Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between poverty and inequality: Concepts and measurement

This paper defines and maps the variety of different concepts and measures of poverty and inequality that have been developed and used in research. These reflect differing views of what constitutes a minimum acceptable quality of life and how the disparity between the least and most well-off in society should be defined. Since the analytical conclusions of poverty and inequality research can depend on the concepts and measures chosen, it is worth laying out their underlying rationales. In this paper we discuss the concepts of poverty and inequality in broad terms; the focal variables of poverty and inequality that have been proposed in the literature, from unidimensional monetary indicators to broader multidimensional and subjective concepts; the issues in conducting comparative analyses of poverty and inequality over subgroups of individuals, households, countries and over time; and the properties of measures proposed for summarising levels of poverty and inequality over the population.
Maternal characteristics and behavioural/emotional problems in preschoolers: how they relate to sleep rhythmic movements at sleep onset

Sleep rhythmic movements have been speculated to be a form of self‐soothing. While this sleep‐related movement has been associated with lower socioeconomic status, psychopathologies and maternal characteristics, prospective studies with sizeable sample and objective measurements are lacking. The objectives were: (a) to identify maternal characteristics predicting sleep rhythmic movements in children; and (b) to document behavioural/emotional problems in preschoolers with sleep rhythmic movements. Participants were mother–child dyads (N = 529) from the Adversity: Maternal Adversity, Vulnerability and Neurodevelopment cohort. Questionnaires evaluating socioeconomic status (prenatal), maternal depressive symptoms (prenatal, 48 months), sleep rhythmic movements (12, 18, 24, 36, 48 months), maternal anxiety trait (24 months) and children's behavioural/emotional problems (48 months) were used. Maternal sensitivity (accuracy and appropriateness of mother's responses to her baby's needs) was assessed objectively with a filmed mother–infant interaction (6 months). Generalized estimating equation was used to investigate associations between sleep rhythmic movements and maternal characteristics (depression, anxiety and sensitivity). Linear regressions were used to assess associations between sleep rhythmic movements and behavioural/emotional problems in children. Lower maternal sensitivity, higher maternal depressive symptoms and lower socioeconomic status predicted sleep rhythmic movements in children (p < 0.05). To our knowledge, this is the first study showing that sleep rhythmic movements are associated with lower maternal sensitivity, measured objectively. This study also builds on previous reports, by documenting an association between sleep rhythmic movements and behavioural/emotional problems even in preschoolers. The presence of psychosocial factors in sleep rhythmic movements aetiology should be considered in treatment.
Automatic diameter and orientation distribution determination of fibrous materials in micro X‐ray CT imaging data

Fibrous nanomaterials such as electrospun materials have many uses ranging from tissue engineering to biosensors. High‐resolution imaging is an important component in the characterization of these materials. Important parameters required to predict and study the properties of fibre rich materials include diameter and orientation distribution as well as fibre spacing. The orientations and the relative dimensions of the fibres can be measured via specially designed imaging software. Difficulties in this measurement process can arise if fibres are distributed in close proximity to each other in relation to the resolution of the imaging modality. For example, if some automation is required in the measurement process and, particularly, if the automated processes are not designed for situations where the fibres are in close proximity to each other. This work is therefore concerned with the development of automated measurement techniques to provide estimates of the diameters of fibres and also the orientation distribution. The software automatically detects special points in the fibrous materials where fibres can be considered to have some delineation from surrounding fibres. These sparse points are considered to be points at which estimates of the fibres' properties can be quantified. Aligned and randomly distributed electrospun poly(caprolactone) nanofibres were prepared. Imaging of these materials was performed with an X‐ray Computer Tomography system with an image voxel size of 0.15 × 0.15 × 0.15 μm3. Scanning Electron Microscopy images were also obtained. Fibre diameters estimated using images from both modalities using the developed techniques were found to be in agreement. Orientation distribution was summarized with multiscale entropy and found to be consistent with visual observation across different scales.
Firm Strategies in Open Internet of Things Business Ecosystems: Framework and Case Study

