Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-sectional estimates revealed high HIV incidence in Botswana rural communities in the era of successful ART scale-up in 2013-2015

Background Botswana is close to reaching the UNAIDS “90-90-90” HIV testing, antiretroviral treatment (ART), and viral suppression goals. We sought to determine HIV incidence in this setting with both high HIV prevalence and high ART coverage. Methods We used a cross-sectional approach to assessing HIV incidence. A random, population-based sample of adults age 16–64 years was enrolled in 30 rural and peri-urban communities as part of the Botswana Combination Prevention Project (BCPP), from October 2013 –November 2015. Data and samples from the baseline household survey were used to estimate cross-sectional HIV incidence, following an algorithm that combined Limiting-Antigen Avidity Assay (LAg-Avidity EIA), ART status (documented or by testing ARV drugs in plasma) and HIV-1 RNA load. The LAg-Avidity EIA cut-off normalized optical density (ODn) was set at 1.5. The HIV-1 RNA cut-off was set at 400 copies/mL. For estimation purposes, the Mean Duration of Recent Infection was 130 days and the False Recent Rate (FRR) was evaluated at values of either 0 or 0.39%. Results Among 12,610 individuals participating in the baseline household survey, HIV status was available for 12,570 participants and 3,596 of them were HIV positive. LAg-Avidity EIA data was generated for 3,581 (99.6%) of HIV-positive participants. Of 326 participants with ODn ≤1.5, 278 individuals were receiving ART verified through documentation and were considered to represent longstanding HIV infections. Among the remaining 48 participants who reported no use of ART, 14 had an HIV-1 RNA load ≤400 copies/mL (including 3 participants with ARVs in plasma) and were excluded, as potential elite/viremic controllers or undisclosed ART. Thus, 34 LAg-Avidity-EIA-recent, ARV-naïve individuals with detectable HIV-1 RNA (>400 copies/mL) were classified as individuals with recent HIV infections. The annualized HIV incidence among 16–64 year old adults was estimated at 1.06% (95% CI 0.68–1.45%) with zero FRR, and at 0.64% (95% CI 0.24–1.04%) using a previously defined FRR of 0.39%. Within a subset of younger individuals 16–49 years old, the annualized HIV incidence was estimated at 1.29% (95% CI 0.82–1.77%) with zero FRR, and at 0.90% (95% CI 0.42–1.38%) with FRR set to 0.39%. Conclusions Using a cross-sectional estimate of HIV incidence from 2013–2015, we found that at the time of near achievement of the UNAIDS 90-90-90 targets, ~1% of adults (age 16–64 years) in Botswana’s rural and peri-urban communities became HIV infected annually.
Automatic Generation of Saliency-based Areas of Interest for the Visualization and Analysis of Eye-tracking Data

Areas of interest (AOIs) are a powerful basis for the analysis and visualization of eye-tracking data. They allow to relate eye-tracking metrics to semantic stimulus regions and to perform further statistics. In this work, we propose a novel method for the automated generation of AOIs based on saliency maps. In contrast to existing methods from the state-of-the-art, which generate AOIs based on eye-tracking data, our method generates AOIs based solely on the stimulus saliency, mimicking thus our natural vision. This way, our method is not only independent of the eye-tracking data, but allows to work AOI-based even for complex stimuli, such as abstract art, where proper manual deﬁnition of AOIs is not trivial. For evaluation, we cross-validate support vector machine classiﬁers with the task of separating visual scanpaths of art experts from those of novices. The motivation for this evaluation is to use AOIs as projection functions and to evaluate their robustness on different feature spaces. A good AOI separation should result in different feature sets that enable a fast evaluation with a widely automated work-ﬂow. The proposed method together with the data shown in this paper is available as part of the software http://www.ti. uni-tuebingen.de/Eyetrace.1751. 0.html.
Diagnostic, preventive and therapeutic evidence in obstetrics for the implementation of patient blood management: a systematic review protocol

