Miracle-dz/UniFAR-FTUs huggingface dataset:
Transplantation of embryonic spleen tissue reveals a role for adult non-lymphoid cells in initiating lymphoid tissue organization

In this report we describe a transplantation system where embryonic spleens are grafted into adult hosts. This model can be used to analyze the cellular and molecular requirements for the development and organization of splenic microenvironments. Whole embryonic day 15 (ED15) spleens, grafted under the kidney capsule of adult mice, were colonized by host‐derived lymphocytes and DC and developed normal splenic architecture. Grafts were also able to form germinal centers in response to T‐dependent antigen. Using this system we demonstrated that adult host‐derived lymphotoxin (LT) α was sufficient for the development of ED15 LTα−/− grafts. Grafting of ED15 LTα−/− spleens into RAG−/− hosts followed by transfer of LT α−/− splenocytes revealed no requirement for lymphocyte‐derived LT α in the induction of CCL21 or the development of T‐zone stroma. These data suggest that interactions between adult lymphoid‐tissue inducer‐like cells and embryonic stromal cells initiated T‐zone development. Furthermore, adult lymphoid tissue inducer‐like cells were shown to develop from bone marrow‐derived progenitors. The model described here demonstrates a method of transferring whole splenic microenvironments and dissecting the stromal and hematopoietic signals involved in spleen development and organization.
‘If we stop, the world stops’: the 2018 feminist strike in Spain

ABSTRACT Under the slogan ‘If we stop, the world stops’, the Spanish feminist movement organized a 24-hour labour, education, care and consumption women’s strike on 8 March 2018 that amazed both international and national public opinion. After all, Spain has traditionally been a Catholic country and its feminist movement is barely institutionalized, lacking both structure and funding. The present profile sets out to account for the 8M strike and points to a long organization process within a broad protest cycle as keys to its success.
A Workplace Study of the Adoption of Information Visualization Systems. Proceedings of I-KNOW'03: 3rd International Conference on Knowledge Management, Graz, Austria

This paper reports an ongoing longitudinal study of the adoption of information visualization systems by administrative data analysts. Participants were initially excited about the anticipated potential of visual data analysis for their work, but gradually discovered difficulties that eventually precluded a true integration of the visualization system into their daily work practices. These difficulties are unrelated to the specific visualization system used. We conclude that data analysts can take much better advantage of the benefits of information visualization systems when these systems are redesigned to be complementary products of current data analysis and workflow systems, rather than being stand-alone products as is currently the case. Our study offers some insights about how this complementarity can be achieved.
Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression

The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. “Quality by Design”, mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the “knowledge space” is a summary of all process knowledge obtained during product development, and the “design space” is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause–effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.
dotRL: A platform for rapid Reinforcement Learning methods development and validation

This paper introduces dotRL, a platform that enables fast implementation and testing of Reinforcement Learning algorithms against diverse environments. dotRL has been written under . NET framework and its main characteristics include: (i) adding a new learning algorithm or environment to the platform only requires implementing a simple interface, from then on it is ready to be coupled with other environments and algorithms, (ii) a set of tools is included that aid running and reporting experiments, (iii) a set of benchmark environments is included, with as demanding as Octopus-Arm and Half-Cheetah, (iv) the platform is available for instantaneous download, compilation, and execution, without libraries from different sources.
Effects of visuospatial and verbal processing on traumatic intrusions

The present study tested predictions derived from the Dual Representation Theory (DRT) of memory intrusions in Posttraumatic Stress Disorder (PTSD). It was hypothesized that engagement in a visuospatial task after watching a trauma film would result in a reduction in intrusions and better intentional memory recall relative to a verbal and non-task control conditions, and that engagement in a verbal task would have opposite effects. The present study further examined the effects of visuospatial and verbal tasks on trauma-related memory when these tasks were matched in difficulty and engagement levels. In Study 1, participants in visuospatial, verbal, and control conditions were presented with a trauma film. Then they were asked to record intrusions over a one-week period and their intentional memory recall was assessed one week later. Contrary to predictions, the three conditions failed to differ significantly on intrusions or intentional memory recall. Study 2 attempted to replicate more directly prior findings by using the visuospatial task ÂiÂ®TetrisÂiÂ¯ that has been used successfully in prior studies. In contrast to hypothesis and previous research, there were no between-condition differences in intrusions. Exploratory analyses in Study 1 and Study 2 did reveal significant positive relations between anxiety sensitivity and PTSD symptoms, even after controlling for depression and trait anxiety. The findings implicating anxiety sensitivity in PTSD are discussed in the context of treatment and prevention efforts. Discussion focuses on the implications of the inconsistent experimental findings for an evaluation of the validity of the DRT.
GA-optimized neuro-fuzzy approach for nonlinear system modeling

