Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Investigation of Iron-Responsive Microsomal Proteins, including MirC, in Aspergillus fumigatus

The functionality of many microsome-associated proteins which exhibit altered abundance in response to iron limitation in Aspergillus fumigatus is unknown. Here, we generate and characterize eight gene deletion strains, and of most significance reveal that MirC (AFUA_2G05730) contributes to the maintenance of intracellular siderophore [ferricrocin (FC)] levels, augments conidiation, confers protection against oxidative stress, exhibits an intracellular localization and contributes to fungal virulence in the Galleria mellonella animal model system. FC levels were unaffected following deletion of all other genes encoding microsome-associated proteins. MirC does not appear to play a role in either siderophore export from, or uptake into, A. fumigatus. Label-free quantitative proteomic analysis unexpectedly revealed increased abundance of siderophore biosynthetic enzymes. In addition, increased expression of hapX (7.2 and 13.8-fold at 48 and 72 h, respectively; p < 0.001) was observed in ΔmirC compared to wild-type under iron-replete conditions by qRT-PCR. This was complemented by significantly elevated extracellular triacetylfusarinine C (TAFC; p < 0.01) and fusarinine C (FSC; p < 0.05) siderophore secretion. We conclude that MirC plays an important role in FC biosynthesis and contributes to the maintenance of iron homeostasis in A. fumigatus.
MassNet: a functional annotation service for protein mass spectrometry data

Although mass spectrometry has been frequently used to identify proteins, there are no web servers that provide comprehensive functional annotation of those identified proteins. It is necessary to provide such web service due to a rapid increase in the data. We, therefore, introduce MassNet, which provides (i) physico-chemical analysis information, (ii) KEGG pathway assignment (iii) Gene Ontology mapping and (iv) protein–protein interaction (PPI) prediction for the data from MASCOT, Prospector and Profound. MassNet provides the prediction information for PPIs using both 3D structural interaction and experimental interaction deposited in PSIMAP, BIND, DIP, HPRD, IntAct, MINT, CYGD and BioGrid. The web service is freely available at http://massnet.kr or http://sequenceome.kobic. re.kr/MassNet/.
Effects of environmental tobacco smoke exposure on brain functioning in never‐smoking adolescents

Brain functioning, as indexed by event‐related potentials (ERPs) representing smoking cue reactivity, inhibitory control, and reward processing, has been found to be compromised in smokers. However, whether environmental tobacco smoke (ETS) exposure in never smokers results in similar brain changes is unknown. This question is particularly relevant during adolescence, given ongoing brain maturation and a high risk of smoking initiation. The present study tested the associations between ETS exposure and ERPs reflecting cue reactivity (P3, LPP), inhibitory control (N2, P3), and reward processing (anticipation P3 (P3), feedback‐related negativity (FRN)) among never‐smoking adolescents.
More is Simpler: Effectively and Efficiently Assessing Node-Pair Similarities Based on Hyperlinks

Similarity assessment is one of the core tasks in hyperlink analysis. Recently, with the proliferation of applications, e.g., web search and collaborative filtering, SimRank has been a well-studied measure of similarity between two nodes in a graph. It recursively follows the philosophy that "two nodes are similar if they are referenced (have incoming edges) from similar nodes", which can be viewed as an aggregation of similarities based on incoming paths. Despite its popularity, SimRank has an undesirable property, i.e., "zero-similarity": It only accommodates paths with equal length from a common "center" node. Thus, a large portion of other paths are fully ignored. This paper attempts to remedy this issue. (1) We propose and rigorously justify SimRank*, a revised version of SimRank, which resolves such counter-intuitive "zero-similarity" issues while inheriting merits of the basic SimRank philosophy. (2) We show that the series form of SimRank* can be reduced to a fairly succinct and elegant closed form, which looks even simpler than SimRank, yet enriches semantics without suffering from increased computational cost. This leads to a fixed-point iterative paradigm of SimRank* in O(Knm) time on a graph of n nodes and m edges for K iterations, which is comparable to SimRank. (3) To further optimize SimRank* computation, we leverage a novel clustering strategy via edge concentration. Due to its NP-hardness, we devise an efficient and effective heuristic to speed up SimRank* computation to O(Knm) time, where m is generally much smaller than m. (4) Using real and synthetic data, we empirically verify the rich semantics of SimRank*, and demonstrate its high computation efficiency.
The effects of manipulated augmented sensory feedback on error detection when using a touch screen