Introduction Patientblood management (PBM) is defined as the application of evidence-based diagnostic, preventive and therapeutic approaches designed to maintain haemoglobin concentration, optimise haemostasis and minimise blood loss in an effort to improve patient outcome. We propose a protocol for the assessment of the evidence of diagnostic, preventive and therapeutic approaches for the management of relevant outcomes in obstetrics with the aim to create a framework for PBM implementation. Methods and analysis Diagnostic, preventive and therapeutic tools will be considered in the gynaecological conditions and obstetrics setting (antenatal care, peripartum care and maternity care). For each condition, (1) clinical questions based on prioritised outcomes will be developed; (2) evidence will be retrieved systematically from electronic medical literature (MEDLINE, EMBASE, the Cochrane Library, Web of Science, and CINAHL); (3) quality of the reviews will be assessed using the AMSTAR (A Measurement Tool to Assess Systematic Reviews) checklist; quality of primary intervention studies will be assessed using the risk of bias tool (Cochrane method); quality of diagnostic primary studies will be assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies); (4) the Grading of Recommendations Assessment, Development and Evaluation method will be applied to rate the quality of the evidence and to develop recommendations. Ethics and dissemination For each diagnostic, preventive or therapeutic intervention evaluated, a manuscript comprising the evidence retrieved and the recommendation produced will be provided and published in peer-reviewed journals. Ethical approval is not required.
Epithelial Cells Attenuate Toll-Like Receptor-Mediated Inflammatory Responses in Monocyte-Derived Macrophage-Like Cells to Mycobacterium tuberculosis by Modulating the PI3K/Akt/mTOR Signaling Pathway

Both alveolar macrophages (AMs) and alveolar epithelial cells (AECs) are main targets of Mycobacterium tuberculosis (M. tuberculosis (Mtb)). Intercellular communications between mucosal AECs and AMs have important implications in cellular responses to exogenous insults. However, molecular mechanisms underpinning interactions responding to Mtb remain largely unknown. In this study, impacts of AECs on Toll-like receptor- (TLR-) mediated inflammatory responses of AMs to Mtb virulent strain H37Rv were interrogated using an air-liquid interface (ALI) coculture model of epithelial A549 cells and U937 monocyte-derived macrophage-like cells. Results showed that Mtb-activated TLR-mediated inflammatory responses in U937 cells were significantly alleviated when A549 cells were coinfected with H37Rv, in comparison with the infection of U937 cells alone. Mechanistically, PI3K/Akt/mTOR signaling was involved in the epithelial cell-modulated Mtb-activated TLR signaling. The epithelial cell-attenuated TLR signaling in U937s could be reversed by PI3K inhibitor LY294002 and mTOR inhibitor rapamycin, but not glycogen synthase kinase 3β inhibitor LiCl, suggesting that the epithelially modulated-TLR signaling in macrophages was in part caused by inhibiting the TLR-triggered PI3K/Akt/mTOR signaling pathway. Together, this study demonstrates that mucosal AEC-derived signals play an important role in modulating inflammatory responses of AMs to Mtb, which thus also offers an insight into cellular communications between AECs and AMs to Mtb infections.
Strategic distribution of seeds to support diffusion in complex networks

Usually, the launch of the diffusion process is triggered by a few early adopters–i.e., seeds of diffusion. Many studies have assumed that all seeds are activated once to initiate the diffusion process in social networks and therefore are focused on finding optimal ways of choosing these nodes according to a limited budget. Despite the advances in identifying influencing spreaders, the strategy of activating all seeds at the beginning might not be sufficient in accelerating and maximising the coverage of diffusion. Also, it does not capture real scenarios in which marketing campaigns continuously monitor and support the diffusion process by seeding more nodes. More recent studies investigate the possibility of activating additional seeds as the diffusion process goes forward. In this work, we further examine this approach and search for optimal ways of distributing seeds during the diffusion process according to a pre-allocated seeding budget. Theoretically, we show that a universally best solution does not exist, and we prove that finding an optimal distribution of supporting seeds over time for a particular network is an NP-hard problem. Numerically, we evaluate several seeding strategies on different networks regarding maximising the coverage and minimising the spreading time. We find that each network topology has a best strategy given some spreading parameters. Our findings can be crucial in identifying the best strategies for budget allocation in different scenarios such as marketing or political campaigns.
A Method to Form Smooth Films of Liquid Metal Supported by Elastomeric Substrate

