Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights Into DNA Helicases as Druggable Targets for Cancer Therapy

Small molecules that deter the functions of DNA damage response machinery are postulated to be useful for enhancing the DNA damaging effects of chemotherapy or ionizing radiation treatments to combat cancer by impairing the proliferative capacity of rapidly dividing cells that accumulate replicative lesions. Chemically induced or genetic synthetic lethality is a promising area in personalized medicine, but it remains to be optimized. A new target in cancer therapy is DNA unwinding enzymes known as helicases. Helicases play critical roles in all aspects of nucleic acid metabolism. We and others have investigated small molecule targeted inhibition of helicase function by compound screens using biochemical and cell-based approaches. Small molecule-induced trapping of DNA helicases may represent a generalized mechanism exemplified by certain topoisomerase and PARP inhibitors that exert poisonous consequences, especially in rapidly dividing cancer cells. Taking the lead from the broader field of DNA repair inhibitors and new information gleaned from structural and biochemical studies of DNA helicases, we predict that an emerging strategy to identify useful helicase-interacting compounds will be structure-based molecular docking interfaced with a computational approach. Potency, specificity, drug resistance, and bioavailability of helicase inhibitor drugs and targeting such compounds to subcellular compartments where the respective helicases operate must be addressed. Beyond cancer therapy, continued and new developments in this area may lead to the discovery of helicase-interacting compounds that chemically rescue clinically relevant helicase missense mutant proteins or activate the catalytic function of wild-type DNA helicases, which may have novel therapeutic application.
Human Detection , Tracking and Activity Recognition from Video

​—Human detection, tracking and activity recognition is an important area of research with applications ranging from entertainment, robotics, surveillance and elderly care. In this project, I implemented and developed various human detection and tracking algorithms using Robot Operating System (ROS) in Linux and using MATLAB R2016b in Windows. Implementing the algorithms using Kinect and MATLAB, real-time human detection and skeleton tracking was achieved with 30 fps. The second part of the project was to collect data to implement various machine learning algorithms to predict human activity. Currently, the system detects 3 activities with 96.02% accuracy using Decision Trees for one human. Keywords—Human detection, skeleton tracking, activity detection and classification
Exercise Training Plus Sildenafil Treatment: Role on Autonomic and Inflammatory Markers

Abstract The current study aimed to determine the effects of sildenafil-associated aerobic exercise training (ET) on the physical performance, hemodynamic, autonomic and inflammatory parameters of rats. Male Wistar rats were randomly assigned to: sedentary rats placebo-treated (SP); sedentary rats sildenafil-treated (SS); trained rats placebo-treated (TP); and trained rats sildenafil-treated (TS). Sildenafil treatment consisted of 8 weeks of daily oral gavage (1.5 mg/kg), one hour before the session of ET (60–75% of maximal running speed, 5 days/week, for 8 weeks). After ET period, physical capacity, hemodynamic, autonomic and skeletal muscle inflammatory profile were assessed. Chronic sildenafil treatment causes an additional increase of physical capacity in aerobically trained rats. However, these beneficial effects were accompanied by unwanted alterations, as increased of arterial pressure and peripheral sympathetic modulation, as well as exacerbated inflammatory status on skeletal muscle of rats. Taken together, these data suggest the positive and negative effects of sildenafil chronic administration, associated to aerobic ET, at doses used in clinical practice. This report stresses the importance of paying greater attention to the indiscriminate use of this substance in high-performance sports.
Small-sized, printed 2.4/5-GHz WLAN notebook antenna aimed for 4×4 multiple transmit/receive antennas in future Gbps communications

To embrace Gbps data rate with higher through-put performance, 4×4 wireless local area network (WLAN) antennas embedded in the notebook computers can be foreseen in the near future. This will create more challenges for antenna engineers because antenna real estate is already a very limited commodity for current large screen-to-body-ratio notebooks. To deploy more antennas, the single antenna itself must be even smaller and yet does not compromise on its antenna performance. In this paper, a small-sized, 2.4/5-GHz WLAN monopole antenna well suited for large screen-to-body-ratio notebook computers is presented. The antenna height was 4 mm only and constructed on a low-cost dielectric substrate of size 4 mm × 30 mm to be easily placed within the ultra-thin bezel surrounding the top edge of the notebook display. The antenna consisted of two radiating arms of unequal lengths to generate the lower and the higher bands. By properly loading each radiating arm with a shorting chip inductor, the proposed antenna was capable of generating the 2.4-GHz (2400–2484 MHz) and the 5-GHz (5150–5825 MHz) WLAN bands with a very low antenna profile. Details of the proposed design are described and discussed in the article.
A timer for analyzing temporally dynamic changes in transcription during differentiation in vivo

