Miracle-dz/UniFAR-FTUs huggingface dataset:
A 28-Year History of HIV-1 Drug Resistance and Transmission in Washington, DC

Washington, DC consistently has one of the highest annual rates of new HIV-1 diagnoses in the United States over the last 10 years. To guide intervention and prevention strategies to combat DC HIV infection, it is helpful to understand HIV transmission dynamics in a historical context. Toward this aim, we conducted a retrospective study (years 1987–2015) of 3,349 HIV pol sequences (1,026 bp) from 1,995 individuals living in the DC area belonging to three different cohorts. We coupled HIV sequence data with clinical information (sex, risk factor, race/ethnicity, viral load, subtype, anti-retroviral regimen) to identify circulating drug resistant mutations (DRM) and transmission clusters and assess their persistence over time. Of the transmission clusters identified in the DC area, 78.0 and 31.7% involved MSM and heterosexuals, respectively. The longest spread of time for a single cluster was 5 years (2007–2012) using a distance-based network inference approach and 27 years (1987–2014) using a maximum likelihood phylogenetic approach. We found eight subtypes and nine recombinants. Genetic diversity increased steadily over time with a slight peak in 2009 and remained constant thereafter until 2015. Nucleotide diversity also increased over time while relative genetic diversity (BEAST) remained relatively steady over the last 28 years with slight increases since 2000 in subtypes B and C. Sequences from individuals on drug therapy contained the highest total number of DRMs (1,104–1,600) and unique DRMs (63–97) and the highest proportion (>20%) of resistant individuals. Heterosexuals (43.94%), MSM (40.13%), and unknown (44.26%) risk factors showed similar prevalence of DRMs, while injection drug users had a lower prevalence (33.33%). Finally, there was a 60% spike in the number of codons with DRMs between 2007 and 2010. Past patterns of HIV transmission and DRM accumulation over time described here will help to predict future efficacy of ART drugs based on DRMs persisting over time and identify risk groups of interest for prevention and intervention efforts within the DC population. Our results show how longitudinal data can help to understand the temporal dynamics of HIV-1 at the local level.
Maternal circulating miRNAs that predict infant FASD outcomes influence placental maturation

Maternal gestational circulating microRNAs, predictive of adverse infant outcomes, including growth deficits, following prenatal alcohol exposure, contribute to placental pathology by impairing the EMT pathway in trophoblasts. Prenatal alcohol exposure (PAE), like other pregnancy complications, can result in placental insufficiency and fetal growth restriction, although the linking causal mechanisms are unclear. We previously identified 11 gestationally elevated maternal circulating miRNAs (HEamiRNAs) that predicted infant growth deficits following PAE. Here, we investigated whether these HEamiRNAs contribute to the pathology of PAE, by inhibiting trophoblast epithelial–mesenchymal transition (EMT), a pathway critical for placental development. We now report for the first time that PAE inhibits expression of placental pro-EMT pathway members in both rodents and primates, and that HEamiRNAs collectively, but not individually, mediate placental EMT inhibition. HEamiRNAs collectively, but not individually, also inhibited cell proliferation and the EMT pathway in cultured trophoblasts, while inducing cell stress, and following trophoblast syncytialization, aberrant endocrine maturation. Moreover, a single intravascular administration of the pooled murine-expressed HEamiRNAs, to pregnant mice, decreased placental and fetal growth and inhibited the expression of pro-EMT transcripts in the placenta. Our data suggest that HEamiRNAs collectively interfere with placental development, contributing to the pathology of PAE, and perhaps also, to other causes of fetal growth restriction.
Slow sinusoidal tilt movements demonstrate the contribution to orthostatic tolerance of cerebrospinal fluid movement to and from the spinal dural space