Several methods are proposed to manipulate and pattern liquid metal films into elastic conductors but all lack precise control over the film thickness and roughness, thereby limiting its uniformity, stability, and reproducibility. Here, an approach relying solely on wetting phenomena is proposed to produce smooth film of liquid gallium (Ga) on extended surface areas with controlled thickness and electrical properties. The surface chemistry and topography of silicone rubber (poly(dimethylsiloxane)) is engineered with microstructured pillars and gold precoating layer to produce Ga superlyophilic substrates. Physical vapor deposition of Ga on such substrates leads to the formation of smooth and homogeneous films by imbibition of the surface topography rather than coalescence and formation of Ga drops. By capillarity, Ga accumulates in between the pillars up to their top surface, forming a smooth film with a root mean square roughness (Rq) smaller than 100 nm. The wetting conditions and electromechanical properties of the resulting films are compared based on the selection of the microtexture patterns and a model of the film sheet resistance as a function of the texture geometrical parameters is established.
Adaptive stabilisation of discrete LTI plant with bounded disturbances via finite capacity channel

ABSTRACT The paper is devoted to adaptive feedback stabilisation for discrete linear time-invariant plants with bounded disturbances and quantisation. The quantisation occurs due to the finite capacity of the discrete-time two-way communication channel. A simple adaptive controller based on the Yakubovich's recursive goal inequalities method is designed. The bound for the minimum channel capacity sufficient to stabilise any stabilisable system with unknown parameters is evaluated for any prespecified compact set of unknown parameters. It is shown that if the channel capacity exceeds the above bound then the upper bound for limit output error is proportional to the disturbance intensity and inversely proportional to the exponential of the channel capacity. The design and performance of the proposed algorithm are illustrated by an example.
Thrombus Permeability in Admission Computed Tomographic Imaging Indicates Stroke Pathogenesis Based on Thrombus Histology

Background and Purpose— Intracranial thrombi can be characterized according to their permeability as measured by contrast agent penetration. Thrombus composition and its associated pathogenesis are important factors affecting treatment and secondary prevention. We aimed to explore the histopathologic factors explaining the heterogeneity of thrombus permeability measures and evaluated potential correlations with stroke pathogenesis. Methods— Thrombus densities were measured in thin-slice noncontrast computed tomography and automatically aligned computed tomographic angiography images of 133 patients with large-vessel occlusions of the middle cerebral artery. Change in thrombus attenuation (&Dgr;t) and corrected void fraction (&egr;; attenuation increase corrected for contralateral artery densities) were calculated. First, these thrombus perviousness measures were correlated with histological thrombus components (especially fractions of fibrin-platelet accumulation and red blood cells) and stroke pathogenesis (n=32). For validation, an association between perviousness and pathogenesis was assessed in a second, independent cohort (n=101). Results— Thrombus perviousness estimates were correlated with both fibrin/platelets fractions (&Dgr;t: r=0.43, P=0.016/&egr;: r=0.45, P=0.01) and inversely with red blood cells counts (&Dgr;t: r=−0.46, P=0.01/&egr;: r=−0.49, P=0.006). In the first cohort, &Dgr;t was substantially higher in samples from patients with cardioembolic stroke pathogenesis as compared with noncardioembolic-derived thrombi (P=0.026). In the validation cohort, thrombus perviousness measures differed significantly between cardioembolic (&Dgr;t: median [ interquartile range]=12.53 [8.70–17.90]; &egr;: median [interquartile range]=0.054 [0.036–0.082]) and noncardioembolic thrombi (&Dgr;t: median [interquartile range]=3.2 [2.17–6.44], P<0.001; &egr;: median [interquartile range]=0.020 [0.011–0.027], P<0.001) and were associated with pathogenesis (&Dgr;t: &bgr;=0.45, P=0.016/&egr;: &bgr;=83.6, P=0.013) in a binary logistic regression model. Conclusions— Permeable thrombi showed a strong correlation with lower fractions of red blood cells counts and more fibrin/platelets conglomerations, concurrent with an association with cardioembolic origin. This novel information about thrombus perviousness may be valuable as a new and simple to acquire imaging marker for identifying stroke pathogenesis using early and readily available imaging.
Efficient Global Registration for Nominal/Actual Comparisons