Understanding the mechanisms of cellular differentiation is challenging because differentiation is initiated by signaling pathways that drive temporally dynamic processes, which are difficult to analyze in vivo. We establish a new tool, Timer of cell kinetics and activity (Tocky; or toki [time in Japanese]). Tocky uses the fluorescent Timer protein, which spontaneously shifts its emission spectrum from blue to red, in combination with computer algorithms to reveal the dynamics of differentiation in vivo. Using a transcriptional target of T cell receptor (TCR) signaling, we establish Nr4a3-Tocky to follow downstream effects of TCR signaling. Nr4a3-Tocky reveals the temporal sequence of events during regulatory T cell (Treg) differentiation and shows that persistent TCR signals occur during Treg generation. Remarkably, antigen-specific T cells at the site of autoimmune inflammation also show persistent TCR signaling. In addition, by generating Foxp3-Tocky, we reveal the in vivo dynamics of demethylation of the Foxp3 gene. Thus, Tocky is a tool for cell biologists to address previously inaccessible questions by directly revealing dynamic processes in vivo.
New Attempts in Binary Image Registration

The work reported in the paper addresses the problem of binary image registration. The proposed methodology is based on an evolution strategy (ES) developed to align two binary images. The fitness function is defined in terms of mutual information computed between the sensed image and the target image. We use various recombination schemes involving standard local/global convex and discrete crossover procedures as well as hybrid methods. The proposed hybridizations are developed based on some inner bond between the chromosome alleles. In the final stage we evaluate the similarity between the resulted image and the target using both quantitative and qualitative measures. Also, in order to experimentally establish the performances of our method, we compare it against one of the most commonly used procedures for image registration in case of rigid type perturbation, namely PAT registration. The experimental results together with some conclusive remarks regarding the quality of the proposed methodology are reported in the final part of the paper.
Implementation of a Reference Architecture for Cyber Physical Systems to support Condition Based Maintenance

This paper presents the implementation of a reference architecture for Cyber Physical Systems (CPS) to support Condition Based Maintenance (CBM) of industrial assets. The article focuses on describing how the MANTIS Reference Architecture is implemented to support predictive maintenance of clutch-brake assets fleet, and includes the data analysis techniques and algorithms implemented at platform level to facilitate predictive maintenance activities. These technologies are (1) Root Cause Analysis powered by Attribute Oriented Induction Clustering and (2) Remaining Useful Life powered by Time Series Forecasting. The work has been conducted in a real use case within the EU project MANTIS.
Personalising the TV Experience with Augmented Reality Technology: Synchronised Sign Language Interpretation

This paper explores the potential of augmented reality technology as a novel way to allow users to view a sign language interpreter through an optical head-mounted display while watching a TV programme. We address the potential of augmented reality for personalisation of TV access services as part of closed laboratory investigations. Based on guidelines of regulatory authorities and research on traditional sign language services on TV, as well as feedback from experts, we justify our two design proposals. We describe how we produced the content for the AR prototype applications and what we have learned during the process. Finally, we develop questions for our upcoming user studies.
Trajectory outlier detection based on partition-and-detection framework

Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. In this paper, a trajectory outlier detection based on local outlier fraction algorithm (TODLOF) is proposed to detect outliers in the trajectory dataset based on the partition-and-detection framework. When partitioning a trajectory, a minimum description length principle (MDL) based method is adopted. The local outlier factor (LOF) is used as the basis for judging the outlier in the detection stage, which improve the accuracy of the anomaly detection. Finally, experiments were carried out, with hurricane trajectory data and animal migration data as inputs, to prove that this algorithm can detect anomaly trajectories efficiently. And an online version of this algorithm is also presented to meet the requirements of real-time application.
Macroscopic Cerebral Tumor Growth Modeling From Medical Images: A Review

