Miracle-dz/UniFAR-FTUs huggingface dataset:
Mycoplasma genitalium

Background Mycoplasma genitalium is very difficult to grow in culture but has been more able to be studied for disease associations since the advent of research molecular amplification assays. Polymerase chain reaction (PCR) and other molecular assays have demonstrated an association with adverse disease outcomes, such as urethritis or nongonococcal urethritis in men and adverse reproductive sequelae in women-for example, cervicitis, endometritis, and pelvic inflammatory disease (PID), including an association with risk for human immunodeficiency virus. The lack of commercially available diagnostic assays has limited widespread routine testing. Increasing reports of high rates of resistance to azithromycin detected in research studies have heightened the need available commercial diagnostic assays as well as standardized methods for detecting resistance markers. This review covers available molecular methods for the diagnosis of M. genitalium and assays to predict the antibiotic susceptibility to azithromycin. Methods A PubMed (US National Library of Medicine and National Institutes of Health) search was conducted for literature published between 2000 and 2016, using the search terms Mycoplasma genitalium, M. genitalium, diagnosis, and detection. Results Early PCR diagnostic tests focused on the MPa adhesion gene and the 16S ribosomal RNA gene. Subsequently, a transcription-mediated amplification assay targeting ribosomes was developed and widely used to study the epidemiology of M. genitalium. Newer methods have proliferated and include quantitative PCR for organism load, AmpliSens PCR, PCR for the pdhD gene, a PCR-based microarray for multiple sexually transmitted infections, and multiplex PCRs. None yet are cleared by the Food and Drug Administration in the United States, although several assays are CE marked in Europe. As well, many research assays, including PCR, gene sequencing, and melt curve analysis, have been developed to detect the 23S ribosomal RNA gene mutations that confer resistance to azithromycin. One recently developed assay can test for both M. genitalium and azithromycin resistance mutations at the same time. Conclusions It is recommended that more commercial assays to both diagnose this organism and guide treatment choices should be developed and made available through regulatory approval. Research is needed to establish the cost-effectiveness of routine M. genitalium testing in symptomatic patients and screening in all individuals at high risk of acquiring and transmitting sexually transmitted infections.
Biosorption of Zn 2+ and Ni 2+ , Cu 2+ by Four Kinds of Active Microalgae

To investigate the affinity of the terrestrial microalgae to metal ions (Zn2+, Cu2+ and Ni2+) in aqueous solutions, four kinds of living algae (Oscillatoria, Botryococcus, Scenedesmus and Spirulina platensis Geitl, abbreviated as CZ, PTZ, SZ and LXZ) were chosen to biosorption experiment, and the mechanism of algae biosorption to metal ions were explored by fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Results show that four kinds of algae have a good affinity for Cu, Zn, the removal rate could reach more than 95%; while for Ni, compared with CZ, SZ and PTZ, the adsorption rate of LXZ is weak. The results of FTIR and SEM experiments revealed that the function groups such as carboxyl, hydroxyl, amino, sulfonic groups and so on are the main groups which played important role in the procedure of absorbing metal ions from water aqueous. Specific algae have different affinity to different metals which will be great helpful in the field of biosorption.
A Maximum Feasible Subsystem for Globally Optimal 3D Point Cloud Registration

In this paper, a globally optimal algorithm based on a maximum feasible subsystem framework is proposed for robust pairwise registration of point cloud data. Registration is formulated as a branch-and-bound problem with mixed-integer linear programming. Among the putative matches of three-dimensional (3D) features between two sets of range data, the proposed algorithm finds the maximum number of geometrically correct correspondences in the presence of incorrect matches, and it estimates the transformation parameters in a globally optimal manner. The optimization requires no initialization of transformation parameters. Experimental results demonstrated that the presented algorithm was more accurate and reliable than state-of-the-art registration methods and showed robustness against severe outliers/mismatches. This global optimization technique was highly effective, even when the geometric overlap between the datasets was very small.
The Role of Risk Perception in Flu Vaccine Behavior among African‐American and White Adults in the United States

