Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.
A Mixed-Norm Laplacian Regularized Low-Rank Representation Method for Tumor Samples Clustering

Tumor samples clustering based on biomolecular data is a hot issue of cancer classifications discovery. How to extract the valuable information from high dimensional genomic data is becoming an urgent problem in tumor samples clustering. In this paper, we introduce manifold regularization into low-rank representation model and present a novel method named Mixed-norm Laplacian regularized Low-Rank Representation (MLLRR) to identify the differentially expressed genes for tumor clustering based on gene expression data. Then, in order to advance the accuracy and stability of tumor clustering, we establish the clustering model based on Penalized Matrix Decomposition (PMD) and propose a novel cluster method named MLLRR-PMD. In this method, the cancer clustering research includes three steps. First, the matrix of gene expression data is decomposed into a low rank representation matrix and a sparse matrix by MLLRR. Second, the differentially expressed genes are identified based on the sparse matrix. Finally, the PMD is applied to cluster the samples based on the differentially expressed genes. The experiment results on simulation data and real genomic data illustrate that MLLRR method enhances the robustness to outliers and achieves remarkable performance in the extraction of differentially expressed genes.
COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term ‘event’ refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: discover temporal relationship patterns between event locations, i.e., repeated cases when there is a specific temporal relationship (same time, before, or after) between events occurring at two locations. This can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.
iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features

As one of the new posttranslational modification, lysine glutarylation has been identified in both prokaryotic and eukaryotic cells. These glutarylated proteins are involved in various cellular functions, such as translation, metabolism, and exhibited diverse subcellular localizations. Experimental identification of lysine glutarylation sites was founded in 2014 and also identified its deglutarylase sirturn 5(SIRT 5). Computational prediction of lysine glutarylation could be a complementary way to the experimental technique. In this work, the lysine glutarylation predictor iGlu-Lys has been developed based on the machine learning scheme. We have selected the best feature scheme which took the amino acid pair order and special-position information into account from four constructions. The machine learning algorithm support vector machine has been adopted and its performance has been measured for different window length of peptides. In the 10-fold cross-validation with window length 19, the AUC and MCC were 0.8944 and 0.5098, respectively. Different ROC curves in 6-, 8-, and 10-fold cross-validations were very close which illustrated the robustness of our predictor. The results of iGLu-Lys were better than the existing method GlutPred. Meanwhile, a free webserver for iGlu-Lys is accessible at http://app.aporc. org/iGlu-Lys/.
Bearing-Based Network Localizability: A Unifying View

This paper provides a unifying view and offers new insights on bearing-based network localizability, that is the problem of establishing whether a set of directions between pairs of nodes uniquely determines (up to translation and scale) the position of the nodes in d-space. If nodes represent cameras then we are in the context of global structure from motion. The contribution of the paper is theoretical: first, we rewrite and link in a coherent structure several results that have been presented in different communities using disparate formalisms; second, we derive some new localizability results within the edge-based formulation.
A Review of Obstructive Sleep Apnea Detection Approaches

Sleep disorders are a common health condition that can affect numerous aspects of life. Obstructive sleep apnea is one of the most common disorders and is characterized by a reduction or cessation of airflow during sleep. In many countries, this disorder is usually diagnosed in sleep laboratories, by polysomnography, which is an expensive procedure involving much effort for the patient. Multiple systems have been proposed to address this situation, including performing the examination and analysis in the patient's home, using sensors to detect physiological signals that are automatically analyzed by algorithms. However, the precision of these devices is usually not enough to provide clinical diagnosis. Therefore, the objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends. The performance of different algorithms and methods for apnea detection through the use of different sensors (pulse oximetry, electrocardiogram, respiration, sound, and combined approaches) has been evaluated. 84 original research articles published from 2003 to 2017 with the potential to be promising diagnostic tools have been selected to cover multiple solutions. This paper could provide valuable information for those researchers who want to carry out a hardware implementation of potential signal processing algorithms.
The Influence of Culture on Healthy Relationship Formation and Teen Dating Violence: A Qualitative Analysis of South Asian Female Youth Residing in the United States

