Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosing oceanic nutrient deficiency

The supply of a range of nutrient elements to surface waters is an important driver of oceanic production and the subsequent linked cycling of the nutrients and carbon. Relative deficiencies of different nutrients with respect to biological requirements, within both surface and internal water masses, can be both a key indicator and driver of the potential for these nutrients to become limiting for the production of new organic material in the upper ocean. The availability of high-quality, full-depth and global-scale datasets on the concentrations of a wide range of both macro- and micro-nutrients produced through the international GEOTRACES programme provides the potential for estimation of multi-element deficiencies at unprecedented scales. Resultant coherent large-scale patterns in diagnosed deficiency can be linked to the interacting physical–chemical–biological processes which drive upper ocean nutrient biogeochemistry. Calculations of ranked deficiencies across multiple elements further highlight important remaining uncertainties in the stoichiometric plasticity of nutrient ratios within oceanic microbial systems and caveats with regards to linkages to upper ocean nutrient limitation. This article is part of the themed issue ‘Biological and climatic impacts of ocean trace element chemistry’.
Weighted Effect Coding for Observational Data with wec

Weighted effect coding refers to a specific coding matrix to include factor variables in generalised linear regression models. With weighted effect coding, the effect for each category represents the deviation of that category from the weighted mean (which corresponds to the sample mean). This technique has particularly attractive properties when analysing observational data, that commonly are unbalanced. The wec package is introduced, that provides functions to apply weighted effect coding to factor variables, and to interactions between (a.) a factor variable and a continuous variable and between (b.) two factor variables.
Comparison of sequential multiplex PCR, sequetyping and whole genome sequencing for serotyping of Streptococcus pneumoniae

Streptococcus pneumoniae is one of the major causes of pneumonia, meningitis and other pneumococcal infections in young children and elders. Determination of circulating S. pneumoniae serotypes is an essential service by public health laboratories for the monitoring of putative serotype replacement following the introduction of pneumococcal conjugate vaccines (PCVs) and of the efficacy of the immunization program. The Quellung method remains the gold standard for typing S. pneumoniae. Although this method is very effective, it is also costly, time consuming and not totally reliable due to its subjective nature. The objectives of this study were to test and evaluate the efficiency of 3 different molecular methods compared to the Quellung method. Sequential multiplex PCR, sequetyping and whole genome sequencing (WGS) were chosen and tested using a set of diverse S. pneumoniae. One-hundred and eighteen isolates covering 83 serotypes were subjected to multiplex PCR and sequetyping while 88 isolates covering 53 serotypes were subjected to WGS. Sequential multiplex PCR allowed the identification of a significant proportion (49%) of serotypes at the serogroup or subset level but only 27% were identified at the serotype level. Using WGS, 55% to 60% of isolates were identified at the serotype level depending on the analysis strategy used. Finally, sequetyping demonstrated the lowest performance, with 17% of misidentified serotypes. The use of Jin cpsB database instead of the GenBank database slightly improved results but did not significantly impact the efficiency of sequetyping. Although none of these molecular methods may currently replace the Quellung method, WGS remains the most promising molecular pneumococcal serotyping method.
Laboratory Characterization of Fine Aggregate Cementitious Material

Abstract : Personnel of the Geotechnical and Structures Laboratory, U.S. Army Engineer Research and Development Center, conducted a laboratory investigation to characterize the strength and constitutive property behavior of a fine aggregate cemented material (FACM). The FACM was designed to have a 34-MPa (5,000-psi) unconfined strength and to contain no coarse aggregate. Forty-three mechanical property tests two hydrostatic compression tests (HC), four unconfined compression tests (UC), 16 triaxial compression tests (TXC), two uniaxial strain tests (UX), four uniaxial strain load/biaxial strain unloading tests (UX/BX), three uniaxial strain load/constant volume tests (UX/CV), three uniaxial strain load/constant strain path tests (UX/SP), five direct pull tests (DP), one conventional triaxial extension test (CTE), and three reduced triaxial extension tests (RTE)_were successfully completed. In addition to the mechanical property tests, nondestructive pulse-velocity measurements were performed on each specimen. The TXC tests exhibited a continuous increase in principal stress difference with increasing confining stress. A recommended compression failure surface was developed from the TXC and UC test results. Test data from the RTE, CTE, and DP tests were used to develop a recommended extension failure surface for FACM. Results from the stress paths of the strain path tests and the recommended compression failure surface exhibited good agreement.
Effects of micro- and macro-vascular perfusion during radiofrequency tumor ablation

