Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Animal Species Detection Using Robust Principal Component Analysis and Large Displacement Optical Flow

This paper examines the problem of detecting various types of animals in video sequences taken in the wild. Given that manually extracting and labeling an animal’s position in a video sequence is very labor-intensive, an automatic solution would be extremely useful. We propose a method that provides accurate localization of the animals using a very general approach. Our technique ﬁrst separates the background from the video frames using Robust Principal Component Analysis. It then locates and ﬁlters candidate regions containing the animal using local entropy and large displacement optical ﬂow respectively. We also verify the effectiveness of our algorithm on video sequences of various types of animals
Iterative Learning Control and System Identificati n of the Antagonistic Knee M scle Complex During Gait Using Functional Electrical Stimulation

Functional Electrical Stimulation (FES) can be used to support the gait of stroke patients. By measuring joint angles and adjusting the stimulation intensities automatically to the current need of the patient, setup times can be reduced and time-variant effects like muscle fatigue can be compensated. This was achieved in recent publications by using Iterative Learning Control (ILC) on the ankle complex. In this paper we consider FES of the antagonistic knee muscle complex (quadriceps and hamstring muscles) that controls knee flexion/extension. We used a coactivation strategy in order to map the two stimulation channels to a single control input. A large class of dynamic models was obtained by system identification based on data from two experiments: one with standing subjects and one with subjects walking on a treadmill while being stimulated during different time segments of the gait cycle. Time delays, system poles, and in particular the system gains were found to vary largely. Furthermore, large differences were observed between muscle dynamics in standing pose and during walking. We designed an iterative learning controller that is stable for almost all models. In experiments with eight healthy subjects walking on a treadmill, the ILC was found to reduce deviations from a reference trajectory to about five degrees within two strides.
Identification of User Interests in Social Media by Giray Havur

Social media has taken an important part in our lives in a short amount of time. People share parts of their experiences, opinions, and interests with others in a timely-fashion on these platforms. In recent years, fast growth of user population in social media is not only driving the research towards analyzing its inhabitants for fulfilling their expectations but also making it a very crucial information source for decision making processes in societies and in businesses. In this work, we propose methods for identifying users and their interests by using the multimedia data shared in social media. We show effectiveness of these methods in three applications. Our first application considers extracting political interests of Turkish Twitter users. We collect tweets that include a set of predefined words representing two different political stances in Turkey. We extract profile images of the users who wrote those tweets and apply a computer vision technique called image context extraction on this set of images to obtain some textual explanations for each picture. The main goal of this work is inferring proportions of two different political stances to forecast results of March 2014 local elections. Our results show that the proportions obtained from our method are almost the same as the vote percentages of two parties. In our second application, we find Facebook profiles of people whose identification information (Name, surname and location) is given by querying Facebook Graph API. Each query result returns a number of profiles due to people having same name. We refine these results by checking location in profile pages online. Our method achieves a successful match rate of 88% (1332/1500 people). The third application deals with building a community about a given topic of interest by condensing existing communities in a social media platform. We collect members of the communities about the given topic in a set and apply our relevance scoring method on these members. Those who receive a score below a threshold value are assumed to be irrelevant to given topic and they are eliminated so that remaining users in the set are the ones relevant to given topic. We validated the results of our framework by a user-study. There is a 76% of match between user labelled and automated results.
RELATIONSHIP BETWEEN DAMAGE TO NONSTRUCTURAL BUILDING ELEMENTS AND THE SEISMIC INTENSITIES OF RECENT EARTHQUAKES IN JAPAN

In this study, we examined the relationship between the seismic intensities and the percentage of buildings with damaged nonstructural elements based on damage survey data taken after five earthquakes that occurred recently in Japan. The following characteristics are found for damage to nonstructural elements in the areas where the JMA instrumental seismic intensity were under 6. 1) The percentage of buildings in which damage was found at exterior, interior and sanitary accommodations vary according to the type of earthquakes and the characteristics of seismic motions while the percentage of buildings with moved and overturned furniture do not vary. 2) At JMA seismic intensity levels over 5, more than 20 percent of buildings were damaged and needed some amount of money to repair. 3) At JMA seismic intensity levels over 5.5, electric power supply, water supply, lighting and elevator systems stopped in more than 20 percent of buildings although disruption to buildings systems except for water supply and elevator recovered within one day.
jggc-wave monitoring of hydrate-bearing sand during CH 4 – CO 2 replacement

