Miracle-dz/UniFAR-FTUs huggingface dataset:
An overlap between the control of programmed cell death 1 in Bacillus anthracis and sporulation 2 3

22 The Staphylococcus aureus cid and lrg operons have been shown to control cell death 23 and lysis in a manner thought to be analogous to programmed cell death (apoptosis) in 24 eukaryotic organisms. Although orthologous operons are present in a wide variety of bacterial 25 species, members of the Bacillus cereus group are unique in that they have a total of four cid-26 /lrg-like operons. Two of these operons are similar to the S. aureus cid and lrg operons while 27 the other two (designated clhAB-1 and clhAB-2) are unique to this group. In the current study, 28 the functions and regulation of these loci were examined. Interestingly, the B. anthracis lrgAB 29 mutant displayed decreased stationary phase survival, whereas the clhAB-2 mutant exhibited 30 increased stationary phase survival compared to the parental and complementation strains. 31 However, neither mutation had a dramatic effect on murein hydrolase activity or autolysis. 32 Furthermore, a quantitative analysis of sporulation efficiency revealed both mutants formed 33 fewer spores compared to the parental strain. Similar to S. aureus, B. anthracis lrgAB 34 transcription was shown to be induced by gramicidin and CCCP, agents known to dissipate the 35 proton motive force, in a lytSR-dependent manner. Northern blot analyses also revealed a 36 positive role of lytSR in the clhAB-2 transcription. Taken together, the results of this study 37 demonstrate that the B. anthracis lrgAB and clhAB-2 play important roles in the control of cell 38 death and lysis and reveal a previously unrecognized role of this system in sporulation. 39 3 INTRODUCTION 40
Boosting-based multiple kernel learning for image re-ranking

Re-ranking the returned images from a query relies on two important steps to improve its effectiveness: the estimation of the image relevance and the enhancement of the similarity function. However, attaining an effective visual similarity and an accurate re-ranking are quite challenging. We address these issues by first evaluating the image relevance to the query from the dataset according to the visual features and the co-occurrence of local patches of images. Then we boost the visual similarity measure associated with image relevance, and propose an enhancement algorithm, called Boosting-MKL, which not only incrementally learns the feature fusion but also generally preserves the initial local ranking. Specifically, we perform a random walk over a similarity graph for re-ranking. The experimental results demonstrate that our proposed approach significantly improves the effectiveness of visual similarity measure and the performance of image reranking.
Monitoring forest/non-forest land use conversion rates with annual inventory data

Summary The transitioning of land from forest to other uses is of increasing interest as urban areas expand and the world’s population continues to grow. Also of interest, but less recognized, is the transitioning of land from other uses into forest. In this paper, we show how rates of conversion from forest to non-forest and non-forest to forest can be estimated in the US from a continuously improving publicly available annual forest inventory database, under varying definitions of conversion. Two estimation approaches are considered and contrasted. The approaches are a simple ratio estimator and the weighted maximum likelihood estimator. The latter involves a statistical procedure that incorporates the binomial nature of the indicator variables, the transition of mapped plot conditions and an intuitively appealing way to combine data from varying remeasurement periods for a temporally dependent variable.
ALMOST FLAT MANIFOLDS

1.1. We denote by V a connected ^-dimensional complete Riemannian manifold, by d = d(V) the diameter of V, and by c = c(V) and c~ = c~(V), respectively, the upper and lower bounds of the sectional curvature of V. We set c = c(V) = max (| c1, | c~ |). We say that F i s e-flat, e > 0, if cd 0. b. Every compact nil-manifold possesses an e-flat metric for any e > 0. {A manifold is called a nil-manifold if it admits a transitive action of a nilpotent Lie group; see 4.5. ) The second example shows that for n > 3, e > 0 there are infinitely many e-flat ^-dimensional manifolds with different fundamental groups. 1.3. Define inductively ext(x) = exp (eXi_λ(x)\ exo(x) — x, and set e(ή) = exp (—eXj(n)), where j = 200. (We are generous everywhere in this paper because the true value of the constants is unknown.) 1.4. Main Theorem. Let V be a compact έ(n)-flat manifold, and π its fundamental group. Then: (a) There exists a maximal nilpotent normal divisor N C π (b) ord(πlN) 0), then its fundamental group π and every subgroup of π can be generated by 3 elements. (ii) If d(V) -K,K>0, then π can be generated by N < 3 ex2(nK&) elements; if π is a free group and KQ) 1 < e(n), then π is generated by one element.
Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models