Tumor ablation using radiofrequency (RF) energy is clinically used for treatment of various cancer types. During RF ablation, an electrode is inserted into a tumor under imaging-guidance, and the tumor is heated by RF electric current and cancer cells killed above temperatures of ~50 °C. One of the major factors affecting tissue temperature and ablation zone dimensions is tissue perfusion. To examine perfusion effects, we created Finite Element Method computer models of a clinically used RF ablation device, including temperature-dependent electrical and thermal tissue properties. Microvascular perfusion was modeled according to Pennes' Bioheat Equation, and was varied with temperature to include perfusion cessation due to coagulation at high temperatures. Microvascular perfusion rate was varied to represent variations between patients by +/-1 standard deviation based on prior data measured in humans. Macro-vascular perfusion was modeled by including a large vessel (10 mm diameter) in the model geometry, and assigning a convective heat transfer coefficient as a boundary condition at the vessel wall. The vessel resulted in local deviation of the ablation zone around the vessel, and resulted in a region of viable tissue near the vessel wall. Microvascular perfusion affected overall size and geometry of the ablation zone. Ablation zone volume for average microvascular perfusion was 20.1 cm3, and was 16.6 and 25.3 cm3 when perfusion rate was increased or reduced by 1 standard deviation. Both micro- and macrovascular perfusion considerably affect tissue temperature and ablation zone. Patient-specific data on perfusion would allow for more accurate estimates of ablation zone dimensions and improved treatment planning.
Study of endodontic working length of Korean posterior teeth

Objectives: The aim of this study was to investigate average working lengths of Korean posterior teeth and evaluate validity of endodontic file length. Materials and Methods: The endodontic working length of the posterior teeth of 670 Korean patients were measured than each mean value and standard deviation were investigated than the frequency deviation and standard deviation per each length were calculated. Results: Among the canals of premolar, 66.5% of canal length was marked under 20 mm by endodontic working length and 95.4% could be measured under 22 mm and Among the canals of molars, 95.5% of canal length was marked under 20 mm endodontic working length . Conclusions: With the result of measurement of endodontic working length of premolars of Korean, it sug- gested that 23 mm endodontic file is more proper than the 21 mm and 25 mm file on the market.
Low-velocity impact damage monitoring of a sandwich composite wing

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low-energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient structural health monitoring methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this article, a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical feature matrices, composed of features extracted from the time and frequency domains, are developed. Kernel principal component analysis is used to compress and classify the statistical feature matrices. Compared with traditional principal component analysis algorithm, kernel principal component analysis method shows better feature clustering and damage quantification capabilities. A new damage index, formulated using the Mahalanobis distance, is defined to quantify impact damage. The developed methodology has been validated using low-velocity impact experiments with a sandwich composite wing.
Learning Robust Subspace Clustering

We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often violated for real-world observations, as they can be corrupted by errors or deviate from ideal models. We propose to address this by learning a linear transformation on subspaces using matrix rank, via its convex surrogate nuclear norm, as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same subspace, and, at the same time, forces a high-rank structure for data from different subspaces. In this way, we reduce variations within the subspaces, and increase separations between the subspaces for more accurate subspace clustering. This learned Robust Subspace Clustering framework significantly enhances the performance of existing subspace clustering methods. To exploit the low-rank structures of the transformed subspaces, we further introduce a subspace clustering technique, called Robust Sparse Subspace Clustering, which efficiently combines robust PCA with sparse modeling. We also discuss the online learning of the transformation, and learning of the transformation while simultaneously reducing the data dimensionality. Extensive experiments using public datasets are presented, showing that the proposed approach significantly outperforms state-ofthe-art subspace clustering methods.
A study of social-affective communication: Automatic prediction of emotion triggers and responses in television talk shows

Advancements in spoken language technologies have allowed users to interact with computers in an increasingly natural manner. However, most conversational agents or dialogue systems are yet to consider emotional awareness in interaction. To consider emotion in these situations, social-affective knowledge in conversational agents is essential. In this paper, we present a study of the social-affective process in natural conversation from television talk shows. We analyze occurrences of emotion (emotional responses), and the events that elicit them (emotional triggers). We then utilize our analysis for prediction to model the ability of a dialogue system to decide an action and response in an affective interaction. This knowledge has great potential to incorporate emotion into human-computer interaction. Experiments in two languages, English and Indonesian, show that automatic prediction performance surpasses random guessing accuracy.
Septal Deformation Patterns Delineate Mechanical Dyssynchrony and Regional Differences in Contractility: Analysis of Patient Data Using a Computer Model