Standing up elicits a host of cardiovascular changes which all affect the cerebral circulation. Lowered mean arterial blood pressure (ABP) at brain level, change in the cerebral venous outflow path, lowered end‐tidal PCO2 (PETCO2), and intracranial pressure (ICP) modify cerebral blood flow (CBF). The question we undertook to answer is whether gravity‐induced blood pressure (BP) changes are compensated in CBF with the same dynamics as are spontaneous or induced ABP changes in a stable position. Twenty‐two healthy subjects (18/4 m/f, 40 ± 8 years) were subjected to 30° and 70° head‐up tilt (HUT) and sinusoidal tilts (SinTilt, 0°↨60° around 30° at 2.5–10 tilts/min). Additionally, at those three tilt levels, they performed paced breathing at 6–15 breaths/min to induce larger than spontaneous cardiovascular oscillations. We measured continuous finger BP and cerebral blood flow velocity (CBFv) in the middle cerebral artery by transcranial Doppler to compute transfer functions (TFs) from ABP‐ to CBFv oscillations. SinTilt induces the largest ABP oscillations at brain level with CBFv gains strikingly lower than for paced breathing or spontaneous variations. This would imply better autoregulation for dynamic gravitational changes. We demonstrate in a mathematical model that this difference is explained by ICP changes due to movement of cerebrospinal fluid (CSF) into and out of the spinal dural sack. Dynamic cerebrovascular autoregulation seems insensitive to how BP oscillations originate if the effect of ICP is factored in. CSF‐movement in‐and‐out of the spinal dural space contributes importantly to orthostatic tolerance by its effect on cerebral perfusion pressure.
Up‐regulation of hepatic lipolysis stimulated lipoprotein receptor by leptin: a potential lever for controlling lipid clearance during the postprandial phase

As a hepatic receptor for triglyceriderich lipoproteins, the lipolysis‐stimulated lipoprotein receptor (LSR) may be involved in the dynamics of lipid distribution between the liver and peripheral tissues. Here, we explore the potential role of leptin in regulating LSR. At physiological concentrations (1–10 ng/ml), leptin increased LSR protein and mRNA levels in Hepa1–6 cells through an ERK1/2‐dependent and α‐amanitin‐sensitive pathway. In vivo, leptin treatment of C57BL6/Rj mice (1 µg2×/d, 8 d) led to a significant increase in hepatic LSR mRNA and protein, decreased liver triglycerides and increased VLDL secretion as compared to controls. LSR+/− mice with elevated postprandial lipemia placed on a high‐fat (60% kcal) diet exhibited accelerated weight gain and increased fat mass as compared to controls. While plasma leptin levels were increased 3‐fold, hepatic leptin receptor protein levels and phosphorylation of ERK1/2 were significantly reduced. Therefore, leptin is an important regulator of LSR protein levels providing the means for the control of hepatic uptake of lipids during the postprandial phase. However, this may no longer be functional in LSR+/− mice placed under a chronic dietary fat load, suggesting that this animal model could be useful for the study of molecular mechanisms involved in peripheral leptin resistance. —Stenger, C. , Hanse, M. , Pratte, D. , Mbala, M. ‐L. , Akbar, S. , Koziel, V. , Escanyé, M. ‐C. , Kriem, B. , Malaplate‐Armand, C. , Olivier, J.‐L. , Oster, T. , Pillot, T. , Yen, F. T. Up‐regulation of hepatic lipolysis stimulated lipoprotein receptor by leptin: a potential lever for controlling lipid clearance during the postprandial phase. FASEB J. 24, 4218–4228 (2010). www.fasebj. org
Microplastic samplings and inverse trajectory recognition in the Mediterranean Sea

In this work data of the Goletta Verde microplastic sampling campaign conducted, in 2015, in the Mediterranean Sea are presented together with the marine circulation analysis of the same domain. Both surface and 15m-depth sampling are realized in nine different Italian locations. A detailed analysis of wind stress, surface and subsurface velocity fields is conducted, on a smaller domain comprising two critical sampling locations, to estimate possible pollutant pathways. Inverse trajectories technique is used to evaluate feasible microplastic sources and accumulation zones.
A Generalized Model for Two-Element Biomimetic Antenna Arrays

Biomimetic antenna arrays (BMAAs) are antenna systems enabling an improved angle estimation for small apertures by mimicking the hearing system of the fly Ormia ochracea. This is achieved by a special kind of coupling applied to the antenna elements inspired by the insect. However, recent designs of BMAAs rely on a strong mutual coupling between the antenna elements. In this paper, it will be shown that the strong mutual coupling is not needed for a BMAA at the antenna side, and a generalized model of the biomimetic antenna system is theoretically derived, evaluated, and verified by measurements. This new model also gives an intuitive insight into the working principle of the biomimetic antenna system.
What Language? - The Choice of an Introductory Programming Language