In order to characterize the behavior of nonlinear dynamic systems many different approaches have been proposed in recent years. One of the best black-box models employed to deal with system nonlinearities is the combination of artificial neural network (ANN) and fuzzy logic system (FLS), which is known as neuro-fuzzy system. However, the gradient-based nature of this combination causes some deficiencies. Therefore, in this paper, an optimization approach which utilizes genetic algorithm (GA) as a derivativefree optimizer is proposed for both designing the structure of neuro-fuzzy model and assessing the model parameters. The whole proposed approach is applied to approximate: first, a nonlinear plant; next, nonlinear dynamic behavior of magneto-rheological (MR) damper, which is widely used in semi-active control of structures and its identification is significantly difficult due to inherent hysteretic and highly nonlinear behavior of the device. Comparisons between the responses of the models and the reference data show high accuracy and feasibility of the proposed approach.
A Review of some Developments in Collocation Algorithms

This paper reviews some developments in error estimation and mesh selection for collocation methods for ordinary differential equations. The basic idea of collocation has great generality and simplicity. Given some operator equation we look for an approximate solution in the form of a linear combination of some fixed basis functions. The coefficients in the linear combination are found by substitution into the equation, then by satisfying any boundary conditions and the differential equation at certain ‘collocation’ points. The number of points is chosen so that the number of equations is equal to the number of unknowns. The idea applies to a wide range of operator equations, but the discussion here will be confined to ordinary differential equations, mainly higher order boundary value problems. One particular attraction is the ease with which boundary conditions involving higher derivatives can be dealt with. For differential equations, unlike integral equations, collocation leads directly to a set of algebraic equations for the parameters. The most common sets of functions used are global or piecewise polynomials. These are easily differentiated so that substitution in the equations is easy. Some care is needed in the choice of collocation points if an effective algorithm is to be obtained. The aim of this paper is to review error estimation for these methods and mesh selection for piecewise polynomial collocation. Some mesh selection criteria which take into account the way the error arises are introduced. These criteria are, however, expensive, and in spite of their good motivation they do not seem to produce significantly better distributions than simpler criteria, so
Minimum-Energy Broadcast Routing in Dynamic Wireless Networks

One of the new challenges facing research in wireless networks is the design of algorithms and protocols that are energy aware. A good example is the minimum-energy broadcast routing problem for a static network in the plane, which attracted a great deal of attention these past years. The problem is NPhard and its approximation ratio complexity is a solution proved to be within a factor 6 of the optimal, based on finding a Minimum Spanning Tree of the static planar network. In this paper, we use for the first time the evolving graph combinatorial model as a tool to prove an NP-Completeness result, namely that computing a Minimum Spanning Tree of a planar network in the presence of mobility is actually NP-Complete. This result implies that the above approximation solution cannot be used in dynamic wireless networks. On the positive side, we give a polynomial-time algorithm to build a rooted spanning tree of an on/off network, that minimizes the maximum energy used * The views expressed in this article are the sole responsibility of the authors and in no way represent the view of the European Commission and its services.
Geodesic Forests for Image Editing

A Geodesic Forest is a new representation of digital color images which yields flexible and efficient editing algorithms. In this paper an image is decomposed into a collection of trees (a forest) whose branches follow directions of minimum variation. This representation enables expensive, 2D, edge-aware processing to be cast as efficient one-dimensional operations along the tree branches. Existing and novel contrast-sensitive editing tasks can now be achieved by simple and effective algorithms acting on the same tree-based image decomposition. The contribution of this paper is three-fold: i) We introduce the Geodesic Forests image representation which unifies a number of previously diverse editing techniques; ii) We present a GPU-CUDA algorithm for the efficient decomposition of an image into a complete set of disjoint geodesic trees; iii) We describe a number of simple algorithms to generate existing and new edge-aware image and video effects. The effectiveness of our algorithms is demonstrated with a number of applications such as: texture flattening, ink painting, data-aware resizing, diffusive painting and geodesic plotting. The high level of parallelism of our algorithms enables them to be applied interactively to high-resolution images (∼ 15Mpixel), and video data. CR Categories: I.4. 10 [Computing Methodologies]: Image Processing and Computer Vision—Image Representation; I.4. 3 [Computing Methodologies]: Image Processing and Computer Vision— Enhancement; I.3. 3 [Computer Graphics]: Image Generation—;
Ashley Sides Reality of the conjunction fallacy Running head : REALITY OF THE CONJUNCTION FALLACY On the reality of the conjunction fallacy

