Miracle-dz/UniFAR-FTUs huggingface dataset:
Unusual Properties of Catalase A (KatA) of Pseudomonas aeruginosa PA14 Are Associated with Its Biofilm Peroxide Resistance

ABSTRACT Pseudomonas aeruginosa is a ubiquitous environmental bacterium whose major catalase (KatA) is highly stable, extracellularly present, and required for full virulence as well as for peroxide resistance in planktonic and biofilm states. Here, we dismantled the function of P. aeruginosa KatA (KatAPa) by comparing its properties with those of two evolutionarily related (clade 3 monofunctional) catalases from Bacillus subtilis (KatABs) and Streptomyces coelicolor (CatASc). We switched the coding region for KatAPa with those for KatABs and CatASc, expressed the catalases under the potential katA-regulatory elements in a P. aeruginosa PA14 katA mutant, and verified their comparable protein levels by Western blot analysis. The activities of KatABs and CatASc, however, were less than 40% of the KatAPa activity, suggestive of the difference in intrinsic catalatic activity or efficiency for posttranslational activity modulation in P. aeruginosa. Furthermore, KatABs and CatASc were relatively susceptible to proteinase K, whereas KatAPa was highly stable upon proteinase K treatment. As well, KatABs and CatASc were undetectable in the extracellular milieu. Nevertheless, katA Bs and catA Sc fully rescued the peroxide sensitivity and osmosensitivity of the katA mutant, respectively. Both catalase genes rescued the attenuated virulence of the katA mutant in mouse acute infection and Drosophila melanogaster models. However, the peroxide susceptibility of the katA mutant in a biofilm growth state was rescued by neither katA Bs nor catA Sc. Based on these results, we propose that the P. aeruginosa KatA is highly stable compared to the two major catalases from gram-positive bacteria and that its unique properties involving metastability and extracellular presence may contribute to the peroxide resistance of P. aeruginosa biofilm and presumably to chronic infections.
A type-sensitive preprocessor for haskell

This paper presents a preprocessor which generates code from type de(cid:12)nitions. The preprocessor uses rules to describe the pattern of the produced code. In this way, instances may be derived for type classes. Utility functions for manipulating types, especially records, can also be produced. The set of rules is easily extendible; by adding derivation rules for new type classes, instance derivation is possible for classes de(cid:12)ned outside the prelude.
Parallel Applications And Tools For Cloud Computing Environments

The main research focus of SALSA project is two-fold. First, we investigate new programming models of parallel multicore computing and Cloud/Grid computing. It aimed at developing and applying parallel and distributed Cyberinfrastructure to support large scale data analysis. Second, we develop user-friendly and integrated Cloud computing environments and tools to provide our parallel algorithms and Cloud infrastructures as services.
New fuzzy rules for improved image segmentation

The extended fuzzy rules for image segmentation (EFRIS) algorithm initially splits all segmented regions into mutually exclusive 4-connected objects, from which the largest one in each region is designated as its main object. A drawback of this approach is that it is less effective when the main objects are relatively small and some of the minor objects are completely surrounded and connected to the main object of another region. Besides, defining insufficient merging rules, EFRIS also only considers the surrounding main objects in the original order that the regions were segmented, which is undesirable. In this paper, a new general segmentation algorithm called modified extended fuzzy rules for image segmentation (MEFRIS) is presented, which addresses these problems and whose improved segmentation performance is analysed and numerically evaluated. The results are also contrasted with both the original generic fuzzy rule-based image segmentation (GFRIS) and EFRIS algorithms.
IN VIVO GIBBERELLIN A9 METABOLISM BY AZOSPIRILLUM SP. IN dy DWARF RICE MUTANT SEEDLINGS

Azospirillum sp. are bacteria beneficial to inoculated plants, and the effect is partially attributed to gibberel lin production. Deutero-labelled GA9 was fed to dy mutant rice-dwarf seedlings inoculated with A. lipoferum or A. brasilense and/or treated with Prohexadione-Ca, an inhibitor of late steps in gibberellin biosynthesis. According to the shoot growth response, the mutant showed the ability to perform 3? -hydroxylation of the d2GA9 fed. This ability was partially blocked by Prohexadione -Ca, suggesting that GA9 may have some activity per se. After the feeding was done along with Azospirillum sp. inoculation, the bacteria performed 3? -hydroxylation as shown by shoot growth promotion and corroborated by GC-MS detection of d2GA3. The growth promotion was blocked by Prohexadione-Ca but in a lower extent as compared with non-inoculated controls, suggesting that the inhibitor worked on the 3? hydroxylation performed on the substrate fed but on the microorganism capacity to produce “active” gibberellins.
Calibrate kinect to use in computer vision, simplified and precise

