Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploration of “localness” on twitter during the 2012 U.S. elections

This poster presents an exploratory method used to analyze the geography of Twitter use. A shift has taken place in political speech from local to diffuse online environments, which necessitates the use of new tools and methods to analyze discourse. As such, this study presents a geo‐spatial analysis of tweets to members of the U.S. Congress in 2012 to aid researchers carrying out comparable research in the future. This poster also presents summarized findings about the sometimes localized, often national distribution of geo‐located tweets to Congress members. This poster concludes by calling for discussion about spatial analysis and social media tools, while highlighting methodological problems encountered in this study and offering directions for future research.
Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose

Lung cancer mortality rate can be significantly reduced by up to 20% through routine low-dose computed tomography (LDCT) screening, which, however, has high sensitivity but low specificity, resulting in a high rate of false-positive nodules. Combining PET with CT may provide more accurate diagnosis for indeterminate screening-detected nodules. In this work, we investigated low-dose dynamic 18F-FDG PET in discrimination between benign and malignant nodules using a virtual clinical trial based on patient study with ground truth. Six patients with initial LDCT screening-detected lung nodules received 90 min single-bed PET scans following a 10 mCi FDG injection. Low-dose static and dynamic images were generated from under-sampled list-mode data at various count levels (100%, 50%, 10%, 5%, and 1%). A virtual clinical trial was performed by adding nodule population variability, measurement noise, and static PET acquisition start time variability to the time activity curves (TACs) of the patient data. We used receiver operating characteristic (ROC) analysis to estimate the classification capability of standardized uptake value (SUV) and net uptake constant Ki from their simulated benign and malignant distributions. Various scan durations and start times (t*) were investigated in dynamic Patlak analysis to optimize simplified acquisition protocols with a population-based input function (PBIF). The area under curve (AUC) of ROC analysis was higher with increased scan duration and earlier t*. Highly similar results were obtained using PBIF to those using image-derived input function (IDIF). The AUC value for Ki using optimized t* and scan duration with 10% dose was higher than that for SUV with 100% dose. Our results suggest that dynamic PET with as little as 1 mCi FDG could provide discrimination between benign and malignant lung nodules with higher than 90% sensitivity and specificity for patients similar to the pilot and simulated population in this study, with LDCT screening-detected indeterminate lung nodules.
Neighbor Embedding by Soft Kendall Correlation

Correlation-based embedding of complex data relationships in a Euclidean space is studied. The proposed soft formulation of Kendall correlation allows for gradient-based optimization of scatter point neighborhood relationships for reconstructing original data neighbors. The approach is able to handle asymmetric data relations provided in the form of a general scoring matrix. Scale and shift invariance properties of correlation help circum-venting typical embedding distortion artefacts in dimension reduction and data embedding scenarios.
Distinct substrate specificities of human GlcNAc-6-sulfotransferases revealed by mass spectrometry–based sulfoglycomic analysis

Sulfated glycans are known to be involved in several glycan-mediated cell adhesion and recognition pathways. Our mRNA transcript analyses on the genes involved in synthesizing GlcNAc-6-O–sulfated glycans in human colon cancer tissues indicated that GlcNAc6ST-2 (CHST4) is preferentially expressed in cancer cells compared with nonmalignant epithelial cells among the three known major GlcNAc-6-O-sulfotransferases. On the contrary, GlcNAc6ST-3 (CHST5) was only expressed in nonmalignant epithelial cells, whereas GlcNAc6ST-1 (CHST2) was expressed equally in both cancerous and nonmalignant epithelial cells. These results suggest that 6-O-sulfated glycans that are synthesized only by GlcNAc6ST-2 may be highly colon cancer–specific, as supported by immunohistochemical staining of cancer cells using the MECA-79 antibody known to be relatively specific to the enzymatic reaction products of GlcNAc6ST-2. By more precise MS-based sulfoglycomic analyses, we sought to further infer the substrate specificities of GlcNAc6STs via a definitive mapping of various sulfo-glycotopes and O-glycan structures expressed in response to overexpression of transfected GlcNAc6STs in the SW480 colon cancer cell line. By detailed MS/MS sequencing, GlcNAc6ST-3 was shown to preferentially add sulfate onto core 2–based O-glycan structures, but it does not act on extended core 1 structures, whereas GlcNAc6ST-1 prefers core 2–based O-glycans to extended core 1 structures. In contrast, GlcNAc6ST-2 could efficiently add sulfate onto both extended core 1– and core 2–based O-glycans, leading to the production of unique sulfated extended core 1 structures such as R-GlcNAc(6-SO3−)β1-3Galβ1–4GlcNAc(6-SO3−)β1–3Galβ1–3GalNAcα, which are good candidates to be targeted as cancer-specific glycans.
Ma2/d promotes myonuclear positioning and association with the sarcoplasmic reticulum

