticeship-style learning) was developed. The second cycle took place with four expert representatives, one from each sector, accreditation bodies and two students; in the form of a focus group. I presented the professional learning programme at the focus group and opened the table for discussion on how the programme could be improved. All of the feedback given was incorporated into the programme. Conclusions showed that there is a requirement for a professional learning programme to meet both sector and student requirements, whilst all stakeholders agreed that classroom pedagogies must also continue. Resources offered must sustain North Cyprus tourism-specific contents, yet include online pedagogies and practical training.
Moral judgment and hormones: A systematic literature review

This systematic review of the literature aims to evaluate possible associations between moral judgment and hormones. The electronic databases PsycINFO, PubMed, Scielo, Web of Science, Scopus, and LILACS were used. Twenty studies with different methodological designs were reviewed, covering the hormones cortisol, oxytocin, and testosterone, assessing aspects related to polymorphisms in receptor genes, endogenous levels, and exogenous administration. Taken together, the reviewed studies showed a trend towards an association between hormones and moral judgment, with important specificities involving biological, environmental, and individual aspects. Endogenous levels of cortisol, released under stress, showed negative associations with altruistic and utilitarian decisions only in highly emotionally charged dilemmas. Oxytocin receptor gene polymorphisms (rs2268498, rs237889, and rs2254298) and acute administration of this hormone were associated with variability in moral judgment, with sex as an important moderating variable. Testosterone studies have tended to show a positive association with utilitarian moral judgments, particularly in female and in individuals with low prenatal exposure to this hormone. Knowing how hormones influence moral judgment may help expand our understanding of the plurality of human behavior. However, this area of research is new and still little explored, which does not allow for conclusions with a high level of evidence. Subsequent research will benefit from methodological improvements to extend current findings.
“Slim-Mesh” Technique for Giant Ventral Hernia

Background and Objective: We devised a sutureless “Slim-Mesh” technique to treat ventral hernias, including large-giant/massive ones, reduce intra- and postoperative complications, and lower operation time. Methods: Between September 1, 2009 and October 31, 2020, 43 patients with large (10 – 14.9 cm)-giant (15 – 19.9 cm) and massive (≥ 20 cm) ventral hernia were operated at our Department with the above technique. This was a prospective (79%)-retrospective study. Results: This study comprised 22 males and 21 females. Mean age was 63 years. Large-giant and massive hernias were found intraoperatively in 37 and 6 cases respectively. Mean operation time for all hernias was 116 minutes, 104 for large-giant hernias, and 190 for massive. In 53.4% of cases, hernia-neck operative measurement was larger than preoperative size. In 25.5% of cases, laparoscopy found satellite hernias previously undetected by ultrasound- and/or computed tomography scan. A composite mesh and a noncomposite mesh were used in 95% and 5% of cases respectively. For mesh fixation, titanium tacks and absorbable straps were used in 14% and 86% of cases respectively. Mean length of hospital stay was 2.3 days. Mean follow-up time was 3 years and 4 months. In our study, there were 5 early postoperative complications: 3 seromas, 1 trocar-site hernia, and 1 case of cystitis. We found 2 late small symptomless recurrences (4.6%). Conclusion: The sutureless “Slim-Mesh” technique facilitates intra-abdominal introduction, as well as the handling and fixation of giant and monster (36 × 26 cm) meshes. In our experience, “Slim-Mesh” is safe, simple, and fast, and economical even for large-giant/massive ventral hernia repair.
International consultation on incontinence questionnaire – Urinary incontinence short form ICIQ-UI SF: Validation of its use in a Danish speaking population of municipal employees

