Miracle-dz/UniFAR-FTUs huggingface dataset:
Preexposure Prophylaxis Monitoring in New York City: A Public Health Approach

The scale-up of preexposure prophylaxis (PrEP) represents a paradigm shift in HIV prevention that poses unique challenges for public health programs. Monitoring of PrEP implementation at the population level is a national priority, with particular significance in New York City (NYC) given the substantial HIV burden and the prominence of PrEP in state and local Ending the Epidemic program plans. We highlight the importance of local monitoring and evaluation of PrEP implementation outcomes and describe the experience at the NYC Health Department, which includes engaging communities, triangulating a variety of data sources regarding PrEP implementation, and leveraging those data to help guide programming. In NYC, we used data from national surveillance systems and incorporated PrEP-related indicators into existing local data collection systems to help illustrate gaps in PrEP awareness and use. Ultimately, ensuring that PrEP achieves the desired impact at the population level depends on identifying disparities through appropriate and accurate measurement, and addressing them through evidence-based programs.
Universal intracellular biomolecule delivery with precise dosage control

We demonstrate quantitatively controlled delivery of DNA, mRNA, and protein into primary cells using nanostraw electroporation. Intracellular delivery of mRNA, DNA, and other large macromolecules into cells plays an essential role in an array of biological research and clinical therapies. However, current methods yield a wide variation in the amount of material delivered, as well as limitations on the cell types and cargoes possible. Here, we demonstrate quantitatively controlled delivery into a range of primary cells and cell lines with a tight dosage distribution using a nanostraw-electroporation system (NES). In NES, cells are cultured onto track-etched membranes with protruding nanostraws that connect to the fluidic environment beneath the membrane. The tight cell-nanostraw interface focuses applied electric fields to the cell membrane, enabling low-voltage and nondamaging local poration of the cell membrane. Concurrently, the field electrophoretically injects biomolecular cargoes through the nanostraws and into the cell at the same location. We show that the amount of material delivered is precisely controlled by the applied voltage, delivery duration, and reagent concentration. NES is highly effective even for primary cell types or different cell densities, is largely cargo agnostic, and can simultaneously deliver specific ratios of different molecules. Using a simple cell culture well format, the NES delivers into >100,000 cells within 20 s with >95% cell viability, enabling facile, dosage-controlled intracellular delivery for a wide variety of biological applications.
A Pitch-Matched Front-End ASIC With Integrated Subarray Beamforming ADC for Miniature 3-D Ultrasound Probes

This paper presents a front-end application-specified integrated circuit (ASIC) integrated with a 2-D PZT matrix transducer that enables in-probe digitization with acceptable power dissipation for the next-generation endoscopic and catheter-based 3-D ultrasound imaging systems. To achieve power-efficient massively parallel analog-to-digital conversion (ADC) in a 2-D array, a 10-bit 30 MS/s beamforming ADC that merges the subarray beamforming and digitization functions in the charge domain is proposed. It eliminates the need for costly intermediate buffers, thus significantly reducing both power consumption and silicon area. Self-calibrated charge references are implemented in each subarray to further optimize the system-level power efficiency. High-speed datalinks are employed in combination with the subarray beamforming scheme to realize a 36-fold channel-count reduction and an aggregate output data rate of 6 Gb/s for a prototype receive array of $24 \times 6$ elements. The ASIC achieves a record power efficiency of 0.91 mW/element during receive. Its functionality has been demonstrated in both electrical and acoustic imaging experiments.
China DSM Generation and Accuracy Acessment Using ZY3 Images

We generated DSM of all China using over 37 thousand stereo and tri-stereo ZY3 images. The original RPCs were modified though bundle adjustment oriented by SRTM. DSMs were generated through dense tri-stereo matching based on a tri-epipolar method. Modified Belief Propagation was used as global method which was more robust than standard SGM. All original DSMs were multi degree overlapping. For every 0.0001 degree horizontal grid geographic coordinate, a middle height was calculated from all DSMs containing this coordinate. All other heights whose distance from this middle height bigger than a threshold were discarded and the rest heights were averaged as result. Over 330 thousand check points were used to estimate the height accuracy of all DSMs. The RMSE is about 4 meters.
Promoting cross-jurisdictional primary health care research: developing a set of common indicators across 12 community-based primary health care teams in Canada