The purpose of the present study was to determine which augmented sensory modality would best develop subjective error-detection capabilities of learners performing a spatial-temporal task when using a touch screen monitor. Participants were required to learn a 5-digit key-pressing task in a goal time of 2550 ms over 100 acquisition trials on a touch screen. Participants were randomized into 1 of 4 groups: 1) visual-feedback (colour change of button when selected), 2) auditory-feedback (click sound when button was selected), 3) visual-auditory feedback (both colour change and click sound when button was selected), and 4) no-feedback (no colour change or click sound when button was selected). Following each trial, participants were required to provide a subjective estimate regarding their performance time in relation to the actual time it took for them complete the 5-digit sequence. A no-KR retention test was conducted approximately 24-hours after the last completed acquisition trial. Results showed that practicing a timing task on a touch screen augmented with both visual and auditory information may have differentially impacted motor skill acquisition such that removal of one or both sources of augmented feedback did not result in a severe detriment to timing performance or error detection capabilities of the learner. The present study reflects the importance of multimodal augmented feedback conditions to maximize cognitive abilities for developing a stronger motor memory for subjective error-detection and correction capabilities.
Pattern-based core word recognition to support ontology matching

Ontology matching is a crucial issue in the domain of semantic web and data interoperability. In this paper, a core word based method for measuring similarity from the semantic level of ontology entities is described. In ontology, most labels of entities are compound words rather than single meaningful words. However, the main meaning is represented usually by one word of them, which is called core word. The core word is learned by investigating certain patterns, which are defined based on part of speech POS and linguistics knowledge. The other information is noted as complementary information. An algorithm is given to measure the similarity between a pair of compound words and short texts. In order to support diverse situation, especially when core words cannot be recognized, non semantic based ontology matching techniques are applied from lexical and structural level of ontology. The described method is tested on real ontology and benchmarking data sets. It showed good matching ability and obtained promising results.
A Mathematical Model for Bicriteria in Constrained Three Stage Flow Shop Scheduling

This paper presents a bicriteria in n-jobs, three machines flow shop scheduling to minimize the total elapsed time and rental cost of the machines taken on rent under a specified rental policy in which the processing time, independent setup time each associated with probabilities including transportation time and job block criteria. Further the concept of the break down interval for which the machines are not available for the processing is included. A heuristic approach to find optimal or near optimal sequence has been discussed. A computer programme followed by a numerical illustration is given to substantiate the algorithm.
An NLMS algorithm with variable step-size updates through selective inputs

This paper presents a variable step-size normalized least-mean-square (VSS-NLMS) algorithm using only one parameter. Numerous VSS-NLMS algorithms lead to fast convergence rate and low misadjustment. However, they depend on several parameters that are not simple to tune on practice. The proposed VSS-NLMS algorithm leads to fast convergence rate and low misadjustment although the algorithm uses only one parameter. Further, the parameter can be easily tuned through input data.
Premature Termination of MexR Leads to Overexpression of MexAB-OprM Efflux Pump in Pseudomonas aeruginosa in a Tertiary Referral Hospital in India

