Miracle-dz/UniFAR-FTUs huggingface dataset:
The current status of species recognition and identification in Aspergillus

The species recognition and identification of aspergilli and their teleomorphs is discussed. A historical overview of the taxonomic concepts starting with the monograph of Raper & Fennell (1965) is given. A list of taxa described since 2000 is provided. Physiological characters, particularly growth rates and the production of extrolites, often show differences that reflect phylogenetic species boundaries and greater emphasis should be placed on extrolite profiles and growth characteristics in species descriptions. Multilocus sequence-based phylogenetic analyses have emerged as the primary tool for inferring phylogenetic species boundaries and relationships within subgenera and sections. A four locus DNA sequence study covering all major lineages in Aspergillus using genealogical concordance theory resulted in a species recognition system that agrees in part with phenotypic studies and reveals the presence of many undescribed species not resolved by phenotype. The use of as much data from as many sources as possible in making taxonomic decisions is advocated. For species identification, DNA barcoding uses a short genetic marker in an organism”s DNA to quickly and easily identify it to a particular species. Partial cytochrome oxidase subunit 1 sequences, which are used for barcoding animal species, were found to have limited value for species identification among black aspergilli. The various possibilities are discussed and at present partial β-tubulin or calmodulin are the most promising loci for Aspergillus identification. For characterising Aspergillus species one application would be to produce a multilocus phylogeny, with the goal of having a firm understanding of the evolutionary relationships among species across the entire genus. DNA chip technologies are discussed as possibilities for an accurate multilocus barcoding tool for the genus Aspergillus.
Optimization under Uncertainty for Reliable Unconventional Play Evaluation. A Case Study in Vaca Muerta Shale Gas Blocks, Argentina

The objective of this work is to present a comprehensive workflow to optimize the value of a hydrocarbon asset evaluation project under high degrees of uncertainty. This workflow is applicable to both conventional and unconventional assets. However, because of the considerable level of subsurface uncertainty and high initial costs (mainly due to drilling and hydraulic fracturing operations), unconventional resources are good examples for demonstrating the benefits of the workflow. For the case of an unconventional asset, well spacing and perforation cluster spacing are usually the decision parameters that need to be optimized to increase its value. The workflow begins with the construction of a representative base case single well gas simulation model for production history matching. Petrophysical, geological, geomechanical, stimulation, completions and production data are interpreted and analyzed together to better understand drivers that could be influencing the production. If this can be repeated with several wells in the block with sufficient production data, the process is enriched as so the level of confidence, as the range of history-matching parameters from these different wells across the block can be captured for sensitivity and uncertainty analysis. Several sets of sensitivities and uncertainty runs are then performed to get a probabilistic production profile in the presence of the most influential parameters. It is important to highlight that usually, the limited number of wells, short production histories, different dynamic behavior in neighboring blocks and the lack of necessary data to help understand well performance all contribute to the high uncertainty in predicting production. Given the high cost of drilling and hydraulic fracturing and on the other hand the high gas price in Argentina, optimizing well spacing and cluster spacing are critical parameters in the process of unconventional resource evaluation considering the high degree of uncertainty.
Level of Education and Category Fluency Task among Spanish Speaking Elders: Number of Words, Clustering, and Switching Strategies

ABSTRACT It has been well documented that education influences the individual's performance on category fluency tasks but it is still unclear how this effect may differ across the different types of category tasks (i.e., animals, fruits, vegetables and clothing). This study aims (1) to analyze the effect of the level of education on four different types of category fluency tasks among elder Hispanic Americans and (2) to provide normative information on a population with different education levels that was previously screened for neurological and psychiatric conditions. In addition this study examines the semantic strategies used by these individuals to complete the fluency tasks. The sample included 105 healthy Hispanic individuals (age 55–98; 29 males and 76 females) divided into three education groups (<6, 6–11 and >11 years of education). Results showed that after controlling for age and gender, education has a main effect and is a strong predictor of performance in verbal fluency for the categories animals and clothing with increasing educational attainment being associated with higher category fluency scores and with more switches between categories. These findings suggest that the category fruit is less influenced by level of education than the other three semantic categories and may be a more appropriate test across different educational groups. Results from this study provide a reference for clinicians assessing verbal fluency in Spanish speaking populations.
Effects of forest roads and tree removal in or near wetlands of the Pacific Northwest : a literature synthesis

Literature Synthesis The literature synthesis includes the review of over 600 publications with relevance to forest practices and wetlands. One key finding of the literature synthesis is highlighting the lack of research related to wetlands and forest practices. Because of the paucity of wetland specific research in forested areas of the Pacific Northwest, much of the material in the report heavily relied on research related to stream and riparian zones. Dr. Adamus used his experience and best professional judgment to make connections between forest practices and processes and functions in riparian zones that influence wetlands. The literature review covered many important topics including hydrology, water quality, nutrients, chemicals, soils, and habitat for fish and wildlife.
Mining Multi-Dimensional Constrained Gradients in Data Cubes