Visual sensors, active or passive, play an important role in computer vision and in visual sensors, calibration is of utmost importance. Kinect as a new developed sensor for use as a Natural User Interface is being utilized in different fields especially CV. This integrated system beside other sensors, contains two visual sensors of active and passive that demands a process of calibration. Among different methods of calibration, image-based calibration for data-fusion purposes, has lowest computational cost and can be quite simple and precise. In this study, 2 different methods, consisting of a physical interior distortion model and an eight parameters registration equation have been proposed. Besides computed parameters and their precision, a table of distortion values is introduced that can be used in registration level. Finally to evaluate chosen proposed method, a simple registration of processed data is utilized and results are discussed.
Neuropsychoanalysis in Practice: Brain, Self and Objects

In Neuropsychoanalysis in Practice , Georg Northoff discusses the various neuronal mechanisms that may enable the transformation of neuronal into psychological states, looking at how these processes are altered in psychiatric disorders like depression and schizophrenia. He focuses specifically on how the brain is organized and how this organization enables the brain to differentiate between neuronal and psychodynamic states, that is, the brain and the psyche. This leads him to discuss not only empirical issues but also conceptual problems, for instance, the concept of the brain. Neuropsychoanalysis in Practice applies these concepts and mechanisms to explain the various symptoms observed in psychiatric disorders such as depression and schizophrenia. In addition to the empirical issues, he also discusses various conceptual and methodological issues that are relevant in linking neuroscience and psychoanalysis, developing a novel transdisciplinary framework for linking neuroscience, psychoanalysis and philosophy.
Statistical and Stochastic Modeling of Gang Rivalries in Los Angeles

Gang violence has plagued the Los Angeles policing district of Hollenbeck for over half a century. With sophisticated models, police may better understand and predict the region’s frequent gang crimes. The purpose of this paper is to model Hollenbeck’s gang rivalries. A self-exciting point process called a Hawkes process is used to model rivalries over time. While this is shown to fit the data well, an agent based model is presented which is able to accurately simulate gang rivalry crimes not only temporally but also spatially. Finally, we compare random graphs generated by the agent model to existing models developed to incorporate geography into random graphs.
Are Just-InTime Lectures Effective At Teaching ?

A ‘‘Just-In-Time Lecture’’ is a low-cost, flexible technology for creating and presenting educational material to groups and individuals. A review of learning concepts and education research supports the notion that Just-In-Time Lecture technology should be as effective as traditional classroom instruction. This finding is based on (1) a large body of research on related technologies, including television and videodisc training, (2) the fact that classroom instruction translates directly to the Just-In-Time Lecture medium, (3) the applicability of Just-In-Time Lectures to a variety of skill categories (e.g. Bloom’s taxonomy), and (4) positive experience using Just-In-Time Lectures at two universities for classroom and individual instruction.
A Large Probabilistic Semantic Network Based Approach to Compute Term Similarity

Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications. Currently, there are two main approaches for this task, namely the knowledge based and the corpus based approaches. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Contrary to these existing techniques, we propose an efficient and effective approach for semantic similarity using a large scale semantic network. This semantic network is automatically acquired from billions of web documents. It consists of millions of concepts, which explicitly model the context of semantic relationships. In this paper, we first show how to map two terms into the concept space, and compare their similarity there. Then, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Finally, we conduct extensive studies to demonstrate that our approach can accurately compute the semantic similarity between terms of MWEs and with ambiguity, and significantly outperforms 12 competing methods under Pearson Correlation Coefficient. Meanwhile, our approach is much more efficient than all competing algorithms, and can be used to compute semantic similarity in a large scale.
Low complexity decision directed channel tracking for MIMO WLAN system

It is expected that the next generation wireless local area networks will be predominantly based on MIMO-OFDM physical layer. The performance of coherent MIMO-OFDM based systems critically depends on the availability of accurate channel estimations. Preambles, at the beginning of each packet, are used to estimate the channels in IEEE 802.11n systems. However, in mobile communication, when the channel is time varying, the channel variations within a given packet increases the packet error rate (PER) in the system. So the channel variations have to be tracked by the receiver. This paper proposes a low complexity channel tracking scheme for 802.11n systems to improve the PER. The delay introduced by the channel tracking scheme is within the acceptable limits.
Draft Genome Sequence of Oil-Degrading Bacterium Gallaecimonas pentaromativorans Strain YA_1 from the Southwest Indian Ocean

ABSTRACT Gallaecimonas pentaromativorans has been previously reported to be capable of degrading crude oil and diesel oil. G. pentaromativorans strain YA_1 was isolated from the southwest Indian Ocean and can degrade crude oil. This study reports the draft genome sequence of G. pentaromativorans, which can provide insights into the mechanisms of microbial oil biodegradation.
Poincaré: A Hyperbolic Data Center Architecture