Seasonal flu vaccination rates are low for U.S. adults, with significant disparities between African and white Americans. Risk perception is a significant predictor of vaccine behavior but the research on this construct has been flawed. This study addressed critical research questions to understand the differences between African and white Americans in the role of risk perception in flu vaccine behavior: (1) What is the dimensionality of risk perception and does it differ between the two races? (2) Were risk perceptions of white and African‐American populations different and how were sociodemographic characteristics related to risk for each group? (3) What is the relation between risk perception and flu vaccine behaviors for African Americans and whites? The sample, drawn from GfK's Knowledge Panel, consisted of 838 whites and 819 African Americans. The survey instrument was developed from qualitative research. Measures of risk perception included cognitive and emotional measures of disease risk and risk of side effects from the vaccine. The online survey was conducted in March 2015. Results showed the importance of risk perception in the vaccine decision‐making process for both racial groups. As expected, those who got the vaccine reported higher disease risk than those who did not. Separate cognitive and emotional factors did not materialize in this study but strong evidence was found to support the importance of considering disease risk as well as risk of the vaccine. There were significant racial differences in the way risk perception predicted behavior.
An embedded system based solution for exudate extraction

One of the major disorders associated with Diabetes Mellitus is Diabetic Retinopathy. It is a condition where exudates are found in the retina of the eye. In order to assist the ophthalmologists to diagnose the same, many automated algorithms are being proposed. This paper deals with one such algorithm for exudate extraction. The methodology has been realized using embedded systems, which includes MATLAB and Spartan 3e FPGA. 81.6% accuracy has been attained. This paper also proves that embedded system approach is faster than mere software approach by 2 times.
An exploratory quality assessment of orthodontic clinical guidelines using the AGREE II instrument

Objectives Clinical guidelines act as a means of assisting clinicians in improving the quality of healthcare provided. We aim to assess the quality of currently available international orthodontic guidelines. Materials and methods A cross-sectional systematic assessment of orthodontic clinical guidelines was undertaken. A Medline search using the keywords 'guideline', 'orthodontics', and 'dent', and search of specific dental and orthodontic organization websites for orthodontic-related clinical guidelines. Relevant guidelines published between 1999 and 2012 in English were identified. Draft guidelines and endorsements were excluded. The quality of each guideline was evaluated by a single calibrated assessor using the AGREE II instrument. Variation in the quality of guidelines produced by different organizations was also assessed. Results Seventeen guidelines were included in this study. Overall, the reporting of scope and purpose (84.31, 95% CI: 70.91-97.72) and clarity of presentation (75.49, 95% CI: 61.68-89.3) domains of the AGREE II instrument were deemed of high quality. Lower scores were obtained for the following domains: rigour of development (52.08, 95% CI: 37.59-66.57), editorial independence (47.06, 95% CI: 20.6-73.51), stakeholder agreement (46.41, 95% CI: 29.66-63.15), and applicability (27.45, 95% CI: 8.26-46.64). There was a noted difference in the individual domain scores of orthodontic guidelines produced by the different organizations. Conclusions In relation to the AGREE II instrument, the quality of orthodontic guidelines for use in clinical practice are deemed sub-optimal. Variation in the quality of guidelines produced different organizations is evident.
The costs of avoiding environmental impacts from shale‐gas surface infrastructure