Constrained gradient analysis (similar to the “cubegrade” problem posed by Imielinski, et al. [9]) is to extract pairs of similar cell characteristics associated with big changes in measure in a data cube. Cells are considered similar if they are related by roll-up, drill-down, or 1-dimensional mutation operation. Constrained gradient queries are expressive, capable of capturing trends in data and answering “what-if” questions. To facilitate our discussion, we call one cell in a gradient pair probe cell and the other gradient cell. An efficient algorithm is developed, which pushes constraints deep into the computation process, finding all gradient-probe cell pairs in one pass. It explores bi-directional pruning between probe cells and gradient cells, utilizing transformed measures and dimensions. Moreover, it adopts a hyper-tree structure and an H-cubing method to compress data and maximize sharing of computation. Our performance study shows that this algorithm is efficient and scalable.
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding

Image analysis methods that are based on exact blur values are faced with the computational complexities due to blur measurement error. This atmosphere encourages scholars to look for handcrafted and learned features for finding depth from a single image. This paper introduces a novel exact realization for blur measures on digital images and implements it on a new measure of defocus Gaussian blur at edge points in Depth From Defocus (DFD) methods with the potential to change this atmosphere. The experiments on real images indicate superiority of the proposed measure in error performance over conventional learned features in the state-of the-art single image based depth estimation methods.
A Parallel Computing Method for Community Structure Detection Based on BSP Model

Since the conventional algorithm for community structure detection in a stand-alone environment cannot handle the giant network whose number of nodes is more than 10 5 , and the widely used MapReduce method has a limitation on dealing with excessive I/O operations during the iterative process, an efficient parallel computing method based on BSP (Bulk Synchronous Parallel) model for detecting community structure is proposed in this paper. The Fast Newman method is improved into parallel calculations with multiple steps under the framework of BSP model. It is more efficient to discover community structures in the large scale network. In order to testify the performance of the proposed method, a hama platform was built up on the same cluster of the hadoop platform. And a dataset, at a scale of 10 6 , was also simulated for the experiments. It is approved that the proposed method is not only able to solve the issue of memory overrun in the conventional calculation on a stand-alone computer, but also to improve the performance effectively comparing to the MapReduce model. The proposed method has high practical value in large scale networks.
Translation by Quasi Logical Form Transfer

The paper describes work on applying a general purpose natural language processing system to transfer-based interactive translation. Transfer takes place at the level of Quasi Logical Form (QLF), a contextually sensitive logical form representation which is deep enough for dealing with cross-linguistic differences. Theoretical arguments and experimental results are presented to support the claim that this framework has good properties in terms of modularity, compositionality, reversibility and monotonicity.
Segmentation of touching characters in printed document recognition

A new discrimination function for segmenting touching characters based on both pixel projection and profile projection is presented. A dynamic recursive segmentation algorithm is developed for effectively segmenting touching characters. Contextual information and a spelling checker are used to correct errors caused by incorrect recognition and segmentation. The 99.85% top recognition accuracy has been achieved, while the minimum accuracy is 99.4% based on 12 real documents. <<ETX>>
User-Controlled, Robust Natural Language Generation from an Evolving Knowledge Base

In this paper we describe a natural language generation system which produces complex sentences from a biology knowledge base. The NLG system allows domain experts to discover errors in the knowledge base and generates certain parts of answers in response to users’ questions in an e-textbook application. The system allows domain experts to customise its lexical resources and to set parameters which influence syntactic constructions in generated sentences. The system is capable of dealing with certain types of incomplete inputs arising from a knowledge base which is constantly edited and includes a referring expression generation module which keeps track of discourse history. Our referring expression module is available for download as the open source Antfarm tool 1 .
Hybrid Tikhonov + total-variation regularization for imaging large-contrast media by full-waveform inversion

Full waveform inversion (FWI) is a waveform matching procedure which can potentially provide subsurface models with a wavelength-scale resolution. However, sophisticated regularization techniques are required to decrease the sensitivity of FWI to the initial model and noise and reduce the ill-posedness of the problem resulting from uneven illumination. The subsurface may be considered as a combination of a blocky part and a smoothly-varying part. Due to the difference in statistical properties of each part, different techniques are needed to regularize them. To tackle this issue, we propose a new hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers. The Tikhonov regularization is used to stabilize the reconstruction of the smoothly-varying background part of the subsurface, while the TV regularization is used for recovering the large contrasts associated with salt bodies for example. The new Tikhonov-TV (TT) regularization is implemented in frequency-domain FWI based on wavefield reconstruction, an efficient penalty method to extend the parameter-search space, using an iterative refinement strategy and the split Bregman technique. The relevance of the TT-regularized FWI is illustrated with two synthetic examples, a toy example and a target of the large-contrast 2004 BP salt model. The results show that the TT method outperforms the TV method in recovering both the smooth and blocky parts of the subsurface.
Engineering the lutein epoxide cycle into Arabidopsis thaliana