The replacement of methane for carbon dioxide in natural gas hydrate-bearing sediments is a promising technology in view of a more sustainable use of fossil fuels. While previous studies have shown successful CH4–CO2 replacement in hydrates, the mechanical response of hydrate-bearing sediments during CO2 injection, CH4–CO2 replacement, and CH4 production needs to be adequately understood in order to avert production problems such as borehole instability, sand production, and buckling of the casing. We take advantage of the characteristics of elastic mechanical wave propagation in sediments to monitor CH4 hydrate-bearing sands before, during, and after CO2 injection. Results show that CH4–CO2 replacement occurs without a loss of stiffness in the granular medium. This implies that CO2-flooded sandy reservoirs can remain mechanically stable during and after CH4 gas production. On the other hand, dry CO2 dissolves
Multilevel selection as Bayesian inference, major transitions in individuality as structure learning

Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive combination of pre-adapted sub-solutions in the framework of learning theory, no general mathematical formalization of this analogy has been provided yet. Here we show, building on former results connecting replicator dynamics and Bayesian update, that (i) evolution of a hierarchical population under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models and (ii) evolutionary transitions in individuality, driven by synergistic fitness interactions, is equivalent to learning the structure of hierarchical models via Bayesian model comparison. These correspondences support a learning theory-oriented narrative of evolutionary complexification: the complexity and depth of the hierarchical structure of individuality mirror the amount and complexity of data that have been integrated about the environment through the course of evolutionary history.
An Open-Format Enteroid Culture System for Interrogation of Interactions Between Toxoplasma gondii and the Intestinal Epithelium

When transmitted through the oral route, Toxoplasma gondii first interacts with its host at the small intestinal epithelium. This interaction is crucial to controlling initial invasion and replication, as well as shaping the quality of the systemic immune response. It is therefore an attractive target for the design of novel vaccines and adjuvants. However, due to a lack of tractable infection models, we understand surprisingly little about the molecular pathways that govern this interaction. The in vitro culture of small intestinal epithelium as 3D enteroids shows great promise for modeling the epithelial response to infection. However, the enclosed luminal space makes the application of infectious agents to the apical epithelial surface challenging. Here, we have developed three novel enteroid-based techniques for modeling T. gondii infection. In particular, we have adapted enteroid culture protocols to generate collagen-supported epithelial sheets with an exposed apical surface. These cultures retain epithelial polarization, and the presence of fully differentiated epithelial cell populations. They are susceptible to infection with, and support replication of, T. gondii. Using quantitative label-free mass spectrometry, we show that T. gondii infection of the enteroid epithelium is associated with up-regulation of proteins associated with cholesterol metabolism, extracellular exosomes, intermicrovillar adhesion, and cell junctions. Inhibition of host cholesterol and isoprenoid biosynthesis with Atorvastatin resulted in a reduction in parasite load only at higher doses, indicating that de novo synthesis may support, but is not required for, parasite replication. These novel models therefore offer tractable tools for investigating how interactions between T. gondii and the host intestinal epithelium influence the course of infection.
Sex Differences in White Matter Pathways Related to Language Ability

