Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilayer Graph Signal Clustering

Multilayer graphs are commonly used to model relationships of diﬀerent types be-tween data points. In this paper, we pro-pose a method for multilayer graph data clustering, which combines the diﬀerent graph layers in the Riemann manifold of Semi-Positive Deﬁnite (SPD) graph laplacian matrices. The resulting combination can be seen as a low-dimensional representation of the original data points. In addition, we consider that data can also carry signal values and not only graph information. We thus pro-pose new clustering solution for such hybrid data by training a neural network such that the transformed data points are orthonormal, and their distance on the aggregated graph is minimized. Experiments on synthetic and real data show that our method leads to a signiﬁcant improvement with respect to state-of-the-art clustering algorithms for graph data.
Patients’ attendance patterns to different healthcare settings and perceptions of stakeholders regarding screening for chronic, non-communicable diseases in high street dental practices and community pharmacy: a cross-sectional study

Objective Non-communicable diseases (NCDs) impose a significant health and economic burden. This study aimed to assess the differential attendance patterns of public to different healthcare professionals and gauge the opinions of key stakeholders towards screening of NCDs by allied healthcare professionals. Design Questionnaires were designed piloted and subsequently completed by key stakeholders. The results were analysed descriptively. Setting Public questionnaires were undertaken in a West Midlands transport station and Public Markets. High street dental and community pharmacy settings were selected via local clinical and research networks. Healthcare professionals were identified using professional networks and were emailed a web link to an online survey. Participants 1371 members of the public, 1548 patients and 222 healthcare professionals (doctors general practitioner (GP), dentists general dental practitioner (GDP) and pharmacists) completed the questionnaires. Outcome measures The outcome was to compare attendance patterns at GDP and GP practices to determine whether different populations were more likely to access different healthcare professionals, this included determining when patients were last screened for NCDs by their GP. Additionally, the willingness of patients to undergo the required intervention and the opinions of stakeholders regarding the concept of screening for the specified NCDs in general dental and community pharmacy settings were also explored. Results 12% of patients who reported seeing a GDP biannually reported that they had not had contact with a GP in the last year. Over 61% of the public reported attending a GDP biannually, of this group 48% reported having never had a check-up at the GP. All stakeholders surveyed were in broad support of the concept of allied health professionals undertaking screening for specific general health conditions. Conclusions This study has established that allied healthcare professionals may have access to different cohorts of the population to GPs. If GDPs and pharmacists have access to patients who are not using healthcare services elsewhere, they may be ideally placed to risk assess, and where appropriate offer preventative advice and test for NCDs.
High miR-139-3p expression predicts a better prognosis for hepatocellular carcinoma: a pooled analysis

Objective To observe the expression and clinical significance of micro RNA (miR)-139-3p in liver cancer tissues, and to explore its relationship with miR-139-3p target genes related to the prognosis of hepatocellular carcinoma (HCC). Methods A total of 362 patients with HCC were included in the study. Liver hepatocellular carcinoma data were obtained directly from The Cancer Genome Atlas data portal . The bioinformatics analysis tool TargetScan was applied to predict miR-139-3p target genes. Results Survival time was significantly higher in patients with high miR-139-3p expression, compared with the low miR-139-3p expression group. Bioinformatics analysis showed that miR-139-3p target genes ISG20L2, RAD54B, KIAA0101, and PIGS were significantly negatively correlated with miR-139-3p expression. Conclusions High miR-139-3p expression in HCC tissues was indicative of good patient prognosis. miR-139-3p target genes ISG20L2, RAD54B, KIAA0101, and PIGS were related to HCC prognosis.
Propagation of information along the cortical hierarchy as a function of attention while reading and listening to stories