ABSTRACT The cytoplasm of striated myofibers contains a large number of membrane organelles, including sarcoplasmic reticulum (SR), T-tubules and the nuclear membrane. These organelles maintain a characteristic juxtaposition that appears to be essential for efficient inter-membranous exchange of RNA, proteins and ions. We found that the membrane-associated Muscle-specific α2/δ (Ma2/d) subunit of the Ca2+ channel complex localizes to the SR and T-tubules, and accumulates at the myonuclear surfaces. Furthermore, Ma2/d mutant larval muscles exhibit nuclear positioning defects, disruption of the nuclear-SR juxtapositioning, as well as impaired larval locomotion. Ma2/d localization at the nuclear membrane depends on the proper function of the nesprin ortholog Msp300 and the BAR domain protein Amphiphysin (Amph). Importantly, live imaging of muscle contraction in intact Drosophila larvae indicated altered distribution of Sarco/Endoplamic Reticulum Ca2+-ATPase (SERCA) around the myonuclei of Ma2/d mutant larvae. Co-immunoprecipitation analysis supports association between Ma2/d and Amph, and indirectly with Msp300. We therefore suggest that Ma2/d, in association with Msp300 and Amph, mediates interactions between the SR and the nuclear membrane. Summary: A novel function for the Ma2/d auxiliary subunit of the Ca2+ channel in promoting the perinuclear association of the sarcoplasmic reticulum and in maintaining myonuclear position in muscles.
Effect of selective versus nonselective cyclooxygenase inhibitors on gastric ulceration scores and intestinal inflammation in horses

OBJECTIVE To determine whether a cyclooxygenase (COX)-2 selective nonsteroidal anti-inflammatory drug (NSAID) would reduce gastric ulceration and gastrointestinal (GI) inflammation compared with a non-COX selective NSAID. STUDY DESIGN Randomized block design. ANIMALS Twenty-five healthy adult horses. METHODS Horses were randomly assigned to receive placebo (n = 5), phenylbutazone (n = 10), or firocoxib (n = 10) administered daily for 10 days. Gastroscopy was performed on days 0 and 10, and both squamous and glandular ulcers were scored according to established scoring criteria. Fecal samples were collected on days 0, 10, and 20 to test for fecal myeloperoxidase (MPO) concentration by enzyme-linked immunosorbent assay. RESULTS Both classes of NSAID induced GI injury as determined by gastric ulceration scores and fecal MPO. Glandular gastric ulceration scores and fecal MPO concentrations were higher in horses treated with phenylbutazone at day 10 (P < .001 and P = .0018, respectively). Increases in fecal MPO were significantly decreased 10 days following cessation of treatment for firocoxib but remained greater than baseline for the phenylbutazone group. CONCLUSION Although both classes of NSAID induced gastric ulceration, the COX-2 selective NSAID firocoxib induced less severe glandular ulceration. Although there were increases in fecal MPO in both groups after 10 days of treatment, this increase was significant only in horses receiving the nonselective COX inhibitor phenylbutazone. CLINICAL SIGNIFICANCE These findings suggest that both classes of NSAID induce GI injury in horses; however, at the dosages used in this study, the COX-2 selective NSAID firocoxib resulted in less severe injury.
Using Features From Pre-trained TimeNET For Clinical Predictions