Evidence from functional imaging studies points to a role for gender in language ability. However, recent studies suggest that sex differences in the neural basis of language are still unclear, reflecting a complex interaction between sex and language ability. We used diffusion weighted magnetic resonance imaging and global probabilistic tractography to investigate white matter (WM) pathways between 32 male and 35 age- and IQ-matched female adult participants in relation to their verbal abilities. Males showed higher fractional anisotropy (FA) in the left anterior thalamic radiations (ATR), right cingulum-angular bundle, right corticospinal tract, bilateral superior longitudinal fasciculus-temporal terminations, bilateral uncinate fasciculus (UNC), and corpus callosum-forceps minor when compared with the female group. In contrast, females showed higher radial diffusivity (RD) in the left ATR and left UNC when compared to the male group. The relationship between WM metrics and verbal ability also differed across the two groups: a negative correlation between verbal comprehension index (VCI) and FA as well as axial diffusivity (AD) in left cingulum-cingulate gyrus (CCG) supracallosal bundle in males but not in females; a negative correlation between verbal IQ (VIQ) and FA in the right corticospinal tract (CST), and a positive correlation between VCI and RD in corpus callosum-forceps minor in the female but not in the male group. A direct comparison of these correlation coefficients yielded significant differences between the groups for the VCI-AD and VIQ -FA associations. The findings may reflect sex differences in WM related to language ability.
Can a Like Save the Planet? Comparing Antecedents of and Correlations Between Environmental Liking on Social Media, Money Donation, and Volunteering

Due to the societal dissemination of digital technology, people are increasingly experiencing environmental topics through digital media channels such as social networks. Several researchers therefore have proposed these channels as a possibility to strengthen sustainable development based on their cost-efficient nature. But while prior studies have investigated isolated factors for understanding environmental social media behavior, there is still scarce understanding of the relevant underlying motivational factors and possible connections with more traditional environmental behaviors. Therefore, the present study applied the established socio-psychological model of goal-directed behavior and compared the desires for liking as a fundamental form of digital social media behavior with the desires for two traditional environmental behaviors (money donation and volunteering) in a cross-sectional research design. Within the biodiversity conservation case of the Andean bear (Tremarctos ornatus) in Ecuador, we compared the antecedents for the desires for the corresponding environmental behaviors (RQ1) as well as their connections between each other (RQ2). Within a sample of 407 Ecuadorian students (Mage = 20.94 years, SD = 2.25, 61.2% female), we found the marginal effect of perceived behavioral control on the desires for liking on social media as the main difference concerning the antecedents of the behaviors because money donation and volunteering largely depended on personal resources such as time or money. Furthermore, gender emerged as the second main difference between the behaviors due to it only being predictive for the liking motivation. Enjoyment was the only variable that coherently predicted all three behaviors. Finally, desires for liking on social media predicted both other behaviors in robust regression analyses, but were only predictive for volunteering in corresponding path models. The results illustrate how cost-efficient digital environmental behaviors such as liking may be suitable for sparking low-level environmental action, which may entail more pronounced forms of environmental activism, at least when they involve feasible personal costs. Overall, the findings are in line with prior research regarding the less demanding nature of liking, but further elaborate on the importance of gender for digital environmental behavior and correlates between digital and classical environmental behaviors.
Exosome Determinants of Physiological Aging and Age-Related Neurodegenerative Diseases

Aging is consistently reported as the most important independent risk factor for neurodegenerative diseases. As life expectancy has significantly increased during the last decades, neurodegenerative diseases became one of the most critical public health problem in our society. The most investigated neurodegenerative diseases during aging are Alzheimer disease (AD), Frontotemporal Dementia (FTD) and Parkinson disease (PD). The search for biomarkers has been focused so far on cerebrospinal fluid (CSF) and blood. Recently, exosomes emerged as novel biological source with increasing interest for age-related neurodegenerative disease biomarkers. Exosomes are tiny Extracellular vesicles (EVs; 30–100 nm in size) released by all cell types which originate from the endosomal compartment. They constitute important vesicles for the release and transfer of multiple (signaling, toxic, and regulatory) molecules among cells. Initially considered with merely waste disposal function, instead exosomes have been recently recognized as fundamental mediators of intercellular communication. They can move from the site of release by diffusion and be retrieved in several body fluids, where they may dynamically reflect pathological changes of cells present in inaccessible sites such as the brain. Multiple evidence has implicated exosomes in age-associated neurodegenerative processes, which lead to cognitive impairment in later life. Critically, consolidated evidence indicates that pathological protein aggregates, including Aβ, tau, and α-synuclein are released from brain cells in association with exosomes. Importantly, exosomes act as vehicles between cells not only of proteins but also of nucleic acids [DNA, mRNA transcripts, miRNA, and non-coding RNAs (ncRNAs)] thus potentially influencing gene expression in target cells. In this framework, exosomes could contribute to elucidate the molecular mechanisms underneath neurodegenerative diseases and could represent a promising source of biomarkers. Despite the involvement of exosomes in age-associated neurodegeneration, the study of exosomes and their genetic cargo in physiological aging and in neurodegenerative diseases is still in its infancy. Here, we review, the current knowledge on protein and ncRNAs cargo of exosomes in normal aging and in age-related neurodegenerative diseases.
A Sensitivity Analysis Comparison of Three Models for the Dynamics of Germinal Centers

