Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproduction of menstrual changes in transplanted human endometrial tissue in immunodeficient mice.

BACKGROUND Cultures of human endometrial tissue are useful for analysing the mechanisms underlying the menstrual cycle. However, long-term culture of endometrial tissue is difficult in vitro. Xenotransplantation of normal human endometrial tissue into immunodeficient mice could allow prolonged survival of the transplanted tissues. METHODS Proliferative-phase endometrial tissue samples from three women were transplanted into the subcutaneous space of ovariectomized, immunodeficient, non-obese diabetic (NOD)/severe combined immunodeficiency (SCID)/gammaC(null) (NOG) mice. The mice were treated with 17beta-estradiol (E2) for the first 14 days after transplantation, followed by E2 plus progesterone for the next 14 days. The transplants were investigated morphologically and immunohistochemically at various times after implantation. RESULTS The transplanted tissues contained large numbers of small glands, pseudostratification of the nuclei and dense stroma after treatment with E2 alone. After treatment with E2 plus progesterone, subnuclear vacuolation, luminal secretion and decidualization of the stroma were observed. When the hormone treatment ceased, tissue destruction occurred and the transplants returned to the proliferative phase. Lymphocytes were identified immunohistochemically: the numbers of CD56-positive and CD16-negative cells increased significantly in the stroma during the late secretory phase (day 28). CONCLUSIONS Human endometrial tissue transplanted into NOG mice showed similar histological changes to eutopic endometrial tissue during treatment with sex steroid hormones for 1 month. Moreover, lymphocytes were produced in the transplanted human endometrial tissue. This system represents a new experimental model of the human endometrium in vivo.
Turnout detection and classification using a modified HOG and template matching

This paper presents a railway track and turnout detection and turnout classification algorithm. The railway track extraction is based on an edge detection using the width of the rolling pads. This edge detection scheme is then used as an input to the RANSAC algorithm to determine the model of the rails knowing their gauge. The turnout detection scheme is based on the Histogram of Oriented Gradient (HOG) and Template Matching (TM). The turnout classification is based on HOG. The detection results show (i) reliable performance for our railway track extraction scheme; (ii) a correction rate of 97.31 percent for the turnout detection scheme using a Support Vector Machine (SVM) classifier. The turnout classification has correction rate of 98.72 percent using SVM.
Observations of the intermediate and benthic nepheloid layers in southern Lake Michigan during the summer of 1995

During the summer of 1995 time series measurements of water transparency, water temperature, and current velocity were made at stations located in 28, 58, and 100 m of water in southern Lake Michigan. Inertial internal waves were the dominant feature of the lake circulation. These waves caused variations in the thickness and in the vertical distribution of suspended sediment in the benthic nepheloid layer. An intermediate nepheloid layer located at the base of the thermocline was also affected by the inertial waves. This layer moves up and down in response to movement of the thermocline due to both inertial waves and to upwelling and downwelling events. Although a direct link between inertial wave action and changes in the benthic nepheloid layer could not be established, the data strongly suggest that the layer is maintained by local resuspension due to a combination of inertial wave action and longer-term processes. measurements show a well-developed bnl in each of the profiles. The thickness of the bnl varied
HIGH‐DIMENSIONAL LEARNING FRAMEWORK   FOR ADAPTIVE DOCUMENT FILTERING *

We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high‐dimensional on‐line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on‐line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them.
Action-Space Knowledge Transfer in MDP ’ s : Formalism , Suboptimality Bounds , and Algorithms ?

Temporal-difference reinforcement learning (RL) has been successfully applied in several domains with large statesets. Largeaction sets, however, have received considerably less attention. This pape r studies the use of knowledge transfer between related tasks to accelerate lea rning with large action sets. We introduce action transfer , a technique that extracts the actions from the (near-)optimal solution to the first task and uses them in place of the full action set when learning any subsequent tasks. When optimal ac tions make up a small fraction of the domain’s action set, action transfer c an substantially reduce the number of actions and thus the complexity of the problem. However, action transfer betweendissimilar tasks can be detrimental. We present a novel formalism of related tasks and use it to derive a bound on the subopti mality of action transfer. We additionally bound action-transfer suboptim al ty in generic MDP’s and analyze the feasibility of provably reliableaction transfer. We build on this analysis to propose randomized task perturbation (RTP), an enhancement to action transfer that makes it robust to unrepresentative sour ce tasks. The empirical results in this paper show the potential of RTP action transf er to substantially expand the applicability of RL to problems with large action sets.
Studies of the cell surface of mouse dendritic cells and other leukocytes