Predictive models based on Recurrent Neural Networks (RNNs) for clinical time series have been successfully used for various tasks such as phenotyping, in-hospital mortality prediction, and di-agnostics. However, RNNs require large labeled data for training and are computationally expensive to train. Pre-training a network for some supervised or unsupervised tasks on a dataset, and then ﬁne-tuning via transfer learning for a related end-task can be an efﬁcient way to leverage deep models for scenarios that lack in either computational resources or labeled data, or both. In this work, we consider an approach to leverage a deep RNN – namely TimeNet [ Malhotra et al. , 2017 ] – that is pre-trained on a large number of diverse publicly available time-series from UCR Repository [ Chen et al. , 2015 ] . TimeNet maps varying-length time series to ﬁxed-dimensional feature vectors and acts as an off-the-shelf feature extractor. TimeNet-based approach overcome the need for hand-crafted features, and allows for use of traditional easy-to-train and interpretable linear models for the end-task, while still leveraging the features from a deep neural network. Empirical evaluation of the proposed approach on MIMIC-III 1 data suggests promising direction for future exploration: our results are comparable to existing benchmarks while our models require lesser training and hyper-parameter tuning effort.
Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks

With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. Being it's objective the investigation of social structures through the use of networks and graph theory, Social Network Analysis is, nowadays, an important research area in several domains. Social Network Analysis cope with different problems namely network metrics, models, visualization and information spreading, each one with several approaches, methods and algorithms. One of the critical areas of Social Network Analysis involves the calculation of different centrality measures (i.e.: the most important vertices within a graph). Today, the challenge is how to do this fast and efficiently, as many increasingly larger datasets are available. Recently, the need to apply such centrality algorithms to non static networks (i.e.: networks that evolve over time) is also a new challenge. Incremental and dynamic versions of centrality measures are starting to emerge (betweenness, closeness, etc). Our contribution is the proposal of two incremental versions of the Laplacian Centrality measure, that can be applied not only to large graphs but also to, weighted or unweighted, dynamically changing networks. The experimental evaluation was performed with several tests in different types of evolving networks, incremental or fully dynamic. Results have shown that our incremental versions of the algorithm can calculate node centralities in large networks, faster and efficiently than the corresponding batch version in both incremental and full dynamic network setups.
On Rich Clubs of Path-Based Centralities in Networks

Many scale-free networks exhibit a "rich club" structure, where high degree vertices form tightly interconnected subgraphs. In this paper, we explore the emergence of "rich clubs" in the context of shortest path based centrality metrics. We term these subgraphs of connected high closeness or high betweeness vertices as rich centrality clubs (RCC). Our experiments on real world and synthetic networks high- light the inter-relations between RCCs, expander graphs, and the core-periphery structure of the network. We show empirically and theoretically that RCCs exist, if the core-periphery structure of the network is such that each shell is an expander graph, and their density decreases from inner to outer shells. We further demonstrate that in addition to being an interesting topological feature, the presence of RCCs is useful in several appli- cations. The vertices in the subgraph forming the RCC are effective seed nodes for spreading information. Moreover, networks with RCCs are robust under perturbations to their structure. Given these useful properties of RCCs, we present a network modification model that can efficiently create a RCC within net- works where they are not present, while retaining other structural properties of the original network. The main contributions of our paper are: (i) we demonstrate that the formation of RCC is related to the core-periphery structure and particularly the expander like properties of each shell, (ii) we show that the RCC property can be used to find effective seed nodes for spreading information and for improving the resilience of the network under perturbation and, finally, (iii) we present a modification algorithm that can insert RCC within networks, while not affecting their other structural properties. Taken together, these contributions present one of the first comprehensive studies of the properties and applications of rich clubs for path based centralities.
Evaluation of the airway microbiome in nontuberculous mycobacteria disease

