Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Site Validation in India of the Line Probe Assay for the Rapid Diagnosis of Multi-Drug Resistant Tuberculosis Directly from Sputum Specimens

Rifampicin (R) and isoniazid (H) are key first-line anti-tuberculosis drugs. Failure to detect resistance to these two drugs early results in treatment failure and poor clinical outcomes. The study purpose was to validate the use of the GenoType MTBDRplus line probe assay (LPA) to detect resistance to R and H in Mycobacterium tuberculosis strains directly from smear-positive sputum samples in India. Method Smear positive sputum specimens from 320 patients were subjected to LPA and results compared against those from conventional Lowenstein Jensen (LJ) culture and drug susceptibility testing (C&DST). All specimens with discordant R DST results were subjected to either sequencing of the rpoB gene and/or repeat DST on liquid culture (MGIT 960) at a National Reference Laboratory. Results Significantly higher proportion of interpretable results were observed with LPA compared to LJ C&DST (94% vs. 80%, p-value <0.01). A total of 248 patients had both LJ and LPA DST results available; 232 (93.5%) had concordant R DST results. Among the 16 discordant R DST results, 13 (81%) were resolved in agreement with LPA results. Final LPA performance characteristics were sensitivity 96% (CI: 90%–98%), specificity 99% (CI: 95%–99%), positive predictive value 99% (CI: 95%–99%), and negative predictive value 95% (CI: 89%–98%). The median turnaround testing time, including specimen transportation time, on LPA was 11 days as compared with 89 days for LJ C&DST. Conclusions LPA proved highly accurate in the rapid detection of R resistance. The reduction in time to diagnosis may potentially enable earlier commencement of the appropriate drug therapy, leading to some reduction of transmission of drug-resistant strains.
The Gaussian Process Density Sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skull-reconstruction task.
Hierarchical Clustering of Features on Categorical Data of Biomedical Applications

Data mining became increasingly important in bioinformatics and biomedical area during last decade. Various data mining methods, such as association rule mining and clustering, have successfully revealed previous unknown knowledge in bioinformatics and biomedical area. Within the categorical data mining domain, one of the major tasks is to understand the relationship among features. However, none of previous methods addresses the problem of revealing the relationship among the features if a priori knowledge is unknown. In this paper, a previous neglected problem, feature association mining problem is defined. While feature selection method identifies the features associated with the pre-defined target concept, the feature association mining has to be done without any a priori knowledge. To find out the feature association in the categorical data, based on the contingency table, we first define a distance measure to identify the closeness between features. Then, we propose a hierarchical clustering algorithm to group together the closest features. Having built the hierarchical tree, we expect the domain experts be able to understand the feature’s relationship and be able to utilize this knowledge for further analysis. Our experiments on real data sets show that our solution does find interesting pattern in feature groups.
Context-Dependent Automatic Response Generation Using Statistical Machine Translation Techniques

Developing a system that can automatically respond to a user’s utterance has recently become a topic of research in natural language processing. However, most works on the topic take into account only a single preceding utterance to generate a response. Recent works demonstrate that the application of statistical machine translation (SMT) techniques towards monolingual dialogue setting, in which a response is treated as a translation of a stimulus, has a great potential, and we exploit the approach to tackle the context-dependent response generation task. We attempt to extract relevant and significant information from the wider contextual scope of the conversation, and incorporate it into the SMT techniques. We also discuss the advantages and limitations of this approach through our experimental results.
CFD Simulation and Mitigation with Boiling Liquid Expanding Vapor Explosion (BLEVE) Caused by Jet Fire

