Miracle-dz/UniFAR-FTUs huggingface dataset:
In-Depth Two-Year Study of Phenolic Profile Variability among Olive Oils from Autochthonous and Mediterranean Varieties in Morocco, as Revealed by a LC-MS Chemometric Profiling Approach

Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars (“Picholine Marocaine”, “Dahbia”, “Haouzia” and “Menara”), and nine Mediterranean varieties recently introduced in Morocco (“Arbequina”, “Arbosana”, “Cornicabra”, “Frantoio”, “Hojiblanca”, “Koroneiki”, “Manzanilla”, “Picholine de Languedoc” and “Picual”), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils.
Very-Old Rural Adults: Functional Status and Social Support

This 20-year follow-up study examined the physical, psychological, and social outcomes, and social supports of 49 survivors of an original sample of 418 rural adults who were aged 65 years and older in 1976. The majority of adults lived alone but received regular assistance. Families, especially adult children, were their primary social supports. Very few formal supports were used. Psychological well-being among the very-old was good as expressed through life satisfaction and morale, despite multiple health problems and moderate levels of dependence on others for activities of daily living and instrumental activities of daily living. Both the cross-sectional and the longitudinal data from this study support the need to rethink issues of social support, independence, and family relationships in very-old age.
Making Nature Valuable, Not Profitable: Are Payments for Ecosystem Services Suitable for Degrowth?

The growth economy imposes multiple crises on humanity and the natural world. To challenge this economic growth imperative, the degrowth movement emerges as a dissident response. Although within an economic growth perspective, payments for ecosystem services (PES) have also been proposed to attenuate the negative impacts of capitalism, as a redistributive mechanism that is claimed to deliver equitable conservation and sustainability. Degrowth has notably similar concerns, although it is inclined to argue against PES traditional ideologies and practices, which lead conservation to perceive nature within economic growth and market ideologies, diminishing the relationship between humans and nature. In spite of that, PES are becoming a strong trend in environmental governance. This paper attempts to examine whether PES are, and how they could be suitable for degrowth, through the lens of its main sources. In order to integrate PES and degrowth, it could require a PES reconceptualization. Although we assert that PES are not the most appropriate instrument for conservation, we argue that maybe PES could contribute to degrowth as a transition instrument toward fostering better practices. However, it is important to elucidate how they can be used and under which circumstances they could be appropriate.
Training auditory discrimination: a single case study

This paper presents a case study of a 7-year-old girl with severe language disorder. It explores her input language processing skills using theoretically motivated questions derived from a psycholinguistic framework. It compares her performance on experimental tasks to age matched controls (n = 28). This exploration revealed multiple processing deficits, including auditory discrimination problems. The second phase of the study was a treatment period targeting auditory discrimination. The child’s auditory discrimination improved but these gains did not generalize to other language processes. The paper discusses the results with reference to a developing system.
The Four Ps, Narrative Psychiatry, and the Story of George Engel

Jonathan Bolton’s (2014) “Case Formulation after Engel” gives an insightful presentation of the difficulties teaching quality clinical work in psychiatry. Plus, he provides a valuable heuristic for organizing psychiatric thought through the “4P model. ” The catalyst for his article seems to come from teaching psychiatric residents a course in ‘case formulation’ at a time when other faculty are saying ‘case formulation is considered passé. ’ This dismissive denigration puts bolton in a difficult situation and suggests a need for allies. One place to look for allies is in the world of narrative medicine and psychiatry. bolton does not define case formulation, but he does describe some of the functions it provides for clinical work. Most of these functions are narrative functions:
Understanding Negations in Information Processing: Learning from Replicating Human Behavior

Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding preferences based on user reviews or social media. However, it is difficult for computer programs to correctly infer meaning from narrative content. One major challenge is negations that invert the interpretation of words and sentences. As a remedy, this paper proposes a novel learning strategy to detect negations: we apply reinforcement learning to and a policy that replicates the human perception of negations based on an exogenous response, such as a user rating for reviews. Our method yields several benefits, as it eliminates the former need for expensive and subjective manual labeling in an intermediate stage. Moreover, the inferred policy can be used to derive statistical inferences and implications regarding how humans process and act on negations.
Effects of Muscarinic M1 Receptor Blockade on Cocaine-Induced Elevations of Brain Dopamine Levels and Locomotor Behavior in Rats

