Miracle-dz/UniFAR-FTUs huggingface dataset:
Correction of the retinal dystrophy phenotype of the RCS rat by viral gene transfer of Mertk

The Royal College of Surgeons (RCS) rat is a widely studied animal model of retinal degeneration in which the inability of the retinal pigment epithelium (RPE) to phagocytize shed photoreceptor outer segments leads to a progressive loss of rod and cone photoreceptors. We recently used positional cloning to demonstrate that the gene Mertk likely corresponds to the retinal dystrophy (rdy) locus of the RCS rat. In the present study, we sought to determine whether gene transfer of Mertk to a RCS rat retina would result in correction of the RPE phagocytosis defect and preservation of photoreceptors. We used subretinal injection of a recombinant replication-deficient adenovirus encoding rat Mertk to deliver the gene to the eyes of young RCS rats. Electrophysiological assessment of animals 30 days after injection revealed an increased sensitivity of treated eyes to low-intensity light. Histologic and ultrastructural assessment demonstrated substantial sparing of photoreceptors, preservation of outer segment structure, and correction of the RPE phagocytosis defect in areas surrounding the injection site. Our results provide definitive evidence that mutation of Mertk underlies the RCS retinal dystrophy phenotype, and that the phenotype can be corrected by treatment of juvenile animals. To our knowledge, this is the first demonstration of complementation of both a functional cellular defect (phagocytosis) and a photoreceptor degeneration by gene transfer to the RPE. These results, together with the recent discovery of MERTK mutations in individuals with retinitis pigmentosa, emphasize the importance of the RCS rat as a model for gene therapy of diseases that arise from RPE dysfunction.
Neurological Soft Signs in Aging, Mild Cognitive Impairment, and Alzheimer’s Disease – The Impact of Cognitive Decline and Cognitive Reserve

Objectives: Neurological soft signs (NSS), i.e., minor motor and sensory changes, are a common feature in severe psychiatric disorders. We sought to establish the frequency of NSS in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) on basis of a large population-based sample and to identify their neuropsychological correlates including cognitive reserve. Methods: Neurological soft signs were examined using an abbreviated version of the Heidelberg NSS Scale in 221 “old” participants born between 1930 and 1932 (63 with MCI, 15 with AD, 143 healthy old controls) and 256 healthy “young” participants (born between 1950 and 1952) of the population-based interdisciplinary longitudinal study of aging. Subjects received thorough neuropsychological testing; years of school education were used as a proxy for cognitive reserve. Results: Neurological soft signs scores were significantly (p < 0.001) higher in the AD patients (5.6 ± 3.11) than in the healthy old controls (2.8 ± 1.90) and in the MCI patients (3.0 ± 1.96). This result was confirmed after years of school education, which were inversely correlated (r = −0.25; p < 0.001) with NSS were entered as a covariate. In the patients, but not in the controls, NSS were significantly correlated with deficits in executive functioning and visuospatial functioning. Comparison of NSS scores between “old” (2.84 ± 1.9) and “young” (2.46 ± 1.97) controls yielded only minor, non-significant differences after education (13.86 ± 3.0 vs. 14.61 ± 2.48 years, respectively) was controlled for. Conclusion: Our results demonstrate that NSS are frequently found in mild AD, but not in MCI. NSS refer to frontal-executive deficits and visuospatial dysfunction rather than age per se and can be partly compensated for by cognitive reserve.
Self-disgust in personality disorders: the role of childhood abuse and trauma, emotional invalidation and shame

This thesis examines predictors of self-disgust among a sample who screened positive for personality disorders. It is a joint thesis with ‘The role of self-disgust in non-suicidal self-injury among individuals with personality disorder’ (Schwaiger, 2016). Part 1, the literature review, examines whether self-disgust is associated with psychopathology (as evidenced by a DSM-5 mental health disorder). Seventeen studies indicate that self-disgust is associated with a wide range of mental health disorders including Depression, Borderline Personality Disorder and Eating Disorders. The evidence is limited by the use of largely non-clinical samples, cross- sectional designs, and by theoretical and methodological limitations regarding the operationalisation and measurement of the self-disgust construct. Part 2, the empirical paper investigates predictors of self-disgust using a cross- sectional web-based survey. Self-disgust was elevated in those classified as PD according to a brief screening tool (n =188), relative to a non-PD sample (n=133). Childhood abuse and trauma and shame independently predicted self-disgust in those who screened positive for a probable PD. Self-disgust also mediated the relationship between childhood abuse and trauma and the likelihood of PD. Part 3, the critical appraisal considers outstanding issues regarding the psychometric measurement of self-disgust. Directions for further research are discussed, as are the strengths and limitations of utilising web-based research and social media to aid recruitment in psychological research. Personal reflections are finally offered on how the research has impacted me as a researcher and clinician.
Polyandry in dragon lizards: inbred paternal genotypes sire fewer offspring

