Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.
Impact of Imperfect Channel State Information on Bi-Directional Communications With Relay Selection

In this correspondence, we study the effect of channel estimation error to the reception reliability for decode-forward based two-way relaying and physical layer network coding. In particular, we focus on the scenario where the channel estimation error is inversely proportional to signal-to-noise ratio (SNR). To such a scenario, analytic results have been developed to show that symbol error probability can approach zero by increasing SNR and extra diversity gains can still be achieved by applying relay selection.
Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers

This paper presents a study of feature selection methods effect, using a filter approach, on the accuracy and error of supervised classification of cancer. A comparative evaluation between different selection methods: Fisher, T-Statistics, SNR and ReliefF, is carried out, using the dataset of different cancers; leukemia cancer, prostate cancer and colon cancer. The classification results using k nearest neighbors (KNN) and support vector machine (SVM) classifiers show that the combination between SNR's method and the SVM classifier can present the highest accuracy.
Error Negativity Does Not Reflect Conflict: A Reappraisal of Conflict Monitoring and Anterior Cingulate Cortex Activity

Our ability to detect and correct errors is essential for our adaptive behavior. The conflict-loop theory states that the anterior cingulate cortex (ACC) plays a key role in detecting the need to increase control through conflict monitoring. Such monitoring is assumed to manifest itself in an electroencephalographic (EEG) component, the error negativity (Ne or error-related negativity [ERN]). We have directly tested the hypothesis that the ACC monitors conflict through simulation and experimental studies. Both the simulated and EEG traces were sorted, on a trial-by-trial basis, as a function of the degree of conflict, measured as the temporal overlap between incorrect and correct response activations. The simulations clearly show that conflict increases as temporal overlap between response activation increases, whereas the experimental results demonstrate that the amplitude of the Ne decreases as temporal overlap increases, suggesting that the ACC does not monitor conflict. At a functional level, the results show that the duration of the Ne depends on the time needed to correct (partial) errors, revealing an on-line modulation of control on a very short time scale.
Combining Theory, Model, and Experiment to Explain How Intrinsic Theta Rhythms Are Generated in an In Vitro Whole Hippocampus Preparation without Oscillatory Inputs

Abstract Scientists have observed local field potential theta rhythms (3–12 Hz) in the hippocampus for decades, but understanding the mechanisms underlying their generation is complicated by their diversity in pharmacological and frequency profiles. In addition, interactions with other brain structures and oscillatory drives to the hippocampus during distinct brain states has made it difficult to identify hippocampus-specific properties directly involved in theta generation. To overcome this, we develop cellular-based network models using a whole hippocampus in vitro preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and postinhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. Sparse firing of PYR cells and large excitatory currents onto PV+ cells are present as in experiments. The particular theta frequency is more controlled by PYR-to-PV+ cell interactions rather than PV+-to-PYR cell interactions. We identify two scenarios by which theta rhythms can emerge, and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+ cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+ to PYR cells needs to be larger than from PYR to PV+ cells. Our models can serve as a platform on which to build and develop an understanding of in vivo theta generation.
Contextual Co-occurrence Information for Object Representation and Categorization

Object categorization based on hierarchical context modeling has shown to be useful in large database of object categories, especially, when a large number of object classes needs to be recognized from a range of different scene categories. However, average precision of categorization is still low compared to other existing methods. This may reflect that the contribution of underlying relations between objects has not been fully considered. In this paper, we improve average precision of contextual object recognition by taking advantage of objects co-occurrence information. Our method consists of two main phases. In the first phase, object representation is derived by considering the frequency of objects appeared in each image. The second phase is focused on classification of objects by applying a decision tree algorithm. We use SUN09 database to evaluate our proposed method. This database consists of images spanning from different scene categories and object instances. Our experimental results demonstrate that our proposed method achieves a higher average precision in comparison to a recent similar method by encoding contextual information in an efficient way.
Toward Efficient Segmentation of Brain Tumors Based on Support Vector Machine Classifier Through Optimized RBF Kernel Parameters and Optimal Texture Features

This paper presents a brain tumor automatic segmentation approach applied to magnetic resonance MR images. The authors' approach addresses all types of brain tumors. The proposed method involves therefore: image pre-processing, feature extraction via wavelet transform-spatial gray level dependence matrix WT-SGLDM, dimensionality reduction using genetic algorithm GA, parameters optimization by GA-SVM model and classification of the reduced features using support vector machine SVM. These optimal features and optimized parameters are employed for the segmentation of brain tumor. The resulting method is aimed at early tumor diagnostics support by distinguishing between the brain tissue, benign tumor and malignant tumor tissue. The authors' contribution consists in involving the parameters optimization phase to improve the classification and segmentation results by using GA-SVM model. The segmentation results in different types of brain tissue are evaluated by comparison with the manual segmentation as well as with other existing techniques. The qualitative evaluation shows that their approach outperforms manual segmentation with a Match Percent measure MP equal to 97.08% and 98.89% for the malignant and the benign tumors respectively. The quantitative evaluation displays that the authors' attitude overtakes FCM algorithm with an accuracy rate of 99.69% for benign tumor and 99.36% for malignant tumor.
Where event processing grand challenge meets real-time web: PLAY event marketplace