Abstract Aim To describe the process by which the 12 community-based primary health care (CBPHC) research teams worked together and fostered cross-jurisdictional collaboration, including collection of common indicators with the goal of using the same measures and data sources. Background A pan-Canadian mechanism for common measurement of the impact of primary care innovations across Canada is lacking. The Canadian Institutes for Health Research and its partners funded 12 teams to conduct research and collaborate on development of a set of commonly collected indicators. Methods A working group representing the 12 teams was established. They undertook an iterative process to consider existing primary care indicators identified from the literature and by stakeholders. Indicators were agreed upon with the intention of addressing three objectives across the 12 teams: (1) describing the impact of improving access to CBPHC; (2) examining the impact of alternative models of chronic disease prevention and management in CBPHC; and (3) describing the structures and context that influence the implementation, delivery, cost, and potential for scale-up of CBPHC innovations. Findings Nineteen common indicators within the core dimensions of primary care were identified: access, comprehensiveness, coordination, effectiveness, and equity. We also agreed to collect data on health care costs and utilization within each team. Data sources include surveys, health administrative data, interviews, focus groups, and case studies. Collaboration across these teams sets the foundation for a unique opportunity for new knowledge generation, over and above any knowledge developed by any one team. Keys to success are each team’s willingness to engage and commitment to working across teams, funding to support this collaboration, and distributed leadership across the working group. Reaching consensus on collection of common indicators is challenging but achievable.
A New Optimized Denoising Method applied to the Spot World Heritage Initiative and its Spot 5 Supermode images

In the context of the Spot World Heritage (SWH) initiative, the long term archive of Spot 1 to 5 satellite data will be reprocessed. This initiative gives the opportunity to increase Spot data quality and production efficiency with the introduction of new state-of-the-art processing methods. In this context, we consider the Spot 5 Supermode processing chain and the introduction of a new denoising method. Our objective is to propose the best denoising method in terms of efficiency and accuracy for the Spot 5 Supermode data production. We report two experimentations that lead to the choice of the Non Local Bayes (NLB) denoising method; it is fast, accurate and remains stable in terms of efficiency and quality for different landscapes and image sizes. We also introduce two optimized versions of the NLB algorithm that increase the computation efficiency by a factor up to 4.
A Systematic Review of Single-Dose Aminoglycoside Therapy for Urinary Tract Infection: Is It Time To Resurrect an Old Strategy?

Increasing bacterial resistance and poor patient adherence rates limit the effectiveness of conventional antibiotic therapies for urinary tract infection (UTI). The objective of this study was to investigate whether a single aminoglycoside dose adequately treated UTI. ABSTRACT Increasing bacterial resistance and poor patient adherence rates limit the effectiveness of conventional antibiotic therapies for urinary tract infection (UTI). The objective of this study was to investigate whether a single aminoglycoside dose adequately treated UTI. A systematic search of PubMed/MEDLINE and Google Scholar databases was performed through September 2018 for English language original research articles assessing the efficacy of one-time parenteral aminoglycoside as UTI monotherapy. Of 252 potentially relevant studies, 13 studies met the inclusion criteria, representing 13,804 patients. Patient ages ranged from 2 weeks to >70 years; both inpatient and outpatient settings were represented. Cystitis was more common than pyelonephritis, and more females were represented than males. Escherichia coli was the most commonly isolated uropathogen. The pooled microbiologic cure rate with single-dose aminoglycoside therapy was 94.5% ± 4.3%. Cure was sustained (no recurrence) for 73.4% ± 9.6% of patients at day 30. Lower cure rates were observed among patients with radiographic urinary tract abnormality (chi-square P < 0.01). Across all studies, 63/13,804 (0.5%) cases of nephrotoxicity, vestibular toxicity, or injection site reaction were reported; no hearing loss was observed. Single-dose aminoglycoside therapy appears to be an effective treatment option for lower UTI in nonseptic patients, with minimal toxicity. Additional studies would be beneficial to confirm efficacy for pyelonephritis. When resistance to first-line UTI agents is endemic, aminoglycosides may serve as β-lactam- and fluoroquinolone-sparing options.
Analysing Causal Factors of Peatland Wildfires: A Knowledge-based Approach

When dealing with environmental problems, complexity and uncertainty have become barriers to obtaining reliable information to facilitate good decision-making in fire prevention. This paper proposes and tests a workflow to build the structure of a causal model as a decision support system for fire prone peatlands. We extend the generic process of developing a causal model by incorporating knowledge gained from the literatures and from human experts. We outline the process of automatically generating the relevant variables through literature reviews and constructing a causal model using an online survey and discussions with domain experts. We demonstrate that the structure of the causal model developed can provide logical inferences on the spatial and temporal behaviours of these wildfires.
Polo-Like Kinase 4 (PLK4) Is Overexpressed in Central Nervous System Neuroblastoma (CNS-NB)