The surface of dendritic cells (DC) has been analyzed by means of monoclonal antibodies (Ab) and lactoperoxidase (LPO)-mediated radioiodination. Antigens and other exteriorily disposed polypeptides of purified spleen DC were compared with those of tissue macrophages (Mφ), monocytes, and other bone marrow-derived elements. Quantitative binding studies and autoradiography with (125)I-Ab established that DC expressed high levels of I-A and H-2D, 2 × 10(5) and 1 × 10(5) Ab binding sites per cell, respectively. DC from conventional, germ-free, and specific pathogen-free mice were all rich in Ia. Expression of Ia on B cells was 5-10 percent of that on DC and increased fivefold during lipopolysaccharide mitogenesis. More than 70-90 percent of purified Mφ and monocytes from specific pathogen-free mice were Ia negative, but increased levels of Ia were noted on cells from mice reared under conventional conditions. Thus large amounts of Ia on DC is a constitutive trait, whereas the expression of Ia by other cell types may be governed by the environmental and immunological status of the host. The 2.4G2 Fc receptor Ag was not detected on DC. Peritoneal and spleen Mφ had 10(5) 2.4G2 binding sites/cell, whereas monocytes and lymphocytes were less reactive (1 × 10(4)-3 × 10(4) binding sites/cell). Four other Mφ-related antigens were evaluated. Each had a distinctive tissue distribution and none bound exclusively to Mφ and monocytes. Neither 1.21J (Mac-1) nor F4/80 reacted with DC. Immunoprecipitation studies of externally ((125)I) and biosynthetically ([(35)S]methionine)dabeled cells confirmed the binding data. Sensitive binding assays with (125)I-Ab confirmed previous observations that DC lack Ig and Thy-1. Lyt-1 was also not found on DC, but 5-12 percent of the cells in purified DC preparations expressed both Lyt-2 and Ia. All DC expressed the leukocyte common antigens at levels similar to other leukocytes. The spectrum of surface polypeptides labeled by LPO-mediated iodination was different on Mφ, DC, and lymphocytes. Polypeptides migrating at molecular weights of 155,000, 85,000, and 62,000 appeared to be restricted to DC. These observations establish that the cell surface of DC differs considerably from other leukocytes, including the blood monocyte, and suggest that the DC is part of a unique Ia-rich leukocyte differentiation pathway.
Human melanoma antigen AH is an autoantigenic ganglioside related to GD2

AH antigen, initially defined by an antibody present in a melanoma patient, is a cell surface antigen found on approximately 65% of melanoma cell lines. Absorption analysis indicates that AH is a differentiation antigen marking normal and malignant cells of neuroectodermal origin. The AH determinant has been found to be related to GD2 ganglioside.
Social Creativity : Bringing Different Points of View Together

Complex design problems require more knowledge than any one single person can possess, and the knowledge relevant to a problem is often distributed and controversial. Rather than being a limiting factor, Òsymmetry of ignoranceÓ can provide the foundation for social creativity. Bringing different points of view together and trying to create a shared understanding among all stakeholders can lead to new insights, new ideas, and new artifacts. Social creativity can be supported by new media that allow owners of problems to contribute to framing and solving these problems. These new media need to be designed from a meta-design perspective by creating environments in which stakeholders can act as designers and be more than consumers.
tFacet: Hierarchical Faceted Exploration of Semantic Data Using Well-Known Interaction Concepts

Information stored in the Semantic Web is becoming more and more interesting for average Web users. Due to the complexity of existing tools, however, accessing it is difficult. In this paper we therefore introduce tFacet, a tool that uses well-known interaction concepts to enable hierarchical faceted exploration of semantic data for non-experts. The aim is to facilitate the formulation of semantically unambiguous queries to allow a faster and more precise access to information in the Semantic Web for the broader public.
Non-cell-autonomous role for Cripto in axial midline formation during vertebrate embryogenesis

