Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of the aboriginal house mouse Mus spretus Lataste to tunnels bearing the odours of conspecifics

Abstract Abstract. The behaviour of wild-caught Mus spretus towards tunnels previously occupied by conspecifics, and to the conspecifics themselves, was examined to test whether (1) willingness to investigate and enter tunnels corresponded to the predictions of territorial avoidance, and (2) exposure to substrate odours primed a defensive response to the odour's donor. Adult males and females were provided with a choice of a clean tunnel versus one that had been occupied by themselves, by a conspecific from the same, a neighbouring or a distant area, or by a woodmouse, Apodemus sylvaticus. Mesh caps initially prevented tunnel entry while investigation was measured, then they were removed to record choice of entry. Subsequently, mice met the odour's donor in a neutral arena. Almost all aggression was between mice from different capture areas and affiliative behaviour was between those from the same area. Conspecific odours attracted more investigation than clean tunnels, although odour investigation varied according to the subject and donor's sex and their relative capture areas. Mice investigated then avoided entering a neighbour's tunnels, consistent with predictions of territorial avoidance. Provided with conspecific odours from the same or a distant area versus a clean tunnel, mice generally entered whichever tunnel they had preferred investigating before the tunnels were opened. Regardless of capture area, mice that entered a scented tunnel were significantly more defensive on encountering the donor than those that entered clean tunnels, especially towards female donors. This suggests that mice entering an apparently occupied (scented) area were primed to display submissive behaviour towards a potentially dangerous resident.
Optimal group size and northern bobwhite coveys

Abstract Northern bobwhite, Colinus virginianus , form social units, called coveys, during the nonbreeding season (approximately September–April). Because the evolutionary advantage of this behaviour is generally unknown, we used controlled group size manipulations within an aviary to investigate whether group size influences (1) the time that the covey spends feeding, (2) the percentage of the covey that is vigilant, (3) the overall vigilance of the group and (4) the time to predator detection. We found that increasing group size increased the time that coveys spent in an exposed feeding area, reduced individual vigilance, improved group vigilance and decreased the time to detection of a potential predator. Additionally, we used experimental reductions of wild northern bobwhite coveys to test whether groups size influences (1) individual and covey survival, (2) daily movement in maintaining covey size and (3) mass change. We conducted field research on 12 independent 259-ha study areas (6 control plots and 6 treatments, where 60% of the population was removed) in east-central Kansas, U.S.A. between 9 November and 31 January, 1997–2000. We radio-marked 386 radiocollared individuals that comprised 137 groups on the study areas. Covey size did not differ between or within years or treatments ( X ±SE: 10.98±0.22 individuals). Our results indicate that a stable group size existed between 1 and 22 individuals, with 11 being an optimal group size. Small coveys (1–7 individuals) had lower group persistence and individual survival, and used increased movement to create or join larger groups where survival was higher. Large groups (15–22) had lower individual survival, increased group movement and individual mass loss. Density-dependent feedbacks (e.g. lower survival and increased competition) may have lowered larger coveys to a stable size. Our results suggest the regulation of an optimal covey size of 11 was promoted by high group persistence, low group movement, improved feeding efficiency, improved individual predator detection and improved individual survival. Copyright 2003 Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour.
Disability type, depression, and antidepressants use among older adults in the United States

Abstract Objectives: The study aimed to estimate the prevalence of depression and antidepressant use among older adults with different types of disability. Methods: The study sample consisted of 32,193 adults 50 years and older who participated in the Adult Functioning and Disability supplement of the National Health Interview Survey from 2010–2014. Logistic regression was used to estimate depressive symptoms and self-reported antidepressant use by disability type. Results: One in ten participants reported feeling depressed daily or weekly, and less than half of them reported using antidepressants. Adults with a disability in cognition (odds ratio [OR] = 5.55), mobility (OR = 1.92), vision (OR = 1.91), hearing (OR = 1.88), and self-care (OR = 1.66) were more likely to often feel depressed. Antidepressant use was higher among those with cognition and self-care disability compared with no disabilities. A dose-response association existed between the number of disabilities and depression (AOR = 2.3) and antidepressant use (AOR = 1.39). Conclusions: Various forms of disability are strongly associated with depression in older adults. Antidepressants may be underutilized among older adults with certain impairments, including vision, hearing, and mobility. Future research needs should elucidate the mechanisms linking different disabilities to depression and aim to develop treatments tailored to the needs of older adults with disabilities.
Inflammasome inhibition prevents α-synuclein pathology and dopaminergic neurodegeneration in mice