Neuroblastoma (NB) is the most common extracranial solid tumor in pediatrics, with rare occurrences of primary and metastatic tumors in the central nervous system (CNS). We previously reported the overexpression of the polo-like kinase 4 (PLK4) in embryonal brain tumors. PLK4 has also been found to be overexpressed in a variety of peripheral adult tumors and recently in peripheral NB. Here, we investigated PLK4 expression in NBs of the CNS (CNS-NB) and validated our findings by performing a multi-platform transcriptomic meta-analysis using publicly available data. We evaluated the PLK4 expression by quantitative real-time PCR (qRT-PCR) on the CNS-NB samples and compared the relative expression levels among other embryonal and non-embryonal brain tumors. The relative PLK4 expression levels of the NB samples were found to be significantly higher than the non-embryonal brain tumors (p-value < 0.0001 in both our samples and in public databases). Here, we expand upon our previous work that detected PLK4 overexpression in pediatric embryonal tumors to include CNS-NB. As we previously reported, inhibiting PLK4 in embryonal tumors led to decreased tumor cell proliferation, survival, invasion and migration in vitro and tumor growth in vivo, and therefore PLK4 may be a potential new therapeutic approach to CNS-NB.
A prediction model for hypoxemia during routine sedation for gastrointestinal endoscopy

OBJECTIVES: The current study was designed to assess the clinical predictors of hypoxemia and to develop a multivariable, predictive model for hypoxemia during routine gastrointestinal endoscopy. METHODS: In total, 308 patients were enrolled in the analysis. Demographic data, concurrent chronic disease information, anesthetic dose and Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scores were collected and analyzed statistically. RESULTS: Multivariate logistic regression indicated that age (OR: 1.04; 95%CI 1.01-1.08), body mass index (BMI) (OR: 1.12; 95%CI: 1.02-1.21) and habitual snoring (OR: 3.71; 95%CI: 1.62-8.48) were independently associated with hypoxemia. A logistic regression function (LR model) was developed to predict hypoxemia considering the parameters of -7.73+0.04 age (years), +0.11 BMI, and +1.31 habitual snoring (yes or no). The area under the receiver operating characteristic (ROC) curve for the LR model was 0.76. CONCLUSIONS: The LR model, consisting of age, BMI and habitual snoring, was a useful predictor of hypoxemia during routine sedation for gastrointestinal endoscopy.
Sparse matrix solvers on the GPU: conjugate gradients and multigrid

Many computer graphics applications require high-intensity numerical simulation. We show that such computations can be performed efficiently on the GPU, which we regard as a full function streaming processor with high floating-point performance. We implemented two basic, broadly useful, computational kernels: a sparse matrix conjugate gradient solver and a regular-grid multigrid solver. Real-time applications ranging from mesh smoothing and parameterization to fluid solvers and solid mechanics can greatly benefit from these, evidence our example applications of geometric flow and fluid simulation running on NVIDIA's GeForce FX.
Color: Cycling Offline Learning and Online Representing for Remote Sensing Dataflow

In recent years, many model driven frameworks have achieved great success in remote sensing. For large scale remote sensing dataflow, however, its ability of processing is far from enough. In this paper, we propose a novel cycling data driven framework based on deep learning, which consists of three components: offline learning, online representing and sample refining. That online representing makes predictions on unlabeled remote sensing images and uses them to retrain the model by offline learning while sample refining as a router refines coarse predictions and forwards refined predictions to offline learning. Because the process is like circling between offline learning and online representing, we refer to our framework as circling offline learning and online representing (COLOR). We show the excellent performance and potentiality on airplane object detection by applying a fairly straightforward implement of COLOR using our GF2 airplane object detection dataset. Code and dataset will be made available.
False Positive Mitigation in Behavioral Malware Detection Using Deep Learning

The malicious software is in a continuous development and the anti-malware technologies are advancing as well to keep up. There are proactive detection technologies, based on the analysis of a sample behavior, that succeed in detecting zero-day malware, the downside being the false positives rate. The current paper proposes an approach for mitigating the false positives by introducing a deep learning classifier. This classifier provides a ’’second opinion’’ for the samples that would have been detected by the current state of the art approach. The proposed approach is able to reduce the false positives rate by 97‥, while only losing 12‥ of the legitimate detection.
Attention-mechanism-based tracking method for intelligent Internet of vehicles

Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10%, and the overall precision is higher than Siamese network by 8%.
Do the public treat online and offline equally? An explorative study