The Grand Challenge in Event Processing serves as a common goal and mechanism for coordinating research across the spectrum of people working on event processing. The Grand Challenge is a single, though broad, challenge that impacts society which the community can use as a basis for measuring progress in research and application. In this paper we will present an analysis of the recent development in the realization of the Grand Challenge and elaborate on its implementation using the current development in the so called Real-time Web - a decentralized, global, Internet-like infrastructure, built upon widely-accepted open standards. We present a particular solution that has been developed in the scope of the EU project PLAY (www.play-project.eu), that is aiming to develop an elastic and reliable platform for dynamic complex event-driven interaction in large highly distributed and heterogeneous federated service systems. The main outcome is an Event Marketplace Platform which offers and consumes real-time data of all sorts and across the whole Web. We explain the theoretical principles behind the Event Marketplace, discuss the research and technological challenges required for its efficient development, present the implementation and evaluation details and demonstrate several applications that have been built on the top of the Platform so far.
Survey on Feature Selection

Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.
Tackling tuberculosis: Insights from an international TB Summit in London

Tuberculosis (TB) poses a grave predicament to the world as it is not merely a scientific challenge but a socio-economic burden as well. A prime cause of mortality in human due to an infectious disease; the malady and its cause, Mycobacterium tuberculosis have remained an enigma with many questions that remain unanswered. The ability of the pathogen to survive and switch between varied physiological states necessitates a protracted therapeutic regimen that exerts an excessive strain on low-resource countries. To complicate things further, there has been a significant rise of antimicrobial resistance. Existing control measures, including treatment regimens have remained fairly uniform globally for at least half a century and require reinvention. Overcoming the societal and scientific challenges requires an increase in dialog to identify key regions that need attention and effective partners with whom successful collaborations can be fostered. In this report, we explore the discussions held at the International TB Summit 2015 hosted by EuroSciCon, which served as an excellent platform for researchers to share their recent findings. Ground-breaking results require outreach to affect policy design, governance and control of the disease. Hence, we feel it is important that meetings such as these reach a wider, global audience.
TRIM16 Acts as an E3 Ubiquitin Ligase and Can Heterodimerize with Other TRIM Family Members

The TRIM family of proteins is distinguished by its tripartite motif (TRIM). Typically, TRIM proteins contain a RING finger domain, one or two B-box domains, a coiled-coil domain and the more variable C-terminal domains. TRIM16 does not have a RING domain but does harbour two B-box domains. Here we showed that TRIM16 homodimerized through its coiled-coil domain and heterodimerized with other TRIM family members; TRIM24, Promyelocytic leukaemia (PML) protein and Midline-1 (MID1). Although, TRIM16 has no classic RING domain, three-dimensional modelling of TRIM16 suggested that its B-box domains adopts RING-like folds leading to the hypothesis that TRIM16 acts as an ubiquitin ligase. Consistent with this hypothesis, we demonstrated that TRIM16, devoid of a classical RING domain had auto-polyubiquitination activity and acted as an E3 ubiquitin ligase in vivo and in vitro assays. Thus via its unique structure, TRIM16 possesses both heterodimerization function with other TRIM proteins and also has E3 ubiquitin ligase activity.
Distributed and Online Fair Resource Management in Video Surveillance Sensor Networks

Visual capability introduced to Wireless Sensor Networks (WSNs) render many novel applications that would otherwise be infeasible. However, unlike legacy WSNs which are commercially deployed in applications, visual sensor networks create additional research problems that delays the real-world implementations. Conveying real-time video streams over resource constrained sensor hardware remains to be a challenging task. As a remedy, we propose a fairness-based approach to enhance the event reporting and detection performance of the Video Surveillance Sensor Networks. Instead of achieving fairness only for flows or for nodes as investigated in the literature, we concentrate on the whole application requirement. Accordingly, our Event-Based Fairness (EBF) scheme aims at fair resource allocation for the application level messaging units called events. We identify the crucial network-wide resources as the in-queue processing turn of the frames and the channel access opportunities of the nodes. We show that fair treatment of events, as opposed to regular flow of frames, results in enhanced performance in terms of the number of frames reported per event and the reporting latency. EBF is a robust mechanism that can be used as a stand-alone or as a complementary method to other possible performance enhancement methods for video sensor networks implemented at other communication layers.
Strike a Pose! The Femininity Effect in Collegiate Women’s Sport

The apologetic strategies women employ to manage the cultural tension between athleticism and hegemonic femininity are well documented. Existing research, however, tends to be small-scale. The cumulative symbolic implications of female athlete appearance on cultural ideals remain under-theorized as a result. Our quantitative content analysis of a stratified, random sample of 4,799 collegiate women athletes’ roster photos examined whether sport, school type, and geographical location are related to gendered appearance. Despite important contextual variation, we found overwhelming homogeneity across settings. Our results suggest that the normalization of women’s athleticism is limited and depends on subordinated femininities. Thus, despite some positive changes, team sport still helps stabilize and naturalize the gender order.
Multi-Scale Edge Detection and Object Extraction for Image Retrieval