Mathematical models have been ubiquitously employed in various applications. One of these applications that arose in the past few decades is cerebral tumor growth modeling. Simultaneously, medical imaging techniques, such as magnetic resonance imaging, computed tomography, and positron emission tomography, have witnessed great developments and become the primary clinical procedure in tumors diagnosis and detection. Studying tumor growth via mathematical models from medical images is an important application that is believed to play significant role in cancer treatment by predicting tumor evolution, quantifying the response to therapy, and the effective treatment planning of chemotherapy and/or radiotherapy. In this paper, we focus on the macroscopic growth modeling of brain tumors, mainly glioma, and highlight the current achievements in the state-of-the-art methods. In addition, we discuss some challenges and perspectives on this research that can further promote the research of this field.
Antioxidant Activity of Oral Administration of Rosmarinus Officinalis Leaves Extract on Rat's Hippocampus which Exposed to 6-Hydroxydopamine

Carnosic acid, a diterpene of Rosemarinus officinalis leaves extract (RE), has potent antioxidant activity in vitro. The dopaminergic connection of substantia nigra pars compacta to the hippocampus might be affected by oxidative stress which caused cognitive impairment observed in the early phase of Parkinson's disease (PD). Adult male Wistar rats were lesioned bilaterally by intra-nigral injection of 6-OHDA, and divided into six groups: four groups that orally given RE containing 40% of carnosic acid, at doses of 25, 50 and 100 mg/kg (treated rats) and distilled water (H2O), once daily for a period of 14 days before and after the injury. There were also two another groups as control rats which injected by normal saline and untreated lesion group. The injured animals were evaluated for their spatial memory performance by Morris Water Maze test. Lesioned rats showed significant increase in escape latency, as compared with control group. Two weeks after injury, tissue samples were collected from the hippocampus. Levels of catalase (CAT), glutathione peroxidase (GPX) and superoxide dismutase (SOD), malondialdehyde (MDA) and reactive oxygen species (ROS) were determined. There were significant increase of SOD, GPX and CAT enzymes activities in RE50 treated group as compared to lesioned rats. We found a significant decrease of ROS in RE50 treated group as compared to Lesioned rats. These findings provide evidence that 50 mg/kg of RE decreased oxidative damage of the hippocampus induced by 6-OHDA and serve as potential candidate for the treatment of PD.
Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback

Axiomatic analysis is a well-defined theoretical framework for analytical evaluation of information retrieval models. The current studies in axiomatic analysis implicitly assume that the constraints (axioms) are independent. In this paper, we revisit this assumption and hypothesize that there might be interdependence relationships between the existing constraints. As a preliminary study, we focus on the pseudo-relevance feedback (PRF) models that have been theoretically studied using the axiomatic analysis approach. In this paper, we introduce two novel interdependent PRF constraints which emphasize on the effect of existing constraints on each other. We further modify two state-of-the-art PRF models, log-logistic and relevance models, in order to satisfy the proposed constraints. Experiments on three TREC newswire and web collections demonstrate that the proposed modifications significantly outperform the baselines, in all cases.
EFP and PG-EFP: Epistemic Forward Search Planners in Multi-Agent Domains

This paper presents two prototypical epistemic forward planners, called EFP and PG-EFP, for generating plans in multi-agent environments. These planners differ from recently developed epistemic planners in that they can deal with unlimited nested beliefs, common knowledge, and capable of generating plans with both knowledge and belief goals. EFP is simply a breadth first search planner while PG-EFP is a heuristic search based system. To generate heuristics in PG-EFP, the paper introduces the notion of an epistemic planning graph. The paper includes an evaluation of the planners using benchmarks collected from the literature and discusses the issues that affect their scalability and efficiency, thus identifies potentially directions for future work. It also includes experimental evaluation that proves the usefulness of epistemic planning graphs.
J un 2 01 8 Method to Annotate Arrhythmias by Deep Network

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys); supraventricular tachycardia (Tachy); ventricular flutter or fibrillation (VF/VFL); ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy.
Device‐Level Photonic Memories and Logic Applications Using Phase‐Change Materials

Inspired by the great success of fiber optics in ultrafast data transmission, photonic computing is being extensively studied as an alternative to replace or hybridize electronic computers, which are reaching speed and bandwidth limitations. Mimicking and implementing basic computing elements on photonic devices is a first and essential step toward all‐optical computers. Here, an optical pulse‐width modulation (PWM) switching of phase‐change materials on an integrated waveguide is developed, which allows practical implementation of photonic memories and logic devices. It is established that PWM with low peak power is very effective for recrystallization of phase‐change materials, in terms of both energy efficiency and process control. Using this understanding, multilevel photonic memories with complete random accessibility are then implemented. Finally, programmable optical logic devices are demonstrated conceptually and experimentally, with logic “OR” and “NAND” achieved on just a single integrated photonic phase‐change cell. This study provides a practical and elegant technique to optically program photonic phase‐change devices for computing applications.
The roles of gender and profession on gender role expectations of pain in health care professionals

