Miracle-dz/UniFAR-FTUs huggingface dataset:
Filamentous “Epsilonproteobacteria” Dominate Microbial Mats from Sulfidic Cave Springs

ABSTRACT Hydrogen sulfide-rich groundwater discharges from springs into Lower Kane Cave, Wyoming, where microbial mats dominated by filamentous morphotypes are found. The full-cycle rRNA approach, including 16S rRNA gene retrieval and fluorescence in situ hybridization (FISH), was used to identify these filaments. The majority of the obtained 16S rRNA gene clones from the mats were affiliated with the “Epsilonproteobacteria” and formed two distinct clusters, designated LKC group I and LKC group II, within this class. Group I was closely related to uncultured environmental clones from petroleum-contaminated groundwater, sulfidic springs, and sulfidic caves (97 to 99% sequence similarity), while group II formed a novel clade moderately related to deep-sea hydrothermal vent symbionts (90 to 94% sequence similarity). FISH with newly designed probes for both groups specifically stained filamentous bacteria within the mats. FISH-based quantification of the two filament groups in six different microbial mat samples from Lower Kane Cave showed that LKC group II dominated five of the six mat communities. This study further expands our perceptions of the diversity and geographic distribution of “Epsilonproteobacteria” in extreme environments and demonstrates their biogeochemical importance in subterranean ecosystems.
An Improvement to Fault Localization Technique Based on Branch-Coverage Spectra

For trust in software, developers spend much effort debugging to ensure that software behaviors as expected. Spectrum-based fault localization techniques (SFL) make use of runtime coverage of program elements, like statements, branches and du-pairs, and then check codes in the order of the rank of suspiciousness. So, correct elements with higher suspiciousness than faulty elements cause the loss of precision. In this paper, we focus on a situation where suspiciousness calculated according to coverage and outcome, i.e. Successful or failing, is higher than it should be. It is found that when a branch structure is repeatedly executed, which is normal in real-life programs, and all of its branches are covered within a run, a branch related to faults could lead other branches to be doubted. To reduce effects between branches, we do the following things: First, we utilize branches to monitor program behaviors, second, we take test cases with high similarities as triggered by the same fault, third, for branches mentioned, we propose an algorithm to infer which branch is more likely to be faulty in the failure, finally, experiments based on Siemens benchmark set and flex show that our approach is useful to heighten the ranking of faulty elements by reducing suspiciousness of correct branches.
Rank-constrained PCA for intrinsic images decomposition

Intrinsic image decomposition is an important topic in computer vision and computer graphics applications. However, this is a challenging problem by adopting the information of a single image. Therefore, additional priors or supplementary information such as multiply images or user interactions are necessary to address this problem. In this paper, we propose a novel scheme to use multiple images for intrinsic image decomposition, based on a similar strategy in robust principal component analysis (RPCA). RPCA utilizes the fact that the reflectance layer is common in multiple lights of a scene, and attempts to decompose the data matrix constructed from input images into a low-rank matrix and a sparse matrix. This is possible if the sparse matrix is sufficiently sparse, which is often not the case in computer vision applications. Moreover, the weighting parameter between the low-rank and sparse matrices greatly affects the accuracy of the results, and tuning this parameter can be tricky. This paper proposes a rank-constrained PCA algorithm (RCPCA) for solving background recovering problems. Fixing the rank of the low-rank matrix to be 1 allows RCPCA to better recover the low-rank matrix from the data matrix. Comprehensive tests show that RCPCA produces more stable and accurate results than RP-CA.
A two-stage evolutionary process for designing TSK fuzzy rule-based systems

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (mu, lambda)-evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some three-dimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdani-type fuzzy rule-based systems in the first case, and classical regression and neural modeling in the second.
Face Recognition Across Non-Uniform Motion Blur, Illumination, and Pose

Existing methods for performing face recognition in the presence of blur are based on the convolution model and cannot handle non-uniform blurring situations that frequently arise from tilts and rotations in hand-held cameras. In this paper, we propose a methodology for face recognition in the presence of space-varying motion blur comprising of arbitrarily-shaped kernels. We model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that the set of all images obtained by non-uniformly blurring a given image forms a convex set. We first propose a nonuniform blur-robust algorithm by making use of the assumption of a sparse camera trajectory in the camera motion space to build an energy function with l1-norm constraint on the camera motion. The framework is then extended to handle illumination variations by exploiting the fact that the set of all images obtained from a face image by non-uniform blurring and changing the illumination forms a bi-convex set. Finally, we propose an elegant extension to also account for variations in pose.
Existence of Data and Multiuser Diversities in Noncooperative Mobile Infostation Networks