A new scheme for boundary-based object extraction and description of still images, through multi-scale edge detection, is proposed in this paper. Boundary-based methods try to extract closed contours from individual edge pixels through edge-linking. Our approach is based on a connected structure of edge pixels as the initial edge-linking elements. These connected structures, the sub-segments, are extracted from the Canny edge map of an image. Multiple simplification-scales are derived from applying iterations of the bilateral filter to the image, providing extra information about the relative importance of each sub-segment. Edge-linking towards contour closure is achieved through perceptually-driven minimum cost search. Furthermore, a shape-based description vector is derived from the extracted contours, and retrieval results are obtained via the integration of the whole scheme into MUVIS framework
Greedy linear value-approximation for factored Markov decision processes

Significant recent work has focused on using linear representations to approximate value functions for factored Markov decision processes (MDPs). Current research has adopted linear programming as an effective means to calculate approximations for a <i>given</i> set of basis functions, tackling very large MDPs as a result. However, a number of issues remain unresolved: <i>How accurate are the approximations produced by linear programs? How hard is it to produce better approximations? </i> and <i>Where do the basis functions come from? </i> To address these questions, we first investigate the complexity of minimizing the Bellman error of a linear value function approximation--showing that this is an inherently hard problem. Nevertheless, we provide a branch and bound method for calculating Bellman error and performing approximate policy iteration for general factored MDPs. These methods are more accurate than linear programming, but more expensive. We then consider linear programming itself and investigate methods for automatically constructing sets of basis functions that allow this approach to produce good approximations. The techniques we develop are guaranteed to reduce <i>L</i><inf>1</inf> error, but can also empirically reduce Bellman error.
Recurrent Polynomial Network for Dialogue State Tracking

Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive.
Reconciling the Role of Serotonin in Behavioral Inhibition and Aversion: Acute Tryptophan Depletion Abolishes Punishment-Induced Inhibition in Humans

The neuromodulator serotonin has been implicated in a large number of affective and executive functions, but its precise contribution to motivation remains unclear. One influential hypothesis has implicated serotonin in aversive processing; another has proposed a more general role for serotonin in behavioral inhibition. Because behavioral inhibition is a prepotent reaction to aversive outcomes, it has been a challenge to reconcile these two accounts. Here, we show that serotonin is critical for punishment-induced inhibition but not overall motor response inhibition or reporting aversive outcomes. We used acute tryptophan depletion to temporarily lower brain serotonin in healthy human volunteers as they completed a novel task designed to obtain separate measures of motor response inhibition, punishment-induced inhibition, and sensitivity to aversive outcomes. After a placebo treatment, participants were slower to respond under punishment conditions compared with reward conditions. Tryptophan depletion abolished this punishment-induced inhibition without affecting overall motor response inhibition or the ability to adjust response bias in line with punishment contingencies. The magnitude of reduction in punishment-induced inhibition depended on the degree to which tryptophan depletion reduced plasma tryptophan levels. These findings extend and clarify previous research on the role of serotonin in aversive processing and behavioral inhibition and fit with current theorizing on the involvement of serotonin in predicting aversive outcomes.
Refolding and simultaneous purification by three‐phase partitioning of recombinant proteins from inclusion bodies

Many recombinant eukaryotic proteins tend to form insoluble aggregates called inclusion bodies, especially when expressed in Escherichia coli. We report the first application of the technique of three‐phase partitioning (TPP) to obtain correctly refolded active proteins from solubilized inclusion bodies. TPP was used for refolding 12 different proteins overexpressed in E. coli. In each case, the protein refolded by TPP gave either higher refolding yield than the earlier reported method or succeeded where earlier efforts have failed. TPP‐refolded proteins were characterized and compared to conventionally purified proteins in terms of their spectral characteristics and/or biological activity. The methodology is scaleable and parallelizable and does not require subsequent concentration steps. This approach may serve as a useful complement to existing refolding strategies of diverse proteins from inclusion bodies.
NetClus: A Scalable Framework for Locating Top-K Sites for Placement of Trajectory-Aware Services

Optimal location queries identify the best locations to set up new facilities for providing service to its users. For several businesses such as fuel stations, cellphone base-stations, etc., placement queries require taking into account the mobility patterns (or trajectories) of the users. In this work, we formulate the TOPS (Trajectory-Aware Optimal Placement of Services) query that locates the best k sites on a road network for the prevailing user trajectories. The problem is NP-hard. The greedy approach, which is the state-of-the-art technique for this problem, is not scalable and practical for real urban-scale scenarios, primarily due to its high memory footprint beyond the capabilities of commodity machines. To overcome these challenges, we develop an indexing framework called NETCLUS that derives its power through an unique combination of FM sketches with network clustering. Empirical studies show that NETCLUS requires less than 100 s to answer the TOPS query on real datasets comprising of more than 250,000 sites and 120,000 trajectories.