Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of Co-occurring Comorbidities in People Living With HIV

Abstract Background The aims of this study were to identify common patterns of comorbidities observed in people living with HIV (PLWH), using a data-driven approach, and evaluate associations between patterns identified. Methods A wide range of comorbidities were assessed in PLWH participating in 2 independent cohorts (POPPY: UK/Ireland; AGEhIV: Netherlands). The presence/absence of each comorbidity was determined using a mix of self-reported medical history, concomitant medications, health care resource use, and laboratory parameters. Principal component analysis (PCA) based on Somers’ D statistic was applied to identify patterns of comorbidities. Results PCA identified 6 patterns among the 1073 POPPY PLWH (85.2% male; median age [interquartile range {IQR}], 52 [47–59] years): cardiovascular diseases (CVDs), sexually transmitted diseases (STDs), mental health problems, cancers, metabolic disorders, chest/other infections. The CVDs pattern was positively associated with cancer (r = .32), metabolic disorder (r = .38), mental health (r = .16), and chest/other infection (r = .17) patterns (all P < .001). The mental health pattern was correlated with all the other patterns (in particular cancers: r = .20; chest/other infections: r = .27; both P < .001). In the 598 AGEhIV PLWH (87.6% male; median age [IQR], 53 [48–59] years), 6 patterns were identified: CVDs, chest/liver, HIV/AIDS events, mental health/neurological problems, STDs, and general health. The general health pattern was correlated with all the other patterns (in particular CVDs: r = .14; chest/liver: r = .15; HIV/AIDS events: r = .31; all P < .001), except STDs (r = –.02; P = .64). Conclusions Comorbidities in PLWH tend to occur in nonrandom patterns, reflecting known pathological mechanisms and shared risk factors, but also suggesting potential previously unknown mechanisms. Their identification may assist in adequately addressing the pathophysiology of increasingly prevalent multimorbidity in PLWH.
Dynamic Cipher Puzzle for Efficient Broadcast Authentication in Wireless Sensor Networks

The use of signature-based broadcast authentication for code and data dissemination in wireless sensor networks (WSNs) cannot be avoided. It increases security but requires high computation. Adversaries can exploit the latter condition as an opportunity to send many false signatures. Filtering methods can overcome this vulnerability. Cipher Puzzle is a filtering method that has low storage overhead along with high security, especially against denial of service (DoS) attacks. However, its number of hash iterations cannot be bounded, which causes sender-side delay. This paper proposes a Dynamic Cipher Puzzle (DCP), which uses a threshold function to limit the number of hash iterations. Hence, time at the sender-side can be used more efficiently. Besides, its dynamic puzzle-strength increases the obscurity of the transmitted packet. Simulation and experimental results were analyzed with Arduino 2560. The theoretical results show that the quadratic function outperformed the compared methods. The scheme decreased sender-side delay by 94.6% with a guarantee of zero solution probability in 1.728 ×10−13. The experimental results show that the consumption of resources at the sensor node increases with an acceptable value. Moreover, DCP increases the complexity for the attacker to implement probability and signature-based DoS attacks.
S-phase transcriptional buffering quantified on two different promoters

Transcriptional buffering enforced during DNA replication shows that histone acetylation governs the homeostasis process and can also restrict promoters from reaching maximum transcriptional potential Imaging of transcription by quantitative fluorescence-based techniques allows the examination of gene expression kinetics in single cells. Using a cell system for the in vivo visualization of mammalian mRNA transcriptional kinetics at single-gene resolution during the cell cycle, we previously demonstrated a reduction in transcription levels after replication. This phenomenon has been described as a homeostasis mechanism that buffers mRNA transcription levels with respect to the cell cycle stage and the number of transcribing alleles. Here, we examined how transcriptional buffering enforced during S phase affects two different promoters, the cytomegalovirus promoter versus the cyclin D1 promoter, that drive the same gene body. We found that global modulation of histone modifications could completely revert the transcription down-regulation imposed during replication. Furthermore, measuring these levels of transcriptional activity in fixed and living cells showed that the transcriptional potential of the genes was significantly higher than actual transcription levels, suggesting that promoters might normally be limited from reaching their full transcriptional potential.
Evaluating the Performance of Structure from Motion Pipelines