Growing energy demand has increased the need to manage conflicts between energy production and the environment. As an example, shale‐gas extraction requires substantial surface infrastructure, which fragments habitats, erodes soils, degrades freshwater systems, and displaces rare species. Strategic planning of shale‐gas infrastructure can reduce trade‐offs between economic and environmental objectives, but the specific nature of these trade‐offs is not known. We estimated the cost of avoiding impacts from land‐use change on forests, wetlands, rare species, and streams from shale‐energy development within leaseholds. We created software for optimally siting shale‐gas surface infrastructure to minimize its environmental impacts at reasonable construction cost. We visually assessed sites before infrastructure optimization to test whether such inspection could be used to predict whether impacts could be avoided at the site. On average, up to 38% of aggregate environmental impacts of infrastructure could be avoided for 20% greater development costs by spatially optimizing infrastructure. However, we found trade‐offs between environmental impacts and costs among sites. In visual inspections, we often distinguished between sites that could be developed to avoid impacts at relatively low cost (29%) and those that could not (20%). Reductions in a metric of aggregate environmental impact could be largely attributed to potential displacement of rare species, sedimentation, and forest fragmentation. Planners and regulators can estimate and use heterogeneous trade‐offs among development sites to create industry‐wide improvements in environmental performance and do so at reasonable costs by, for example, leveraging low‐cost avoidance of impacts at some sites to offset others. This could require substantial effort, but the results and software we provide can facilitate the process.
Investigation of Thermal and Hardness Properties of MWCNTs Filled PMMA Polymer Nanocomposites

Different volume fraction () of MWCNTs filled PMMA nanocomposites have been synthesized by in-situ polymerization under the influence of ultrasonic irradiation and were characterized by XRD, TGA and DSC. XRD patterns are suggestive of the formation of MWCNTs filled polymer nanocomposites due to occurrence of MWCNTs peaks in the resulted composite. TGA result shows that MWCNTs filled composite degraded at higher temperature of 328oC compared to that of virgin PMMA and DSC result shows the increase in melting temperature from 369.8oC (virgin PMMA) to 372.68oC for MWCNTs filled PMMA composite which indicates thermal stability of the MWCNTs-PMMA polymer nanocomposite with 0.05 of MWCNTs. Hardness is found to decrease from 88 for virgin PMMA to 45 for 0.1 of MWCNTs filled polymer nanocomposites. The decrease in hardness with increase in volume fraction of MWCNTs may be attributed to weak interfacial interactions between MWCNTs and polymer (PMMA) matrix.
Health literacy and patient outcomes in chronic kidney disease: a systematic review

Background Limited health literacy affects 25% of people with chronic kidney disease (CKD), and may reduce self-management skills resulting in poorer clinical outcomes. By disproportionately affecting people with low socio-economic status and non-white ethnicity, limited health literacy may promote health inequity. Methods We performed a systematic review of quantitative studies of health literacy and clinical outcomes among adults with CKD. Results A total of 29 studies (13 articles; 16 conference abstracts) were included. One included non-USA patients. Of the 29 studies, 5 were cohort studies and 24 were cross-sectional. In all, 18 300 patients were studied: 4367 non-dialysis CKD; 13 202 dialysis; 390 transplant; 341 unspecified. Median study size was 127 [interquartile range (IQR) 92-238)], but 480 (IQR 260-2392) for cohort studies. Median proportion of non-white participants was 48% (IQR 17-70%). Six health literacy measures were used. Outcomes included patient attributes, care processes, clinical/laboratory parameters and 'hard' clinical outcomes. Limited health literacy was significantly, independently associated with hospitalizations, emergency department use, missed dialysis sessions, cardiovascular events and mortality (in cohort studies). Study quality was high (1 study), moderate (3 studies) and poor (25 studies), limited by sampling methods, variable adjustment for confounders and reduced methodological detail given in conference abstracts. Conclusions There is limited robust evidence of the causal effects of health literacy on patient outcomes in CKD. Available evidence suggests associations with adverse clinical events, increased healthcare use and mortality. Prospective studies are required to determine the causal effects of health literacy on outcomes in CKD patients, and examine the relationships between socio-economic status, comorbidity, health literacy and CKD outcomes. Intervention development and evaluation will determine whether health literacy is a modifiable determinant of poor outcomes in CKD.
Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.
Joint Resource Allocation and Dynamic Activation of Energy Harvesting Small Cells in OFDMA HetNets