Attributing higher “probability” to a sentence of form p-and-q compared to p is a reasoning fallacy only if (a) the word “probability” carries its modern, technical meaning, and (b) the sentence p is interpreted as a conjunct of the conjunction p-and-q. Legitimate doubts arise about both conditions in classic demonstrations of the conjunction fallacy. We used betting paradigms and unambiguously conjunctive statements to reduce these sources of ambiguity about conjunctive reasoning. Despite the precautions, conjunction fallacies were as frequent under betting instructions as under standard probability instructions.
Identification of Tenrec ecaudatus, a Wild Mammal Introduced to Mayotte Island, as a Reservoir of the Newly Identified Human Pathogenic Leptospira mayottensis

Leptospirosis is a bacterial zoonosis of major concern on tropical islands. Human populations on western Indian Ocean islands are strongly affected by the disease although each archipelago shows contrasting epidemiology. For instance, Mayotte, part of the Comoros Archipelago, differs from the other neighbouring islands by a high diversity of Leptospira species infecting humans that includes Leptospira mayottensis, a species thought to be unique to this island. Using bacterial culture, molecular detection and typing, the present study explored the wild and domestic local mammalian fauna for renal carriage of leptospires and addressed the genetic relationships of the infecting strains with local isolates obtained from acute human cases and with Leptospira strains hosted by mammal species endemic to nearby Madagascar. Tenrec (Tenrec ecaudatus, Family Tenrecidae), a terrestrial mammal introduced from Madagascar, is identified as a reservoir of L. mayottensis. All isolated L. mayottensis sequence types form a monophyletic clade that includes Leptospira strains infecting humans and tenrecs on Mayotte, as well as two other Malagasy endemic tenrecid species of the genus Microgale. The lower diversity of L. mayottensis in tenrecs from Mayotte, compared to that occurring in Madagascar, suggests that L. mayottensis has indeed a Malagasy origin. This study also showed that introduced rats (Rattus rattus) and dogs are probably the main reservoirs of Leptospira borgpetersenii and Leptospira kirschneri, both bacteria being prevalent in local clinical cases. Data emphasize the epidemiological link between the two neighbouring islands and the role of introduced small mammals in shaping the local epidemiology of leptospirosis.
Sensory Cue Integration with High-Order Deep Neural Networks

Humans can easily capture real world concepts from multi-modal signals by constructing joint representations of these signals. The joint representations may contain abstract information of multiple modalities and relationships across the modalities. Contrary to humans, it is not easy to obtain joint representations reflecting the structure of multimodal data with machine learning algorithms, especially with conventional neural networks. This is because these models only have additive interaction across modalities. To deal with this issue, we propose a novel machine learning algorithm which captures multiplicative interaction between multiple modalities by using high-order edges. With these edges, the proposed method is able to learn the highly non-linear correlation among modalities. In the experimental results, we demonstrate the effect of this high-order interaction by showing improved results compared to other conventional models with a benchmark dataset, MNIST.
Research Statement past and Current Research Organize and Manage Uncertain and Incomplete Data

My research bridges the areas of data mining, machine learning, and bioinformatics. I am particularly interested in scalable data analysis problems for data science with an emphasis on biological applications. In recent years, we have witnessed a dramatic increase in our ability to extract and collect data from the physical world. The flood of data poses great computational challenges and entails interdisciplinary collaboration in order to be made useful. These challenges represent the inherent characteristics of the data: 1) massive volume; 2) variety (e.g., complex structures, heterogeneous types and data sources); and 3) poor quality (e.g., incompleteness, uncertainty and the presence of measurement errors and noise).
On the intersection of ACM curves in $\PP^3$

Bezout's theorem gives us the degree of intersection of two properly intersecting projective varieties. As two curves in P^3 never intersect properly, Bezout's theorem cannot be directly used to bound the number of intersection points of such curves. In this work, we bound the maximum number of intersection points of two integral ACM curves in P^3. The bound that we give is in many cases optimal as a function of only the degrees and the initial degrees of the curves.
Modeling phonetic context with non-random forests for speech recognition