Quite a few educational programming languages have been developed to make programming easier to learn. These languages have seen very little adoption in academia. This raises questions about their suitability for their intended purpose as introductory programming languages. A lot of factors are taken into consideration in choosing an introductory programming language. This work presents the results of a survey of computer science degree programs across the United States. The survey shows not only which introductory programming languages are popular but the reasons behind their popularity. It shows the features that are considered important for an introductory programming language and how well the popular introductory languages support those features. This work shows that some popular educational programming languages do not support some of those features that are considered important.
Micro-Tesla Offset in Thermally Stable AlGaN/GaN 2DEG Hall Plates Using Current Spinning

This article describes the characterization of a low-offset Hall plate using the AlGaN/GaN 2-D electron gas (2DEG). A four-phase current spinning technique was used to reduce the sensor offset voltage to values in the range of ∼20 nV, which corresponds to a low residual offset of ∼3.4 ± 2 <italic>μ</italic>T when supplied with low voltages (0.25–1 V). These offsets are 30x smaller than the values previously reported for GaN Hall plates, and it is on par with state-of-the-art current-spun silicon (Si) Hall plates. In addition, the offset does not exceed 10 <italic>μ</italic>T even at a higher supply voltage of 2 V. Current spinning was done with a relay matrix at a switching frequency of 1 Hz to enable an offset reduction. The sensor also shows stable current-scaled sensitivity over a wide temperature range of −100 °C to 200 °C, with a temperature coefficient of ∼100 ppm/°C. This value is at least 3x better than the state-of-the art Si Hall plates. Additionally, the sensor's voltage-related sensitivity (∼57 mV/V/T) is similar to that of the state-of-the-art Si Hall plates. Because of the low offset values enabled by current spinning, the AlGaN/GaN 2DEG Hall plates are viable candidates for low-field current and magnetic sensing in high-temperature environments.
Improved Vessel Segmentation Using Curvelet Transform and Line Operators

Vessel segmentation from the fundus retinal images is highly significant in diagnosing many pathologies related to eye and other systemic diseases. Even though there are many methods in the literature focusing on this task, most of these methods are not focusing on the small peripheral vessels segmentation. In this paper, we propose a new approach based on curvelet transform and line operators which can segment the small peripheral vessels with very high accuracy resulting in a higher sensitivity compared to the other state-of-the-art methods. In the proposed approach, the contrast between the retinal vessels and the background pixels is enhanced by applying a series of image processing steps involving color space transformation, adaptive histogram equalization, and anisotropic diffusion filtering. Then by using the modified curvelet transform coefficients, the retinal vessel edge contrast is further enhanced. Finally, the vessels are segmented out by applying the line operator response, followed by suitable thresholding to obtain the segmented vessels. Post processing is carried out to remove the scattered unwanted background pixels. The performance of the method is compared against the other state-of-the-art methods using DRIVE as a testing database. An average sensitivity, specificity, accuracy and positive predictive value of 0.7653, 0.9735, 0.9542 and 0.7438 are respectively achieved.
A Physically Embodied Robot Teacher (PERT) as a Facilitator for Peer Learning