Multiple mating in female animals is something of a paradox because it can either be risky (e.g., higher probability of disease transmission, social costs) or provide substantial fitness benefits (e.g., genetic bet hedging whereby the likelihood of reproductive failure is lowered). The genetic relatedness of parental units, particularly in lizards, has rarely been studied in the wild. Here, we examined levels of multiple paternity in Australia's largest agamid lizard, the eastern water dragon (Intellagama lesueurii), and determined whether male reproductive success is best explained by its heterozygosity coefficient or the extent to which it is related to the mother. Female polyandry was the norm: 2/22 clutches (9.2%) were sired by three or more fathers, 17/22 (77.2%) were sired by two fathers, and only 3/22 (13.6%) clutches were sired by one father. Moreover, we reconstructed the paternal genotypes for 18 known mother–offspring clutches and found no evidence that females were favoring less related males or that less related males had higher fitness. However, males with greater heterozygosity sired more offspring. While the postcopulatory mechanisms underlying this pattern are not understood, female water dragons likely represent another example of reproduction through cryptic means (sperm selection/sperm competition) in a lizard, and through which they may ameliorate the effects of male-driven precopulatory sexual selection.
Influencing: A Strategy for Goal Adoption in BDI Agents

In recent years, computing systems based on Belief Desire-Intention (BDI) paradigm have received considerable attention in the area of Agent Technology. This paradigm has been proposed to model human cognitive behaviour in terms of three cognitive states viz., belieJ desire, and intention. It is believed that one’s goal-adoption behavior largely depends on its cognitive states. Hence, an agent (influencing agent) can influence another agent (target agent) by acting on latter’s cognitive state to make it adopt a goal of former’s interest. On giving a formal definition to agent cognitive states we present a fiamework for influencing that achieves goal adoption at target agent. Also the uses of communication tones and social norms in a process of influencing are shown. We explain the use of the proposed framework in a production management scenario.
Monetary Fund and financial crises in the global economy

The rapidly evolving global economy as well as new challenges with economic and social implications make the problem of financial crises, including currency crises, extremely important in the current situation. Especially given the fact that the crises affect various countries and regions of the world. Serious currency crashes have occurred in developing countries with marked structural problems, but also in mature economies which possess fully developed market mechanisms and efficient systemic solutions. In addition, the severely destructive impact on the global financial balance and the scale of potential losses caused by such turbulences indicate that there is a need for a responsible and effective preventive policy on all decision-making levels. It seems that adequate responses and actions on the global level, in addition to actions simultaneously taken by regional organisations and by various countries themselves, can be a basis for creating conditions conducive both to the stabilisation of the entire global economy and to its sustainable growth. The aim of the present study is, therefore, to present a synthetic overview of the crucial qualitative features of currency crises, and to analyse the response of the International Monetary Fund – as one of the key international financial organisations – to the threats resulting from currency crunches. Numerous controversies surrounding the essence of currency crises as well as the functioning and activities of the IMF provide a substantive focus for the present analysis.
Generalization of Workflows in Process-Oriented Case-Based Reasoning

In this paper, we introduce the concept of generalized cases into process-oriented case-based reasoning. We present the formal foundations for the generalization of workflow cases as well as a new algorithm for generalizing semantic workflows, guided by ontological knowledge of the domain. Further, the specialization of workflows w.r. t. a current query is addressed. An experimental evaluation demonstrates the capability of the approach for workflow adaptation showing that the adapted workflows have a similar quality compared to that of original workflows. Furthermore, the retrieval performance can be improved by a reduction of the case-base size while the coverage of cases is significantly increased.
Psychological and Neuroscientific Perspectives on Gratitude as an Emotion

Gratitude is an emotional response when people feel grateful for the good things that happen to them or acknowledgment of a benefit received from another person. Gratitude is an important component of positive psychology and the world’s largest religions emphasize the importance of gratitude in their teaching. Research has begun to explore factors that enhance human life and one such factor is the effect of positive emotions on mental health outcomes. Gratitude is considered to be a positive emotion and grateful individuals tend to be happier, less depressed, less stressed and more satisfied with their lives. Gratitude may have a strong link with mental health and wellbeing and research has recently started to understand its positive effects. Despite the research findings showing the effectiveness of gratitude interventions, the neural mechanisms involved in gratitude are relatively unknown. It is important to investigate the neural processes involved in gratitude because it may provide clues as to how gratitude as a positive emotion can influence mental health and wellbeing. Gratitude has many definitions and this essay will present the different definitions of the experience of gratitude, what happens in the brain when experiencing gratitude and as a positive emotion and how gratitude may benefit our wellbeing and mental health.
Resource Material for Promoting the Bayesian View of Everything