Background— Response to cardiac resynchronization therapy depends both on dyssynchrony and (regional) contractility. We hypothesized that septal deformation can be used to infer integrated information on dyssynchrony and regional contractility, and thereby predict cardiac resynchronization therapy response. Methods and Results— In 132 cardiac resynchronization therapy candidates with left bundle branch block (LBBB)-like electrocardiogram morphology (left ventricular ejection fraction 19±6%; QRS width 170±23 ms), longitudinal septal strain was assessed by speckle tracking echocardiography. To investigate the effects of dyssynchronous activation and differences in septal and left ventricular free wall contractility on septal deformation pattern, we used the CircAdapt computer model of the human heart and circulation. In the patients, 3 characteristic septal deformation patterns were identified: LBBB-1=double-peaked systolic shortening (n=28); LBBB-2=early systolic shortening followed by prominent systolic stretching (n=34); and LBBB-3=pseudonormal shortening with less pronounced late systolic stretch (n=70). LBBB-3 revealed more scar (2 [2–5] segments) compared with LBBB-1 and LBBB-2 (both 0 [0–1], P<0.05). In the model, imposing a time difference of activation between septum and left ventricular free wall resulted in pattern LBBB-1. This transformed into pattern LBBB-2 by additionally simulating septal hypocontractility, and into pattern LBBB-3 by imposing additional left ventricular free wall or global left ventricular hypocontractility. Improvement of left ventricular ejection fraction and reduction of left ventricular volumes after cardiac resynchronization therapy were most pronounced in LBBB-1 and worst in LBBB-3 patients. Conclusions— A double-peaked systolic septal deformation pattern is characteristic for LBBB and results from intraventricular dyssynchrony. Abnormal contractility modifies this pattern. A computer model can be helpful in understanding septal deformation and predicting cardiac resynchronization therapy response.
Coherent light trapping in thin-film photovoltaics

Thin-film photovoltaic technologies have an enormous potential to reduce the cost of solar electricity. However, because thin photoactive layers are used, optical absorption is incomplete unless light-trapping strategies are employed. Since conventional light-trapping approaches based on geometric scattering are less effective in thin-film cells, coherent light-trapping approaches that exploit the wave nature of light are being explored to enhance optical absorption. In this article, we look at the various strategies for coherent light trapping in thin-film solar cells, including photonic crystals, metal nanostructures, and multilayer stacks. The suitability of a particular strategy depends on factors such as configuration of the solar cell, process compatibility, cost, desired angular response, and materials usage. We also discuss the physical limits of light trapping in thin films.
A Dynamic GPS-Free Localization Technique using Progressive Interpolation

Location management in wireless networks refers to a collection of tasks that strive to hide the effects of mobility from the user and/or application. It was realized, quite a while back, that extending emergency 911-like services (E-911) to a continually growing mobile population is one of the extremely important mobility management applications. The bulk of the proposed solutions to emergency location management in wireless environments are either reactive in nature or else rely on GPS-enabled end users. While GPS-enabled devices will become ubiquitous in the future, presently the use of GPS in highly accurate emergency location management is problematic. Unfortunately, GPS does not work well in poor atmospheric conditions, the very conditions under which emergency situations are likely to arise. The main contribution of this paper is the proposed new algorithm that employs a progressive prediction and interpolation technique for GSM systems. The localization results thus achieved are accurate and reliable than those obtained using conventional triangulation techniques.
Polynomial Semantic Indexing

We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a low-rank (but diagonal preserving) representation of our polynomial models to induce feasible memory and computation requirements. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain state-of-the-art performance while providing realistically scalable methods.
PictureRx: Illustrated medication instructions for patients with limited health literacy.

