Miracle-dz/UniFAR-FTUs huggingface dataset:
The Elicitin-Like Glycoprotein, ELI025, Is Secreted by the Pathogenic Oomycete Pythium insidiosum and Evades Host Antibody Responses

Pythium insidiosum is a unique oomycete that can infect humans and animals. Patients with a P. insidiosum infection (pythiosis) have high rates of morbidity and mortality. The pathogen resists conventional antifungal drugs. Information on the biology and pathogenesis of P. insidiosum is limited. Many pathogens secrete proteins, known as effectors, which can affect the host response and promote the infection process. Elicitins are secretory proteins and are found only in the oomycetes, primarily in Phytophthora and Pythium species. In plant-pathogenic oomycetes, elicitins function as pathogen-associated molecular pattern molecules, sterol carriers, and plant defense stimulators. Recently, we reported a number of elicitin-encoding genes from the P. insidiosum transcriptome. The function of elicitins during human infections is unknown. One of the P. insidiosum elicitin-encoding genes, ELI025, is highly expressed and up-regulated at body temperature. This study aims to characterize the biochemical, immunological, and genetic properties of the elicitin protein, ELI025. A 12.4-kDa recombinant ELI025 protein (rELI025) was expressed in Escherichia coli. Rabbit anti-rELI025 antibodies reacted strongly with the native ELI025 in P. insidiosum’s culture medium. The detected ELI025 had two isoforms: glycosylated and non-glycosylated. ELI025 was not immunoreactive with sera from pythiosis patients. The region near the transcriptional start site of ELI025 contained conserved oomycete core promoter elements. In conclusion, ELI025 is a small, abundant, secreted glycoprotein that evades host antibody responses. ELI025 is a promising candidate for development of diagnostic and therapeutic targets for pythiosis.
Long-Term Yield Rates for Actuarial Valuations

Abstract Consider the problem of valuing a life insurance or annuity on a person aged 20. The valuation formula requires that we know the prices of pure-discount bonds with maturities of up to 100 years. This article investigates the problem of estimating the yield rate for a pure-discount bond that matures in 100 years. It is shown how to estimate this yield rate with parametric and nonparametric models on the price of U.S. Treasury strips. Moreover, confidence intervals on these rates are constructed with bootstrap methods.
Monitoring a virtual network infrastructure: an IaaS perspective

Infrastructure as a Service (IaaS) providers keep extending with new features the computing infrastructures they offer on a pay per use basis. In this paper we explore reasons and opportunities to include networking within such features, meeting the demand of users that need composite computing architectures similar to Grids. The introduction of networking capabilities within IaaSs would further increase the potential of this technology, and also foster an evolution of Grids towards a confluence, thus incorporating the experiences matured in this environment. Network monitoring emerges as a relevant feature of such virtual architectures, which must exhibit the distinguishing properties of the IaaS paradigm: scalability, dynamic configuration, accounting. Monitoring tools developed with the same purpose in Grids provide useful insights on problems and solutions.
Segmentation and tracking of interacting human body parts under occlusion and shadowing

The paper presents a system to segment and track multiple body parts of interacting humans in the presence of mutual occlusion and shadow. The color image sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train and classify individual pixel colors. A Markov random field (MRF) framework is used at the blob level to merge the pixels into coherent blobs and to register inter-blob relations. A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures. A two-fold tracking scheme is used which consists of blob to blob matching in consecutive frames and blob to body part association within a frame. The tracking scheme resembles a multi-target, multi-assignment framework. The result is a tracking system that simultaneously segments and tracks multiple body parts of interacting people. Example sequences illustrate the success of the proposed paradigm.
From dusk till dawn: Localisation at night using artificial light sources

This paper is about localising at night in urban environments using vision. Despite it being dark exactly half of the time, surprisingly little attention has been given to this problem. A defining aspect of night-time urban scenes is the presence and effect of artificial lighting - be that in the form of street or interior lighting through windows. By building a model of the environment which includes a representation of the spatial location of every light source, localisation becomes possible using monocular cameras. One of the challenges we face is the gross change in light appearance as a function of distance due to flare, saturation and bleeding - city lights certainly do not appear as point features. To overcome this, we model the appearance of each light as a function of vehicle location, using this to inform our data-association decisions and to regularise the cost function which is used to infer vehicle pose. In this way we develop a place-dependent but stable sensor model which is customised for the particular environment in which we are operating. We demonstrate that our system is able to localise successfully at night over 12 km in situations where a traditional point feature based system fails.
Development and Validation of a Prediction Score for Complications after Hepatectomy in Hepatitis B-Related Hepatocellular Carcinoma Patients