Oral treatment with a brain-penetrant NLRP3 inhibitor has protective effects in preclinical models of Parkinson’s disease. Systemic inflammasome inhibitor for Parkinson’s disease Brain accumulation of misfolded α-synuclein, progressive loss of dopaminergic neurons, and neuroinflammation are major hallmarks of Parkinson’s disease (PD). Although neuroinflammation has been shown to contribute to the pathophysiology of the disease, the mechanisms mediating the activation of inflammatory signals remain unclear. Gordon and colleagues now show that, in mouse models of PD, α-synuclein aggregates promote inflammasome activation in brain microglia. Oral treatment with an inflammasome inhibitor improved motor performance and reduced neuroinflammation, neurodegeneration, and α-synuclein accumulation in mouse models of α-synuclein–mediated toxicity. The results suggest that systemic delivery of inflammasome inhibitors might have therapeutic effects in PD. Parkinson’s disease (PD) is characterized by a profound loss of dopaminergic neurons in the substantia nigra, accompanied by chronic neuroinflammation, mitochondrial dysfunction, and widespread accumulation of α-synuclein–rich protein aggregates in the form of Lewy bodies. However, the mechanisms linking α-synuclein pathology and dopaminergic neuronal death to chronic microglial neuroinflammation have not been completely elucidated. We show that activation of the microglial NLR family pyrin domain containing 3 (NLRP3) inflammasome is a common pathway triggered by both fibrillar α-synuclein and dopaminergic degeneration in the absence of α-synuclein aggregates. Cleaved caspase-1 and the inflammasome adaptor protein apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain (ASC) were elevated in the substantia nigra of the brains of patients with PD and in multiple preclinical PD models. NLRP3 activation by fibrillar α-synuclein in mouse microglia resulted in a delayed but robust activation of the NLRP3 inflammasome leading to extracellular interleukin-1β and ASC release in the absence of pyroptosis. Nanomolar doses of a small-molecule NLRP3 inhibitor, MCC950, abolished fibrillar α-synuclein–mediated inflammasome activation in mouse microglial cells and extracellular ASC release. Furthermore, oral administration of MCC950 in multiple rodent PD models inhibited inflammasome activation and effectively mitigated motor deficits, nigrostriatal dopaminergic degeneration, and accumulation of α-synuclein aggregates. These findings suggest that microglial NLRP3 may be a sustained source of neuroinflammation that could drive progressive dopaminergic neuropathology and highlight NLRP3 as a potential target for disease-modifying treatments for PD.
Comparing feedback from faculty interactions and virtual assessment software in the development of psychomotor skills in preclinical fixed prosthodontics

The purpose of this study was to determine the effectiveness of virtual assessment software as a means of immediate feedback for preclinical fixed prosthodontics course. The subjects of the study were second year dental students with no previous training in fixed prosthodontics. Nine students participated in the study. Participants completed 2 days of didactic training focused on the principles of tooth preparation and the use of intraoral scanners and virtual assessment software. Didactic training was followed by 12 sessions of practical exercises. Students were randomly assigned to one of three groups for training in the preparation of tooth no. 46 for a complete cast crown. Students received feedback from (a) faculty interaction only; (b) interactions with both faculty and virtual assessment software; or (c) interactions with only virtual assessment software. During Sessions 5, 10, and 12, students prepared tooth no. 46 for complete cast crown independently and without any immediate feedback to simulate a practical exam. Prepared teeth were collected at Sessions 5, 10, and 12, and two blinded faculty members assessed the teeth following an established rubric. Results from Session 12 showed that preparations that one of three students and two of three students respectively for student–faculty interaction and student–faculty–Compare software interaction groups did not meet acceptable standards. However, the students in student–Compare software interaction group generated acceptable preparations at week 12. These data suggest that immediate feedback via virtual assessment software may be as effective as one‐on‐one faculty instruction for dental students in fixed prosthodontics.
Circulating miRNA panels for specific and early detection in bladder cancer