How does attention route information from sensory to high-order areas as a function of task, within the relatively fixed topology of the brain? In this study, participants were simultaneously presented with two unrelated stories – one spoken and one written – and asked to attend one while ignoring the other. We used fMRI and a novel inter-subject correlation analysis to track the spread of information along the processing hierarchy as a function of task. Processing the unattended spoken (written) information was confined to auditory (visual) cortices. In contrast, attending to the spoken (written) story enhanced the stimulus-selective responses in early sensory regions and allowed it to spread into higher-order areas. Surprisingly, we found that the story-specific spoken (written) responses for the attended story also reached the opposite secondary visual (auditory) regions. These results demonstrate how attention enhances the processing of attended input and allows it to propagate across brain areas.
Recommendations for the assessment and management of measurable residual disease in adults with acute lymphoblastic leukemia: A consensus of North American experts

Measurable residual disease (MRD) that persists after initial therapy is a powerful predictor of relapse and survival in acute lymphoblastic leukemia (ALL). However, the optimal use of this information to influence therapeutic decisions is controversial. Herein, we comprehensively review the role of MRD assessment in adults with ALL, including methods to quantify residual leukemia cells during remission, prognostic impact of MRD across ALL subtypes, and available therapeutic approaches to eradicate MRD. This review presents consensus statements and provides an evidence‐based framework for practicing hematologists and oncologists to use MRD information to make rational treatment decisions in adult patients with ALL.
Eye Tracking and Gesture Based Interaction for Target Selection on Large Displays

Mouse based target selection requires much movement when locating targets across long distance on large displays. Eye tracking technique can locate targets more easily and quickly across long distance, and has a high potential for fast targets selection on large displays. However, eye tracking still faces to challenges, such as low accuracy, and high error rate of selection operation, especially for gaze-only interaction. This paper proposed a multimodal interaction method that combined gaze with gestures. This method utilized gaze for rough selection first, and then utilized hand gesture to confirm accurate selection. Furthermore, when targets are small and crowded, we used semi-fixed gaze cursor and secondary selection mechanism to optimize the selection process. Finally we conducted a pilot study, and the results showed that the selection speed and accuracy rate of our method was higher than the gaze only input and similar to mouse input.
Commercial Site Recommendation Based on Neural Collaborative Filtering

Commercial site recommendation based on big data is one of the innovative applications in the new retail era. Recently, most studies utilize regression analysis or collaborative filtering to recommend the optimal site based on some features extracted from commercial data, geographic data and other heterogeneous data. Compared to manual features which could not be well-defined, deep learning is able to automatically extract features and give nonlinear and in-depth description of the relationship between variables. Therefore, this paper applies deep learning to the study of commercial site recommendation. We firstly study the usage of NeuMF, a neural collaborative filtering method in commercial site recommendation. Then we propose NeuMF-RS method based on NeuMF method. Finally, we evaluate our proposed model on a real-world dataset collected from Dianping.com. The results indicate that NeuMF-RS outperforms the state-of-the-art methods in commercial site recommendation.
HyperMind Builder: Pervasive User Interface to Create Intelligent Interactive Documents

We introduce a Graphical User Interface (GUI) to create intelligent interactive documents for everyone. The intelligent interactive document refers to a document displaying contents dynamically according to a reader's behavior. To the best of our knowledge, creating such documents requires a certain amount of efforts and implementation skills. By utilizing our system, users including non-technological experts can create interactive documents without any programming. Our system supports many people to enhance the possibility of designing new human-document interactions.
Intrinsic Universal Measurements of Non-linear Embeddings

A basic problem in machine learning is to find a mapping $f$ from a low dimensional latent space to a high dimensional observation space. Equipped with the representation power of non-linearity, a learner can easily find a mapping which perfectly fits all the observations. However such a mapping is often not considered as good as it is not simple enough and over-fits. How to define simplicity? This paper tries to make such a formal definition of the amount of information imposed by a non-linear mapping. This definition is based on information geometry and is independent of observations, nor specific parametrizations. We prove these basic properties and discuss relationships with parametric and non-parametric embeddings.
Pilot Safety Evaluation of a Novel Strain of Bacteroides ovatus