Different kinds of explosions are driven by the internal energy accumulated in compressed gas or superheated liquid. A well-known example of such an explosion is the burst of a vessel with pressure-liquefied substance, known as Boiling Liquid Expanding Vapor Explosion (BLEVE). Hot BLEVE accident is caused mainly by direct heating (pool fire or jet fire) of the steel casing at the vapor side of the tank to temperatures in excess of 400 °C. Thermal insulation around the tank can significantly reduce and retard the excessive heating of the tank casings in a fire. This will allow fire fighters enough time to reach the accident location and to cool the LPG (Liquid Petroleum Gas) tank to avoid the BLEVE, to extinguish the fire or to evacuate the people in the vicinity of the accident. The proposed algorithm addresses several aspects of the BLEVE accident and its mitigation: Computational Fluid Dynamic (CFD) Simulation of jet fire by using fire dynamics simulator (FDS) software by using large eddy simulation (LES); calculation of the convective and radiative heat fluxes by using the impinging jet fire theory; performing thermochemical and heat transfer analysis on the glass-woven vinyl ester coating of the vessel by using FDS software (version 5); and COMSOL Multiphysics (version 4.3b) during the heating phase of composite and calculation of the time period required to evaporate the liquefied propane by using the first and second laws of thermodynamics.
Enumeration, isolation and identification of diazotrophs from Korean wetland rice varieties grown with long‐term application of N and compost and their short‐term inoculation effect on rice plants

Aim: This study has been aimed (i) to isolate and identify diazotrophs from Korean rice varieties; (ii) to examine the long‐term effect of N and compost on the population dynamics of diazotrophs and (iii) to realize the shot‐term inoculation effect of these diazotrophs on rice seedlings.
Subjective Significance Shapes Arousal Effects on Modified Stroop Task Performance: A Duality of Activation Mechanisms Account

Activation mechanisms such as arousal are known to be responsible for slowdown observed in the Emotional Stroop and modified Stroop tasks. Using the duality of mind perspective, we may conclude that both ways of processing information (automatic or controlled) should have their own mechanisms of activation, namely, arousal for an experiential mind, and subjective significance for a rational mind. To investigate the consequences of both, factorial manipulation was prepared. Other factors that influence Stroop task processing such as valence, concreteness, frequency, and word length were controlled. Subjective significance was expected to influence arousal effects. In the first study, the task was to name the color of font for activation charged words. In the second study, activation charged words were, at the same time, combined with an incongruent condition of the classical Stroop task around a fixation point. The task was to indicate the font color for color-meaning words. In both studies, subjective significance was found to shape the arousal impact on performance in terms of the slowdown reduction for words charged with subjective significance.
Submodular Variational Inference for Network Reconstruction

In real-world and online social networks, individuals receive and transmit information in real time. Cascading information transmissions (e.g. phone calls, text messages, social media posts) may be understood as a realization of a diffusion process operating on the network, and its branching path can be represented by a directed tree. The process only traverses and thus reveals a limited portion of the edges. The network reconstruction/inference problem is to infer the unrevealed connections. Most existing approaches derive a likelihood and attempt to find the network topology maximizing the likelihood, a problem that is highly intractable. In this paper, we focus on the network reconstruction problem for a broad class of real-world diffusion processes, exemplified by a network diffusion scheme called respondent-driven sampling (RDS). We prove that under realistic and general models of network diffusion, the posterior distribution of an observed RDS realization is a Bayesian log-submodular model.We then propose VINE (Variational Inference for Network rEconstruction), a novel, accurate, and computationally efficient variational inference algorithm, for the network reconstruction problem under this model. Crucially, we do not assume any particular probabilistic model for the underlying network. VINE recovers any connected graph with high accuracy as shown by our experimental results on real-life networks.
Semantic characterizations of navigational XPath

We give semantic characterizations of the expressive power of navigational XPath (a.k.a. Core XPath) in terms of first order logic. XPath can be used to specify sets of nodes and sets of paths in an XML document tree. We consider both uses. For sets of nodes, XPath is equally expressive as first order logic in two variables. For paths, XPath can be defined using four simple connectives, which together yield the class of first order definable relations which are safe for bisimulation. Furthermore, we give a characterization of the XPath expressible paths in terms of conjunctive queries.
Monitoring, Discussing and Publishing Energy Consumption Data using EnergyUse

We introduce EnergyUse, a collaborative website designed for raising climate change awareness by offering users the ability to view and compare the actual energy consumption of various appliances, and to share and discuss energy conservation tips in an open and social environment. The platform collects data from smart plugs, and exports appliance consumption and community generated energy tips as linked data. EnergyUse is supported by multiples automatic processes that semantically link related contributions, generate appliances descriptions and publish consumption data using the EnergyUse ontology.
Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments

In a partial monitoring game, the learner repeatedly chooses an action, the environment responds with an outcome, and then the learner suffers a loss and receives a feedback signal, both of which are fixed functions of the action and the outcome. The goal of the learner is to minimize his regret, which is the difference between his total cumulative loss and the total loss of the best fixed action in hindsight. Assuming that the outcomes are generated in an i.i. d. fashion from an arbitrary and unknown probability distribution, we characterize the minimax regret of any partial monitoring game with finitely many actions and outcomes. It turns out that the minimax regret of any such game is either zero, Θ( √ T ), Θ(T ), or Θ(T ). We provide a computationally efficient learning algorithm that achieves the minimax regret within logarithmic factor for any game.
Stormwater infrastructure and channel stability: how to assess tributaries in the watershed of Augusta-Richmond County and other urban areas

In 2014, Rocky Creek was 305(b)/303(d) listed for fish, macroinvertebrate communities, and fecal coliform bacteria. Although the city of Augusta has implemented Total Maximum daily Load (TMDL) plans, they may not account for geomorphic responses, which are fundamental in driving habitat types. Many available stream assessments used for identifying and installing best management practices (BMPs) are insufficient when assessing urban streams. These assessments lack parameters for channel stability and geomorphic responses to disturbance (aggradation, widening, and degradation), use baselines from systems not located in urban settings, and/or use qualitative scales. Therefore, we developed and piloted the Augusta Creek Walk Protocol for Rocky Creek, which addresses these issues and decreases subjectivity with a binary or presence/absence scale. This assessment was conducted over 17 transects spaced at approximately 609.6 meters (2,000 ft.) intervals and covering approximately 6.7 miles. We found that all but five cross sections (Upper 2, 2, 3, 13, and 15) along Rocky Creek scored over 60% for channel stability. High scores in our protocol reflect problematic areas and a high frequency of high scores may indicate that most of the reach is prone to flooding or that our methods may need to be adjusted. We also found that parameter score patterns alternated between those high in widening and aggradation, which may reflect channel evolution in response to disturbance for Rocky Creek. This sequence pattern follows those in channel evolution models reiterating that this sequence is characteristic of alluvial systems in adjustment, and therefore our protocol may be accurate in assessing these parameters. In order to have more comparable baselines, we compared our data to those found in other urban systems by investigating a log-linear relationship between bankfull indicators and drainage area (km). We found that drainage area did not explain well the variability of Rocky Creek bankfull measurements. Currently, our assessment continues in the piloting stages. We propose that if used it be used in conjunction with and not as a replacement for other more in depth surveys; it is designed only to target problematic areas relative to cross sections within the stream itself. With results from this study, we recommend that the AugustaRichmond County Engineering Department follow-up with more in depth surveys for the following cross sections: Upper 3, 1, 4, 8-12, and 16. These cross sections scored the highest in channel stability and/or parameters such as, aggradation, widening, and degradation. In addition to developing this protocol we have gathered stormwater infrastructure data, which remain in the process of mapping. Because stormwater discharge contributes to hydrologic regimes, we hope to use these maps along with those of impervious surfaces in future studies to better understand the variability in channel integrity along Rocky Creek.
Unconstrained Functional Networks Classifier

Directly compressible, wax-containing granules having improved flow properties are obtained when waxes are admixed in the melt with certain flow improving additives, followed by cooling and granulation of the admixture. In certain embodiments of the invention, only the wax itself melts in the melt combination of the wax(es) and additives(s), and in other cases both the wax(es) and the additives(s) will both melt. In either case, the melt combination of the wax(es) with the additive(s) yields, upon cooling and granulation, a wax-containing particulate drug diluent having improved and unexpected flow properties.
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction

We provide rigorous guarantees for the regression approach to structured output prediction. We show that the quadratic regression loss is a convex surrogate of the prediction loss when the output kernel satisfies some condition with respect to the prediction loss. We provide two upper bounds of the prediction risk that depend on the empirical quadratic risk of the predictor. The minimizer of the first bound is the predictor proposed by Cortes et al. (2007) while the minimizer of the second bound is a predictor that has never been proposed so far. Both predictors are compared on practical tasks.
Detecting NBTI induced failures in SRAM core-cells