Introduction Gender-related stereotypes of pain may account for some assessment and treatment disparities among patients. Among health care providers, demographic factors including gender and profession may influence the use of gender cues in pain management decision-making. The Gender Role Expectations of Pain Questionnaire was developed to assess gender-related stereotypic attributions of pain regarding sensitivity, endurance, and willingness to report pain, and has not yet been used in a sample of health care providers. The purpose of this study was to examine the presence of gender role expectation of pain among health care providers. It was hypothesized that health care providers of both genders would endorse gender stereotypic views of pain and physicians would be more likely than dentists to endorse these views. Methods One-hundred and sixty-nine providers (89 dentists, 80 physicians; 40% women) were recruited as part of a larger study examining providers’ use of demographic cues in making pain management decisions. Participants completed the Gender Role Expectations of Pain Questionnaire to assess the participant’s views of gender differences in pain sensitivity, pain endurance, and willingness to report pain. Results Results of repeated measures analysis of variance revealed that health care providers of both genders endorsed stereotypic views of pain regarding willingness to report pain (F(1,165)=34.241, P<0.001; d=0.479). Furthermore, female dentists rated men as having less endurance than women (F(1,165)=4.654, P=0.032; d=0.333). Conclusion These findings affirm the presence of some gender-related stereotypic views among health care providers and suggest the presence of a view among health care providers that men are underreporting their pain in comparison to women. Future work can refine the effects of social learning history and other psychosocial factors that contribute to gender and provider differences in pain management decisions.
Planning for Partial Observability by SAT and Graph Constraints

Chatterjee et al. have recently shown the utility of SAT in solving a class of planning problems with partial observability. A core component of their logical formulation of planning is constraints expressing s-t-reachability in directed graphs. In this work, we show that the scalability of the approach can be dramatically improved by using dedicated graph constraints, and that a far broader class of important planning problems can be expressed in terms of s-t-reachability and acyclicity constraints.
Synthetically Generated Semantic Codebook for Bag-of-Visual-Words Based Word Spotting

Word-spotting methods based on the Bag-of-Visual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable "codebook" for a certain dataset has a high computational cost. Therefore, in this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation.
Observers of control state and uncertainty using Takagi-Sugeno fuzzy model

Adding a term called uncertainty in the Takagi-Sugeno (T-S) fuzzy model to cover the difference between a real system concerned and its T-S fuzzy model, this paper proposes novel observers of the control state and the uncertainty. Unlike state observer in the traditional sense that is usually designed as a whole, following dividing the state into two parts by performing a unique matrix transformation, two observers of the state are designed separately in order to eliminate the influence of the uncertainty. Finally, an observer of the uncertainty based on one of state observers is provided.
Spontaneous Coronary Artery Dissection (SCAD): FEMALE SURVIVORS' EXPERIENCES OF STRESS AND SUPPORT

Purpose: Spontaneous coronary artery dissection (SCAD) is a nonatherosclerotic type of acute myocardial infarction that primarily affects young, healthy women without typical risk factors for heart disease. Few investigations have examined psychosocial variables in this population and none have looked at patient perceptions of the experience and stresses associated with having a SCAD event or the resources currently available to SCAD survivors. This investigation describes survivors' subjective experiences of SCAD. Participants also provided information about types and helpfulness of resources available to them post-SCAD, including cardiac rehabilitation. Methods: Participants were recruited online and completed a one-time questionnaire. Results: Participants (n = 409) completed a questionnaire concerning their experiences with their SCAD event in the 1 y and 2 wk prior to the SCAD event. Their responses reflected moderate to high perceptions of stress. Participants experienced the SCAD event as highly stressful and frightening and their heart health presents as a moderate-severe source of current, post-diagnosis stress. Spontaneous coronary artery dissection–based informational support was frequently rated as inadequate, whereas other supportive resources varied in their helpfulness and accessibility. Participants reported positive experiences in cardiac rehabilitation and strong interest in SCAD-specific, professionally led, online patient education and support groups. Conclusions: This study is the largest to date investigation of SCAD survivors and their experiences in this understudied and perhaps underrecognized condition. Opportunities for researchers and providers to develop, tailor, and disseminate SCAD-specific interventions are discussed.