Teen dating violence (TDV) has well-documented detrimental health effects. Scant research has examined the perspectives of ethnically diverse youth about the impact of culture on TDV. We sought to explore the intersection between culture and TDV specifically for South Asian youth residing in the United States. We conducted semi-structured interviews with South Asian youth aged 16 to 21 years. Interviews included three aims: (a) exploring participants’ perspectives on TDV and healthy relationships within the South Asian community, (b) examining how different components of their cultural identity affect their romantic relationships, and (c) understanding ideas for TDV prevention programs for South Asian youth. Interview transcripts were coded using thematic analysis. Twenty-five adolescent females participated; the majority (76%) trace their heritage to India, were born abroad (62%), and are in college (80%). The majority believed that dating was stigmatized within the South Asian community, forcing youth to hide relationships from their parents. Participants described aspects of culture that may negatively impact relationships (e.g., patriarchal attitudes and fear of stigma), as well as those that may protect teens (e.g., close-knit community). Conflicts about dating due to generational differences were also discussed. Participants suggested programs for South Asian youth focused on engaging parents in conversations about TDV and promoting gender equity. About half thought TDV prevention programs should be culturally tailored for South Asian youth, while others believed multicultural programs would be more effective. Although South Asian culture may confer some protection for youth experiencing TDV, stigma against dating and generational differences may create unique challenges. Future work should examine how to mediate parent–adolescent cultural conflicts around dating.
Neural Observer and Adaptive Neural Control Design for a Class of Nonlinear Systems

This paper addresses the problem of adaptive neural tracking control for nonlinear nonstrict-feedback systems. The state variables are immeasurable and only the system output is available. A neural observer is constructed to estimate these unknown system state variables. An observer-based adaptive neural tracking control scheme is developed via backstepping approach. It is shown that the designed controller guarantees that the system output well follows the desired reference signal, and meanwhile, other closed-loop signals remain bounded. Finally, two simulation examples are used to test our results.
Khepri - a Modular View-Based Tool for Exploring (Historical Sociolinguistic) Data

Digital humanities needs tools that better support the core processes of humanistic inquiry. This includes support for handling uncertainty and incompleteness in the data, for interactive exploration, and for fluidly moving between close and distant reading (Gibbs and Owens 2012; Drucker 2011; Jänicke et al. 2015; Caviglia, Ciuccarelli, and Coleman 2012; Uboldi and Caviglia 2015). The Khepri tool presented here is part of a project to develop a modular set of components that take these requirements into account, and can be connected and configured to respond to the needs of a particular humanities task and data. Khepri targets data stored as Linked Data (Heath and Bizer 2011), a set of scalable standards that has gained widespread adoption particularly in the sphere of cultural heritage.
Video Saliency Detection Using Object Proposals

In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg–Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms.
User-study Based Optimization of Fast and Accurate Mahalanobis Brushing in Scatterplots

Brushing is at the heart of most modern visual analytics solutions with coordinated, multiple views and effective brushing is crucial for swift and efﬁcient processes in data exploration and analysis. Given a certain data subset that the user wishes to brush in a data visualization, traditional brushes are usually either accurate (like the lasso) or fast (e.g., a simple geometry like a rectangle or circle). In this paper, we now present a new, fast and accurate brushing technique for scatterplots, based on the Mahalanobis brush, which we have extended and then optimized using data from a user study. We explain the principal, sketch-based model of our new brushing technique (based on a simple click-and-drag interaction), the details of the user study and the related parameter optimization, as well as a quantitative evaluation, considering efﬁciency, accuracy, and also a comparison with the original Mahalanobis brush.
Novel Surgical and Radiologic Classification of the Subtympanic Sinus: Implications for Endoscopic Ear Surgery

Objective The aim of this study is to describe the endoscopic anatomy of the subtympanic sinus (STS), establish a classification according to its extension regarding the level of the facial nerve (FN), and assess the feasibility of the transcanal endoscopic approach to the STS. Study Design Experimental anatomic research. Setting Temporal bone laboratory. Methods We performed endoscopic dissection of 34 human whole head and ear block specimens. Of those, 29 underwent high-resolution computed tomography. The STS was classified according to its extension regarding the level of the FN: type A, no extension medial to the FN; type B, extension to the medial limit of the FN; type C, extension of the sinus medially and posteriorly from the FN into the mastoid cavity. Results The majority of cases (n = 21, 72%) showed a shallow type A STS. We observed a deep type B configuration in 6 cases (21%) and a type C in 2 cases (7%). The STS was completely exposable with a 0° endoscope in 44% of the specimens. Using a 45° endoscope, we gained complete insight in 79%. However, in 21% of the cases, the posteromedial extension of the STS was too deep to be completely explored by an endoscopic transcanal approach. Conclusion The majority of the STS is shallow and does not extend medially from the FN. This morphologic variant allows complete transcanal endoscopic visualization. In more excavated STS, a complete endoscopic exploration is not achievable, and a retrofacial approach may be adopted to completely access the STS.
Protein Kinase C&thgr; Via Activating Transcription Factor 2-Mediated CD36 Expression and Foam Cell Formation of Ly6Chi Cells Contributes to Atherosclerosis