Significance Optimizing the balance between light harvesting and photoprotection holds great promise for improving photosynthetic efficiency and ultimately crop yields. The switch between these two states is regulated by xanthophyll cycling, which occurs in response to changing light conditions. Two xanthophyll cycles have been described in vascular plants: the violaxanthin cycle and the lutein epoxide cycle. The contribution of the lutein epoxide cycle to photosynthesis has been difficult to dissect because the violaxanthin cycle often functions in parallel and responds more rapidly. The introduction of the lutein epoxide cycle into Arabidopsis thaliana creates a model system in which to study this ecologically significant but less well-characterized xanthophyll cycle and reveals its role in modulating a rapidly reversible component of nonphotochemical quenching of chlorophyll a fluorescence in response to light. Although sunlight provides the energy necessary for plants to survive and grow, light can also damage reaction centers of photosystem II (PSII) and reduce photochemical efficiency. To prevent damage, plants possess photoprotective mechanisms that dissipate excess excitation. A subset of these mechanisms is collectively referred to as NPQ, or nonphotochemical quenching of chlorophyll a fluorescence. The regulation of NPQ is intrinsically linked to the cycling of xanthophylls that affects the kinetics and extent of the photoprotective response. The violaxanthin cycle (VAZ cycle) and the lutein epoxide cycle (LxL cycle) are two xanthophyll cycles found in vascular plants. The VAZ cycle has been studied extensively, owing in large part to its presence in model plant species where mutants are available to aid in its characterization. In contrast, the LxL cycle is not found in model plants, and its role in photosynthetic processes has been more difficult to define. To address this challenge, we introduced the LxL cycle into Arabidopsis thaliana and functionally isolated it from the VAZ cycle. Using these plant lines, we showed an increase in dark-acclimated PSII efficiency associated with Lx accumulation and demonstrated that violaxanthin deepoxidase is responsible for the light-driven deepoxidation of Lx. Conversion of Lx to L was reversible during periods of low light and occurred considerably faster than rates previously described in nonmodel species. Finally, we present clear evidence of the LxL cycle’s role in modulating a rapid component of NPQ that is necessary to prevent photoinhibition in excess light.
Exploiting contextual data for event retrieval in surveillance video

Contextual information is vital for the robust extraction of semantic information in automated surveillance systems. We have developed a scene independent framework for the detection of events in which we provide 2D and 3D contextual data for the scene under surveillance via a novel fast and convenient interface tool. In addition, the proposed framework illustrates the use of integral images, not only for detection, as with the classic Viola-Jones object detector, but also for efficient tracking. Finally, we provide a quantitative assessment of the performance of the proposed system in a number of physical locations via groundtruthed datasets.
Breaking the Fog: Defining and Orienting Surfaces in Complex Point Cloud Datasets

We present a vertex clustering algorithm for the purposes of surface determination and normal estimation that can help provide detailed visualizations of complex point cloud datasets. The proposed method combines a novel bucket and layer spatial partitioning scheme, along with an iterative process for surface subdivision based on the optimization of quality-of-fit statistics. Our approach can efficiently decompose and approximate a dataset through the local classification and fitting of surface regions. The algorithm uses a standard least squares approach combined with Delaunay-based triangulation for developing these approximated surfaces. To demonstrate the effectiveness of our approach, we execute the algorithm on several real-world datasets scanned from complex environments. We perform an analysis of the various techniques presented and provide a comparison of our approach with the standard k-nearest neighbors method commonly used for solving this problem. Through this performance analysis we show that as the complexity of the datasets increase, the performance and accuracy of our proposed approach continues to function at an effective level.
A Simpler Constant Factor Approximation for the k-Connected m-Domination Set Problem in Unit Disk Graph