Notes mainly for the use of CMC, Christopher A. Fuchs, and Rüdiger Schack, although anyone is welcome to make use of the notes and even buy in, in whole or in part, if he/she finds the notes convincing. Not everything here is endorsed by Fuchs and Schack, but it is all part of a joint program aimed at producing a Bayesian synthesis. Fuchs, in particular, probably disagrees with every statement about Hamiltonians being the objective part of quantum theory. Nonetheless, nothing should be attributed to me alone until Fuchs and Schack have disavowed it.
Acupuncture Anxiolytic Effects on Physiological and Psychological Assessments for a Clinical Trial

In a randomized controlled trial we examined the effect of acupuncture on anxiety of the adolescent football players prior to the competition using psychological and physiological markers. A total of 45 athletes were equally allocated to either acupuncture group, sham group, or wait-list control group. Thereafter, all participants were asked to complete an anxiety questionnaire before and after the intervention. Their heart rate and skin conductance were also examined before and after the intervention. The results of ANOVA on posttest scores showed that acupuncture had a significant effect on cognitive anxiety (p = 0.001) and somatic anxiety (p < 0.001) but not on self-confidence (p > 0.05). Furthermore, the results showed that acupuncture significantly decreased the skin conductance in acupuncture group compared to sham group (p = 0.006) and wait-list control group (p < 0.001). In conclusion, the results suggested that acupuncture has the capacity to decrease cognitive anxiety and somatic anxiety prior to competition in adolescent athletes, while this was accompanied by significant physiological changes. This trial is registered with IRCT138904074264N1 (IRCT is a Primary Registry in the WHO Registry Network).
Accelerating Branes and the String/Black Hole Transition

String theory in Euclidean flat space with a spacelike linear dilaton contains a D1-brane which looks like a semi-infinite hairpin. In addition to its curved shape, this ``hairpin brane'' has a condensate of the open string tachyon stretched between its two sides. The tachyon smears the brane and shifts the location of its tip. The Minkowski continuation of the hairpin brane describes a D0-brane freely falling in a linear dilaton background. Effects that in Euclidean space are attributed to the tachyon condensate, give rise in the Minkowski case to a stringy smearing of the trajectory of the D-brane by an amount that grows as its acceleration increases. When the Unruh temperature of the brane reaches the Hagedorn temperature of perturbative string theory in the throat, the rolling D-brane state becomes non-normalizable. We propose that black holes in string theory exhibit similar properties. The Euclidean black hole solution has a condensate of a tachyon winding around Euclidean time. The Minkowski manifestation of this condensate is a smearing of the geometry in a layer around the horizon. As the Hawking temperature, T_{bh}, increases, the width of this layer grows. When T_{bh} reaches the Hagedorn temperature, the size of this ``smeared horizon'' diverges, and the black hole becomes non-normalizable. This provides a new point of view on the string/black hole transition.
Time-frequency analysis of non-stationary electrocardiogram signals using Hilbert-Huang Transform

Electrocardiogram (ECG) is a measure of an electrical activity of heart which is used to analyze the functioning of the heart of a person. This analysis helps the physician to diagnose the condition of the patient. As the ECG signals are non-stationary in nature, they cannot be analyzed with earlier techniques as they are limited. We therefore need robust methods like Hilbert-Huang Transform and Wavelet Transform to analyze such signals. In this paper I have employed Hilbert Huang Transform to analyze the ECG signal and plotted the time-frequency plot. HHT is a latest data analysis method proposed by Huang et al. which analyses non-linear and non-stationary signals by decomposing them into Intrinsic Mode Functions (IMF) followed by finding out their instantaneous frequencies of Intrinsic Mode Functions (IMF) with Hilbert Transform.
A Survey on Effective Classification for Text Mining using one-class SVM

Data mining techniques have been widely used to find new patterns and knowledge from large amount of data. While Bayesian models were widely used in the early days, more advanced machine learning methods, such as artificial neural networks and support vector machines, have been applied in recent years these techniques are used in different areas. The problem of text mining has gained increasing attention in recent years because of the large amounts of text data, which are created in a variety of social network, web, and other information-centric applications. Unstructured data is the easiest form of data which can be created in any application scenario. As a result, there has been a tremendous need to design methods and algorithms which can effectively process a wide variety of text applications. This paper will provide an overview of the different methods and algorithms which are common in the text domain, with a particular focus on mining methods. General Terms  To develop filtered text, removing stop words, stemming, html tags etc.  Applying feature selection method TF-IDF.  Extracting high quality ontopic text using PNLH algorithm.
Knowledge Transfer within Formal and Informal Bank Communities: an Ecological Approach.