This research full paper describes our work on the development, and evaluation of a physically-embodied robot teacher (PERT) as a facilitator for Peer Instruction (PI) in a high school science classroom. Embodied pedagogical agents are particularly useful in teaching young learners because of the engagement, and enjoyment they engender. Teaching robots are among possibilities in the future classroom; they may also represent solutions to the looming, acute global teacher scarcity. However, their design creates unique computational challenges. Moreover, robot-human systems involving a single robot in interaction with multiple humans, are not common. PI has been reported in many studies as a learner-centered approach that supports conceptual learning, metacognition, cognitive load sharing, and higher order learning, through peer scaffolding. However, existing studies have reported only applications by human teachers in regular classrooms. This study highlights an approach for enabling a PERT as a PI facilitator. Based on conflicting reports of the significance of agent embodiment for learning, we present the report of two experimental studies from our assessment of the system, employing the mixed method approach. We compared participants’ perception of the robot’s social presence and teaching quality when the robot is physically present and when it is absent (voice-only). Our findings are based on data from 14 participants in each study. The Social Presence indeX (SPiX) and the teacher quality index (TQI) were used for quantitative data collection for social presence and teaching quality, respectively. Qualitative data were based on live observation and participants’ open-ended feedback. Quantitative findings show no significant difference in participants’ perceptions in both conditions but qualitative feedback provided more information. Our findings show: i) students are willing to accept non-human teachers, and ii) their expectations of them are similar to those for human teachers; iii) an improved affective system will promote increased presence; iv) participants noted the robot as ‘neutral’, with important implications for culturally-responsive education; v) robot teachers could be potential technology-inspired solution to global teacher scarcity and vi) in addition to promoting deeper learning, PI can be a useful pedagogical approach for non-human teachers. This study is in line with anticipated ability of future technological advancements to support machines that are as effective as intelligent human tutors.
Identification of a transcription factor in the mosquito‐invasive stage of malaria parasites

Gene expression in Plasmodium parasites undergoes significant changes in each developmental stage, but the transcription factors (TFs) regulating these changes have not been identified. We report here a Plasmodium TF (AP2‐O) that activates gene expression in ookinetes, the mosquito‐invasive form, and has a DNA‐binding domain structurally related to that of a plant TF, Apetala2 (AP2). AP2‐O mRNA is pre‐synthesized by intraerythrocytic female gametocytes and translated later during ookinete development in the mosquito. The Plasmodium TF activates a set of genes, including all genes reported to be required for midgut invasion, by binding to specific six‐base sequences on the proximal promoter. These results indicate that AP2 family TFs have important roles in stage‐specific gene regulation in Plasmodium parasites.
Decoding upper limb kinematic parameters from motor cortical activity using a deep learning algorithm

The current neural interface technology opens a new window for collecting multi-site streams of the firing activities of hundreds of neurons simultaneously. It provokes a need to equip with the means to harness as much information as possible from such huge amounts of neural data. Deep learning algorithms capable of representing latent components in large data may offer a means to extract useful information related to specific behavioral functions from these neural data. In this study, we aimed to decode movement-related information from motor cortical neuronal ensembles in a primate while the animal moved the arm and hand to perform an eight-target center-out task. The previous studies addressed the problem by decoding the velocity parameter to reconstruct arm-movement trajectories. However, as velocity can be decomposed into speed and direction, it may be advantageous to decode each parameter independently. Thus, we decoded speed and direction of the hand separately with the long short-term memory network (LSTM) to from the ensemble of one hundred fifty-eight primary motor cortical neurons. A comparison of the suggested LSTM decoder with traditional decoders directly predicting the velocity parameter using the linear Kalman filter or LSTM demonstrated a significant increase in the performance of reconstructing 2D hand trajectory. Our results may add accumulating evidence to the employment of deep learning algorithms for intracortical brain-machine interfaces and suggest that speed and direction can be decoded independently.
Analyzing the Impact of Leaderboards in Introductory Programming Courses’ Short-Length Activities

This Research Full Paper presents results from gamified in-class interventions with leader-boards. Generally, it is assumed that student expertise is directly related to deliberate and repeated practice. However, finding techniques that foster student engagement in problem-solving may not be the easiest of tasks. To that end, gamification, also known as the use of game design elements in non-game contexts, has proved to be effective in educational scenarios. Concerned with stimulating student engagement in Computer Science, this research aimed to observe the effect of the insertion of leaderboards in short-length in-class experiments performed in two different terms of undergraduate IP courses. $N =84$ students were randomly allocated into one of two types of group: experimental groups with leaderboards; and control groups without knowledge of nor access to leaderboards. Both experiments showed that students in the experimental groups tried and completed more programming problems, on average, than those from their respective control groups. P-values below $\alpha =5$% indicated a real impact from the leaderboard in students’ performances. This research may benefit educators responsible for IP courses that have an online platform at their disposal, by introducing a design of leaderboard that incites student engagement, at least for short-length activities.
3D Indoor Scene Classification using Tri-projection Voxel Splatting