Objectives The present study was undertaken to investigate the mutations that are present in mexR gene of multidrug resistant (MDR) isolates of Pseudomonas aeruginosa collected from a tertiary referral hospital of north east India. Methods 76 MDR clinical isolates of P. aeruginosa were obtained from the patients who were admitted to or attended the clinics of Silchar medical college and hospital. They were screened phenotypically for the presence of efflux pump activity by an inhibitor based method. Acquired resistance mechanisms were detected by multiplex PCR. Real time PCR was performed to study the expression of mexA gene of MexAB-OprM efflux pump in isolates with increase efflux pump activity. mexR gene of the isolates with overexpressed MexAB-OprM efflux pump was amplified, sequenced and analysed. Results Out of 76 MDR isolates, 24 were found to exhibit efflux pump activity phenotypically against ciprofloxacin and meropenem. Acquired resistance mechanisms were absent in 11 of them and among those isolates, 8 of them overexpressed MexAB-OprM. All the 8 isolates possessed mutation in mexR gene. 11 transversions, 4 transitions, 2 deletion mutations and 2 insertion mutations were found in all the isolates. However, the most significant observation was the formation of a termination codon at 35th position which resulted in the termination of the polypeptide and leads to overexpression of the MexAB-OprM efflux pump. Conclusions This study highlighted emergence of a novel mutation which is probably associated with multi drug resistance. Therefore, further investigations and actions are needed to prevent or at least hold back the expansion and emergence of newer mutations in nosocomial pathogens which may compromise future treatment options.
Ubiquitin-Specific Peptidase 5, a Target Molecule of Vialinin A, Is a Key Molecule of TNF-α Production in RBL-2H3 Cells

Tumor necrosis factor alpha (TNF-α), a central mediator of the inflammatory response, is released from basophilic cells and other cells in response to a variety of proinflammatory stimuli. Vialinin A is a potent inhibitor of TNF-α production and is released from RBL-2H3 cells. Ubiquitin-specific peptidase 5 (USP5), a deubiquitinating enzyme, was identified as a target molecule of vialinin A and its enzymatic activity was inhibited by vialinin A. Here we report production of TNF-α is decreased in USP5 siRNA-knockdown RBL-2H3 cells, compared with control cells. The finding of the present study strongly suggests that USP5 is one of the essential molecules for the production of TNF-α in RBL-2H3.
CSG Operations of Arbitrary Primitives with Interval Arithmetic and Real-Time Ray Casting

We apply Knoll et al.'s algorithm [Knoll et al., "Fast ray tracing of arbitrary implicit surfaces with interval and affine arithmetic. ", Comput. Graph. Forum, 28(1):26–40, 2009] to interactively ray-cast constructive solid geometry (CSG) objects of arbitrary primitives represented as implicit functions. Whereas modeling globally with implicit surfaces suffers from a lack of control, implicits are well-suited for arbitrary primitives and can be combined through various operations. The conventional way to represent union and intersection with interval arithmetic (IA) is simply using min and max but other operations such as the product of two forms can be useful in modeling joints between multiple objects. Typical primitives are objects of simple shape, e.g. cubes, cylinders, spheres, etc. Our method handles arbitrary primitives, e.g. superquadrics or non-algebraic implicits. Subdivision and interval arithmetic guarantee robustness whereas GPU ray casting allows for fast and aesthetic rendering. Indeed, ray casting parallelizes efficiently and trivially and thus takes advantage of the continuous increasing computational power of hardware (CPUs and GPUs); moreover it lends itself to multi-bounce effects, such as shadows and transparency, which help for the visualization of complicated objects. With our system, we are able to render multi-material CSG trees of implicits robustly, in interactive time and with good visual quality.
2-D DOA and polarization estimation of LFM signals with one electromagnetic vector sensor

Spatial polarimetric WVD matrix is constructed using Wigner-Ville distribution (WVD), a new algorithm is proposed for two-dimension (2-D) direction of arrival (DOA) and polarization of linear frequency modulation (LFM) signals based on a single electromagnetic vector sensor. The signals with different time-frequency (t-f) signatures permit the source discriminations through the flexibility of selecting t-f region. So by utilizing different t-f regions, the proposed method can distinguish the closely-spaced sources and perform signal selective DOA. Simulations illustrate the efficacy of the proposed algorithm.
Word-sense disambiguation in biomedical ontologies