The capacity of mobile Infostation network can be greatly increased, if, in addition to direct short-range communications between mobile nodes and fixed infostations, nodes also communicate amongst themselves whenever they meet. However, this requires cooperation among mobile nodes that are not necessarily spontaneous for commercial applications. We propose means to create opportunistic cooperation in the context of contention distribution in selfish mobile infostation networks. First, we assume that all nodes have a common interest in all files. We stipulate a social contract such that a bilateral file exchange takes place only when either node obtains something it wants from the exchange. Resulting capacity depends on mobility, the number of files being disseminated, and node density. In addition to the existence of multiuser diversity, our results indicate the existence of data diversity-throughput increases as the number of files of interest to all nodes increases. We also consider the case where nodes have dissimilar interests. Results show that as the level of interest overlap decreases, network performance degrades dramatically. We propose an alternate user strategy in the partially overlapping-interests case and show that network throughput is significantly improved by allowing better use of multiuser diversity. We conclude that through opportunistic cooperation, both data and multiuser diversities exist in noncooperative mobile infostation networks.
Recent Advance in Heterocyclic Organozinc and Organomanganese Compounds; Direct Synthetic Routes and Application in Organic Synthesis

A practical synthetic route for the preparation of 2-pyridyl and 3-pyridyl derivatives has been accomplished by utilizing a simple coupling reaction of stable 2-pyridylzinc bromides and 3-pyridylzinc bromides. The organozincs used in this study were easily prepared via the direct insertion of active zinc into the corresponding bromopyridines. The subsequent coupling reactions with a variety of different electrophiles have afforded the corresponding coupling products. Using highly active manganese, a variety of Grignard-type organomanganese reagents have been obtained. The subsequent coupling reactions of the resulting organomanganese reagents with several electrophiles have also been accomplished under mild conditions.
Logistic regression analysis of biomarker data subject to pooling and dichotomization

There is growing interest in pooling specimens across subjects in epidemiologic studies, especially those involving biomarkers. This paper is concerned with regression analysis of epidemiologic data where a binary exposure is subject to pooling and the pooled measurement is dichotomized to indicate either that no subjects in the pool are exposed or that some are exposed, without revealing further information about the exposed subjects in the latter case. The pooling process may be stratified on the disease status (a binary outcome) and possibly other variables but is otherwise assumed random. We propose methods for estimating parameters in a prospective logistic regression model and illustrate these with data from a population‐based case‐control study of colorectal cancer. Simulation results show that the proposed methods perform reasonably well in realistic settings and that pooling can lead to sizable gains in cost efficiency. We make recommendations with regard to the choice of design for pooled epidemiologic studies. Copyright © 2011 John Wiley & Sons, Ltd.
Towards intelligent hospital devices: Health caring of patients with motor disabilities

In this document we present an intelligence device which includes a monitor system for automatic movements of a robotic hospital bed based on posture classification and identification. This functionality has been developed in response to the requirements defined by the application of a diagnostic methodology [10]. Our diagnostic methodology applied on a Mexican public hospital allowed us to understand that the problem of mobility in elderly people and disabled people involves solutions like devices that attend these individuals to achieve movement without many complications. The movement of these patients can be performed routinely or sporadically during their stay in hospital. For patients who require certain routine application of this movement is designed an intelligent monitor system. This intelligent system allows medical experts to program the movements of the robotic bed considering the patient's posture and time. We will show a methodology for bodies classification in real time. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.
Title The Plenoptic videos : Capturing , Rendering and Compression

This paper presents a system for capturing and rendering a dynamic image-based representation called the plenoptic videos. It is a simplified version of light fields for dynamic environment, where user viewpoints are constrained along the camera plane of a linear array of video cameras. The system consists of a camera array of 8 Sony CCX-Z11 CCD cameras and eight Pentium 4 1.8 GHz computers connected together through a 100 BaseT LAN. Important issues such as multiple camera calibration, real-time compression, decompression and rendering are addressed. Experimental results demonstrated the usefulness of the proposed parallel processing based system in capturing and rendering high quality dynamic image-based representation using off-the-shelf equipment, and its potential applications in visualization and immersive television systems.
Transforming growth factor-beta1 is associated with kidney damage in patients with essential hypertension: renoprotective effect of ACE inhibitor and/or angiotensin II receptor blocker.