We present a typology of strategies employed by firms using the Internet of Things (IoT). The IoT is a distributed network of connected physical objects. As these devices exchange data with each other instead of through an intermediary, the IoT increases complexity of business ecosystems, and opens up new business opportunities. When the platform owner does not own the data and technology is mostly open source, other actors can use and build on them. In addition to platform owner’s strategy, we propose a framework with three additional strategies, based on whether the firms’ offering integrates into the specific industrial value chain or contributes to the IoT ecosystem, and whether the firm offering is by nature stand-alone or systemic. With a multiple case study design, we explore this framework in the setting of 23 firms in a large research project context. The descriptions of the identified IoT strategies support our framework.
Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family

Background: Multiple sclerosis (MS) is a complex disease resulting from the joint effect of many genes. It has been speculated that rare variants might explain part of the missing heritability of MS. Objective: To identify rare coding genetic variants by analyzing a large MS pedigree with 11 affected individuals in several generations. Methods: Genome-wide linkage screen and whole exome sequencing (WES) were performed to identify novel coding variants in the shared region(s) and in the known 110 MS risk loci. The candidate variants were then assessed in 591 MS patients and 3169 controls. Results: Suggestive evidence for linkage was obtained to 7q11.22-q11.23. In WES data, a rare missense variant p.R183C in FKBP6 was identified that segregated with the disease in this family. The minor allele frequency was higher in an independent cohort of MS patients than in healthy controls (1.27% vs 0.95%), but not significant (odds ratio (OR) = 1.33 (95% confidence interval (CI): 0.8–2.4), p = 0.31). Conclusion: The rare missense variant in FKBP6 was identified in a large Dutch MS family segregating with the disease. This association to MS was not found in an independent MS cohort. Overall, genome-wide studies in larger cohorts are needed to adequately investigate the role of rare variants in MS risk.
Replicating Human Hand Synergies Onto Robotic Hands: A Review on Software and Hardware Strategies

This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands. In robotics, this dimensionality reduction can be achieved by coupling some of the degrees of freedom (DoFs) of the robotic hand, that results in a reduction of the needed inputs. Such coupling can be obtained at the software level, exploiting mapping algorithm to reproduce human hand organization, and at the hardware level, through either rigid or compliant physical couplings between the joints of the robotic hand. This paper reviews the main solutions proposed for both the approaches.
Genetically Induced Tumors in the Oncopig Model Invoke an Antitumor Immune Response Dominated by Cytotoxic CD8β+ T Cells and Differentiated γδ T Cells Alongside a Regulatory Response Mediated by FOXP3+ T Cells and Immunoregulatory Molecules

In recent years, immunotherapy has shown considerable promise in the management of several malignancies. However, the majority of preclinical studies have been conducted in rodents, the results of which often translate poorly to patients given the substantial differences between murine and human immunology. As the porcine immune system is far more analogous to that of humans, pigs may serve as a supplementary preclinical model for future testing of such therapies. We have generated the genetically modified Oncopig with inducible tumor formation resulting from concomitant KRASG12D and TP53R167H mutations under control of an adenoviral vector Cre-recombinase (AdCre). The objective of this study was to characterize the tumor microenvironment in this novel animal model with respect to T-cell responses in particular and to elucidate the potential use of Oncopigs for future preclinical testing of cancer immunotherapies. In this study, we observed pronounced intratumoral T-cell infiltration with a strong CD8β+ predominance alongside a representation of highly differentiated γδ T cells. The infiltrating CD8β+ T cells displayed increased expression of the cytotoxic marker perforin when compared with the peripheral T-cell pool. Similarly, there was robust granzyme B staining localizing to the tumors; affirming the presence of cytotoxic immune cells within the tumor. In parallel with this antitumor immune response, the tumors displayed enrichment in FOXP3-expressing T cells and increased gene expression of indoleamine 2,3-dioxygenase 1 (IDO1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and programmed death-ligand 1 (PDL1). Finally, we investigated the Oncopig immune system in mediating antitumor immunity. We observed pronounced killing of autologous tumor cells, which demonstrates the propensity of the Oncopig immune system to recognize and mount a cytotoxic response against tumor cells. Together, these findings suggest innate and adaptive recognition of the induced tumors with a concomitant in vivo suppression of T-cell effector functions. Combined, the data support that the Oncopig may serve as a valuable model for future preclinical testing of immunotherapies aimed at reactivating tumor-directed cytotoxicity in vivo.
Evolutionary and demographic processes shaping geographic patterns of genetic diversity in a keystone species, the African forest elephant (Loxodonta cyclotis)