We investigate global registration methods for Nominal/Actual comparisons, using precise, high-resolution 3D scans. First we summarize existing approaches and requirements for this ﬁeld of application. We then demonstrate that a basic RANSAC strategy, along with a slightly modiﬁed version of basic building blocks, can lead to a high global registration performance at moderate registration times. Speciﬁcally, we introduce a simple feedback loop that exploits the fast convergence of the ICP algorithm to efﬁciently speed up the search for a valid global alignment. Using the example of 3D printed parts and range images acquired by two different high-precision 3D scanners for quality control, we show that our method can be efﬁciently used for Nominal/Actual comparison. For this scenario, the proposed algorithm signiﬁcantly outperforms the current state of the art, with regards to registration time and success rate.
Building knowledge of healthy eating in hospitalized youth: a self‐regulated campaign

Background: Data from the World Health Organization shows obesity has more than doubled worldwide since 1980. Childhood obesity is mainly associated with external and modifiable factors, as eating habits, existing room for promoting healthy lifestyles. Additionally, learning can take place in contexts other than schools, as the hospital, potentiating the time of hospitalization. Self‐regulated learning framework is suited to train the skills necessary for self‐management processes and behavioral changes. The aim was to describe and evaluate a campaign designed to increase knowledge about healthy eating by hospitalized school‐aged youth. Method: A quasi‐experimental design was used. Participants were randomly distributed between the experimental and control groups, each one with 15 participants aged between six and 16 years‐old. Information about healthy eating was not provided directly; participants had to reflect and work to build knowledge through activities inspired in the self‐regulated learning framework. Results: Results showed that the experimental group when compared with their counterparts significantly improved their knowledge on healthy eating after taking part in the campaign. Conclusions: Campaigns that increase knowledge on healthy eating based on the promotion of self‐regulated learning strategies could be incorporated in health promotion programs not only in pediatric units but also in other educational contexts. &NA; Desarrollando conocimientos sobre alimentación saludable en jóvenes hospitalizados: una campaña de autorregulación. Antecedentes: el informe de la Organización Mundial de Salud indicó que la obesidad más que duplicó desde 1980. La obesidad infantil es mayoritariamente asociada a causas externas y modificables, como hábitos de alimentación, y puede ser combatida a través de estilos de vida saludables. Las intervenciones educativas pueden ocurrir en el hospital, potenciando el tiempo de hospitalización. El entrenamiento puede ser realizado en el marco de la autorregulación del aprendizaje. Nuestro objetivo es evaluar una campaña de promoción del conocimiento sobre alimentación saludable entre jóvenes hospitalizados en edad escolar. Método: se usó un diseño cuasi‐experimental. Los participantes fueron distribuidos al azar entre los grupos experimental y control, cada uno con 15 participantes de entre 6 y 16 años. La información sobre alimentación saludable no fue ofrecida directamente, los participantes tuvieron que reflexionar y trabajar para construir conocimiento, a través de actividades inspiradas en el aprendizaje autorregulado. Resultados: los resultados muestran que después de la campaña, el grupo experimental, comparando con el grupo de control, mejoró significativamente su conocimiento sobre alimentación saludable. Conclusiones: campañas para incrementar el conocimiento sobre alimentación saludable basadas en el marco de la autorregulación del aprendizaje podrían ser incorporadas en programas de promoción de la salud.
BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm

Abstract Motivation In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale. Results BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries. Availability and implementation The implementation is available within the BatchI R package at http://zaed.aei. polsl.pl/index.php/pl/111-software. Supplementary information Supplementary data are available at Bioinformatics online.
Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called “AntiMPmod” which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).
Road Lane Semantic Segmentation for High Definition Map

High Definition map (HD Map) is an important part of autonomous driving vehicle. Most conventional method to generate HD map requires expensive system and postprocessing of observed data. In this paper, we propose automatic HD map generating algorithm using just monocular camera without further human labors. The proposed algorithm detects road lane from image and classifies the type of road lane at pixel-level with Fully Convolutional Network (FCN) which outperforms the other semantic segmentation methods. The segmentation results are used to extract lane features, and the features are used for loop-closure detection. Final map is generated with graph-based Simultaneous Localization and Mapping (SLAM) algorithm. The experiment is done with monocular camera mounted on mobile vehicle. In this paper, final map generated by proposed method is compared with aerial view data. The results show that the proposed method can generate reliable map that is comparable to real roads even only the low-cost sensor is used.