Germinal centers (GCs) are transient anatomical microenvironments where antibody affinity maturation and memory B cells generation takes place. In the past, models of Germinal Center (GC) dynamics have focused on understanding antibody affinity maturation rather than on the main mechanism(s) driving their rise-and-fall dynamics. Here, based on a population dynamics model core, we compare three mechanisms potentially responsible for this GC biphasic behavior dependent on follicular dendritic cell (FDC) maturation, follicular T helper (Tfh) cell maturation, and antigen depletion. Analyzing the kinetics of B and T cells, as well as its parameter sensitivities, we found that only the FDC-maturation-based model could describe realistic GC dynamics, whereas the simple Tfh-maturation and antigen-depletion mechanisms, as implemented here, could not. We also found that in all models the processes directly related to Tfh cell kinetics have the highest impact on GC dynamics. This suggests the existence of some still unknown mechanism(s) tuning GC dynamics by affecting Tfh cell response to proliferation-inducing stimuli.
Interrelations Between Ethical Leadership, Green Psychological Climate, and Organizational Environmental Citizenship Behavior: A Moderated Mediation Model

Synthesizing theories of ethical leadership, psychological climate, pro-environmental behavior, and gender, first, we proposed and tested a model linking supervisors’ ethical leadership and organizational environmental citizenship behavior via a green psychological climate. Then, we tested the moderating effect of gender on the indirect (via a green psychological environment) relationship between supervisors’ ethical leadership and organizational environmental citizenship behavior. Time-lagged (three waves, 2 months apart) survey data were collected from 447 employees in various manufacturing and service sector firms operating in China. Data were analyzed using structural equation modeling, bootstrapping, and multigroup techniques to test the hypothesized relationships. The results showed a positive relationship between employee ratings of supervisors’ ethical leadership and organizational environmental citizenship behavior. Moreover, a green psychological climate mediates the relationship between supervisors’ ethical leadership and organizational environmental citizenship behavior. Importantly, the multigroup analysis revealed that gender moderates the indirect relationship (via green psychological climate) between supervisors’ ethical leadership and organizational environmental citizenship behavior. The study carries useful practical implications for policymakers and managers concerned about environmental sustainability.
The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being ‘good’ or ‘bad. ’ Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to ‘bad’ quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). LONI-QC’s functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.
Musicians Show Better Auditory and Tactile Identification of Emotions in Music

Musicians are better at processing sensory information and at integrating multisensory information in detection and discrimination tasks, but whether these enhanced abilities extend to more complex processes is still unknown. Emotional appeal is a crucial part of musical experience, but whether musicians can better identify emotions in music throughout different sensory modalities has yet to be determined. The goal of the present study was to investigate the auditory, tactile and audiotactile identification of emotions in musicians. Melodies expressing happiness, sadness, fear/threat, and peacefulness were played and participants had to rate each excerpt on a 10-point scale for each of the four emotions. Stimuli were presented through headphones and/or a glove with haptic audio exciters. The data suggest that musicians and control are comparable in the identification of the most basic (happiness and sadness) emotions. However, in the most difficult unisensory identification conditions (fear/threat and peacefulness), significant differences emerge between groups, suggesting that musical training enhances the identification of emotions, in both the auditory and tactile domains. These results support the hypothesis that musical training has an impact at all hierarchical levels of sensory and cognitive processing.