Aspiration is associated with nontuberculous mycobacterial (NTM) pulmonary disease and airway dysbiosis is associated with increased inflammation. We examined whether NTM disease was associated with a distinct airway microbiota and immune profile. 297 oral wash and induced sputum samples were collected from 106 participants with respiratory symptoms and imaging abnormalities compatible with NTM. Lower airway samples were obtained in 20 participants undergoing bronchoscopy. 16S rRNA gene and nested mycobacteriome sequencing approaches characterised microbiota composition. In addition, inflammatory profiles of lower airway samples were examined. The prevalence of NTM+ cultures was 58%. Few changes were noted in microbiota characteristics or composition in oral wash and sputum samples among groups. Among NTM+ samples, 27% of the lower airway samples were enriched with Mycobacterium. A mycobacteriome approach identified Mycobacterium in a greater percentage of samples, including some nonpathogenic strains. In NTM+ lower airway samples, taxa identified as oral commensals were associated with increased inflammatory biomarkers. The 16S rRNA gene sequencing approach is not sensitive in identifying NTM among airway samples that are culture-positive. However, associations between lower airway inflammation and microbiota signatures suggest a potential role for these microbes in the inflammatory process in NTM disease. 16S rRNA gene sequencing is not sensitive to detect Mycobacterium but identifies microbiota signatures associated with inflammation http://ow.ly/opXm30ldtQH
Mobility helps problem-solving systems to avoid Groupthink

Groupthink occurs when everyone in a group starts thinking alike, as when people put unlimited faith in a leader. Avoiding this phenomenon is a ubiquitous challenge to problem-solving enterprises and typical countermeasures involve the mobility of group members. Here we use an agent-based model of imitative learning to study the influence of the mobility of the agents on the time they require to find the global maxima of NK-fitness landscapes. The agents cooperate by exchanging information on their fitness and use this information to copy the fittest agent in their influence neighborhoods, which are determined by face-to-face interaction networks. The influence neighborhoods are variable since the agents perform random walks in a two-dimensional space. We find that mobility is slightly harmful for solving easy problems, i.e., problems that do not exhibit suboptimal solutions or local maxima. For difficult problems, however, mobility can prevent the imitative search being trapped in suboptimal solutions and guarantees a better performance than the independent search for any system size.
The Effect of Mobile App Interventions on Influencing Healthy Maternal Behavior and Improving Perinatal Health Outcomes: Systematic Review

Background Perinatal morbidity and mortality are significant public health issues with an enduring impact on the health and well-being of women and their families. Millions of pregnant women now download and use mobile applications to access, store, and share health information. However, little is known about the consequences. An investigation of their impact on perinatal health outcomes is particularly topical. Objective To determine the effects of mobile app interventions during pregnancy on influencing healthy maternal behavior and improving perinatal health outcomes. Methods Searches of PubMed, Embase, the Cochrane Library, CINAHL, WHO Global Health Library, POPLINE, and CABI Global Health were conducted with no date or language restrictions. Randomized and non-randomized studies were included if they reported perinatal health outcomes of interventions targeting pregnant women, using mobile apps compared with other communication modalities or with standard care. The primary outcome measure was the change in maternal behaviors (as defined by trial authors), by intervention goals. Two reviewers independently extracted data using standardized forms. Results Four randomized controlled trials (RCTs) involving 456 participants were included. All studies targeted participants in early pregnancy; however, wide variation was evident in participant characteristics, intervention, and study outcomes measures. Three trials were based in hospital settings, comparing women using mobile apps with routine antenatal care. One community-based trial gave all participants a device to promote physical activity; the intervention arm was also given a mobile app. All studies reported data for the primary outcome measure, describing some benefit from the intervention compared with controls. However, few statistically significant primary or secondary outcomes were reported. Due to insufficient data, the planned meta-analysis and subgroup analyses were not performed. Conclusions Due to limited numbers, heterogeneity of interventions, comparators, and outcome measures, no firm conclusions can be drawn on the effects of mobile application interventions during pregnancy on maternal knowledge, behavior change, and perinatal health outcomes. As millions of women utilize mobile apps during pregnancy, rigorous studies are essential for health care and maternity care providers to optimally design, implement, and evaluate interventions.
Projection-based Visualization of Dynamical Processes on Networks