Several membrane-associated proteins are known to modulate the activity and range of potent morphogenetic signals during development. In particular, members of the EGF-CFC family encode glycosyl-phosphatidylinositol (GPI)-linked proteins that are essential for activity of the transforming growth factor β (TGFβ) ligand Nodal, a factor that plays a central role in establishing the vertebrate body plan. Genetic and biochemical studies have indicated that EGF-CFC proteins function as cell-autonomous co-receptors for Nodal; by contrast, cell culture data have suggested that the mammalian EGF-CFC protein Cripto can act as a secreted signaling factor. Here we show that Cripto acts non-cell-autonomously during axial mesendoderm formation in the mouse embryo and may possess intercellular signaling activity in vivo. Phenotypic analysis of hypomorphic mutants demonstrates that Cripto is essential for formation of the notochordal plate, prechordal mesoderm and foregut endoderm during gastrulation. Remarkably, Cripto null mutant cells readily contribute to these tissues in chimeras, indicating non-cell-autonomy. Consistent with these loss-of-function analyses, gain-of-function experiments in chick embryos show that exposure of node/head process mesoderm to soluble Cripto protein results in alterations in cell fates toward anterior mesendoderm, in a manner that is dependent on Nodal signaling. Taken together, our findings support a model in which Cripto can function in trans as an intercellular mediator of Nodal signaling activity.
Glycosylation in immune cell trafficking

Summary: Leukocyte recruitment encompasses cell adhesion and activation steps that enable circulating leukocytes to roll, arrest, and firmly adhere on the endothelial surface before they extravasate into distinct tissue locations. This complex sequence of events relies on adhesive interactions between surface structures on leukocytes and endothelial cells and also on signals generated during the cell–cell contacts. Cell surface glycans play a crucial role in leukocyte recruitment. Several glycosyltransferases such as α1,3 fucosyltransferases, α2,3 sialyltransferases, core 2 N‐acetylglucosaminlytransferases, β1,4 galactosyltransferases, and polypeptide N‐acetylgalactosaminyltransferases have been implicated in the generation of functional selectin ligands that mediate leukocyte rolling via binding to selectins. Recent evidence also suggests a role of α2,3 sialylated carbohydrate determinants in triggering chemokine‐mediated leukocyte arrest and influencing β1 integrin function. The recent discovery of galectin‐ and siglec‐dependent processes further emphasizes the significant role of glycans for the successful recruitment of leukocytes into tissues. Advancing the knowledge on glycan function into appropriate pathology models is likely to suggest interesting new therapeutic strategies in the treatment of immune‐ and inflammation‐mediated diseases.
Time Series Expression Analyses Using RNA-seq: A Statistical Approach

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.
Local Homing Navigation Based on the Moment Model for Landmark Distribution and Features

For local homing navigation, an agent is supposed to return home based on the surrounding environmental information. According to the snapshot model, the home snapshot and the current view are compared to determine the homing direction. In this paper, we propose a novel homing navigation method using the moment model. The suggested moment model also follows the snapshot theory to compare the home snapshot and the current view, but the moment model defines a moment of landmark inertia as the sum of the product of the feature of the landmark particle with the square of its distance. The method thus uses range values of landmarks in the surrounding view and the visual features. The center of the moment can be estimated as the reference point, which is the unique convergence point in the moment potential from any view. The homing vector can easily be extracted from the centers of the moment measured at the current position and the home location. The method effectively guides homing direction in real environments, as well as in the simulation environment. In this paper, we take a holistic approach to use all pixels in the panoramic image as landmarks and use the RGB color intensity for the visual features in the moment model in which a set of three moment functions is encoded to determine the homing vector. We also tested visual homing or the moment model with only visual features, but the suggested moment model with both the visual feature and the landmark distance shows superior performance. We demonstrate homing performance with various methods classified by the status of the feature, the distance and the coordinate alignment.
The Small RNA ncS35 Regulates Growth in Burkholderia cenocepacia J2315