BACKGROUND Evidence suggests that transforming growth factor-beta1 (TGF-beta(1)) is associated with target organ damage in hypertension. This study aimed to investigate the relationship between TGF-beta(1) levels and kidney damage and renoprotective effects of angiotensin-converting enzyme inhibitor and/or angiotensin II type 1 receptor blocker in patients with essential hypertension (EH). METHODS A total of 156 patients with EH were enrolled and grouped according to albumin-to-creatinine ratio (ACR). Of these, 90 patients with EH underwent a 12-week antihypertensive trial with administration of benazepril, valsartan or both. Serum TGF-beta(1), plasma angiotensin (Ang) II and urinary albumin were quantified by immunoassays. RESULTS Serum TGF-beta1, plasma Ang II and ACR were highly elevated in patients with EH (P < 0.01). There was a positive correlation between serum TGF-beta1 levels and ACR (r = 0.53, P < 0.01). Significant decreases in TGF beta1 and ACR were obtained in all groups at the end of 12-week antihypertensive therapy compared to the baseline values, with the combined group to a greater extent (P < 0.01). Plasma Ang II levels were significantly decreased in the benazepril group but increased in the valsartan group (P < 0.05) while no significant change was observed in the combined group. CONCLUSIONS TGF-beta(1) is highly elevated and strongly associated with urinary albumin excretion in patients with EH. Treatment with benazepril or valsartan attenuates serum TGF-beta(1) levels and microalbuminuria with the combined therapy receiving the greater effect. TGF-beta(1) could be a potential surrogate marker in monitoring the development and progression of kidney damage in EH.
Architectural fabrication of tensile structures with flying machines

The paper presents a manufacturing process for the erection of tensile structures with flying robots. Analogies are drawn to existing methods of aerial construction and robotic fabrication in architecture. Firstly, we describe a set of aerial building instructions for the vehicles, such as an assembly of a node and the erection of a link. Secondly, we investigate combinations of these instructions as prototypical structural arrangements and identify distinct characteristics for architectural production.
Kinetic drag force analysis of micro in-pipe robot

Kinetic drag force is an important performance for micro in-pipe robot. It influences the velocity, traveling distance and load ability of the robot directly. First, a novel creeping micro in-pipe robot is presented in this paper, and the system structure and operating principle are introduced. Then cable drag force, rolling resistance, moment of resistance in elbow, and climbing resistance are analyzed respectively in detail. The analytic model is established and the calculating formulas of resistances mentioned above are obtained. Finally, some useful conclusions are drawn, which are important for optimal design and motion control of the robot.
Where was this SQL query executed? a static concept location approach

Concept location in software engineering is the process of identifying where a specific concept is implemented in the source code of a software system. It is a very common task performed by developers during development or maintenance, and many techniques have been studied by researchers to make it more efficient. However, most of the current techniques ignore the role of a database in the architecture of a system, which is also an important source of concepts or dependencies among them. In this paper, we present a concept location technique for data-intensive systems, as systems with at least one database server in their architecture which is intensively used by its clients. Specifically, we present a static technique for identifying the exact source code location from where a given SQL query was sent to the database. We evaluate our technique by collecting and locating SQL queries from testing scenarios of two open source Java systems under active development. With our technique, we are able to successfully identify the source of most of these queries.
Impedance control network resonant dc-dc converter for wide-range high-efficiency operation