Summary Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models‐3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models‐3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models‐3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models‐3 output. We apply our methods to data on SO2 concentrations, and we obtain high‐resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.
Brain Image Analysis and Atlas Construction

The tremendous pace of development in brain imaging technologies has revolutionized our ability to investigate brain structure and function. Techniques are now available to capture features of anatomy and function at both molecular and whole-brain scales, mapping neuronal dynamics and gene expression as well as growth and degenerative processes that span multiyear time scales. The number of brain imaging investigations is also increasing exponentially. A major goal of these studies is to analyze how the dynamically changing brain varies across age, gender, disease, across multiple imaging modalities, and in large human populations. To tackle these questions, many laboratories are using sophisticated algorithms for brain image analysis. Engineering approaches drawn from computer vision, image analysis, computer graphics, and artificial intelligence research fields are required to manipulate, analyze, and communicate brain data. Novel image analysis algorithms continue to uncover new patterns of altered structure and function in individuals and clinical populations, and mathematical strategies are being developed to relate these patterns to clinical, demographic, and genetic parameters. In this chapter, we review current challenges in brain image analysis, focusing on the main algorithms, their technical foundations, and their scientific and clinical applications. The approaches include methods for automated registration and segmentation, anatomical parameterization and modeling, tissue classification and shape analysis, and pathology detection in individuals or groups. Algorithms are also described for generating digital brain atlases. Atlases are fundamental to brain image analysis, as they offer a powerful framework to synthesize the results of disparate imaging studies. Built from one or more representations of the brain, atlases are annotated representations of anatomy in a 3D coordinate system. They serve as standardized templates on which other brain maps can be overlaid, for subsequent comparison and integration. To align new imaging data with an atlas, a variety of registration algorithms may be employed (see also Chapter 8 for other applications). Once registered, brain maps can be pooled across subjects and combined mathematically and statistically. As such, atlases provide a standardized 3D coordinate system to express observations from different individuals and a framework for interlaboratory communication.
Chromatic Clustering in High Dimensional Space

In this paper, we study a new type of clustering problem, called {\em Chromatic Clustering}, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function is optimized. In this paper, we consider two variants of the problem, chromatic $k$-means clustering (denoted as $k$-CMeans) and chromatic $k$-medians clustering (denoted as $k$-CMedians), and investigate their hardness and approximation solutions. For $k$-CMeans, we show that the additional coloring constraint destroys several key properties (such as the locality property) used in existing $k$-means techniques (for ordinary points), and significantly complicates the problem. There is no FPTAS for the chromatic clustering problem, even if $k=2$. To overcome the additional difficulty, we develop a standalone result, called {\em Simplex Lemma}, which enables us to efficiently approximate the mean point of an unknown point set through a fixed dimensional simplex. A nice feature of the simplex is its independence with the dimensionality of the original space, and thus can be used for problems in very high dimensional space. With the simplex lemma, together with several random sampling techniques, we show that a $(1+\epsilon)$-approximation of $k$-CMeans can be achieved in near linear time through a sphere peeling algorithm. For $k$-CMedians, we show that a similar sphere peeling algorithm exists for achieving constant approximation solutions.
A Systematic Comparison of Training Criteria for Statistical Machine Translation

We address the problem of training the free parameters of a statistical machine translation system. We show significant improvements over a state-of-the-art minimum error rate training baseline on a large ChineseEnglish translation task. We present novel training criteria based on maximum likelihood estimation and expected loss computation. Additionally, we compare the maximum a-posteriori decision rule and the minimum Bayes risk decision rule. We show that, not only from a theoretical point of view but also in terms of translation quality, the minimum Bayes risk decision rule is preferable.
Document Representation and Clustering with WordNet Based Similarity Rough Set Model

Most studies on document clustering till date use Vector Space Model (VSM) to represent documents in the document space, where documents are denoted by a vector in a word vector space. The standard VSM does not take into account the semantic relatedness between terms. Thus, terms with some semantic similarity are dealt with in the same way as terms with no semantic relatedness. Since this unconcern about semantics reduces the quality of clustering results, many studies have proposed various approaches to introduce knowledge of semantic relatedness into VSM model. Those approaches give better results than the standard VSM. However they still have their own issues. We propose a new approach as a combination of two approaches, one of which uses Rough Sets theory and cooccurrence of terms, and the other uses WordNet knowledge to solve these issues. Experiments for its evaluation show advantage of the proposed approach over the others.
Applying evolution programming Search Based Software Engineering (SBSE) in selecting the best open source software maintainability metrics