Abstract The past processes that have shaped geographic patterns of genetic diversity may be difficult to infer from current patterns. However, in species with sex differences in dispersal, differing phylogeographic patterns between mitochondrial (mt) and nuclear (nu) DNA may provide contrasting insights into past events. Forest elephants (Loxodonta cyclotis) were impacted by climate and habitat change during the Pleistocene, which likely shaped phylogeographic patterns in mitochondrial (mt) DNA that have persisted due to limited female dispersal. By contrast, the nuclear (nu) DNA phylogeography of forest elephants in Central Africa has not been determined. We therefore examined the population structure of Central African forest elephants by genotyping 94 individuals from six localities at 21 microsatellite loci. Between forest elephants in western and eastern Congolian forests, there was only modest genetic differentiation, a pattern highly discordant with that of mtDNA. Nuclear genetic patterns are consistent with isolation by distance. Alternatively, male‐mediated gene flow may have reduced the previous regional differentiation in Central Africa suggested by mtDNA patterns, which likely reflect forest fragmentation during the Pleistocene. In species like elephants, male‐mediated gene flow erases the nuclear genetic signatures of past climate and habitat changes, but these continue to persist as patterns in mtDNA because females do not disperse. Conservation implications of these results are discussed.
Detection and characterization of ibropapilloma associated herpesvirus of marine turtles in Rio Grande do Sul , Brazil

Rodenbusch C.R. , Almeida L.L. , Marks F.S. , Ataíde M.W. , Alievi M.M. , Tavares M. , Pereira R.A. & Canal C.W. 2012. Detection and characterization of ibropapilloma associated herpesvirus of marine turtles in Rio Grande do Sul, Brazil. Pesquisa Veterinária Brasileira 32(11):1179-1183. Laboratório de Virologia, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9090, Porto Alegre, RS 91540-000, Brazil. E-mail: carlarodenbusch@yahoo.com. br Fibropapillomatosis (FP) is a benign tumoral disease that affects sea turtles, hampering movement, sight and feeding, ultimately leading to death. In Brazil, the disease was described for the irst time in 1986. Research suggests the involvement of a herpesvirus in association with environmental and genetic factors as causal agents of FP. The objective of the present study was to detect and characterize this herpesvirus in sea turtles living in the coast of state Rio Grande do Sul (RS), Brazil. From October 2008 to July 2010, 14 turtles were observed between the beaches of Torres and Tavares, of which 11 were green turtles (Chelonia mydas) and 3 were loggerhead turtles (Caretta caretta). All turtles were young and mean curved carapace length was 37.71±7.82cm, and varied from 31 to 55cm. Only one green turtle presented a 1cm, papillary, pigmented ibropapilloma. Skin and ibropapilloma samples were analyzed by conventional and real time PCR assays to detect and quantify herpesvirus. All skin samples were negative, though the ibropapilloma specimen was positive in both tests. Viral load was 9,917.04 copies of viral genome per milligram of tissue. The DNA fragment ampli ied from the ibropapilloma sample was sequenced and allocated in the Atlantic phylogeographic group. This study reports the irst molecular characterization of herpesvirus associated with ibropapilloma in turtles from the coast of RS.
Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches towards in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual- or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavours in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.
FOR HIGH DIMENSIONAL GRAPH SIGNALS

In recent years there is a growing interest in operating on graph signals. One systematic and productive such line of work is incorporating sparsity-inspired models to this data type, offering these signals a description as sparse linear combinations of atoms from a given dictionary. In this paper, we propose a dictionary learning algorithm for this task that is capable of handling high dimensional data. We incorporate the underlying graph topology by forcing the learned dictionary atoms to be sparse combinations of graph wavelet functions. The resulting atoms thus adhere to the underlying graph structure and possess a desired multi-scale property, yet they capture the prominent features of the data of interest. This results in both adaptive representations and an efficient implementation. Experimental results on different datasets, representing both synthetic and real network data, demonstrate the effectiveness of the proposed algorithm for graph signal processing.
Using the Viable System Model to Study IT Governance Dynamics: Evidence from a Single Case Study

This paper presents a single descriptive case study in which the viable system model (VSM) is used as a theoretical lens to model an organization’s contemporary IT governance system. The case presented herein was selected specifically for being a digitized company of which we knew that a lot of effort was put recently in their IT governance system. We find that the case company’s IT governance system maps well to the structure and underlying logic of the VSM. This paper contributes to the literature by providing an empirical justification exemplar on the applicability of systemic thinking in general, and the VSM in specific, for modelling enterprise governance and management of IT.
Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing

Searching, browsing, and recommendations are common ways in which the "choice overload" faced by users in the online marketplace can be mitigated. In this paper we propose the use of hierarchical item categories, obtained from implicit feedback data, to enable efficient browsing and recommendations. We present a method of creating hierarchical item categories from implicit feedback data only i.e., without any other information on the items like name, genre etc. Categories created in this fashion are based on users' co-consumption of items. Thus, they can be more useful for users in finding interesting and relevant items while they are browsing through the hierarchy. We also show that this item hierarchy can be useful in making category based recommendations, which makes the recommendations more explainable and increases the diversity of the recommendations without compromising much on the accuracy. Item hierarchy can also be useful in the creation of an automatic item taxonomy skeleton by bypassing manual labeling and annotation. This can especially be useful for small vendors. Our data-driven hierarchical categories are based on hierarchical latent tree analysis (HLTA) which has been previously used for text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so that HLTA can be applied to implicit feedback data.
Alcohol‐mediated miR‐34a modulates hepatocyte growth and apoptosis

MicroRNAs (miRs) have been recently shown to be heavily involved in the development of alcoholic liver disease (ALD) and suggested as a potential therapeutic target in ALD. The miR‐34a was consistently reported to be significantly elevated in several ALD rodent models, but it remains unclear how miR‐34a modulates the cellular behaviours of hepatocytes in ALD development and progression. This study aims to characterize alcohol‐induced miR‐34a impact on hepatocytes growth and apoptosis. The miRNA array was performed to assess changes in miRNA after chronic alcohol feeding. Liver and blood samples were used to examine ALD progression. The miR‐34a was overexpressed in human hepatocytes to evaluate its impact on cell growth and apoptosis. Real‐time quantitative PCR and Western blot were used to determine the growth and apoptosis molecular signalling pathways associated with miR‐34a. Alcohol feeding significantly promoted fatty liver progression, serum ALT levels, apoptosis and miR‐34a expression in rat liver. Overexpression of miR‐34a in human hepatocytes suppressed cell growth signallings, including c‐Met, cyclin D1 and cyclin‐dependent kinase 6 (CDK6). The miR‐34a might also inhibit the expression of sirtuin 1 (Sirt1) and its target, B‐cell lymphoma 2. Interestingly, the expression of miR‐34a reverses the suppressive effects of ethanol on cell growth. But, miR‐34a promotes hepatocyte senescence and apoptosis. Although the miR‐34a‐mediated down‐regulation of cell growth‐associated genes may contribute to cell growth retardation, other miR‐34a targets, such as Sirt1, may reverse this phenotype. Future studies will be needed to clarify the role of miR‐34a in ALD progression.
Ghosts of programming past, present and yet to come

Twenty-five years ago, when the Psychology of Programming Interest Group (PPIG) was established, the concept of programming seemed rather robust and clear. With Turing's characterisation of algorithms at its core, its meaning could be safely broadened to include the many peripheral activities surrounding the production of software that – as can be inferred from the Call for Papers – have become the natural habitat for PPIG. Today, the nature and status of programming is much murkier. For instance, in his state-of-the-art reflections on programming [13], Chris Granger poses the questions What is programming? and What is wrong with programming? Despite the great promise – and perhaps even greater expectations – for applications of computational thinking, programming issues that were perceived as of the essence are no longer so prominently represented in computingin-the-wild. For instance, the programming languages SCRATCH and Go – with radically different motivations – shift the focus from programming paradigms, formal programming language semantics and sophisticated abstract programming constructs to highly pragmatic issues relating to the experience of the developer. This paper makes the case that in understanding this intellectual development in approaches to program-related activity it is helpful to stop stretching the notion of ‘programming’ ever wider and to appeal instead to a broader complementary concept. To this end, in this paper, we propose to confine the term ‘programming’ to the authentic meaning it acquires through Turing’s characterisation, and address the wider agenda to which it has been enlisted – and through which its meaning has been adulterated – by introducing the notion of 'making construals'. This proposal reflects twenty-five years of parallel development of Empirical Modelling, an approach to computing that has taken much inspiration and encouragement from the PPIG agenda [7, 9, 11].