Bacteroides ovatus ELH-B2 is considered as a potential next-generation probiotic due to its preventive effects on lipopolysaccharides-associated inflammation and intestinal microbiota disorders in mice. To study safety issues associated with B. ovatus ELH-B2, we conducted comprehensive and systematic experiments, including in vitro genetic assessments of potential virulence and antimicrobial resistance genes, and an in vivo acute toxicity study of both immunocompetent and immunosuppressed mice via cyclophosphamide treatment. The results indicated that this novel strain is non-toxigenic, fragilysin is not expressed, and most of potential virulence genes are correlated with cellular structures such as capsular polysaccharide and polysaccharide utilizations. The antibiotic resistance features are unlikely be transferred to other intestinal microorganisms as no plasmids nor related genomic islands were identified. Side effects were not observed in mice. B. ovatus ELH-B2 also alleviated the damages caused by cyclophosphamide injection.
FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

Motivation The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain “truth” sets on which to test them and properly measure their performance. Results We introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees, and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. Availability and implementation FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).
FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce discrimination in their predictions. This paper proposes a method to post-process the predictions of a predictive model to make the processed predictions non-discriminatory. The method considers multiple protected variables together. Multiple protected variables make the problem more challenging than a simple protected variable. The method uses a well-cited discrimination metric and adapts it to allow the specification of explanatory variables, such as position, profession, education, that describe the contexts of the applications. It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time. The proposed method is independent of classification methods. It can handle the cases that existing methods cannot handle: satisfying multiple protected attributes at the same time, allowing multiple explanatory attributes, and being independent of classification model types. An evaluation using four real world data sets shows that the proposed method is as effectively as existing methods, in addition to its extra power.
Reproductive suppression in female Alpine marmots, Marmota marmota


We studied mechanisms responsible for reproductive skew in female Alpine marmots. In each social group, only dominant females produced weaned young although subordinate females had similar body condition, were fertile as indicated by high levels of oestradiol during the mating season, and occasionally started pregnancies. During the period of gestation, dominant females initiated significantly more agonistic interactions against subordinate females, resulting in significantly increased levels of glucocorticoids and decreased levels of progesterone in subordinates. Results suggested that reproductive suppression in female Alpine marmots is mediated by the negative effects of stress (glucocorticoids) on the activity of the hypothalamic-pituitary-gonadal axis. The strength of competition between subordinate and dominant females was affected by their relatedness. Dominant females attacked unrelated subordinate females more, whereas amicable behaviour was observed mainly between dominant females and their daughters. These differences could be explained by differences in indirect fitness: related subordinate females benefited from warming the offspring of the dominant female during hibernation. Reproductive suppression was apparently costly for dominant females, because their reproductive success decreased as the number of adult subordinate females in a group increased.  2003 Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour.
Pollinator-mediated selection on floral traits varies in space and between morphs in Primula secundiflora

Abstract Elucidating how variation in selection shapes the evolution of flowers is key to understanding adaptive differentiation processes. We estimated pollinator-mediated selection through female function in L-morph (long-style and short-anther phenotype) and S-morph (short-style and long-anther phenotype) flowers among four Primula secundiflora populations with different pollinator assemblages. Variation in pollinator assemblage strongly contributed to differences in reproductive success among populations and between morphs of the primrose species. A wider corolla tube width was selected in the bumblebee-dominated populations, whereas shorter corolla tube length and wider corolla tube width were selected in the syrphid fly-dominated populations. Morph-specific variation in pollinator-mediated selection on corolla tube length was detected in the syrphid fly-dominated populations. A shorter corolla tube was selected in the L-morph flowers. However, similar selective pressure on this trait was not observed in the S-morph flowers. These results show that variation in pollinator assemblage leads to variation in selection in space and between morphs. The findings highlight the potential forces of different pollinator agents in driving floral evolution in this primrose species.
Multicellular Crosstalk Between Exosomes and the Neurovascular Unit After Cerebral Ischemia. Therapeutic Implications