Cholinergic muscarinic systems have been shown to influence dopaminergic function in the central nervous system. In addition, previous studies of benztropine analogs that inhibit dopamine uptake and show antagonism at muscarinic receptors show these drugs to be less effective than cocaine in producing its various prototypic effects such as locomotor stimulation. Because previous pharmacological studies on these topics have used nonselective M1 antagonists, we examined the interactions of preferential M1 muscarinic antagonists and cocaine. Dose-dependent increases in extracellular levels of dopamine in selected brain areas, the nucleus accumbens (NAc) shell and core, and the prefrontal cortex, were produced by cocaine but not by the preferential M1 antagonists telenzepine and trihexyphenidyl. When administered with cocaine, however, both M1 antagonists dose-dependently increased the effects of cocaine on dopamine in the NAc shell, and these effects were selective in that they were not obtained in the NAc core or in the prefrontal cortex. Telenzepine also increased locomotor activity, although the effect was small compared with that of cocaine. The locomotor stimulant effects of trihexyphenidyl, in contrast, approached those of cocaine. Telenzepine attenuated, whereas trihexyphenidyl enhanced the locomotor stimulant effects of cocaine, with neither drug facilitating cocaine-induced stereotypy. The present results indicate that preferential antagonist effects at muscarinic M1 receptors do not uniformly alter all of the effects of cocaine, nor do they explain the differences in effects of cocaine and benztropine analogs, and that the alterations in dopamine levels in the NAc shell do not predict the behavioral effects of the interactions with cocaine.
The Prevalence of Metabolic Syndrome and Determination of the Optimal Waist Circumference Cutoff Points in a Rural South African Community

OBJECTIVE To determine the prevalence of metabolic syndrome and to define optimal ethnic-specific waist-circumference cutoff points in a rural South African black community. RESEARCH DESIGN AND METHODS This was a cross-sectional survey conducted by random-cluster sampling of adults aged >15 years. Participants had demographic, anthropometric, and biochemical measurements taken, including a 75-g oral glucose tolerance test. Metabolic syndrome was defined using the 2009 Joint Interim Statement (JIS) definition. RESULTS Of 947 subjects (758 women) studied, the age-adjusted prevalence of metabolic syndrome was 22.1%, with a higher prevalence in women (25.0%) than in men (10.5%). Peak prevalence was in the oldest age-group (≥65 years) in women (44.2%) and in the 45- to 54-year age-group in men (25.0%). The optimal waist circumference cutoff point to predict the presence of at least two other components of the metabolic syndrome was 86 cm for men and 92 cm for women. The crude prevalence of metabolic syndrome was higher with the JIS definition (26.5%) than with the International Diabetes Federation (IDF) (23.3%) or the modified Third Report of the National Cholesterol Education Program Adult Treatment Panel (ATPIII) (18.5%) criteria; there was very good agreement with the IDF definition (κ = 0.90 [95% CI 0.87–0.94]) and good concordance with ATPIII criteria (0.77 [0.72–0.82]). CONCLUSIONS There is a high prevalence of metabolic syndrome, especially in women, suggesting that this community, unlike other rural communities in Africa, already has entered the epidemic of metabolic syndrome. Waist circumference cutoff points differ from those currently recommended for Africans.
Teacher/pupil perspectives on computer-augmented physics lessons on measurement in kenyan secondary schools

The application of computers in Kenya is relatively uncommon, and the facilities and stage of professional and organisational development in Kenya appear to reflect this. This article describes the views of a group of pupils and their teacher regarding the impact of a computer-based instruction (CBI) programme that involved the collaborative learning of a physics course on measurement. Three secondary schools and a total of 118 pupils were studied in three classrooms, one class acting as a comparison. The same teacher taught the same physics content to all three classes using one of two teaching methods, namely: (1) the traditional mode and (2) the CBI mode. Selected students and their teacher were observed and interviewed. The qualitative data analysis methods of comparison and clustering were used to identify pattern and themes within the interview data. Results of the study indicate significant improvements in pupils' learning of physics as well as in the development of positive effects and/or socialisation skills. Accounts by the teacher, as well as those by the students, indicate a perception of the potential of CBI to enhance their learning of measurement concepts and methods. For instance, the study revealed that, apart from its positive influence on the pupils' classroom interaction patterns, the pupils in the treatment groups learned the concepts and methods better than their counterparts in the true control group. Furthermore, the experience influenced the teacher's belief about teaching using CBI to augment physics instruction.
Facial Emotion Recognition using Deep Learning