With the ever increase in biomedical literature, text-mining has emerged as an important technology to support bio-curation and search. Word sense disambiguation (WSD), the correct identification of terms in text in the light of ambiguity, is an important problem in text-mining. Since the late 1940s many approaches based on supervised (decision trees, naive Bayes, neural networks, support vector machines) and unsupervised machine learning (context-clustering, word-clustering, co-occurrence graphs) have been developed. Knowledge-based methods that make use of the WordNet computational lexicon have also been developed. But only few make use of ontologies, i.e. hierarchical controlled vocabularies, to solve the problem and none exploit inference over ontologies and the use of metadata from publications. This thesis addresses the WSD problem in biomedical ontologies by suggesting different approaches for word sense disambiguation that use ontologies and metadata. The “Closest Sense” method assumes that the ontology defines multiple senses of the term; it computes the shortest path of co-occurring terms in the document to one of these senses. The “Term Cooc” method defines a log-odds ratio for co-occurring terms including inferred co-occurrences. The “MetaData” approach trains a classifier on metadata; it does not require any ontology, but requires training data, which the other methods do not. These approaches are compared to each other when applied to a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The MetaData approach performs best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The Term Cooc approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The Closest Sense approach achieves on average 80% success rate. Furthermore, the thesis showcases applications ranging from ontology design to semantic search where WSD is important.
Time-Efficient High-Resolution Large-Area Nano-Patterning of Silicon Dioxide

A nano-patterning approach on silicon dioxide (SiO2) material, which could be used for the selective growth of III-V nanowires in photovoltaic applications, is demonstrated. In this process, a silicon (Si) stamp with nanopillar structures was first fabricated using electron-beam lithography (EBL) followed by a dry etching process. Afterwards, the Si stamp was employed in nanoimprint lithography (NIL) assisted with a dry etching process to produce nanoholes on the SiO2 layer. The demonstrated approach has advantages such as a high resolution in nanoscale by EBL and good reproducibility by NIL. In addition, high time efficiency can be realized by one-spot electron-beam exposure in the EBL process combined with NIL for mass production. Furthermore, the one-spot exposure enables the scalability of the nanostructures for different application requirements by tuning only the exposure dose. The size variation of the nanostructures resulting from exposure parameters in EBL, the pattern transfer during nanoimprint in NIL, and subsequent etching processes of SiO2 were also studied quantitatively. By this method, a hexagonal arranged hole array in SiO2 with a hole diameter ranging from 45 to 75 nm and a pitch of 600 nm was demonstrated on a four-inch wafer.
Introducing a software modeling concept in a medium-sized company

Describes, using the Quality Improvement Paradigm (QIP), how an improvement project aimed at improving the modeling and documentation approach of a medium-sized company (Markant Su/spl uml/dwest Software und Dienstleistungs GmbH) was conducted. We discuss the new modeling approach, which may serve for other companies as a template for deriving their own adapted approach. Further, we illustrate our insights from this project that can help in future technology transfer projects. A major characteristic of this project was that it was embedded in a long-term consulting relationship.
The method for mosaicing the low overlap image sequence

The method is proposed for mosaicing the low overlap image sequence. The overlap areas of the low overlap images can be detected robustly by using the way of power-of-two square-image, and the computation of non-matching pixels can be reduced. The brightness of images is pre-processed in the way of diffusing brightness to eliminate the brightness difference in the panorama. The experiment shows that the method has good effect on mosaicing the low overlap images.
Wideband spectrum sensing receiver for cognitive radio application

For effective spectrum utilization, cognitive radio makes use of wideband spectrum sensing receiver. Current wideband receiver architectures need wideband analog to digital converter (ADC) to sample wideband signal. As current state-of-art ADC has limitation in terms of power and sampling rate, there is a need to explore other alternative solutions. Compressive sampling (CS) data acquisition method is one of the alternatives. Cognitive radio signal which is sparse in frequency domain is sampled at sub-Nyquist rate using lower sampling rate ADC. To reduce the receiver complexity we use modulated wideband converter (MWC) architecture, a sub-Nyquist sampling method. The circuit level design of this architecture covers signal within frequency range of 500 MHz to 2.1 GHz, with channel bandwidth of 1600 MHz. By using 8 parallel paths with channel trading factor of 11, effective sampling rate of 550 MHz is achieved for successful support recovery of multi-band input signal of size N=12.
Learning by Demonstration for Planning Activities of Daily Living in Rehabilitation and Assistive Robotics