Online shopping has become a common shopping channel. Consumers can freely choose their preferred shopping channels. However, will the public treat online and offline channel equally? Based on confirmation bias theory, the current study attempt to explore whether there is a prejudice toward online shopping and how prejudice influence consumers’ channel selection behavior. An explorative experiment was conducted with 124 subjects. The results indicate that prejudice toward online shopping does exist and significantly influence consumers channel selection behavior. Furthermore, the uncertainty of online shopping may induce consumer’s prejudice perception through confirmation bias and lead to both seller and product prejudice. Theoretical and practical implications are discussed.
Efficacy of cannabinoids in paediatric epilepsy

There are hundreds of compounds found in the marijuana plant, each contributing differently to the antiepileptic and psychiatric effects. Cannabidiol (CBD) has the most evidence of antiepileptic efficacy and does not have the psychoactive effects of ∆9‐tetrahydrocannabinol. CBD does not act via cannabinoid receptors and its antiepileptic mechanism of action is unknown. Despite considerable community interest in the use of CBD for paediatric epilepsy, there has been little evidence for its use apart from anecdotal reports, until the last year. Three randomized, placebo‐controlled, double‐blind trials in Dravet syndrome and Lennox–Gastaut syndrome found that CBD produced a 38% to 41% median reduction in all seizures compared to 13% to 19% on placebo. Similarly, CBD resulted in a 39% to 46% responder rate (50% convulsive or drop‐seizure reduction) compared to 14% to 27% on placebo. CBD was well tolerated; however, sedation, diarrhoea, and decreased appetite were frequent. CBD shows similar efficacy to established antiepileptic drugs.
An improved genetic algorithm with rolling window technology for dynamic integrated process planning and scheduling problem

In this paper, the combination of improved genetic algorithm (IGA) and rolling window technology is applied to solve the dynamic IPPS problem, and two kinds of disturbances are considered, which are the machine breakdown and new job arrival. To improve the search capability of IGA, special genetic operators are developed and adopted. To improve the local search performance of the proposed algorithm, the whole neighborhood based on random search is added in the mutation operation of IGA. The experiment which is adopted from some famous benchmark problems has been conducted to verify the performance of the proposed algorithm. The result show that the proposed method can solve the dynamic IPPS effectively and achieve satisfactory improvement.
PETRARCH2: Another Event Coding Program

The PETRARCH2 coding program implements a new coding algorithm, based on a syntactic constituency parse, to extract who-did-what-to-whom political event data from structured news stories. Events are coded according to the CAMEO (Gerner et al. 2001) coding ontology. This software improves upon previous-generation coding software such as TABARI (Schrodt 2001) by using a deep syntactic parse rather than shallow parsing.
L1/2 Regularization Sar Imaging Via Complex Image Data: Regularization Parameter Selection for Target Detection Task

In this paper, we proposed an $\mathrm{L}_{1/2}$ regularization SAR imaging method via complex SAR images. Compared with $\mathrm{L}_{1}$ regularization method, our approach can better reconstruct the target with higher grayscale value, which is very important for target detection. In addition, we proposed a novel regularization parameter selection method for target detection task. First, we established the relationship between the regularization parameter and the scene sparsity. Then, by using a square window detector to estimate the scene sparsity, the regularization parameter is iteratively updated to suppress the background clutter at the greatest extent while retain the majority of the target area. Experimental results demonstrated that the images reconstructed by the proposed method can gain better target detection results than traditional imaging method.
Heterogeneity and Diversity of Striatal GABAergic Interneurons: Update 2018

Our original review, “Heterogeneity and Diversity of Striatal GABAergic Interneurons,” to which this is an invited update, was published in December, 2010 in Frontiers is Neuroanatomy. In that article, we reviewed several decades’ worth of anatomical and electrophysiological data on striatal parvalbumin (PV)-, neuropeptide Y (NPY)- and calretinin(CR)-expressing GABAergic interneurons from many laboratories including our own. In addition, we reported on a recently discovered novel tyrosine hydroxylase (TH) expressing GABAergic interneuron class first revealed in transgenic TH EGFP reporter mouse line. In this review, we report on further advances in the understanding of the functional properties of previously reported striatal GABAergic interneurons and their synaptic connections. With the application of new transgenic fluorescent reporter and Cre-driver/reporter lines, plus optogenetic, chemogenetic and viral transduction methods, several additional subtypes of novel striatal GABAergic interneurons have been discovered, as well as the synaptic networks in which they are embedded. These findings make it clear that previous hypotheses in which striatal GABAergic interneurons modulate and/or control the firing of spiny neurons principally by simple feedforward and/or feedback inhibition are at best incomplete. A more accurate picture is one in which there are highly selective and specific afferent inputs, synaptic connections between different interneuron subtypes and spiny neurons and among different GABAergic interneurons that result in the formation of functional networks and ensembles of spiny neurons.