Current trends in cloud computing suggest that both large, public clouds and small, private clouds will proliferate in the near future. Operational requirements, such as high bandwidth, dependability and smooth manageability, are similar for both types of clouds and their underlying data center architecture. Such requirements can be satisfied with utilizing fully distributed, low-overhead mechanisms at the algorithm level, and an efficient layer 2 implementation at the practical level. Adding to this, owners of evolving private data centers are in dire need of an incrementally upgradeable architecture which supports a small roll-out and continuous expansion in small quanta. In order to satisfy both requirements, we propose Poincare, a data center architecture inspired by hyperbolic tessellations, which utilizes low-overhead, greedy routing. On one hand, we show that Poincare scales to support large data centers with low diameter, high bisection bandwidth, inherent multipath and multicast capabilities, and efficient error recovery. On the other hand, Poincare supports incremental plug &amp; play upgradability with regard to both servers and switches.
Cognitive impairment in Parkinson's disease: a study of early-phase amyloid PET and arterial spin labeling perfusion MRI

Cognitive impairment in Parkinson’s disease: a study of early-phase amyloid PET and arterial spin labeling perfusion MRI by Megan Stark The characteristic motor symptoms present in Parkinson’s disease are often accompanied by a range of non-motor symptoms; in particular, cognitive impairment leading to dementia has an 80% cumulative prevalence in PD but with incredibly variable time for dementia onset. This study presents and compares early-phase [18F] Florbetaben (FBB) positron emission tomography (PET) and arterial spin labeling magnetic resonance imaging (ASL MRI) of perfusion in the context of cognitive decline and imminent conversion to dementia in Parkinson’s disease. Patients underwent a comprehensive neuropsychological battery prior to inclusion in the study, which was used to classify patients as PD with normal cognition (PD-N, n=4), mild cognitive impairment (PD-MCI, n=41) or dementia (PDD, n=5), and to assign each patient a summary global cognitive score and an individualized Parkinsons disease dementia risk score (PDDRS). Early-phase FBB PET images, and structural and arterial spin labeled MR images were acquired for each patient, which were then coregistered, normalized and smoothed in preparation for analysis. The relative measures of brain function given by each imaging modality were analysed using the general linear model, in order to identify any association with cognitive status and dementia risk in the subject group. Cognitive decline and increased dementia conversion risk were found to be significantly associated with distinct regions of cortical hypoperfusion as quantified by the ASL data. FBB-derived images did not exhibit any significant association with cognition or dementia risk, and did not correlate significantly with ASL perfusion measures, suggesting that the two techniques are measuring different physiological phenomenon. A network based approach using principal component analysis identified networks of cortical hypoperfusion in the ASL data that related significantly to cognition and risk of conversion to dementia. This thesis raises questions regarding the physiological information presented by earlyphase PET, which remains a worthwhile area of further investigation. The PDDRS-related perfusion network developed here presents a potential biomarker of imminent conversion to dementia in Parkinsons disease.
Meta-learning with kernels and similarity functions for planning of data mining workﬂows

We propose an intelligent data mining (DM) assistant that will combine planning and meta-learning to provide support to users of virtual DM laboratory. A knowledge-driven planner will rely on a data mining ontology to plan the knowledge discovery workﬂow and determine the set of valid operators for each step of this workﬂow. A probabilistic meta-learner will select the most appropriate operators by using relational similarity measures and kernel functions over records of past sessions meta-data stored in a DM experiments repository.
Design of the lower error fixed-width multiplier and its application

This brief develops a general methodology for designing a lower-error two's-complement fixed-width multiplier that receives two n-bit numbers and produces an n-bit product. By properly choosing the generalized index, we derive better error-compensation bias to reduce the truncation error and then construct a lower error fixed-width multiplier, which is area efficient for VLSI implementation. Finally, we successfully apply the proposed fixed-width multiplier to realizing a digital FIR filter, which has shown that the performance is better than that using other fixed-width multipliers.
A Bayesian derivation of generalized coherence detectors

The generalized coherence (GC) estimate is a well studied statistic for detection of a common but unknown signal on several noisy channels. In this paper, it is shown that the GC detector arises naturally from a Bayesian perspective. Specifically, it is derived as a test of the hypothesis that the signals in the channels are independent Gaussian processes against the hypothesis that the processes have some arbitrary correlation. This is achieved by introducing suitable non-informative priors for the covariance matrices across the channels under the two hypotheses. Subsequently, reduced likelihoods are obtained by marginalizing the joint distribution of the data and the covariance matrix in each case. The likelihood ratio is then shown to be a monotonic function of the GC detection statistic. This derivation extends to the case of time-correlated signals, allowing comparison with the generalized likelihood ratio test (GLRT) recently proposed by Ramírez et al.
Using point process models to compare neural spiking activity in the nucleus of Parkinson's patients and a healthy primate