Structure from Motion (SfM) is a pipeline that allows three-dimensional reconstruction starting from a collection of images. A typical SfM pipeline comprises different processing steps each of which tackles a different problem in the reconstruction pipeline. Each step can exploit different algorithms to solve the problem at hand and thus many different SfM pipelines can be built. How to choose the SfM pipeline best suited for a given task is an important question. In this paper we report a comparison of different state-of-the-art SfM pipelines in terms of their ability to reconstruct different scenes. We also propose an evaluation procedure that stresses the SfM pipelines using real dataset acquired with high-end devices as well as realistic synthetic dataset. To this end, we created a plug-in module for the Blender software to support the creation of synthetic datasets and the evaluation of the SfM pipeline. The use of synthetic data allows us to easily have arbitrarily large and diverse datasets with, in theory, infinitely precise ground truth. Our evaluation procedure considers both the reconstruction errors as well as the estimation errors of the camera poses used in the reconstruction.
Correction of Aliasing in Color Doppler Echocardiography Based on Image Processing Technique in Echodynamography

A study about the program of color Doppler to improve blood velocity estimation has been developed. Color Doppler echocardiographic images were acquired using Philips IE33 have a crucial issue the ambiguity of the offset beams when the correct velocity of the blood flow exceeds the velocity of the Nyquist limit. Therefore, velocity dealiasing is needed in quality control of color Doppler echocardiographic data to correct velocities using EDG (echodynamography) based on observer and machine interaction. The program is applied to echocardiography images of aortic stenosis subject as a technique to assess cardiovascular function. One challenging with color Doppler is correcting velocity aliasing. The observer determines the ambiguity area based on his or her knowledge. These results confirm the dealiasing and noise removing are vital elements in improving blood velocity estimation in cardiovascular diagnostic. Therefore, this study could use clinical tools based on color Doppler imaging.
3D Articulated Human Motion Analysis System Using a Single Low-Cost RGB-D Sensor

In this paper, an cost-effective, highly accurate 3D articulated human motion analysis system is proposed. In the proposed system, a single low-cost RGB-D sensor captures a color image and depth one of human motion. Then, a deep learning-based 2D motion analysis process accurately estimates intermediate 2D articulated human motion from the color image. Finally, a color-to-depth warping-based 3D motion transform process effectively compute final 3D articulated human motion from the depth image. The proposed system is cost-effective but highly accurate. Experimental results show that the proposed system outperforms the commercial system using the same RGB-D sensor when compared in terms of accuracy.
Contribution of muscle activity at different gait phases for improving walking performance in chronic stroke patients with hemiparesis

[Purpose] The aim of this study was to clarify the optimal timing for increasing muscle activity in the paralyzed lower limb of stroke survivors by evaluating the relationship between gait muscle activity patterns and gait parameters. [Participants and Methods] Electromyography of the tibialis anterior, soleus, rectus femoris, and biceps femoris on the paralyzed side and spatiotemporal gait parameters were evaluated in 40 chronic post-stroke patients as they walked at a comfortable speed. The normalized average amplitude and asymmetry indexes of each gait phase were calculated. The correlations between gait velocity or asymmetry indexes and the activity amplitudes of various muscles during each gait phase were analyzed. Multiple regression analysis was performed with gait velocity or asymmetry indexes as the response variable and the muscle activity amplitudes in the various gait phases as explanatory variables. [Results] The major determinants of gait velocity were the tibialis anterior activity (β=−0.35) and biceps femoris activity (β=0.45) during the swing phase. In addition, the biceps femoris activity during the swing phase was the major determinant of the asymmetry index for the swing phase duration (β=−0.41). [Conclusion] For patients with hemiparesis, increasing the biceps femoris activity during the swing phase is considered optimal, which may lead to improvement in walking performance.
Resource Mention Extraction for MOOC Discussion Forums

In discussions hosted on discussion forums for massive online open courses (MOOCs), references to online learning resources are often of central importance. They contextualize the discussion, anchoring the discussion participants’ presentation of the issues and their understanding. However, they are usually mentioned in free text, without appropriate hyperlinking to their associated resource. Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within the MOOC forums and also benefit the contextualization of such resources across disparate views. We propose the novel problem of learning resource mention identification in MOOC forums, i.e., to identify resource mentions in discussions and classify them into pre-defined resource types. As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset–dubbed the forum resource mention (FoRM) dataset–to facilitate our current research and future research on this task. The FoRM contains over 10 000 real-world forum threads in collaboration with Coursera, with more than 23 000 manually labeled resource mentions. We then formulate this task as a sequence tagging problem and investigate solution architectures to address the problem. Importantly, we identify two major challenges that hinder the applications of sequence tagging models to the task: 1) the diversity of resource mention expression and (2) long-range contextual dependencies. We address these challenges by incorporating character-level and thread context information into an LSTM–CRF model. First, we incorporate a character encoder to address the out-of-vocabulary problem caused by the diversity of mention expressions. Second, to address the context dependency challenge, we encode thread contexts using an RNN-based context encoder and apply the attention mechanism to selectively leverage useful context information during sequence tagging. The experiments on FoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify two challenges.
Automation of Data Collection Techniques for Recording Food Intake: a Review of Publicly Available and Well-Adopted Diet Apps