Objective and Background The aim of the present study was to develop and validate a prediction score for postoperative complications by severity and guide perioperative management and patient selection in hepatitis B-related hepatocellular carcinoma patients undergoing liver resection. Methods A total of 1543 consecutive liver resections cases were included in the study. Randomly selected sample set of 70% of the study cohort was used to develop a score to predict complications III–V and the remaining 30% was used to validate the score. Based on the preoperative and predictable intraoperative parameters, logistic regression analysis was used to identify risk factors and create an integer score for the predicting of complication. Results American Society of Anesthesiologists category, portal hypertension, major liver resection (more than 3 segments) and extrahepatic procedures were identified as independent predictors for complications III–V by logistic regression analysis. A score system integrating these 4 factors was stratified into three groups and significantly predicted the risk of complications III–V, with a rate of 1.6%, 11.9% and 65.6% for low, moderate and high risk, respectively. Using the score, the complications risk could be predicted accurately in the validation set, without significant differences between predicted (10.4%) and observed (8.4%) risks for complications III–V (P = 0.466). Conclusions Based on four preoperative risk factors, we have developed and validated an integer-based risk score to predict postoperative severe complications after liver resection for hepatitis B-related hepatocellular carcinoma patients in high-volume surgical center. This score may contribute to preoperative risk stratification and clinical decision-making.
On Compositions of Random Functions on a Finite Set

A milling cutter having blades retained against seating surfaces in cutter body slots by resilient leaf springs engaging the blade edge surface that is opposite the seating surface for the blade. The seating surfaces are each part of a tapered wedge element and a threaded adjuster extending through the wedge element into the cutter body, parallel to the wedge seating surface.
DEMIR at ImageCLEFMed 2011: Evaluation of Fusion Techniques for Multimodal Content-based Medical Image Retrieval

This paper present the details of participation of DEMIR (Dokuz Eylul University Multimedia Information Retrieval) research team to the context of our participation to the ImageCLEF 2011 Medical Retrieval task. This year, we evaluated fusion and re-ranking method which is based on the best low level feature of images with best text retrieval result. We improved results by examination of different weighting models for retrieved text data and low level features. We tested multi-modality image retrieval in ImageCLEF 2011 medical retrieval task and obtained the best seven ranks in mixed retrieval, which includes textual and visual modalities. The results clearly show that proper fusion of different modalities improve the overall retrieval performance.
Continuous-time indefinite linear quadratic optimal control with random input gains

In this paper, the indefinite linear quadratic (LQ) optimal control of continuous-time linear time-invariant (LTI) systems with random input gains is studied. One main novelty of this work is the use of channel/controller co-design framework which bridges and integrates the design of the channels and controller. The co-design is carried out by the twist of channel resource allocation, i.e., the channel capacities can be allocated among the input channels by the control designer subject to an overall capacity constraint. With the channel/controller co-design, necessary and sufficient conditions for the well-posedness as well as the attainability of the indefinite LQ problem concerned are obtained. The optimal control law is given by a linear state feedback associated with the mean-square stabilizing solution of a modified algebraic Riccati equation.
Detecting Learner Errors in the Choice of Content Words Using Compositional Distributional Semantics

We describe a novel approach to error detection in adjective‐noun combinations. We present and release a new dataset of annotated errors where the examples are extracted from learner texts and annotated with error types. We show how compositional distributional semantic approaches can be applied to discriminate between correct and incorrect word combinations from learner data. Finally, we show how the output of the compositional distributional semantic models can be used as features in a classifier yielding good precision and accuracy.
Opportunities and Challenges of Engaged Indigenous Scholarship

Management and Organization Review (MOR) is announcing a renewed initiative that seeks to encourage and publish research reporting engaged indigenous scholarship in China. MOR invites empirical as well as conceptual studies of indigenous phenomena related to management and organizations. MOR welcomes exploratory studies of new, emerging, and/or poorly understood indigenous research questions that employ abductive reasoning and creative hunches, as opposed to testing hypotheses deduced from non-indigenous Western theories. Data on indigenous phenomena can come from any source, including qualitative and quantitative data from case studies, field surveys, experiments, and ethnographies.
DECF: A Coarse-Grained Data-Parallel Programming Framework for Seismic Processing