Background: Although the role of thrombin in atherothrombosis is well studied, its role in the pathogenesis of diet-induced atherosclerosis is not known. Methods: Using a mouse model of diet-induced atherosclerosis and molecular biological approaches, here we have explored the role of thrombin and its G protein-coupled receptor signaling in diet-induced atherosclerosis. Results: In exploring the role of G protein-coupled receptor signaling in atherogenesis, we found that thrombin triggers foam cell formation via inducing CD36 expression, and these events require Par1-mediated G&agr;12-Pyk2-Gab1-protein kinase C (PKC)&thgr;-dependent ATF2 activation. Genetic deletion of PKC&thgr; in apolipoprotein E (ApoE)−/− mice reduced Western diet-induced plaque formation. Furthermore, thrombin induced Pyk2, Gab1, PKC&thgr;, and ATF2 phosphorylation, CD36 expression, and foam cell formation in peritoneal macrophages of ApoE−/− mice. In contrast, thrombin only stimulated Pyk2 and Gab1 but not ATF2 phosphorylation or its target gene CD36 expression in the peritoneal macrophages of ApoE−/−:PKC&thgr;−/− mice, and it had no effect on foam cell formation. In addition, the aortic root cross-sections of Western diet-fed ApoE−/− mice showed increased Pyk2, Gab1, PKC&thgr;, and ATF2 phosphorylation and CD36 expression as compared with ApoE−/−:PKC&thgr;−/− mice. Furthermore, although the monocytes from peripheral blood and the aorta of Western diet-fed ApoE−/− mice were found to contain more of Ly6Chi cells than Ly6Clo cells, the monocytes from Western diet-fed ApoE−/−:PKC&thgr;−/− mice were found to contain more Ly6Clo cells than Ly6Chi cells. It is interesting to note that the Ly6Chi cells showed higher CD36 expression with enhanced capacity to form foam cells as compared with Ly6Clo cells. Conclusions: These findings reveal for the first time that thrombin-mediated Par1-G&agr;12 signaling via targeting Pyk2-Gab1-PKC&thgr;-ATF2-dependent CD36 expression might be playing a crucial role in diet-induced atherogenesis.
Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering

Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, different domains may have different cluster structures. Due to rapid growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (including neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability.
Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain–Computer Interfaces

A brain–computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.
IKK promotes cytokine-induced and cancer-associated AMPK activity and attenuates phenformin-induced cell death in LKB1-deficient cells

AMPK activity is stimulated by IKK-mediated phosphorylation in cancer cells and downstream of inflammatory cytokines. An inflammatory way to activate AMPK In its best-known function as a sensor of energy status, the kinase AMPK is activated by phosphorylation mediated by the tumor suppressor LKB1. Antonia and Baldwin characterized a different pathway for AMPK activation involving TAK1, a kinase associated with inflammatory pathways. They found that this process required phosphorylation of AMPK by the TAK1 target IKK and that it occurred independently of LKB1 and, in certain cell lines, independently of TAK1 as well, indicating that TAK1-independent pathways that activate IKK could also stimulate AMPK. Combining an IKK inhibitor with the cancer drug phenformin improved its ability to kill LKB1-deficient cancer cells, highlighting a new potential treatment for cancers lacking this tumor suppressor. The 5′ AMP-activated protein kinase (AMPK) is an energy sensor that is activated upon phosphorylation of Thr172 in its activation loop by the kinase LKB1, CAMKK2, or TAK1. TAK1-dependent AMPK phosphorylation of Thr172 is less well characterized than phosphorylation of this site by LKB1 or CAMKK2. An important target of TAK1 is IκB kinase (IKK), which controls the activation of the transcription factor NF-κB. We tested the hypothesis that IKK acted downstream of TAK1 to activate AMPK by phosphorylating Thr172. IKK was required for the phosphorylation of Thr172 in AMPK in response to treatment with the inflammatory cytokine IL-1β or TNF-α or upon TAK1 overexpression. In addition, IKK regulated basal AMPK Thr172 phosphorylation in several cancer cell types independently of TAK1, indicating that other modes of IKK activation could stimulate AMPK. We found that IKK directly phosphorylated AMPK at Thr172 independently of the tumor suppressor LKB1 or energy stress. Accordingly, in LKB1-deficient cells, IKK inhibition reduced AMPK Thr172 phosphorylation in response to the mitochondrial inhibitor phenformin. This response led to enhanced apoptosis and suggests that IKK inhibition in combination with phenformin could be used clinically to treat patients with LKB1-deficient cancers.
MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.