Restorative strategies after stroke are focused on the remodeling of cerebral endothelial cells and brain parenchymal cells. The latter, i.e., neurons, neural precursor cells and glial cells, synergistically interact with endothelial cells in the ischemic brain, providing a neurovascular unit (NVU) remodeling that can be used as target for stroke therapies. Intercellular communication and signaling within the NVU, the multicellular brain-vessel-blood interface, including its highly selective blood-brain barrier, are fundamental to the central nervous system homeostasis and function. Emerging research designates cell-derived extracellular vesicles and especially the nano-sized exosomes, as a complex mean of cell-to-cell communication, with potential use for clinical applications. Through their richness in active molecules and biological information (e.g., proteins, lipids, genetic material), exosomes contribute to intercellular signaling, a condition particularly required in the central nervous system. Cerebral endothelial cells, perivascular astrocytes, pericytes, microglia and neurons, all part of the NVU, have been shown to release and uptake exosomes. Also, exosomes cross the blood-brain and blood-cerebrospinal fluid barriers, allowing communication between periphery and brain, in normal and disease conditions. As such exosomes might be a powerful diagnostic tool and a promising therapeutic shuttle of natural nanoparticles, but also a means of disease spreading (e.g., immune system modulation, pro-inflammatory action, propagation of neurodegenerative factors). This review highlights the importance of exosomes in mediating the intercellular crosstalk within the NVU and reveals the restorative therapeutic potential of exosomes harvested from multipotent mesenchymal stem cells in ischemic stroke, a frequent neurologic condition lacking an efficient therapy.
Supramolecularly Engineered NIR‐II and Upconversion Nanoparticles In Vivo Assembly and Disassembly to Improve Bioimaging

Contrast agents for bioimaging suffer from low accumulation at lesion area and high uptake in the reticuloendothelial system (RES). Assembly of nanoparticles in vivo improves their enrichment at tumors and inflamed areas. However, uncontrollable assembly also occurs at the liver and spleen owing to the uptake of nanoparticles by the RES. This is known to probably cause a higher bioimaging background and more severe health hazards, which may hamper the clinical application. Herein, a new near‐infrared (NIR)‐controlled supramolecular engineering strategy is developed for in vivo assembly and disassembly between lanthanide upconversion nanoparticles and second near‐infrared window (NIR‐II, 1000–1700 nm) nanoprobes to realize precision bioimaging of tumors. A supramolecular structure is designed to realize assembly via host–guest interactions of azobenzene and β‐cyclodextrin to enhance the retention of NIR‐II nanoprobes in the tumor area. Meanwhile NIR‐laser‐controllable nanoprobes disassembly brings about a reduction in the bioimaging background as well as acceleration of their RES clearance rate. This strategy may also be used in other nano‐to‐micro‐scale contrast agents to improve bioimaging signal‐to‐noise ratio and reduce long‐term cytotoxicity.
Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in the Eastern region of the Democratic Republic of Congo

Background As of October 12, 2018, 211 cases of Ebola virus disease (EVD) were reported in North Kivu Province, Democratic Republic of Congo. Since the beginning of October the outbreak has largely shifted into regions in which active armed conflict is occurring, and in which EVD cases and their contacts are difficult for health workers to reach. We used available data on the current outbreak with case-count time series from prior outbreaks to project the short-term and long-term course of the outbreak. Methods For short and long term projections we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used a negative binomial autoregression for short-term projections, a Theil-Sen regression model for final sizes, and a baseline minimum-information projection using Gott’s law to construct an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20 to October 13, short-term model projections were validated against actual case counts. Results During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of October 13, the stochastic model projected a median case count of 226 cases by October 27 (95% prediction interval: 205–268) and 245 cases by November 10 (95% prediction interval: 208–315), while the auto-regression model projects median case counts of 240 (95% prediction interval: 215–307) and 259 (95% prediction interval: 216–395) cases for those dates, respectively. Projected median final counts range from 274 to 421. Except for Gott’s law, the projected probability of an outbreak comparable to 2013–2016 is exceedingly small. The stochastic model estimates that vaccine coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. Conclusions Based on our projections we believe that the epidemic had not yet peaked at the time of these estimates, though a trajectory on the scale of the West African outbreak is exceedingly improbable. Validating our models in real time allowed us to generate more accurate short-term forecasts, and this process may provide a useful roadmap for real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.