The nature of an Open Source Software development paradigm forces individual practitioners and organization to adopt software through trial and error approach. This leads to the problems of coming across software and then abandoning it after realizing its lack of important qualities to suit their requirements or facing negative challenges in maintaining the software. These contributed by lack of recognizing guidelines to lead the practitioners in selecting out of the dozens available metrics, the best metric(s) to measure quality OSS. In this study, the novel results provide the guidelines that lead to the development of metrics model that can select the best metric(s) to predict maintainability of Open Source Software.
Dynamic Data Reallocation in Hybrid Disk Arrays

Current disk arrays consist purely of hard disk drives, which normally provide huge storage capacities with low cost and high throughput for data-intensive applications. Nevertheless, they have some inherent disadvantages such as long access latencies and energy inefficiency due to their build-in mechanical mechanisms. Flash-memory-based solid state disks, on the other hand, although currently more expensive and inadequate in write cycles, offer much faster random read accesses and are much more robust and energy efficient. To combine the complementary merits of hard disks and flash disks, in this paper, we propose a hybrid disk array architecture named hybrid disk storage (HIT) for data-intensive applications. Next, a dynamic data redistribution strategy called performance, energy, and reliability balanced (PEARL), which can periodically redistribute data between flash disks and hard disks to adapt to the changing data access patterns, is developed on top of the HIT architecture. Comprehensive simulations using real-life traces demonstrate that compared with existing data placement techniques, PEARL exhibits its strength in both performance and energy consumption without impairing flash disk reliability.
Efficient Active Learning of Halfspaces via Query Synthesis

Active learning is a subfield of machine learning that has been successfully used in many applications including text classification and bioinformatics. One of the fundamental branches of active learning is query synthesis, where the learning agent constructs artificial queries from scratch in order to reveal sensitive information about the true decision boundary. Nevertheless, the existing literature on membership query synthesis has focused on finite concept classes with a limited extension to real-world applications. In this paper, we present an efficient spectral algorithm for membership query synthesis for halfspaces, whose sample complexity is experimentally shown to be near-optimal. At each iteration, the algorithm consists of two steps. First, a convex optimization problem is solved that provides an approximate characterization of the version space. Second, a principal component is extracted, which yields a synthetic query that shrinks the version space exponentially fast. Unlike traditional methods in active learning, the proposed method can be readily extended into the batch setting by solving for the top k eigenvectors in the second step. Experimentally, it exhibits a significant improvement over traditional approaches such as uncertainty sampling and representative sampling. For example, to learn a halfspace in the Euclidean plane with 25 dimensions and an estimation error of 1E-4, the proposed algorithm uses less than 3% of the number of queries required by uncertainty sampling.
An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching

Summary Metabolic models have the potential to impact on genome annotation and on the interpretation of gene expression and other high throughput genome data. The genome of Streptomyces coelicolor genome has been sequenced and some 30% of the open reading frames (ORFs) lack any functional annotation. A recently constructed metabolic network model for S. coelicolor highlights biochemical functions which should exist to make the metabolic model complete and consistent. These include 205 reactions for which no ORF is associated. Here we combine protein functional predictions for the unannotated open reading frames in the genome with ‘missing but expected’ functions inferred from the metabolic model. The approach allows function predictions to be evaluated in the context of the biochemical pathway reconstruction, and feed back iteratively into the metabolic model. We describe the approach and discuss a few illustrative examples.
Learners' acceptance of Learning Management Systems: Developing a theoretical framework

The use of information communication technology to build human resources is a vital prerequisite for the development of knowledge-based economy. Learning Management Systems (LMS) do not only provide academic institutions with effective and efficient means to build human resources but also enable them to efficiently and effectively codify and share their academic knowledge. The success of learning management systems in academic institution may be initiated by instructors' acceptance; however, it survives in the long run by learners' continuous acceptance and utilization. Consequently, the objective of this paper is to provide a comprehensive look at the critical factors that influence learners' acceptance of LMS and consequently their actual use. These critical factors are related to the major entities of LMS: the learner, the instructor, the course, the classmates, the organization, and the technology.
Using fast matrix multiplication in bio-inspired computation for complex optimization problems