Banks are gradually becoming aware that knowledge cannot be treated as an organisational asset without the active and voluntary participation of the communities that are its true owners. A shift to thinking of employees as volunteers requires us to think of the organisation as a complex ecology in which the number of causal factors renders pseudo-rational prescriptive models redundant [15]. The aim of the paper is to propose to the reader an holistic understanding of the different types of community and community interactions within a bank, rooted in the cultural and situational context of both that organisation, its changing environment and the network of formal and informal communities that make it a living entity. In order to do this, we have decided to apply a very peculiar model, named “Cynefin Model” [15], very useful in order to understand the flow of knowledge between communities within an organisation. 1. A Premise: What is a Knowledge Ecology? Knowledge ecology essentially refers to a field of management theory and practice, focused on the relational and social aspects of knowledge creation and utilization. Its primary domain of action is the design and support of knowledge ecosystems, in which information, ideas, and inspiration cross-fertilize and feed on one another. It primarily focuses on the social networks analysis of individuals in contrast to the overly technological emphasis of traditional knowledge management systems (the Newtonian approach) on computers and information technology networks [5] and is made up of knowledge nodes and knowledge exchanges or flows. In the knowledge ecology the basis for cooperation and survival is differentiation and integration between the knowledge nodes. Highly differentiated knowledge nodes can collaborate to accomplish specific actions and may dissolve thereafter. However, collaboration between such nodes would require that they be able to 'relate' to one another under an overarching mission or theme [15]. Within a knowledge ecology, focus on people does not only imply understanding of knowledge exchanges and relationships based on such exchanges. It also implies understanding of how such knowledge influences action or potential for action based on such exchanges. 2. The Cynefin Model Applied to Banks’ Organization The Cynefin model (fig. 1) [15] has been used to assist banks to understand the ecology of knowledge. Cynefin is a Welsh word with no direct equivalent in English and it is translated as habitat, as an adjective acquainted or familiar, but dictionary definitions fail to do it justice. C y n e f i n m o d e l : c u l t u r a l s e n s e m a k i n g F i g u r e 1 R e s t r i c t e d O p e n L ea rn in g Trining P r o f e s s i o n a l L o g i c a l E x p e r t L a n g u a g e I n f o r m a l I n t e r -d e p e n d e n t S y m b o l i c L a n g u a g e U n c h a r t e d I n n o v a t i v e E m e r g e n t L a n g u a g e B u r e a u c r a t i c S t r u c t u r e d C o m m o n L a n g u a g e C U L T U R E S E N S E M A K IN G
Machine Learning Based Auto-Tuning for Enhanced OpenCL Performance Portability

Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good performance on another device. In this paper, we use machine learning-based auto-tuning to address this problem. Benchmarks are run on a random subset of the entire tuning parameter configuration space, and the results are used to build an artificial neural network based model. The model can then be used to find interesting parts of the parameter space for further search. We evaluate our method with different benchmarks, on several devices, including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970 GPU. Our model achieves a mean relative error as low as 6.1%, and is able to find configurations as little as 1.3% worse than the global minimum.
Calibration of Projector with Fixed Pattern and Large Distortion Lens in a Structured Light System

The most critical factor affects accuracy of a Structured Light System (SLS) is calibration. Camera calibration is easy to complete because of its extensive study. To simplify projector calibration, previous work models the projector as an inverse camera and tries to build similar 3D-2D mapping data for projector calibration. Achieved mapping data is directly fed to some classic two-step camera calibration methods. When projector comes with a large distortion lens, this kind of methods will fail because their first steps use closed-form solution to calculate initial guess for optimization in next steps. We proposed a new method to calibrate the projector by removing its distortion first. Because projector cannot “see” anything, not like camera case, constraints such as “straight lines remain straight” working just on 2D image is invalid for distortion estimation. With 3D-2D mapping data, the estimation will involve several extra unknowns into a non-linear optimization. We use partial mapping data whose 2D points in a “small central area” of projector pattern image to acquire an initial guess for those unknowns, and then use all mapping data to refine them and estimate distortion parameters. Experiments show our method can still calibrate the projector when classic methods fail.
Orthogonality improved GFDM with low complexity implementation

The Generalized Frequency Division Multiplexing (GFDM) has been proposed as a flexible modulation scheme for future cellular systems. It extends the traditional OFDM concept to a general framework by trading the orthogonality for the flexibility of employing different pulse shapes, which allows to improve the frequency localization feature. Nevertheless, considering its practical usage, the complexity issue has to be addressed. In this paper, we present a low complexity algorithm for the GFDM implementation which, under certain conditions, yields less computational complexity. Furthermore, we introduce a Span-Factor Manipulation (SFM) concept that can possibly compensate for the orthogonality loss, eventually turning the GFDM into an orthogonality improved system.