Facial emotion recognition is one of the most important cognitive functions that our brain performs quite efficiently. State of the art facial emotion recognition techniques are mostly performance driven and do not consider the cognitive relevance of the model. This project is an attempt to look at the task of emotion recognition using deep belief networks which is cognitively very appealing and at the same has been shown to perform very well for digit recognition (Hinton et.al. 2006). We look at the effects of varying number of hidden layers and hidden units on the performance of the model and attempt to develop important insights into the features learnt by the model. Also we observe that as found various psychological findings our model finds lower spatial frequency more useful for recognizing facial expressions than higher spatial frequency data.
Image representations using multiscale differential operators

Differential operators have been widely used for multiscale geometric descriptions of images. The efficient computation of these differential operators is always desirable. Moreover, it has not been clear whether such representations are invertible. For certain applications, it is usually required that such representations should be invertible so that one can facilitate the processing of information in the transform domain and then recover it. In this paper, such problems are studied. We consider multiscale differential representations of images using different types of operators such as the directional derivative operators and Laplacian operators. In particular, we provide a general approach to represent images by their multiscale and multidirectional derivative components. For practical implementation, efficient pyramid-like algorithms are derived using the spline technique for both the the composition and reconstruction of images. It is shown that using these representations various meaningful geometric information of images can be extracted at multiple scales; therefore, these representations can be used for edge based image processing purposes. Furthermore, the intrinsic relationships of the proposed representations with the compact wavelet models, and some classical multiscale approaches are also elucidated in the paper.
Temporal and spatial genetic variation in spawning grounds of European hake (Merluccius merluccius) in the Bay of Biscay

Polymorphism at five microsatellite loci were screened to determine the genetic variability and the temporal stability of population structure in natural populations of European hake (Merluccius merluccius, L.) within the Bay of Biscay. In addition, the control region (900 bp) and two protein coding genes (ATPase, subunits 6 and 8, 842 bp and a partial sequence of the ND1, 800 bp) of the mitochondrial DNA (mtDNA) were sequenced from geographically distant populations from the extremes of the species range. One hundred individuals from either side of a supposed stock boundary within the bay were collected in autumn 1997. This sampling strategy was repeated during hake spawning seasons in late spring of 1998 and 1999. Low levels of population subdivision were found between putative populations within years. Similarly, low levels of differentiation were found between autumn 1997 northern samples and spring 1998 southern samples which were collected 7 months later on spawning grounds. These results are discussed in relation to ecological, behavioural and oceanographic information. Sampling effects, which may influence these results, are also discussed. Theta (θ) estimates were significantly different from zero in every other pairwise comparison between geographical areas (north and south of the Bay of Biscay) and between years within the same area (P < 0.05). Hierarchical analysis of molecular variance (amova) does not confirm the temporal persistence of population structure. These results are discussed in relation to variance in reproductive success, and temporal spawning patterns, which may exist within the bay. mtDNA variability was very low between geographically distant samples from Norway and the Mediterranean Sea with only 10 variable sites found in a total of 2542 bp of mtDNA, these differences being exclusively in the d‐loop.
High performance iris recognition based on LDA and LPCC

In this paper, the iris recognition algorithm based on LPCC and LDA is first presented. So far, the two algorithms are not found for iris recognition in literature. In addition, a simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the probabilistic neural network (PNN). Finally, a comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The proposed algorithms can achieve 100% recognition rates and the result is encouraging
Keyword-driven image captioning via Context-dependent Bilateral LSTM

Image captioning has recently received much attention. Existing approaches, however, are limited to describing images with simple contextual information, which typically generate one sentence to describe each image with only a single contextual emphasis. In this paper, we address this limitation from a user perspective with a novel approach. Given some keywords as additional inputs, the proposed method would generate various descriptions according to the provided guidance. Hence, descriptions with different focuses can be generated for the same image. Our method is based on a new Context-dependent Bilateral Long Short-Term Memory (CDB-LSTM) model to predict a keyword-driven sentence by considering the word dependence. The word dependence is explored externally with a bilateral pipeline, and internally with a unified and joint training process. Experiments on the MS COCO dataset demonstrate that the proposed approach not only significantly outperforms the baseline method but also shows good adaptation and consistency with various keywords.
Graph Wavelets via Sparse Cuts

Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Processing on Graphs and, in particular, graph wavelets can exploit the intrinsic smoothness of these datasets in order to represent them in a compact and accurate manner. However, how to discover wavelet bases that capture the geometry of the data with respect to the signal as well as the graph structure remains an open problem. In this paper, we study the problem of computing graph wavelet bases via sparse cuts in order to produce low-dimensional encodings of data-driven bases. This problem is connected to known hard problems in graph theory (e.g. multiway cuts) and thus requires an efficient heuristic. We formulate the basis discovery task as a relaxation of a vector optimization problem, which leads to an elegant solution as a regularized eigenvalue computation. Moreover, we propose several strategies in order to scale our algorithm to large graphs. Experimental results show that the proposed algorithm can effectively encode both the graph structure and signal, producing compressed and accurate representations for vertex values in a wide range of datasets (e.g. sensor and gene networks) and significantly outperforming the best baseline.
Molecular mechanics models for tetracycline analogs

Tetracyclines (Tcs) are an important family of antibiotics that bind to the ribosome and several proteins. To model Tc interactions with protein and RNA, we have developed a molecular mechanics force field for 12 tetracyclines, consistent with the CHARMM force field. We considered each Tc variant in its zwitterionic tautomer, with and without a bound Mg2+. We used structures from the Cambridge Crystallographic Data Base to identify the conformations likely to be present in solution and in biomolecular complexes. A conformational search by simulated annealing was undertaken, using the MM3 force field, for tetracycline, anhydrotetracycline, doxycycline, and tigecycline. Resulting, low‐energy structures were optimized with an ab initio method. We found that Tc and its analogs all adopt an extended conformation in the zwitterionic tautomer and a twisted one in the neutral tautomer, and the zwitterionic‐extended state is the most stable in solution. Intermolecular force field parameters were derived from a standard supermolecule approach: we considered the ab initio energies and geometries of a water molecule interacting with each Tc analog at several different positions. The final, rms deviation between the ab initio and force field energies, averaged over all forms, was 0.35 kcal/mol. Intramolecular parameters were adopted from either the standard CHARMM force field, the ab initio structure, or the earlier, plain Tc force field. The model reproduces the ab initio geometry and flexibility of each Tc. As tests, we describe MD and free energy simulations of a solvated complex between three Tcs and the Tet repressor protein. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
Face Recognition Based on Nonlinear DCT Discriminant Feature Extraction Using Improved Kernel DCV

This letter proposes a nonlinear DCT discriminant feature extraction approach for face recognition. The proposed approach first selects appropriate DCT frequency bands according to their levels of nonlinear discrimination. Then, this approach extracts nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. the improved kernel discriminative common vector (KDCV) method. Experiments on the public FERET database show that this new approach is more effective than several related methods.
Planners as Urban Managers: An Exploration of the Attitudes and Self-Image of Senior British Planners

The question of the nature and homogeneity of planners' attitudes and self-image is central to the current debate on planners as urban managers. If planners do possess an ‘institutional mind’ which is closely attuned to the ‘ruling ideas of a ruling elite’, and if their role in determining who gains and who loses in the process of planned urban development is influenced by a coherent professional ideology, then planners can be expected to share distinctive orientations towards the holders of political office, towards the urban environment itself, and towards specific social groups. In this paper we explore this issue, drawing on data from a survey of senior British planners. It is shown that British planners are drawn from a fairly narrowly defined spectrum of society and that they do in fact share distinctive attitudinal orientations. They subscribe to professional roles as mediators, guardians of the physical environment, and, above all, managers of the urban system. It is suggested that the ‘ideal type’ of planning personality centres on a managerial ethos which is directed towards ‘problem solving’ in a rationalist, positivistic way within a strictly defined hierarchy of authority. There is evidence, however, that there exist several subtypes of planning personality within this overall orientation.
Preference Learning for Adaptive Interaction

This extended abstract describes ongoing work in the development of an intelligent assistant that interacts with its user in a personalized fashion, deciding whether, when and how to interact based on a user’s needs and preferences. I consider two types of users: people who work in an office environment and require assistance with managing their daily meeting and project schedule, and people with cognitive disabilities who require guidance in performing their daily tasks. I will examine various interaction types including reminders, requests for confirmation of a task having been completed, requests for permission to assist in the performance of a task, and requests for feedback. Both explicitly stated and implicit user preferences will be used as input to the learning mechanism, and the assistant will be evaluated based on its learning efficiency and the level of user satisfaction it achieves. This development of a preference learning model for interaction management constitutes the primary contribution of my work.