Miracle-dz/UniFAR-FTUs huggingface dataset:
Doxycycline has distinct apicoplast-specific mechanisms of antimalarial activity

Doxycycline (DOX) is a key antimalarial drug thought to kill Plasmodium parasites by blocking protein translation in the essential apicoplast organelle. Clinical use is primarily limited to prophylaxis due to delayed second-cycle parasite death at 1-3 μM serum concentrations. DOX concentrations >5 μM kill parasites with first-cycle activity but have been ascribed to off-target mechanisms outside the apicoplast. We report that 10 μM DOX blocks apicoplast biogenesis in the first cycle and is rescued by isopentenyl pyrophosphate, an essential apicoplast product, confirming an apicoplast-specific mechanism. Exogenous iron rescues parasites and apicoplast biogenesis from first-but not second-cycle effects of 10 μM DOX, revealing that first-cycle activity involves a metal-dependent mechanism distinct from the delayed-death mechanism. These results critically expand the paradigm for understanding the fundamental antiparasitic mechanisms of DOX and suggest repurposing DOX as a faster-acting antimalarial at higher dosing whose multiple mechanisms would be expected to limit parasite resistance.
Life-long persistence of infectious Zika virus: inflammation and behavioral sequela

The recent spread of Zika virus (ZIKV) and its association with congenital defects and neurological disorders has created an urgent need to understand the pathogenesis of ZIKV and identify therapeutic strategies that will prevent or eliminate them. The neurodevelopmental defects associated with ZikV infections early in pregnancy are well documented, however the potential defects associated with infections in late pregnancy and perinatal period are less well characterized. Further, the long-term sequelae of these infections are not fully understood. Immunocompetent C57BL/6 mice infected at one day old (P1), which neurodevelopmentally model late pregnancy in humans, develop a transient neurological syndrome including unsteady gait, kinetic tremors, severe ataxia and seizures 10-15 days post-infection (dpi) but symptoms subside after a week, and most animals survive. Despite apparent recovery, MRI of convalescent mice shows reduced cerebellar volume that correlates with altered coordination and motor function as well as hyperactivity and impulsivity. Persistent mRNA levels of pro-inflammatory genes including Cd80, Il-1α, and Ifn-γ together with Cd3, Cd8 and perforin (PrfA), suggested the persistence of a low-grade inflammatory process. Further, the brain parenchyma of convalescent mice harbor multiple small foci with viral antigen, active apoptotic processes in neurons, and cellular infiltrates, surrounded by activated astrocytes and microglia as late as 1-year post-infection. Detection of negative-sense strand viral RNA and replication of virus derived from these convalescent mice by blinded passage in Vero cells confirmed that low levels of live ZikV persist in CNS of convalescent mice life-long. Our studies establish that Zika can establish reservoirs in CNS and suggest that anti-viral treatment that clears virus from the CNS as well as long-term neurological and behavioral monitoring may be needed for patients known to be exposed to ZikV at an early age. Author’s summary The congenital brain malformations associated with ZikV infections early in pregnancy are well documented, however whether apparently asymptomatic perinatal exposure could lead to long term sequelae is not fully understood. Using a non-lethal neonatal mouse model, we examine host-pathogen interactions, anatomical changes and behavioral patterns by following survivors of the acute infection for over 1 year. We discover that infectious Zika virus has the potential to remain in the CNS for life, lodged within small foci surrounded by gliosis and infiltrating immune cells that may act to limit the viral spread, but also interfere with healing and contribute to life-long neuropathic and behavioral sequelae. These results suggest that anti-viral treatment and long-term neurological and behavioral monitoring may be indicated for patients known to have been exposed to Zika virus, regardless of neurodevelopmental disease severity.
Reanalysis of an Allocentric Navigation Strategy Scale based on Item Response Theory