Negative Bias Temperature Instability (NBTI) is a degradation phenomenon that occurs in PMOS transistors during circuit lifetime. Recent works have proposed transistor level and circuit level models that allow designers to deal with such phenomenon. Based on these models and taking into account Random Dopant Fluctuation (RDF), we study the possibility of detecting SRAM core-cells that are prone to NBTI failures during post-production test. For this purpose, we introduce a statistical simulation method that allows estimating the amount of NBTI affected core-cells that pass or fail under given test conditions. Supply voltage, temperature, word line pulse width, word line pulse voltage and bit line voltage are the parameters considered as test conditions. An industrial core-cell design with a 65 nm technology is used as case study.
Removing Partial Occlusion from Blurred Thin Occluders

We present a method to remove partial occlusion that arises from out-of-focus thin foreground occluders such as wires, branches, or a fence. Such partial occlusion causes the irradiance at a pixel to be a weighted sum of the radiances of a blurred foreground occluder and that of the background. The result is that the background component has lower contrast than it would if seen without the occluder. In order to remove the contribution of the foreground in such regions, we characterize the position and size of the occluder in a narrow aperture image. In subsequent images with wider apertures, we use this characterization to remove the contribution of the foreground, thereby restoring contrast in the background. We demonstrate our method on real camera images without assuming that the background is static.
The Impact of Climate on Hydrological Extremes

High and low flows and associated floods and droughts are extreme hydrological phenomena mainly caused by meteorological anomalies and modified by catchment processes and human activities. They exert increasing on human, economic, and natural environmental systems around the world. In this context, global climate change along with local fluctuations may eventually trigger a disproportionate response in hydrological extremes. This special issue focuses on observed extreme events in the recent past, how these extremes are linked to a changing global/regional climate, and the manner in which they may shift in the coming years.
Alternate pathways involving Sgs1/Top3, Mus81/ Mms4, and Srs2 prevent formation of toxic recombination intermediates from single-stranded gaps created by DNA replication

Toxic recombination events are detected in vegetative Saccharomyces cerevisiae cells through negative growth interactions between certain combinations of mutations. For example, mutations affecting both the Srs2 and Sgs1 helicases result in extremely poor growth, a phenotype suppressed by mutations in genes that govern early stages of recombination. Here, we identify a similar interaction involving double mutations affecting Sgs1 or Top3 and Mus81 or Mms4. We also find that the primary DNA structures that initiate these toxic recombination events cannot be double-strand breaks and thus are likely to be single-stranded DNA. We interpret our results in the context of the idea that replication stalling leaves single-stranded DNA, which can then be processed by two competing mechanisms: recombination and nonrecombination gap-filling. Functions involved in preventing toxic recombination would either avoid replicative defects or act on recombination intermediates. Our results suggest that Srs2 channels recombination intermediates back into the gap-filling route, whereas Sgs1/Top3 and Mus81/Mms4 are involved in recombination and/or in replication to allow replication restart.
Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.
Is cross-sample entropy a valid measure of synchronization between sequences of RR interval and pulse transit time?

Synchronization provides an insight into mechanisms underlying the interaction among bivariate physiological signals where their coupling is not known a priori. Cross-sample entropy (C-SampEn) has been used to quantify their synchronization. However, traditional C-SampEn has a poor statistical stability because a rigid decision rule is applied to define the similarity between two vectors. In this study, a fuzzy membership function was implemented to redefine the decision rule in C-SampEn with its performance evaluated using simulated and real cardiovascular coupling signals (RR interval and pulse transit time sequences from 10 normal subjects and 10 heart failure patients). Simulation results verified the decrease of both C-SampEn with increasing coupling degree. The analysis of cardiovascular coupling signals demonstrated a significant difference between normal and heart failure patients (normal 1.17 ± 0.09 vs. heart failure 1.02 ± 0.10, P<;0.01) with the improved C-SampEn, but not the traditional C-SampEn. Our improved C-SampEn provides a better understanding of the different cardiovascular coupling between normal subjects and heart failure patients.