There has been a proliferation in the development of diet-related smartphone applications (mHealth) that support diet monitoring and can provide health-beneficial interventions. With these developments, the collection of accurate dietary consumption data is becoming an important field in mHealth, as less automated data collection techniques (DCT’s) are often associated with underreporting, ineffective or non-tailored interventions, and self-selection of motivated users and/or high attrition rates. Interventions that incorporate more automated or passive DCT’s have been linked with greater potential for user adoption and engagement, it remains unclear however what DCT’s exist and to what extent mHealth apps incorporate such techniques. As such, the purpose of this study is to investigate the presence of DCTs in well-adopted dietary apps and provide an overview of existing and emerging approaches.
Falsification of Type at Work: Assessment of Prevalence and Investigation of Predictors

BACKGROUND: Occupational-stress, job-satisfaction and poor health outcomes are closely related and strongly pertain to individuals’ mental health and physiological well-being. Falsification of Type is a growing term in the field of organisational psychology that measures occupational stress when working in a job that does not match one’s, natural leader. AIM: The present work aims at determining the prevalence of falsification of type and associated socio-demographic and work-related factors. METHODS: The study sample consists of 150 researchers working at the National Research Centre of Egypt. Participants were asked to complete a self-report Falsification of Type Questionnaire, Andrews and Withey scale for Job Satisfaction, in addition to socio-demographic and work-related variables. Statistics included descriptive and comparative analyses. A regression model was built with falsification of the type as the dependent variable. RESULTS: Facilities showed the highest rate of dissatisfaction in the Job Satisfaction Questionnaire. The most prominent manifestations of falsification were fatigue and irritability, and its predictors were the position, interpersonal relationships, facilities and sex according to the regression model. Falsification of type could seriously contribute to occupational stress. Job satisfaction is highly about falsification. CONCLUSION: More research on the Falsification of Type at work is recommended with the greater attention of employers to the importance of the concept of person-job fit.
Doxorubicin-loaded protease-activated near-infrared fluorescent polymeric nanoparticles for imaging and therapy of cancer

Introduction Despite significant progress in the field of oncology, cancer remains one of the leading causes of death. Chemotherapy is one of the most common treatment options for cancer patients but is well known to result in off-target toxicity. Theranostic nanomedicines that integrate diagnostic and therapeutic functions within an all-in-one platform can increase tumor selectivity for more effective chemotherapy and aid in diagnosis and monitoring of therapeutic responses. Material and methods In this work, theranostic nanoparticles were synthesized with commonly used biocompatible and biodegradable polymers and used as cancer contrast and therapeutic agents for optical imaging and treatment of breast cancer. These core–shell nanoparticles were prepared by nanoprecipitation of blends of the biodegradable and biocompatible amphiphilic copolymers poly(lactic-co-glycolic acid)-b-poly-l-lysine and poly(lactic acid)-b-poly(ethylene glycol). Poly-l-lysine in the first copolymer was covalently decorated with near-infrared fluorescent Alexa Fluor 750 molecules. Results The spherical nanoparticles had an average size of 60–80 nm. The chemotherapeutic drug doxorubicin was encapsulated in the core of nanoparticles at a loading of 3% (w:w) and controllably released over a period of 30 days. A 33-fold increase in near-infrared fluorescence, mediated by protease-mediated cleavage of the Alexa Fluor 750-labeled poly-l-lysine on the surface of the nanoparticles, was observed upon interaction with the model protease trypsin. The cytocompatibility of drug-free nanoparticles and growth inhibition of drug-loaded nanoparticles on MDA-MB-231 breast cancer cells were investigated with a luminescence cell-viability assay. Drug-free nanoparticles were found to cause minimal toxicity, even at high concentrations (0.2–2,000 µg/mL), while doxorubicin-loaded nanoparticles significantly reduced cell viability at drug concentrations >10 µM. Finally, the interaction of the nanoparticles with breast cancer cells was studied utilizing fluorescence microscopy, demonstrating the potential of the nanoparticles to act as near-infrared fluorescence optical imaging agents and drug-delivery carriers. Conclusion Doxorubicin-loaded, enzymatically activatable nanoparticles of less than 100 nm were prepared successfully by nanoprecipitation of copolymer blends. These nanoparticles were found to be suitable as controlled drug delivery systems and contrast agents for imaging of cancer cells.
Mechanisms and Modulation of Oxidative/Nitrative Stress in Type 4 Cardio-Renal Syndrome and Renal Sarcopenia