This paper introduces a new resonant converter architecture that utilizes multiple inverters and a lossless impedance control network (ICN) to maintain zero voltage switching (ZVS) and near zero current switching (ZCS) across wide operating ranges. Hence, the ICN converter is able to operate at fixed frequency and maintain high efficiency across wide ranges in input and output voltages and output power. The ICN converter architecture enables increase in switching frequency (hence reducing size and mass) while achieving very high efficiency. A prototype 200 W, 500 kHz ICN resonant converter designed to operate over an input voltage range of 25 V to 40 V and output voltage range of 250 V to 400 V is built and tested. The prototype ICN converter achieves a peak efficiency of 97.2%, maintains greater than 96.2% full power efficiency at 250 V output voltage across the nearly 2:1 input voltage range, and maintains full power efficiency above 94.6% across its full input and output voltage range. It also maintains efficiency above 93.4% over a 10:1 output power range across its full input and output voltage range owing to the use of burst-mode control.
A Spectacular Hα Complex in Virgo: Evidence for a Collision between M86 and NGC 4438 and Implications for the Collisional ISM Heating of Ellipticals

Deep wide-field Hα+[N II] imaging around the Virgo Cluster giant elliptical galaxy M86 reveals a highly complex and disturbed ISM/ICM. The most striking feature is a set of Hα filaments which clearly connect M86 with the nearby disturbed spiral NGC 4438 (23′ = 120 kpc projected away), providing strong evidence for a previously unrecognized collision between them. Spectroscopy of selected regions shows a fairly smooth velocity gradient between M86 and NGC 4438, consistent with the collision scenario. Such a collision would impart significant energy into the ISM of M86, probably heating the gas and acting to prevent the gas from cooling to form stars. We propose that cool gas stripped from NGC 4438 during the collision and deposited in its wake is heated by shocks, ram pressure drag, or thermal conduction, producing most of the Hα filaments. Some Hα filaments are associated with the well-known ridge of bright X-ray emission to the NW of the nucleus, suggesting that the collision is responsible for peculiarities of M86 previously ascribed to other effects. M86 is radio-quiet; thus AGN heating is unlikely to play a significant role. The M86 system has implications for understanding the role of gravitational interactions in the heating of the ISM in ellipticals, and how collisions in clusters transform galaxies.
Modelling the Costs and Effects of Selective and Universal Hospital Admission Screening for Methicillin-Resistant Staphylococcus aureus

Background Screening at hospital admission for carriage of methicillin-resistant Staphylococcus aureus (MRSA) has been proposed as a strategy to reduce nosocomial infections. The objective of this study was to determine the long-term costs and health benefits of selective and universal screening for MRSA at hospital admission, using both PCR-based and chromogenic media-based tests in various settings. Methodology/Principal Findings A simulation model of MRSA transmission was used to determine costs and effects over 15 years from a US healthcare perspective. We compared admission screening together with isolation of identified carriers against a baseline policy without screening or isolation. Strategies included selective screening of high risk patients or universal admission screening, with PCR-based or chromogenic media-based tests, in medium (5%) or high nosocomial prevalence (15%) settings. The costs of screening and isolation per averted MRSA infection were lowest using selective chromogenic-based screening in high and medium prevalence settings, at $4,100 and $10,300, respectively. Replacing the chromogenic-based test with a PCR-based test costs $13,000 and $36,200 per additional infection averted, and subsequent extension to universal screening with PCR would cost $131,000 and $232,700 per additional infection averted, in high and medium prevalence settings respectively. Assuming $17,645 benefit per infection averted, the most cost-saving strategies in high and medium prevalence settings were selective screening with PCR and selective screening with chromogenic, respectively. Conclusions/Significance Admission screening costs $4,100–$21,200 per infection averted, depending on strategy and setting. Including financial benefits from averted infections, screening could well be cost saving.
Spam filtering using a Markov random field model with variable weighting schemas

In this paper we present a Markov random field model based approach to filter spam. Our approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. We propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Our results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov random field.
Convergence Across Castes

Over the past 25 years there has been a remarkable economic catch-up by scheduled castes and tribes (SC/STs) towards non-SC/ST levels in the terms of their education attainment levels, their occupation choices as well as wage and consumption levels. This is remarkable given the centuries of socio-economic deprivation faced by these backward castes. In this paper we first document these facts and then examine the relative contributions of different factors, in particular, aggregate and sectoral shocks, toward this catch-up. We then develop a multi-sector model with the two types of agents to show that aggregate TFP shocks can induce a convergence between the two groups without any other concurrent redistributive policy changes as long as there exists an initial policy of education subsidies for the relatively disadvantaged group.