—Placement of deep brain stimulating electrodes in the subthalamic nucleus (STN) to treat Parkinson’s disease (PD) also allows the recording of single neuron spiking activity. Analyses of these unique data offer an important opportunity to better understand the pathophysiology of PD. Despite the point process nature of PD neural spiking activity, point process methods are rarely used to analyze these recordings. We develop a point process representa- tion of PD neural spiking activity using a generalized linear model
A Framework for Comprehensive Automated Evaluation of Concurrent Online Checkers

This paper proposes a framework for automated evaluation of concurrent online checkers. The novelty of the underlying approach lies in its completeness (i.e. ability of formally proving the presence or absence of true misses), minimal fault detection latency and accurate, fully automated evaluation of the fault detection characteristics of the checkers. The methodology consists of creating a pseudo-combinational version of the circuit under test, specifying the environment in terms of valid input stimuli and providing the assertions for generating the checkers, which will thereafter be evaluated by the framework. In this paper, a case-study on the control part (routing and arbitration) of a Network-on-Chip (NoC) router has been carried out. It shows on a realistic application that the framework is capable of accurately and formally evaluating the quality of individual concurrent checkers which constitutes an important task in fault tolerant system design. The case study shows that the proposed approach helps achieving high fault coverage in a single clock-cycle.
Cytoskeletal Linker Protein Dystonin Is Not Critical to Terminal Oligodendrocyte Differentiation or CNS Myelination

Oligodendrocyte differentiation and central nervous system myelination require massive reorganization of the oligodendrocyte cytoskeleton. Loss of specific actin- and tubulin-organizing factors can lead to impaired morphological and/or molecular differentiation of oligodendrocytes, resulting in a subsequent loss of myelination. Dystonin is a cytoskeletal linker protein with both actin- and tubulin-binding domains. Loss of function of this protein results in a sensory neuropathy called Hereditary Sensory Autonomic Neuropathy VI in humans and dystonia musculorum in mice. This disease presents with severe ataxia, dystonic muscle and is ultimately fatal early in life. While loss of the neuronal isoforms of dystonin primarily leads to sensory neuron degeneration, it has also been shown that peripheral myelination is compromised due to intrinsic Schwann cell differentiation abnormalities. The role of this cytoskeletal linker in oligodendrocytes, however, remains unclear. We sought to determine the effects of the loss of neuronal dystonin on oligodendrocyte differentiation and central myelination. To address this, primary oligodendrocytes were isolated from a severe model of dystonia musculorum, Dstdt-27J, and assessed for morphological and molecular differentiation capacity. No defects could be discerned in the differentiation of Dstdt-27J oligodendrocytes relative to oligodendrocytes from wild-type littermates. Survival was also compared between Dstdt-27J and wild-type oligodendrocytes, revealing no significant difference. Using a recently developed migration assay, we further analysed the ability of primary oligodendrocyte progenitor cell motility, and found that Dstdt-27J oligodendrocyte progenitor cells were able to migrate normally. Finally, in vivo analysis of oligodendrocyte myelination was done in phenotype-stage optic nerve, cerebral cortex and spinal cord. The density of myelinated axons and g-ratios of Dstdt-27J optic nerves was normal, as was myelin basic protein expression in both cerebral cortex and spinal cord. Together these data suggest that, unlike Schwann cells, oligodendrocytes do not have an intrinsic requirement for neuronal dystonin for differentiation and myelination.
Authentication Scheme for Passwords using Color and Text

The most common method used for authentication is textual passwords. But textual passwords are in risk to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are helpless to shoulder surfing as well as it has higher storage and computational complexity. To address this problem, text can be combined with colors to generate passwords for authentication. The combination of color and text password with efficient matching between the two provides authentication as well as security to the user. Hence using the technique of integration with color and textual password is proposed to generate passwords which are resistant to shoulder surfing.
Performance Analysis for Lattice-Based Speech Indexing Approaches Using Words and Subword Units

Lattice-based speech indexing approaches are attractive for the combination of short spoken segments, short queries, and low automatic speech recognition (ASR) accuracies, as lattices provide recognition alternatives and therefore tend to compensate for recognition errors. Position-specific posterior lattices (PSPLs) and confusion networks (CNs), two of the most popular lattice-based approaches, both reduce disk space requirements and are more efficient than raw lattices. When PSPLs and CNs are used in a word-based fashion, they cannot handle OOV or rare word queries. In this paper, we propose an efficient approach for the construction of subword-based PSPLs (S-PSPLs) and CNs (S-CNs) and present a comprehensive performance analysis of PSPL and CN structures using both words and subword units, taking into account basic principles and structures, and supported by experimental results on Mandarin Chinese. S-PSPLs and S-CNs are shown to yield significant mean average precision (MAP) improvements over word-based PSPLs and CNs for both out-of-vocabulary (OOV) and in-vocabulary queries while requiring much less disk space for indexing.