In this paper, we propose a new method for classifying a 3D scene. A 3D scene consists of a large collection of 3D shape objects. The complexity of a 3D scene makes it hard for us to classify which 3D scene we are dealing with. For instance, a 3D scene of a “room” may be an office, a kitchen, a living room, or a bed room. Here we propose a novel approach to classifying a 3D scene with Tri-projection Voxel Splatting (TVS), taking into account the voxel density along the depth direction. In TVS we first normalize a 3D scene in terms of position and size, followed by converting data into point clouds, via voxelization, and by projecting the scene on three perpendicular planes, reflecting the voxel density along the depth direction. Subsequently, we merge the three projected images, and we finally apply deep learning to predict the class of each 3D scene. To demonstrate the effectiveness of our proposed method (TVS), we conducted experiments with 3D indoor scene dataset extracted from Princeton University's SUNCG dataset. From the experiments, our proposed method outperformed the previous methods.
Metallic Array on a Biased Ferrite Substrate as a Reconfigurable Reflectarray Antenna

A reconfigurable reflect-array antenna which exploits the magnetic anisotropy of a biased ferrite substrate is studied. The unit cell of such an antenna is composed of two centered metal square loops placed on the grounded ferrite substrate. The analysis of such a unit cell for extracting its scattering information is carried out by Galerkin’s Method of Moments (MoM). In this solution, a modelling of transmission line (TL) in Fourier domain is used for evaluating the dyadic Green’s function. By such a computational technique, the phase variations of unit cell for ferrite’s different bias modes is calculated. The obtained phase diagram is used in the design of a reflectarray with 17×17 cells at central frequency 12.5GHz. It is theoretical shown that changing the bias mode of ferrite leads to beam rotation of the designed reflectarray antenna.
Three-Stage Hybrid Fault Diagnosis for Rolling Bearings With Compressively Sampled Data and Subspace Learning Techniques

To avoid the burden of much storage requirements and processing time, this paper proposes a three-stage hybrid method, compressive sampling with correlated principal and discriminant components (CS-CPDC), for bearing faults diagnosis based on compressed measurements. In the first stage, CS is utilized to obtain compressively sampled signals from raw vibration data. In the second stage, an effective multistep feature learning algorithm obtains fewer features from correlated principal and discriminant attributes from the compressively sampled signals, which are then concatenated to increase the performance. In the third stage, with these concatenated features, multiclass support vector machine is used to train, validate, and classify bearing faults. Results show that the proposed method, CS-CPDC, offers high classification accuracies, reduced computation time, and storage requirement, with fewer measurements.
BcsQ is an essential component of the Escherichia coli cellulose biosynthesis apparatus that localizes at the bacterial cell pole

Biofilms are microbial communities characterized by three‐dimensional growth resulting from the ability of individual cells to adhere to each other as well as to produce an extracellular matrix that ensures biofilm physical cohesion. Numerous bacteria produce cellulose as a biofilm matrix polymer, a property relying on the expression of bacterial cellulose synthesis (Bcs) proteins and their post‐translational activation upon binding of cyclic di‐guanosine mono‐phosphate second messenger (c‐di‐GMP) produced by diguanylate cyclases. In Escherichia coli and other Enterobacteriaceae, two genes of unknown function, yhjR and yhjQ, are located upstream of the bcs genes. Here, we show that yhjQ, but not yhjR, is essential for cellulose biosynthesis; it has therefore been renamed bcsQ. Using a green fluorescent protein (GFP) fusion approach, we demonstrate that BcsQ, a MinD homologue, displays a polar localization and that cell‐to‐cell adhesion is initiated through production of cellulose at the BcsQ‐labelled pole. Although we did not detect a similar localization for other Bcs proteins, immunogold labelling of cellulose itself at the pole of individual bacteria indicates the localized activity of the cellulose biosynthesis apparatus. These results therefore suggest that BcsQ could participate in spatial restriction of cellulose biosynthesis activity in Enterobacteriaceae.