Abstract Focusing on 12 allocentric/survey-based strategy items of the Navigation Strategy Questionnaire (Zhong & Kozhevnikov, 2016), the current study applied item response theory-based analysis to determine whether a bidimensional model could better describe the latent structure of the survey-based strategy. Results from item and model fit diagnostics, categorical response and item information curves showed that an item with the lowest rotated component loading (.27) [SURVEY12], could be considered for exclusion in future studies; and that a bidimensional model with three preference-related items constituting a content factor offered a better representation of the latent structure than a unidimensional model per se. Mean scores from these three items also correlated significantly with a pointing-to-landmarks task to the same relative magnitude as the mean scores from all items, and all items excluding SURVEY12. These findings gave early evidence suggesting that the three preference-related items could constitute a subscale for deriving quick estimates of large-scale allocentric spatial processing in healthy adults in both experimental and clinical settings. Potential cognitive and brain mechanisms were discussed, followed by calls for future studies to gather greater evidence confirming the predictive validity of the full and sub scales, along with the design of new items focusing on environmental familiarity.
Weather-Induced Moods and Stock-Return Autocorrelation

Abstract Moods affect investors’ attention, memory, and capacity to process information. Inattentive investors delay the price adjustment process, thus leading to a positive autocorrelation of asset returns. In this study, I investigate the relationship between weather-induced moods and stock-return autocorrelation in the Stock Exchange of Thailand from January 2, 1991, to December 29, 2017. Only good moods contribute significantly to return autocorrelation.
Extended perfusion protocol for MS lesion quantification

Abstract This study aims to examine a time-extended dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocol and report a comparative study with three different pharmacokinetic (PK) models, for accurate determination of subtle blood–brain barrier (BBB) disruption in patients with multiple sclerosis (MS). This time-extended DCE-MRI perfusion protocol, called Snaps, was applied on 24 active demyelinating lesions of 12 MS patients. Statistical analysis was performed for both protocols through three different PK models. The Snaps protocol achieved triple the window time of perfusion observation by extending the magnetic resonance acquisition time by less than 2 min on average for all patients. In addition, the statistical analysis in terms of adj-R 2 goodness of fit demonstrated that the Snaps protocol outperformed the conventional DCE-MRI protocol by detecting 49% more pixels on average. The exclusive pixels identified from the Snaps protocol lie in the low k trans range, potentially reflecting areas with subtle BBB disruption. Finally, the extended Tofts model was found to have the highest fitting accuracy for both analyzed protocols. The previously proposed time-extended DCE protocol, called Snaps, provides additional temporal perfusion information at the expense of a minimal extension of the conventional DCE acquisition time.
Review on Structural Health Monitoring in Metal Aviation Based on Fiber Bragg Grating Sensing Technology

The complexity of modern aircraft structures continues to increase, and advanced structural damage detection technologies need to be applied on aircraft. Structural health monitoring technology can monitor the structural health status response in real time by advanced sensors, evaluate the residual life of the structure, and make maintenance decisions on the aircraft structure. It is a necessary method to keep the aircrafts operating safely and reliably in service. The paper summarizes the requirements of aircraft structural health monitoring and the development of multi-type monitoring technology, especially the Fiber Bragg Grating (FBG) Sensor, which is promising in damage monitoring and shows great application potential. Then the research progress of two key aspects in structural health monitoring is described. Finally, the development trend of FBG sensors for aircraft structure in structural health monitoring technology is prospected.
Design and Modelling of a Modular Variable Stiffness Actuator

This paper presents the design and modeling of a modular variable stiffness joint intended for human-robot interaction. The functional concept is based on bidirectional antagonistic variable stiffness (BAVS) principle, in which elastic actuators are connected in parallel to output link, and an internal torque is generated by the antagonistic motion of two motors to adjust the stiffness. Different from conventional variable stiffness actuators (VSAs) that introduce extra motor to adjust stiffness, BAVS concept treats both motors as main actuators, thereby improving the joint's torque density. The mechanical realization of BAVS principle employs a differential mechanism and makes use of two harmonic reducers in a differential manner. The flexibility of the joint is provided by a non-linear elastic element consisting of linear spring and cam roller mechanism. Low torque density and non-modular design are the main reasons limiting VSAs' application in multi-degree-of-freedom robotic systems. In response to this, the proposed joint is designed to be compact and modular. This article describes the basic principles, the mechanical design and the dynamic modeling of the proposed joint. The preliminary simulation results are presented to illustrate the high torque density and wide stiffness range of the joint.
Interference Suppression and Signal Restoration Using Kalman Filter in Automotive Radar Systems