Introduction Worldwide, the estimated prevalence of urinary incontinence is 8.7%. Urinary incontinence is more frequent in women than in men. Posing the right questions is crucial, when diagnosing urinary incontinence, but also to evaluate the need of treatment and treatment effect. Therefore, reliable and validated questionnaires within this area are needed. Even though the International Consultation on Incontinence Questionnaire–Urinary Incontinence Short Form (ICIQ-UI SF) has been used on a daily basis in the Danish Urogynaecological Database since 2006, it has not yet been validated in a Danish population of both men and women. Objective To test the reliability and validity of the Danish version of the ICIQ-UI SF in a Danish speaking population of men and women among municipal employees. Methods Content validity was evaluated with semi-structured interviews. A quantitative field test was performed, in which the questionnaire was distributed electronically to municipal workers by E-mail. Statistical methods included item characteristics (missings, kurtosis and skewness), internal consistency (Chronbach’s alfa), test-retest (ICC), construct validity (known group validation), and floor and ceiling effect. Results A number of 1814 Danish municipal workers completed the questionnaire. Of the total number of responders, 426 were invited to complete the questionnaire twice (for test-retest) and 215 (50.5%) of these completed the questions again two weeks later. Statistical analyses of the ICIQ-UI SF demonstrated no floor and ceiling effects, skewness was zero and kurtosis 0.00–0.49. Cronbach’s alfa was 0.87 and intraclass correlation coefficient 0.73. Two out of three hypotheses were accepted in the known-groups validation. Conclusion This study offers an adaptation of the ICIQ-UI SF to a Danish setting. The Danish ICIQ-UI SF demonstrated acceptable reliability and validity. However, clinicians should consider the relatively high measurement error.
Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.
Endocrine and molecular factors of increased female reproductive performance in the Dummerstorf high-fertility mouse line FL1

The Dummerstorf high-fertility mouse line FL1 is a worldwide unique selection experiment for increased female reproductive performance. After more than 190 generations of selection, these mice doubled the amount of offspring per litter compared to the unselected control line. FL1 females have a superior lifetime fecundity and the highest Silver fecundity index that has been described in mice, while their offspring show no signs of growth retardation. The reasons for the increased reproductive performance remained unclear. Thus, this study aims to characterize the Dummerstorf high-fertility mouse line FL1 on endocrine and molecular levels on the female side. We analyzed parameters of the hypothalamic pituitary gonadal axis on both hormonal and transcriptional levels. Gonadotropin-releasing hormone and follicle-stimulating hormone (FSH) concentrations were decreased in FL1 throughout the whole estrous cycle. Luteinizing hormone (LH) was increased in FL1 mice in estrus. Progesterone concentrations were decreased in estrus in FL1 mice and not affected in diestrus. We used a holistic gene expression approach in the ovary to obtain a global picture of how the high-fertility phenotype is achieved. We found several differentially expressed genes in the ovaries of FL1 mice that are associated with different female fertility traits. Our results indicate that ovulation rates in mice can be increased despite decreased FSH levels. Cycle-related alterations of progesterone and LH levels have the potential to improve follicular maturation, and interactions of endocrine and molecular factors lead to enhanced follicular survival, more successful folliculogenesis and therefore higher ovulation rates in female FL1 mice.
Mean-level correspondence and moment-to-moment synchrony in adolescent and parent affect: Exploring associations with adolescent age and internalizing and externalizing symptoms

Interactions with parents are integral in shaping the development of children’s emotional processes. Important aspects of these interactions are overall (mean level) affective experience and affective synchrony (linkages between parent and child affect across time). Respectively, mean-level affect and affective synchrony reflect aspects of the content and structure of dyadic interactions. Most research on parent–child affect during dyadic interactions has focused on infancy and early childhood; adolescence, however, is a key period for both normative emotional development and the emergence of emotional disorders. We examined affect in early to mid-adolescents (N = 55, Mage = 12.27) and their parents using a video-mediated recall task of 10-min conflict-topic discussions. Using multilevel modeling, we found evidence of significant level-2 effects (mean affect) and level-1 effects (affective synchrony) for parents and their adolescents. Level-2 and level-1 associations were differentially moderated by adolescent age and adolescent internalizing and externalizing symptoms. More specifically, parent–adolescent synchrony was stronger when adolescents were older and had more internalizing problems. Further, more positive adolescent mean affect was associated with more positive parent affect (and vice versa), but only for dyads with low adolescent externalizing problems. Results underscore the importance of additional research examining parent–child affect in adolescence.