Modern speech recognition systems typically cluster triphone phonetic contexts using decision trees. In this paper we describe a way to build multiple complementary decision trees from the same data, for the purpose of system combination. We do this by jointly building the decision trees using an objective function that has an added entropy term to encourage diversity among the decision trees. After the trees are built, the systems are built in the standard way and the emission probabilities are combined during decoding. Experiments on multiple datasets show gains from the use of multiple trees, at the expense of evaluating multiple models in test time.
Once-daily budesonide MMX in active, mild-to-moderate ulcerative colitis: results from the randomised CORE II study

Objective Budesonide MMX is a novel oral formulation of budesonide that uses Multi-Matrix System (MMX) technology to extend release to the colon. This study compared the efficacy of budesonide MMX with placebo in patients with active, mild-to-moderate ulcerative colitis (UC). Design Patients were randomised 1:1:1:1 to receive budesonide MMX 9 mg or 6 mg, or Entocort EC 9 mg (budesonide controlled ileal-release capsules; reference arm) or placebo once daily for 8 weeks. The primary endpoint was combined clinical and endoscopic remission, defined as UC Disease Activity Index score ≤1 with a score of 0 for rectal bleeding and stool frequency, no mucosal friability on colonoscopy, and a ≥1-point reduction in endoscopic index score from baseline. Results 410 patients were evaluated for efficacy. Combined clinical and endoscopic remission rates with budesonide MMX 9 mg or 6 mg, Entocort EC and placebo were 17.4%, 8.3%, 12.6% and 4.5%, respectively. The difference between budesonide MMX 9 mg and placebo was significant (OR 4.49; 95% CI 1.47 to 13.72; p=0.0047). Budesonide MMX 9 mg was associated with numerically higher rates of clinical (42.2% vs 33.7%) and endoscopic improvement (42.2% vs 31.5%) versus placebo. The rate of histological healing (16.5% vs 6.7%; p=0.0361) and proportion of patients with symptom resolution (23.9% vs 11.2%; p=0.0220) were significantly higher for budesonide MMX 9 mg than placebo. Adverse event profiles were similar across groups. Conclusion Budesonide MMX 9 mg was safe and more effective than placebo at inducing combined clinical and endoscopic remission in patients with active, mild-to-moderate UC.
Towards a Followee Recommender System for Information Seeking Users in Twitter

Micro-blogging activity taking place in sites such as Twitter gains everyday more importance as a source of real-time information and news spreading medium. Finding relevant information sources among the increasing number of Twitter members is essential for users needing to cope with real-time information. In this paper we study Twitter aiming at generating a set of recommendations to a target user consisting in people who publish tweets that might be interesting to him/her. We evaluate and compare two recommendation approaches: the first selects a set of candidate recommendations using only the network topology and the second exploits the user-generated content available in their tweets. We report the results of a set of controlled experiments with real users carried out to evaluate and compare the performance of both algorithms.
Recent Trends in Rapid Environmental Monitoring of Pathogens and Toxicants: Potential of Nanoparticle-Based Biosensor and Applications

Of global concern, environmental pollution adversely affects human health and socioeconomic development. The presence of environmental contaminants, especially bacterial, viral, and parasitic pathogens and their toxins as well as chemical substances, poses serious public health concerns. Nanoparticle-based biosensors are considered as potential tools for rapid, specific, and highly sensitive detection of the analyte of interest (both biotic and abiotic contaminants). In particular, there are several limitations of conventional detection methods for water-borne pathogens due to low concentrations and interference with various enzymatic inhibitors in the environmental samples. The increase of cells to detection levels requires long incubation time. This review describes current state of biosensor nanotechnology, the advantage over conventional detection methods, and the challenges due to testing of environmental samples. The major approach is to use nanoparticles as signal reporter to increase output rather than spending time to increase cell concentrations. Trends in future development of novel detection devices and their advantages over other environmental monitoring methodologies are also discussed.
Mining and Exploring Unstructured Customer Feedback Data Using Language Models and Treemap Visualizations

We propose an approach for exploring large corpora of textual customer feedback in a guided fashion, bringing order to massive amounts of unstructured information. The prototypical system we implemented allows an analyst to assess labelled clusters in a graphical fashion, based on treemaps, and perform drill-down operations to investigate the topic of interest in a more fine-grained manner. Labels are chosen by simple but effective term weighting schemes and lay the foundations for assigning feedback postings to clusters. In order to allow for drill-down operations leading to new clusters of refined information, we present an approach that contrasts foreground and background models of feedback texts when stepping into the currently selected set of feedback messages. The prototype we present is already in use at various Siemens units and has been embraced by marketing analysts.