When a radar equipped on an approaching vehicle transmits a signal whose frequency band overlaps with our own radar system, mutual interference problems arise. When mutual interference occurs, the target detection performance is degraded because the target signals are masked by the high power of direct interference signals. Therefore, we propose a signal processing technique to restore signals distorted by mutual interference in an automotive radar system to increase the reliability of target detection. First, since it is necessary to recognize the period where the interference occurred, a method to find the period of interference based on peak detection is presented. Then, the Kalman filter is employed to recover the distorted signal in the interference region by using the undistorted portion of the signal as its input. In simulations using two frequency modulated continuous wave radar systems, the influence of interference was effectively mitigated with our proposed method and target information was correctly estimated.
Visually Impaired Friendly E-commerce website

A website design that allows visually impaired and blind users to access and shop without any assistance. The system gives voice instruction to users and an input is requested from the user. The user is required to hover over the entire page using the curser to access different voice messages for various fields. The user provides input using the keyboard keys thus taking the process forward. The website is developed using ASP.NET with its database stored in SQL server. The website also provides recommendation based on customer selection using collaborative filtering algorithm. It is a process of filtering information or patterns using techniques involving collaboration of data gathered from different agents and data sources [1]. In this paper effectiveness and efficiency of the developed website is also calculated based on the usability study conducted with three evaluation methods tested by 4 blind individuals in which the entire system is divided into 5 tasks.
Multipath Delay Estimation Using Signal Power Measurements from Multiple Carrier Frequencies

Global Navigation Satellite Systems (GNSS) provide users with outdoor positioning, and their applications are increasing in, e.g., navigation, autonomous driving and the Internet of Things. The main error of satellite navigation is caused by tall buildings, which reflect or block the signals. To estimate the delay of these reflections, we developed a method based on signal power measurements from multiple carrier frequencies. This article presents this new method, together with an analysis of its sensitivity to signal power measurement noise. The findings show that the novel method’s performance is better than state of the art methods, as long as the standard deviation of the measurement noise is relatively low. Thus, the method can be used to correct time of flight measurements, especially for satellite navigation systems.
Different Techniques for Identification of a Bone Fracture in Analysis of Medical Image

Medical Imaging is the practice used to promote body images for medical uses to pinpoint diseases. There are many different techniques available for the identification of fractures in different types of bones. This paper reviews the major techniques appropriate to a search of medical image and compiles over 50 additions to the field, most of which came in the last year. This survey uses different fracture identifying techniques for image segmentation, image classification, fracture detection and other assignments. An analysis is contributed to the study on application area: Flat bone, Short bone, Long bone, Irregular bone, Sesamoid bone. A summary of this is ending with a demanding discussion of directions, test and open challenges for prospect research.
MMSE Based Compressed Sensing Algorithms for Channel Estimation in VLC

Channel estimation is an important research area in a wireless communication system. The recent Compressed sensing (CS) theory, has a significant place in channel estimation. Most of the channel estimation algorithm uses the least square (LS) methods to solve the underdetermined system of equations. But in digital communication engineering, this introduces noise enhancement. This paper proposes, minimum mean square error (MMSE) based compressed sensing (CS) algorithms to overcome the noise enhancement problem. Simulation results show that MMSE based methods have significant improvements over LS methods at higher SNR.
Player Management in Soccer Using Particle Swarm Optimization

Manual searching and optimization are less efficient as what is conventionally followed by Coaches or Managers in Soccer to select a squad of 18 members for an upcoming game in soccer, which is less efficient in their approach to problems which are voluminous and technically inclined and involves high complexity and large impediments. Since Soccer involves huge set of data which needs to be gathered as and when an event occurs at real time it becomes more challenging as there can be critical scenarios raised during practice sessions or matches and it is difficult to correlate all the events manually to take decisions. Hence evolutionary intelligence based computational algorithm such as Particle Swarm Optimization algorithm is applied to measure various parameters of trainings and matches on players hence optimizing the players leading to an efficient player selection. The actual data of a private club was taken to implement the algorithm using Particle Swarm Optimization (PSO) for predicting the coefficients of various models to implement on the proposed five models. To minimize the total cost incurred to the club on players we deploy a fitness function to achieve it.
Robust Manhattan Non-negative Matrix Factorization for Image Reconstruction