Bladder cancer is the 9th leading cause of cancer death worldwide. The major problem in bladder cancer is primarily the high recurrence rate after drug treatment and resection. Although conventional screening methods, such as cystoscopy, urinary cytology and ultrasound sonography, have become widely used in clinical settings, the diagnostic performance of these modalities is unsatisfactory due to low accuracy or high invasiveness. Because circulating micro RNA (miRNA) profiles have recently been reported as an attractive tool for liquid biopsy in cancer screening, here, we performed global miRNA profiling of 392 serum samples of bladder cancer patients with 100 non‐cancer samples and 480 samples of other types of cancer as controls. We randomly classified the bladder cancer and control samples into 2 cohorts, a training set (N = 486) and a validation set (N = 486). By comparing both controls, we identified specific miRNA, such as miR‐6087, for diagnosing bladder cancer in the training and validation sets. Furthermore, we found that a combination of 7 miRNA (7‐miRNA panel: miR‐6087, miR‐6724‐5p, miR‐3960, miR‐1343‐5p, miR‐1185‐1‐3p, miR‐6831‐5p and miR‐4695‐5p) could discriminate bladder cancer from non‐cancer and other types of tumors with the highest accuracy (AUC: .97; sensitivity: 95%; specificity: 87%). The diagnostic accuracy was high, regardless of the stage and grade of bladder cancer. Our data demonstrated that the 7‐miRNA panel could be a biomarker for the specific and early detection of bladder cancer.
Interventional Robustness of Deep Latent Variable Models

The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is of central importance for data efficient and robust use of neural networks. Various approaches aiming towards this goal have been proposed in the recent time – validating existing work is hence a crucial task to guide further development. Previous validation methods focused on shared information between generative factors and learned features. The effects of rare events or cumulative influences from multiple factors on encodings, however, remain uncaptured. Our experiments show that this already becomes noticeable in a simple, noise free dataset. This is why we introduce the interventional robustness score, which provides a quantitative evaluation of robustness in learned representations with respect to interventions on generative factors and changing nuisance factors. We show how this score can be estimated from labeled observational data, that may be confounded, and further provide an efficient algorithm that scales linearly in the dataset size. The benefits of our causally motivated framework are illustrated in extensive experiments.
The Determinants of Market-Implied Recovery Rates

In the presence of recovery risk, the recovery rate is a random variable whose risk-neutral expectation can be inferred from the prices of defaultable instruments. I extract market-implied recovery rates from the term structures of credit default swap spreads for a sample of 497 United States (U.S.) corporate issuers over the 2005–2014 period. I analyze the explanatory factors of market-implied recovery rates within a linear regression framework and also within a Tobit model, and I compare them with the determinants of historical recovery rates that were previously identified in the literature. In contrast to their historical counterparts, market-implied recovery rates are mostly driven by macroeconomic factors and long-term, issuer-specific variables. Short-term financial variables and industry conditions significantly impact the slope of market-implied recovery rates. These results indicate that the design of a recovery risk model should be based on specific market factors, not on the statistical evidence that is provided by historical recovery rates.
Avian navigation: from historical to modern concepts

Studies on avian navigation began at the end of the 19th century with testing various hypotheses, followed by large-scale displacement experiments to assess the capacity of the birds' navigational abilities. In the 1950s, the first theoretical concepts were published. Kramer proposed his ‘Map-and-Compass’ model, assuming that birds establish the direction to a distant goal with the help of an external reference, a compass. The model describes homing as a two-step process, with the first step determining the direction to the goal as a compass course and the second step locating this course with the help of a compass. This model was widely accepted when numerous experiments with clock-shifted pigeons demonstrated the use of the sun compass, and thus a general involvement of compass orientation, in homing. The ‘map’ step is assumed to use local site-specific information, which led to the idea of a ‘grid map’ based on environmental gradients. Kramer's model still forms the basis of our present concept on avian homing, yet route integration with the help of an external reference provides an alternative strategy to determine the home course, and the magnetic compass is a second compass mechanism available to birds. These mechanisms are interrelated by ontogenetic learning processes. A two-step process, with the first step providing the compass course and the second step locating this course with the help of a compass, appears to be a common feature of avian navigation tasks, yet the origin of the compass courses differs between tasks according to their nature, with courses acquired by experience for flights within the home range, courses based on navigational processes for returning home, and courses derived from genetically coded information in first-time migrants. Compass orientation thus forms the backbone of the avian navigational system. Copyright 2003 The Association for the Study of Animal Behaviour. Published by Elsevier Science Ltd. All rights reserved.
Attitudes towards domestic violence in 49 low- and middle-income countries: A gendered analysis of prevalence and country-level correlates