Over years, many efforts are made for the problem of constructing quality fault-tolerant virtual backbones in wireless network. In case that a wireless network consists of physically equivalent nodes, e.g. with the same communication range, unit disk graph (UDG) is widely used to abstract the wireless network and the problem is formulated as the minimum k-connected mdominating set problem on the UDG. So far, most results are focused on designing a constant factor approximation algorithm for this NP-hard problem under two positive integers k and m satisfying m ≥ k ≥ 1 and k ≤ 3. Very recently, Shi et. al. and Fukunaga separately introduced constant factor approximation algorithms for the problem with m ≥ k ≥ 1. However, we found the structures of the algorithms are extremely complicated, and thus it would be difficult to implement and use them in practice. Motivated by such observation, this paper introduces a novel approximation algorithm for the problem with m ≥ k ≥ 1. This algorithm is based on our new technique which first computes a 1-connected m-dominating set D and repeatedly (a) decomposes D into an i-connected block tree, with i = 2, 3, ··· , k, and (b) use this graph structure to improve the connectivity of D, until D becomes k-connected. We provide a rigorous theoretical analysis to prove that the proposed algorithm is correct and its approximation ratio is a constant. We compare the structure of our algorithm against the existing ones and show our algorithm is much simpler to understand and implement.
Chapter 1 The Motivational Subsystem

• Sustainability: An agent must attend to its essential needs, such as hunger and thirst, and also know to avoid serious physical dangers, and so on (Toates 1986). • Purposefulness: The action of an agent must be chosen in accordance with some criteria, instead of completely randomly (Hull 1943, Anderson 1993), and those criteria are related to enhancing sustainability of an agent (Toates 1986). • Focus: An agent must be able to focus its activities with respect to specific purposes. That is, its actions need to be consistent, persistent, and contiguous, with respect to its purposes (Toates 1987). However, an agent needs to be able to give up some of its activities, temporally or permanently, when necessary (Simon 1967, Sloman 1986). • Adaptivity: An agent must be able to adapt its behavior (i.e., to learn) for the sake of improving its purposefulness, sustainability, and focus.
Allosensitization Does Not Increase the Risk of Xenoreactivity to α1,3-Galactosyltransferase Gene-Knockout Miniature Swine in Patients on Transplantation Waiting Lists

Background. The recent availability of α1,3-galactosyltransferase knockout (GalT-KO) miniature swine has eliminated anti-Gal antibodies as the major barrier to xenotransplantation, potentially bringing this modality closer to clinical application. Highly-allosensitized patients, who have poor prospects of receiving a suitable cross-match negative human organ, might be the first patients to benefit from xenotransplantation of porcine organs. However, concerns exist regarding cross-reactivity of alloreactive anti-human leukocyte antigen (HLA) antibodies against xenogeneic swine leukocyte antigen (SLA) antigens. We have investigated this question using sera from such patients on GalT-KO target cells. Methods. Using flow cytometry and complement-dependent cytotoxicity (CDC) assays, we have tested a panel of 88 human serum samples from patients awaiting cadaveric renal allotransplantation for reactivity against: 1) human; 2) standard miniature swine; and 3) GalT-KO peripheral blood lymphocytes (PBL) and cultured endothelial cells. Results. Anti-swine IgM and IgG antibody binding, as well as CDC, were significantly attenuated on GalT-KO versus standard swine. No correlation was found between the degree of anti-human panel reactive antibodies (PRA) and xenoreactivity against either standard or GalT-KO miniature swine. Treatment of sera with dithiothreitol (DTT) showed that the majority of remaining lymphocytotoxicity against GalT-KO swine was mediated by preformed IgM antibodies. Patients with high alloreactivity but low anti-GalT-KO xenoreactivity were readily identified. Conclusions. Highly allosensitized patients awaiting renal transplants appear to be at no increased risk of xenosensitization over their non-sensitized cohorts, and could therefore be candidates for xenotransplantation using GalT-KO swine donors.
Rib Reconstruction of the Absent Mandibular Condyle in Children

Objectives To describe pediatric costochondral graft reconstruction of the absent mandibular condyle and to report the short-term and long-term outcomes and complications associated with performing this procedure in young children. Study Design Case series with a retrospective chart review. Setting Pediatric otolaryngology clinic and tertiary children’s hospital in a metropolitan area. Subjects and Methods All children treated for an absent mandibular condyle with a costochondral graft at Children’s Hospitals and Clinics of Minnesota were identified from 2002 through 2011, and a retrospective chart review was performed. Results Ten patients aged 3 to 11 years were identified. The most common diagnosis, in 8 of 10 patients, was oculo-auriculo-vertebral syndrome. Three of the patients had a tracheostomy, of which 1 was decannulated following condylar reconstruction. Functional improvement, defined as improved symmetry, chewing, or better oral opening, was observed in 8 of 10 patients. Five patients have required no further surgeries to date, with a mean follow-up time of 3.9 years. Severe overgrowth of the graft was noted in 1 case, and partial or complete resorption of the graft was also noted in 3 cases. Overgrowth occurred after 5.7 years, whereas resorption occurred after an average of 2.5 years. Conclusions Costochondral grafts are an excellent surgical treatment option for children with severe mandibular malformations. Short-term results show particular improvement in function and mandibular alignment. The mean follow-up time with no revision surgery was substantial and indicates that rib grafting is a good addition to the armamentarium of treatment for this patient population.