Small RNAs play an important role in the survival of bacteria in diverse environments. We explored the physiological role of ncS35, a small RNA expressed in B. cenocepacia J2315, an opportunistic pathogen in cystic fibrosis patients. In cystic fibrosis patients, infections can lead to “cepacia syndrome,” a rapidly progressing and often fatal pneumonia. Infections with Burkholderia spp. are difficult to threat with antibiotics because of their high intrinsic resistance and ability to form biofilms. We show that ncS35 attenuates the growth and reduces the metabolic rate of B. cenocepacia and influences biofilm structure. This demonstrates that as-yet-uncharacterized small RNAs with regulatory function can influence physiological traits of B. cenocepacia that are relevant for infection. ABSTRACT Burkholderia cenocepacia J2315 is a member of the B. cepacia complex. It has a large genome with three replicons and one plasmid; 7,261 genes code for annotated proteins, while 113 code for functional RNAs. Small regulatory RNAs of B. cenocepacia have not yet been functionally characterized. We investigated a small regulatory RNA, designated ncS35, that was discovered by differential RNA sequencing. Its expression under various conditions was quantified, and a deletion mutant, ΔncS35, was constructed. Compared to planktonic growth in a rich medium, the expression of ncS35 was elevated when B. cenocepacia J2315 was grown in biofilms and in minimal medium. Cells of the deletion mutant showed increased aggregation, higher metabolic activity, a higher growth rate, and an increased susceptibility to tobramycin. A transcriptomic analysis revealed upregulation of the phenylacetic acid and tryptophan degradation pathways in ΔncS35. Computational target prediction indicated that ncS35 likely interacts with the first gene of the tryptophan degradation pathway. Overall, we demonstrated that small RNA ncS35 is a noncoding RNA with an attenuating effect on the metabolic rate and growth. It is possible that slower growth protects B. cenocepacia J2315 against stressors acting on fast-dividing cells and enhances survival under unfavorable conditions. IMPORTANCE Small RNAs play an important role in the survival of bacteria in diverse environments. We explored the physiological role of ncS35, a small RNA expressed in B. cenocepacia J2315, an opportunistic pathogen in cystic fibrosis patients. In cystic fibrosis patients, infections can lead to “cepacia syndrome,” a rapidly progressing and often fatal pneumonia. Infections with Burkholderia spp. are difficult to threat with antibiotics because of their high intrinsic resistance and ability to form biofilms. We show that ncS35 attenuates the growth and reduces the metabolic rate of B. cenocepacia and influences biofilm structure. This demonstrates that as-yet-uncharacterized small RNAs with regulatory function can influence physiological traits of B. cenocepacia that are relevant for infection.
Incremental Analog-to-Digital Converters for High-Resolution Energy-Efficient Sensor Interfaces

Integrated sensor interface circuits require power-efficient high-accuracy data converters. In many applications, the best choice is to use incremental A/D converters (IADCs) incorporating extended counting. In this paper, we discuss the operation and design of IADCs, including single-loop and MASH architectures. When using a direct-feed-forward modulator, the IADC accumulates the residue voltage, and it is easily to implement a hybrid scheme of extended counting. Several hybrid schemes are review and discussed. A multi-step extended counting scheme is discussed for high-resolution power-efficient conversion. Optimal trade-off between high order and high oversampling ratio is critical for energy efficiency. A two-step IADC is discussed, which extends the performance of an Nth-order IADC close to that of a (2N-1)th-order IADC, with reduced power. An implemented device uses the circuitry of a second-order IADC (IADC2) to achieve a performance close to that of a third-order IADC. The two-step operation can be extended to multi-step one, and the SQNR performance can be increased significantly. The two-step operation can be extended to multi-step operation, which can boost up the order of SQNR and further improve the energy efficiency drastically.
Cancer subtype identification pipeline: A classifusion approach

Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers.
Running with Technology

The use of wearable devices during running has become commonplace. Although there is ongoing research on interaction techniques for use while running, the effects of the resulting interactions on the natural movement patterns have received little attention so far. While previous studies on pedestrians reported increased task load and reduced walking speed while interacting, running movement further restricts interaction and requires minimizing interferences, e.g. to avoid injuries and maximize comfort. In this paper, we aim to shed light on how interacting with wearable devices affects running movement. We present results from a motion-tracking study (N=12) evaluating changes in movement and task load when users interact with a smartphone, a smartwatch, or a pair of smartglasses while running. In our study, smartwatches required less effort than smartglasses when using swipe input, resulted in less interference with the running movement and were preferred overall. From our results, we infer a number of guidelines regarding interaction design targeting runners.