This letter presents a motion planning system for robotic devices to be adopted in assistive or rehabilitation scenarios. The proposed system is grounded on a learning by demonstration approach based on dynamic movement primitives (DMP) and presents a high level of generalization allowing the user to perform activities of daily living. The proposed approach has been experimentally validated on a robotic arm (i.e., the Kuka LWR4+) attached to a human subject wrist. Two experimental sessions have been carried out in order to: 1) evaluate the differences between our approach and the one proposed in “Dynamical movement primitives: Learning attractor models for motor behaviors” (A. J. Ijspeert et al., Neural Comput., 2013) in terms of reconstruction error between the demonstrated trajectory and the learned one, and in terms of memory size required to record the database of DMP parameters; and 2) measure the generalization level of the proposed system with respect to the variation of the object positions by evaluating the success rate of the task execution. The experimental results demonstrate that the proposed approach allows 1) reproducing the user's personal motion style with high accuracy and 2) efficiently generalizing with respect to the change of object position. Furthermore, a significant reduction of memory allocation for the database can be achieved, with a consequent significant computational time saving.
A Kalman Smoothing Algorithm for Speech Enhancement Based on the Properties of Vocal Tract Varying Slowly

The linear prediction coefficients obtained from noisy speech have an important impact on improving the quality of the enhanced speech in the speech enhancement algorithm based on the Kalman Smoother. According to the properties of the slow changes of the vocal tract, this paper proposes a novel Kalman smoothing algorithm for speech enhancement based on vocal tract parameters smoother. Firstly, the linear prediction coefficients are converted into the line spectrum frequency parameters. Then, these parameters of the adjacent frames are smoothed before they transform into state transition matrix. Experimental results indicate that the proposed Kalman smoothing algorithm for speech enhancement based on vocal tract parameters smoother can suppress the sudden changes of residual noise energy and improve the quality of enhanced speech. The quality of the enhanced speech is evaluated by means of segmental SNR and ITU-PESQ scores. Experimental results indicate that the proposed algorithm achieves obvious improvements compared with conventional Wiener filter.
Evaluation of interest point detectors for Visual SLAM

: In this paper we present several interest points detectors and we analyze their suitability when used as landmark extractors for vision-based simultaneous localization and mapping. For this purpose, we evaluate the detectors according to their repeatability under changes in viewpoint and scale. These are the desired requirements for visual landmarks. Several experiments were carried out using sequence of images captured with high precision. The sequences represent planar objects as well as 3D scenes.
DP RIETI Discussion Paper Series 13E-041 Competition , Productivity Growth , and Structural Change

Extending the endogenous growth model proposed by Young (1998), we construct a two-sector growth model that explains the observed pattern of structural change. Unlike existing studies, we assume neither non-homothetic preferences nor exogenous differential in productivity growth among different sectors. Our key assumption is that any two goods produced by firms in a sector are closer substitutes than those produced by firms in other sectors, which gives rise to an endogenous differential in productivity growth among different sectors. When the two composite goods are poor substitutes, the share of employment gradually shifts from the sector with a low markup to that with a high markup. To test the validity of the prediction of our model, we also estimate industry-level total factor productivity (TFP) growth and markup using Japanese firm-level panel data. The empirical results show a negative correlation between estimated markup and long-term TFP growth, and a positive correlation between the growth of industrial labor input and markup, which supports our theoretical results. Finally, in contrast to the previous studies of structural change that consider competitive economies, we study the socially optimal allocation and characterize the optimal tax policies.