Non-negative matrix factorization (NMF) and its improvement methods fail to image reconstruction while the image dataset is contaminated by salt and pepper noise. To address this issue, robust Manhattan NMF (RMahNMF) based matrix completion is proposed to restore the corrupted data. Experiments show that RMahNMF is more effective and robust in image reconstruction than other NMF methods.
On the Measurement of Range and its Time-Derivatives in LFMCW Radar

This paper examines the simultaneous measurement of range and its time-derivatives in Linear Frequency Modulated Continuous Wave (LFMCW) radar. A signal model is developed to highlight the influence of velocity modulations on estimating higher range derivatives. Experimental results demonstrate the validity of the signal model and the merits of simultaneously estimating range and its derivatives such as acceleration and jerk.
Privacy in Indoor Positioning Systems: A Systematic Review

This article proposes a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on estimated user mobility cases.
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Sensors must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements. We combine real measurements with simulated interference in order to create input-output data suitable for training the model. We analyze the performance to model complexity relation on simulated and measurement data, based on an extensive parameter search. Further, a finite sample size performance comparison shows the effectiveness of the model trained on either simulated or real data as well as for transfer learning. A comparative performance analysis with the state of the art emphasizes the potential of CNN-based models for interference mitigation and denoising of realworld measurements, also considering resource constraints of the hardware.
Dual-band Bandpass filter using SIW cavity with E-shaped DGS

This paper presents the design of broadly separated second-order dual-band bandpass filter (BPF) using substrate integrated waveguide (SIW) cavity with E-shaped defected ground structure (DGS). The lower frequency passband is generated by back to back coupled DGSs. The back to back coupled E-shaped DGS gives two transmission zeros (TZs) which increase roll-off characteristics of first passband and increase attenuation between the passbands. Also, the SIW cavity with slot perturbation has been used to shift down TE102 mode and give a passband along with TE101 mode at high frequency. The proposed filter is synthesized using RT/duroid 5880 substrate of thickness 0.51 mm. The center frequencies are 5.37 GHz and 10.38 GHz and the ratio of center frequencies is 1.93.
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting

Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost –around 40% lower– than the original k-means.
Security and Privacy of mHealth Applications: A Scoping Review

While digital health or mHealth applications (apps) have become accessible resources for the support of personal health, the privacy and security of users’ data have been the subject of concern and controversy. As large numbers of mHealth apps are created and are increasingly widely used by people with various health conditions, it is crucial to have clear and valid methods for evaluating the data practices within them. Recent regulatory initiatives such as the European Union’s General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have had the effect of raising awareness and establishing a minimal set of expectations. However, they do not in themselves address the issue of the development of systems which meet privacy and security requirements. There is a growing body of research on evaluation techniques and frameworks to support the assessment of the privacy and security of health apps, and guidelines to support their design. However, it can be challenging to navigate this space and choose appropriate techniques for a given context. Addressing this issue, this paper examines the recent literature on security and privacy of m-Health applications, using a scoping review methodology. It analyses data security and privacy evaluation techniques and frameworks that have been proposed for mHealth applications, as well as relevant research-based design recommendations. This work consolidates recent research on the topic to support researchers, app designers, end users, and healthcare professionals in designing, evaluating, recommending and adopting mHealth applications.
Detection of Glioma from Brain MRI through Adaptive Segmentation and Runlength of Centralized Patterns

Brain tumor detection and the amount of severity may be a difficult downside for the radiologists. This project is aimed to produce a completely unique clinical network to help radiologists and clinicians in time period. An adaptive mean thresholding is used for segmentation and graylevel runlength matrix are determined from LBP image of therefore obtained segmental image. The features are developed from gray level runlength matrix. Classification is finished with discriminant analysis classifier. This system will not only detect the tumors, but will also identify its grading in terms of its severity. The most frequent tumor among different types of tumors is brain tumor, this projected system will test the presence of low grade tumor such as Astrocytoma and Ependymoma, moreover as high grade tumors like Oligodendroglioma and glioblastoma Multiforme.