Chronic kidney disease (CKD) is a public health problem and a recognized risk factor for cardiovascular diseases (CVD). CKD could amplify the progression of chronic heart failure leading to the development of type 4 cardio-renal syndrome (T4CRS). The severity and persistence of heart failure are strongly associated with mortality risk in T4CRS. CKD is also a catabolic state leading to renal sarcopenia which is characterized by the loss of skeletal muscle strength and physical function. Renal sarcopenia also promotes the development of CVD and increases the mortality in CKD patients. In turn, heart failure developed in T4CRS could result in chronic muscle hypoperfusion and metabolic disturbances leading to or aggravating the renal sarcopenia. The interplay of multiple factors (e.g., comorbidities, over-activated renin-angiotensin-aldosterone system [RAAS], sympathetic nervous system [SNS], oxidative/nitrative stress, inflammation, etc.) may result in the progression of T4CRS and renal sarcopenia. Among these factors, oxidative/nitrative stress plays a crucial role in the complex pathomechanism and interrelationship between T4CRS and renal sarcopenia. In the heart and skeletal muscle, mitochondria, nicotinamide adenine dinucleotide phosphate (NADPH) oxidases, uncoupled nitric oxide synthase (NOS) and xanthine oxidase are major ROS sources producing superoxide anion (O2·−) and/or hydrogen peroxide (H2O2). O2·− reacts with nitric oxide (NO) forming peroxynitrite (ONOO−) which is a highly reactive nitrogen species (RNS). High levels of ROS/RNS cause lipid peroxidation, DNA damage, interacts with both DNA repair enzymes and transcription factors, leads to the oxidation/nitration of key proteins involved in contractility, calcium handling, metabolism, antioxidant defense mechanisms, etc. It also activates the inflammatory response, stress signals inducing cardiac hypertrophy, fibrosis, or cell death via different mechanisms (e.g., apoptosis, necrosis) and dysregulates autophagy. Therefore, the thorough understanding of the mechanisms which lead to perturbations in oxidative/nitrative metabolism and its relationship with pro-inflammatory, hypertrophic, fibrotic, cell death and other pathways would help to develop strategies to counteract systemic and tissue oxidative/nitrative stress in T4CRS and renal sarcopenia. In this review, we also focus on the effects of some well-known and novel pharmaceuticals, nutraceuticals, and physical exercise on cardiac and skeletal muscle oxidative/nitrative stress in T4CRS and renal sarcopenia.
LaFFT: Length-Aware FFT Based Fingerprinting for Encrypted Network Traffic Classification

Encrypted traffic classficiation has become an emergent and challenging task for network monitoring and management. Traditional classification methods for encrypted traffic rely on complex statistical characteristic construction and in-depth packet resolution, which produce huge loads. In this paper, we develop Length-aware FFT (LaFFT) fingerprinting to identify different encrypted application traffic with packet length sequences. We apply FFT to packet length sequences to generate the frequency domain vectors as LaFFT features. We verify the distinguishability of LaFFT fingerprinting by data analysis. Furthermore, the linear inseparability and the front superiority of LaFFT fingerprinting are demonstrated by comprehensive experiments. In the real-world dataset, the LaFFT fingerprinting with random forest classifier can achieve 96.8% TPR, 0.32% FPR and 0.959 FFT, which significantly outperform the state-of-the-art methods.
A Propound Hybrid Approach for Personalized Online Product Recommendations

ABSTRACT The main aim of e-commerce websites is to turn their visitors into customers. For this purpose, recommender system is used as a tool that helps in turning clicks into purchases. Obtaining explicit ratings often faces problems such as authenticity of the ratings given by customers and queries that leads to low accuracy of the recommendations. Implicit ratings play a vital role in providing refined ranking of products. Preference level of the customers are predicted based on collaborative filtering (CF) approach using implicit details and mining click stream paths of like-minded users. Extracting the similarity among products using sequential patterns improves the accuracy of ranking. Integrating these two approaches improves the recommendation quality. Based on the results of experiment carried out to compare the performance of CF, sequential path of products viewed and integration of the two, we conclude that integration of mentioned approaches is superior to the existing ones.
RETRACTED ARTICLE: Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms demands an efficient layout of the wind farms. This layout determines the location of each turbine in the wind farm. Due to its sheer complexity, the wind farm layout design problem is considered a complex optimization problem. In recent years, several attempts have been made to develop techniques and algorithms for optimization of wind farms. This paper proposes yet another optimization algorithm based on the cuckoo search (CS), which is a recent optimization method. The proposed cuckoo search algorithm is compared with genetic algorithm which is by far the highest utilized algorithm for wind farm layout design. Empirical results indicate that the proposed cuckoo search algorithm outperformed the genetic algorithm for the given test scenarios in terms of yearly power output and efficiency.