We jointly optimize resource allocation with the dynamic activation of energy harvesting base stations in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. We consider both energy harvesting constraints and interference constraints along with time-variation in channel condition, user activity, and energy arrival. We optimize the trade-off between throughput performance of the small cell (or hotspot) users and the associated power cost by maximizing the net reward, where positive reward is associated with achievable throughput of the hotspot users and negative reward with the corresponding non-renewable power consumption. Quality-of-service requirements of hotspot users as well as macrocell users are considered in the optimization problem. Assuming the availability of non-causal information, we propose offline resource allocation algorithm using discrete binary particle swarm optimization and dual decomposition technique. Assuming the availability of statistical information of future values, we propose dynamic programming-based online algorithm. Finally, we propose simple and greedy online algorithm assuming lack of any kind of future information. Numerical results demonstrate the performances of the proposed offline, dynamic programming-based online, and greedy online algorithms and highlight the scenarios, where the performance of the proposed algorithms is significantly better than the baseline schemes.
Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.
The Effect of Investor Inattention on Voluntary Disclosure

We employ a quasi-natural experiment to examine the effect of investor inattention on firms’ voluntary disclosure. While prior research focuses on when managers make mandatory disclosures within a given quarter, we examine whether investor inattention influences what managers voluntarily disclose during a quarter. Consistent with inattention leading to looser monitoring from institutional investors, we hypothesize and find that voluntary disclosure becomes more opportunistic and less transparent when investors are inattentive. Specifically, we find evidence of an increase in aggressive non-GAAP disclosures and a decrease in management guidance when investor inattention is high. Overall, our analyses suggest that investor inattention has negative consequences for firms’ voluntary disclosure decisions, complementing prior research that investigates the consequences of investor inattention.
Resource Allocation for Weighted Sum-Rate Maximization in Multi-User Full-Duplex Device-to-Device Communications: Approaches for Perfect and Statistical CSIs

In this paper, we investigate the resource allocation problem for multi-user full-duplex device-to-device (D2D) underlay communication, considering both perfect channel state information (CSI) and statistical CSI scenarios. In perfect CSI scenario, the weighted sum-rate maximization problem under cellular users’ minimum rate constraints is formulated as a mixed integer programming problem. To solve the challenging problem, we decouple it into two subproblems as power allocation and channel assignment. Then we proposed a power allocation algorithm based on difference of two convex functions programming and a channel assignment algorithm based on Kuhn–Munkres algorithm, respectively. In statistical CSI scenario, we formulate the resource allocation problem as an outage probability constrained weighted ergodic sum-rate maximization problem. To solve the problem, the closed-form expressions of outage probability and weighted ergodic sum-rate are derived first. Then we decouple resource allocation problem into power allocation and channel assignment. An optimization solution that consists of a 2-D global searching and Kuhn–Munkres algorithm is then developed. Simulation results demonstrate that the proposed algorithms can improve the weighted sum-rate of full-duplex D2D communications significantly both in perfect CSI and statistical CSI scenarios and confirm the accuracy of our derived closed-form expressions.
Interactive Document Clustering Revisited: A Visual Analytics Approach

Document clustering is an efficient way to get insight into large text collections. Due to the personalized nature of document clustering, even the best fully automatic algorithms cannot create clusters that accurately reflect the user»s perspectives. To incorporate the user»s perspective in the clustering process and, at the same time, effectively visualize document collections to enhance user's sense-making of data, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user's feedback. First, the initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. Second, the clustering result is visualized to the user. A collection of coordinated visualization modules and document projection is designed to guide the user towards a better insight into the document collection and clusters. The user changes clusters and key-terms iteratively as a feedback to the clustering algorithm until the result is satisfactory. In key-term based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. A set of quantitative experiments, a use case, and a user study have been conducted to show the advantages of the approach for document analytics based on clustering.