Background Violence against women by an intimate partner (DV) is a serious public health and human rights issue. Attitudes justifying DV strongly predict its perpetration and victimisation. This paper presents gendered ecological analyses of the societal acceptance of DV in 49 low- and middle-income countries (LMICs) across geographical regions. Methods and findings We utilised data from 49 Demographic and Health Surveys conducted between 2005 and 2017, United Nations Statistics and topic-specific meta-databases. DV acceptance was measured as the justification of ‘wife-beating’ in at least one of five circumstances, and by the summative scale. Stepwise multiple linear regression examined country-level social, economic and political empowerment predictors of societal acceptance of DV amongst women, men, and the aggregate gender difference. Women were more likely than men to justify DV in Sub-Saharan Africa and South (east) Asia with societal acceptance of DV being more widespread in these regions compared with Latin America, the Caribbean, Central/West Asia and Europe. Political conflict and limited economic rights for women were associated with higher levels of DV acceptance amongst women and men. Men in more democratic countries were less likely to justify DV. Amongst women, higher national female literacy rates predicted lower levels of justification. There were higher levels of DV acceptance amongst women and a wider aggregate gender difference in countries with a larger representation of women in national parliament. Conclusion Justification of DV is widespread amongst women and men in LMICs with acceptance rates varying across countries and regions. Gender differences in the impact of contextual factors on DV acceptance supports a gendered approach to national-level interventions. Our findings highlight the need for tailored interventions targeting DV acceptance in conflict-impacted societies. The emphasis of inter(national) policies on the ‘empowerment’ domains of widely-used gender (in)equality indices need to be coupled with strategies tackling discriminatory gender norms.
Detour behaviour in the domestic chick: searching for a disappearing prey or a disappearing social partner

Abstract Chicks,Gallus gallus domesticus, of 2 and 6 days of age were presented with a goal-object that was made to disappear behind one of two screens opposite each other. Chicks proved able to choose the correct screen when the goal-object was a social partner (i.e. a red ball on which they had been imprinted), whereas they searched at random behind either screen when the goal-object was a palatable prey (i.e. a mealworm). Chicks, however, appeared able to make use of the directional cue provided by the movement of the mealworm when tested in the presence of a cagemate. These results suggest that previous failure to obtain detour behaviour in the double screen test in the chick was not due to a cognitive limitation, but rather to the evocation of fear responses to the novel environment that interfered with the correct execution of the spatial task.
Modelling Traffic Assignment Objectives with Emission Cost Functions

Vehicle emissions make up a significant proportion of greenhouse gases, which has provoked interest in capturing emissions in traffic models. This paper provides methods of modelling vehicle emissions for specific emission types in the context of traffic assignment to provide traffic flow patterns with minimal vehicle emissions. Speed limits are incorporated to enable the identification of the minimum total vehicle emissions for a traffic network with given fixed demand. Using a method of emission costing, such as CO2-equivalent or health risk values, similar approaches can be applied to an objective function with a weighted combination of emissions, given a general link cost function that captures a range of emissions. These methods are applied to an example network to indicate the differences in emissions for different flow patterns. Bounds on the lowest emissions possible, for given traffic demand, for each emission type are stated.
Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria.
Efficacy of bio-optimized extracts of turmeric and essential fennel oil on the quality of life in patients with irritable bowel syndrome

Background The use of herbal products to treat irritable bowel syndrome (IBS), a disease that frequently affects the quality of life (QoL), is still under evaluation. This open pilot study assessed the efficacy of bio-optimized extracts of turmeric and essential fennel oil (Enterofytol®) in IBS patients. Methods A total of 211 patients (14% diarrhea-predominant, IBS-D; 24% constipation-predominant, IBS-C; 62% mixed, IBS-M) were enrolled by general practitioners and completed questionnaires measuring symptom severity and QoL before and after Enterofytol®, two capsules b.i. d. for one month, followed by two capsules q.d. for another month. Results IBS severity index and QoL were inversely related. A significant reduction in the severity index and an improvement in QoL were evident following treatment in all IBS subgroups. IBS-D patients showed the worst clinical picture at entry, with the highest IBS severity index and the lowest QoL score, compared with IBS-C and IBS-M subtypes. IBS-D patients, however, also showed the most pronounced response to therapy, considering both scores. The improvement in the IBS severity index was independent of age and sex. Conclusions Results from this “real-life” study show that the combination of turmeric and essential fennel oil over two months improves both symptoms and QoL in IBS patients, irrespectively of age, sex, initial severity of symptoms and IBS-subtypes, suggesting a potential role for the natural treatment of IBS.