OBJECTIVES To describe the role of illustrated medication instructions in pharmacy practice, the evidence for their use, and our experience with their implementation. PRACTICE DESCRIPTION PictureRx is applicable to all pharmacy practice settings. PRACTICE INNOVATION PictureRx enables pharmacists to rapidly produce evidence-based, illustrated medication instructions that are well understood by patients of all health literacy levels. RESULTS PictureRx has been studied in a number of settings. The tool was successfully deployed at a busy outpatient pharmacy and in a medical clinic for the underserved and was pilot tested among elderly community-dwelling Medicare recipients. In each of these settings, PictureRx was received favorably by patients, pharmacists, and other health care providers. It improved patients' satisfaction with the pharmacy and knowledge about their medications. Ongoing research is assessing whether PictureRx enhances medication management among Latinos. CONCLUSION PictureRx helps pharmacists address challenges related to low health literacy and can be implemented in a broad range of practice environments. Ongoing research will delineate the extent to which PictureRx reduces health disparities.
Antiretroviral therapy in acute and recent HIV infection: a prospective multicenter stratified trial of intentionally interrupted treatment

Background:Antiretroviral therapy in early HIV infection may enhance outcome and viral control may be better in acute versus recent infection 24 weeks after treatment interruption. Methods:A prospective trial of treatment stratified by acute versus recent HIV-1 infection. If HIV viral load <50 copies/ml after at least 52 weeks, treatment was interrupted. If viremia rebounded, treatment and interruption were repeated. The primary endpoint was maintaining viral load less than 5000 copies/ml for 24 weeks following treatment interruption. Results:Of the 121 patients enrolled at 15 sites, ninety-five percent were men, median age was 34 years; 69% were white. Median viral load was higher in acute HIV-1 infection (210 000 copies/ml) than recent HIV-1 infection (43 000 copies/ml). The 73 primary endpoint patients (28 acute HIV-1 infection, 45 recent HIV-1 infection) had significantly higher baseline CD4+ T-cell counts (P = 0.044) and lower viral load (P = 0.016). The primary endpoint was achieved in 29 (40%) of the 73 and in 24% of the 121 enrolled overall. There was no significant outcome difference (P = 0.81) between the acute HIV-1 infection [43%, 95% confidence interval (CI) 24–63%] and recent HIV-1 infection (38%, 95% CI 24–53%) groups. Differences after longer follow-up can not be ascertained by this trial. Baseline viral load less than 100 000/ml 22/46 (48%) compared with more than 100 000/ml, 7/27 (26%) and higher baseline CD4+ immune activation predicted success. Conclusion:Forty percent of patients treated during acute HIV-1 infection or recent HIV-1 infection sustained a viral load less than 5000 copies/ml after 24 weeks of treatment interruption.
Enhancing Feature Selection Using Word Embeddings: The Case of Flu Surveillance

Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A "flu infection" concept is formulated and used to reduce spurious and potentially confounding features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12% and 28.7%, respectively.
Vertical Transmission of Feather Lice Between Adult Blackbirds Turdus merula and Their Nestlings: A Lousy Perspective

Abstract There is limited information about the natural history of the transmission of feather lice (Phthiraptera) from parent birds to their young. This article therefore examines the transmission of 4 species of feather lice from parent blackbirds to their nestlings in an English population, and addresses questions formulated from the perspective of the lice. The lice that disperse onto the several young in the nest were mostly found on the larger chicks, those with higher survival prospects. The lice dispersing to chicks were overwhelmingly nymphs, which cannot be sexed morphologically, and so the prediction that the adult lice dispersing would be disproportionately female, potential founders of a new population, was only supported for the most numerous species, Brueelia merulensis. There was no evidence that louse dispersal to chicks was density dependent and more likely when the parents were more heavily infested. Finally, I predicted that lice might aggregate on female blackbirds, which undertake more brooding, to increase their chance of transmission to nestlings. For 1 louse species, B. merulensis, prevalence, but not louse intensity, was higher on female than male blackbirds. For 2 other louse species, Philopterus turdi and Menacanthus eurysternus, no differences between male and female blackbirds were detected.
QoS ASSURED DYNAMIC BANDWIDTH ALLOCATION IN EPON-LTE NETWORKS 1

Ensuring stringent Quality of Service (QoS) requirements is a critical issue in determining the performance of the network. Added to that, when the network happen to be converged Ethernet Passive Optical Network (EPON) and Long Term Evolution (LTE) which is essential for today's bandwidth-intensive customer needs, it is still more critical. Hence, allocation of resources to the end-points is the crucial process in such a converged network. This paper reports a new scheme for allocating time-slots to the end user services in converged EPON and LTE networks. And it is enhanced to support QoS by establishing a mapping mechanism between the LTE bearers and EPON queues. Simulation results reveal that throughput and other QoS services, such as jitter, delay excel in the employed scheme than the traditional one.