Population-based search heuristics such as evolutionary algorithms or ant colony optimization have been widely used to tackle complex problems in combinatorial optimization. In many cases these problems involve the optimization of an objective function subject to a set of constraints which is very large. In this paper, we examine how population-based search heuristics can be sped up by making use of fast matrix multiplication algorithms. First, we point out that this approach is applicable to the wide class of problems which can be expressed as an Integer Linear Program (ILP). Later on, we investigate the speedup that can be gained by the proposed approach in our experimental studies for the multidimensional knapsack problem.
An optimized forwarding protocol for lifetime extension of wireless sensor networks

Summary Optimized routing (from source to sink) in wireless sensor networks (WSN) constitutes one of the key design issues in prolonging the lifetime of battery-limited sensor nodes. In this paper, we explore this optimization problem by considering different cost functions such as distance, remaining battery power, and link usage in selecting the next hop node among multiple candidates. Optimized selection is carried out through fuzzy inference system (FIS). Two differing algorithms are presented, namely optimized forwarding by fuzzy inference systems (OFFIS), and two-layer OFFIS (2L-OFFIS), that have been developed for ﬂat and hierarchical networks, respectively. The proposed algorithms are compared with popular routing protocols that are considered as the closest counterparts such as minimum transmit energy (MTE) and low energy adaptive clustering hierarchy (LEACH). Simulation results demonstrate the superiority of the proposed algorithms in extending the WSN lifetime. Copyright © 2008 John Wiley & Sons, Ltd.
Changes in Coronary Blood Flow and Other Hemodynamic Indicators Induced by Stressful Interviews

&NA; Thirty male subjects, aged 29–69, were studied before and during stressful interviews. Coronary blood flow was measured using the radioactive Diodrast technique developed by Sevelius and Johnson. Blood pressure and pulse rate were also measured; cardiac output and peripheral resistance were calculated. Significant alterations in these parameters occurred in a patterned way which corresponded to judgments of the patients' feeling states.
An accuracy assessment of different rigid body image registration methods and robotic couch positional corrections using a novel phantom.

PURPOSE Image guided radiotherapy (IGRT) using cone beam computed tomography (CBCT) images greatly reduces interfractional patient positional uncertainties. An understanding of uncertainties in the IGRT process itself is essential to ensure appropriate use of this technology. The purpose of this study was to develop a phantom capable of assessing the accuracy of IGRT hardware and software including a 6 degrees of freedom patient positioning system and to investigate the accuracy of the Elekta XVI system in combination with the HexaPOD robotic treatment couch top. METHODS The constructed phantom enabled verification of the three automatic rigid body registrations (gray value, bone, seed) available in the Elekta XVI software and includes an adjustable mount that introduces known rotational offsets to the phantom from its reference position. Repeated positioning of the phantom was undertaken to assess phantom rotational accuracy. Using this phantom the accuracy of the XVI registration algorithms was assessed considering CBCT hardware factors and image resolution together with the residual error in the overall image guidance process when positional corrections were performed through the HexaPOD couch system. RESULTS The phantom positioning was found to be within 0.04 (σ = 0.12)°, 0.02 (σ = 0.13)°, and -0.03 (σ = 0.06)° in X, Y, and Z directions, respectively, enabling assessment of IGRT with a 6 degrees of freedom patient positioning system. The gray value registration algorithm showed the least error in calculated offsets with maximum mean difference of -0.2(σ = 0.4) mm in translational and -0.1(σ = 0.1)° in rotational directions for all image resolutions. Bone and seed registration were found to be sensitive to CBCT image resolution. Seed registration was found to be most sensitive demonstrating a maximum mean error of -0.3(σ = 0.9) mm and -1.4(σ = 1.7)° in translational and rotational directions over low resolution images, and this is reduced to -0.1(σ = 0.2) mm and -0.1(σ = 0.79)° using high resolution images. CONCLUSIONS The phantom, capable of rotating independently about three orthogonal axes was successfully used to assess the accuracy of an IGRT system considering 6 degrees of freedom. The overall residual error in the image guidance process of XVI in combination with the HexaPOD couch was demonstrated to be less than 0.3 mm and 0.3° in translational and rotational directions when using the gray value registration with high resolution CBCT images. However, the residual error, especially in rotational directions, may increase when the seed registration is used with low resolution images.