AbstractDynamical processes on networks play an important role in systems biology and statistical physics. To understandthese processes, it is essential to understand which topological properties of the network are the main factorsfor the dynamics. We present a visualization approach that allows for such investigations. For visual encoding ofthe network topology, we use a node-link diagram. The nodes, however, are placed according to the dynamicalprocesses. We use a projection method of the time series data to generate animations that maintain the mentalmap and exhibit the behavior of the dynamics. Suitable coloring schemes for the nodes encode the current valuesof the dynamics and individual nodes can be investigated with linked views to a time series plot. We present casestudies to demonstrate that our approach is effective for the observation whether the dynamical processes followthe network topology.1. IntroductionThe complex networks found in biological systems and thevarious classes of random graphs (like [ER59], [BA99],[WS98], etc.) are typically high-dimensional objects thatcannot be embedded in a low-dimensional (2D, 3D) space.Even in systems, where the nodes of the network are spa-tially arranged, like in many transportation networks, ﬁndinga functionally meaningful placing of nodes is non-trivial. Inthe German long-distance train connection network, for ex-ample, the travel time distances do not at all match geograph-ical distances [FKW 10]. The purpose of typical networkvisualization algorithms is fundamentally different from ourgoals: We strive for network visualizations that are guidedby dynamical data or functional information.Dynamical processes on networks are deﬁned in form ofa graph G =(V;E)with a set of verticesV and a set of edgesE ˆV V and the dynamics in form of dynamical variablesd(v;t)for each vertex v 2V and time t 2[t
Relational Proportions Between Objects and Attributes

Analogical proportions are statements of the form “A is to B as C is to D”, where A,B,C,D are items of the same nature, or not. In this paper, we more particularly consider “relational proportions” of the form “object A has the same relationship with attribute a as object B with attribute b”. We provide a formal definition for relational proportions, and investigate how they can be extracted from a formal context, in the setting of formal concept analysis.
Stochastic extra-gradient based alternating direction methods for graph-guided regularized minimization

In this study, we propose and compare stochastic variants of the extra-gradient alternating direction method, named the stochastic extra-gradient alternating direction method with Lagrangian function (SEGL) and the stochastic extra-gradient alternating direction method with augmented Lagrangian function (SEGAL), to minimize the graph-guided optimization problems, which are composited with two convex objective functions in large scale. A number of important applications in machine learning follow the graph-guided optimization formulation, such as linear regression, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regression. We conduct experiments on fused logistic regression and graph-guided regularized regression. Experimental results on several genres of datasets demonstrate that the proposed algorithm outperforms other competing algorithms, and SEGAL has better performance than SEGL in practical use.
Mapping the frontal alpha asymmetry indicators of habitual emotion regulation: a data-driven approach

Frontal alpha asymmetry (FAA) has been explored extensively in affective neuroscience. To determine which FAA indicators are linked with the habitual use of emotion regulation, we recorded the resting electroencephalogram alpha activity. First, we reduced all of the FAA indicators to four factors by exploratory factor analysis. Next, we carried out a multiple regression analysis to determine which factors could significantly predict the habitual use of emotion-regulation strategies. According to the results, greater left-asymmetric activation at the dorsolateral prefrontal cortex predicted increased use of reappraisal, whereas at the ventrolateral prefrontal cortex, it was predicted by right-asymmetric activation. Greater left-asymmetric frontal activity at the posterior dorsolateral prefrontal cortex was related to increased use of distraction and suppression. These results suggest that resting FAA in the prefrontal cortex region plays a significant role in the habitual use of emotion regulation. Video abstract: http://links.lww. com/WNR/A485