In this paper, we present a distributed execution control framework (DECF) for seismic processing. The DECF framework employs a coarse-grained data-parallel execution model within a cluster environment and achieves high throughput. The DECF schedules processing modules written as sequential programs to run simultaneously on multiple computers, or multiple CPU cores. The DECF hides the messy details of scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between processing modules.We also introduce a parallel implementation of 3D pre-stack kirchhoff time migration algorithm using MPI and share the practical lessons learned from the process of designing, implementing, and maintaining it, which are of great help to the future work of the DECF.
Effects of dual-task training on balance and executive functions in Parkinson's disease: A pilot study

Abstract The aim of this study was to analyze the efficacy of cognitive–motor dual-task training compared with single-task training on balance and executive functions in individuals with Parkinson's disease. Fifteen subjects, aged between 39 and 75 years old, were randomly assigned to the dual-task training group (n = 8) and single-task training group (n = 7). The training was run twice a week for 6 weeks. The single-task group received balance training and the dual-task group performed cognitive tasks simultaneously with the balance training. There were no significant differences between the two groups at baseline. After the intervention, the results for mediolateral sway with eyes closed were significantly better for the dual-task group and anteroposterior sway with eyes closed was significantly better for the single-task group. The results suggest superior outcomes for the dual-task training compared to the single-task training for static postural control, except in anteroposterior sway with eyes closed.
Estimation and control of sensorless brushless DC motor drive using Extended Kalman Filter

This paper presents a method for estimation of the rotor speed and position of a BLDC motor. As BLDC motors are non-linear systems, Extended Kalman Filter (EKF), an advanced version of the Kalman filter has been used for designing the control algorithm for the motor. In the proposed work, the motor state variables are estimated using an Extended Kalman Filter, using the measurements of the stator line voltages and currents alone. The estimated rotor speed has been used for the closed loop speed control of the BLDC motor and the simulation results have been verified.
Proving Data Structure Properties by Automatic Induction

We consider the problem of automated program verification with emphasis on reasoning about dynamically manipulated data structures. Presently, the state-of-the-art methods are limited to the unfold-and-match (U+M) paradigm where predicates are transformed by fold/unfold operations induced from their recursive definitions. A crucial limitation of U+M is that it cannot in general prove properties between different predicates. Our contribution is a method which can automatically detect and employ induction hypothesis in the proof process, thereby providing a systematic and general method for reasoning about different predicates for the first time. After arguing that the need for this is in fact widespread in practice, we finally demonstrate our method experimentally. We consider the problem of automated program verification with emphasis on reasoning about dynamically manipulated data structures. In automated verification, pre/post conditions are typically specified for each method/procedure (and an invariant given for each loop) before the reasoning system automatically checks if each given program code is correct with respect to the given pre/post/invariant annotations. The verification of properties of the dynamically modified heap is a big challenge for logical methods. This is because typical correctness properties of heaps require complex combinations of structure, data, and separation. This paper presents a proof method for general properties of structure, data and separation. Our framework is not restricted to any decidable class and instead attempts to go beyond the classes covered by present methods. The State-of-the-Art: Unfold and Match Automated proof of heap properties ‐ usually formalized using separation logic and extended with recursively defined shape predicates ‐ “relies on decidable sub-classes together with the corresponding proof systems based on folding/unfolding strategies for recursive shape definitions” [19]. Informally, the state-of-theart [5, 21, 20] can be classified as “unfold-and-match” (U+M) techniques. The proof search proceeds by repetitively applying (un)folding strategies, until all the reduced predicates in the RHS of the obligation can be canceled out by corresponding predicates in the LHS via simple, non-recursive, matching method. More precisely, the only way to manipulate predicates which are user-defined by means of recursive rules is, presently, to em
Characterization and Structure Prediction of Partial Length Protein Sequences of pcoA, pcoR and chrB Genes from Heavy Metal Resistant Bacteria from the Klip River, South Africa

The Klip River has suffered from severe anthropogenic effects from industrial activities such as mining. Long-term exposure to heavy metal pollution has led to the development of heavy metal resistant strains of Pseudomonas sp. KR23, Lysinibacillus sp. KR25, and E. coli KR29. The objectives of this study were to characterize the genetics of copper and chromate resistance of the isolates. Copper and chromate resistance determinants were cloned and sequenced. Open reading frames (ORFs) related to the genes CopA and CopR were identified in E. coli KR29, PcoA in Lysinibacillus sp. KR25 and none related to chromate resistance were detected. The 3D-models predicted by I-TASSER disclose that the PcoA proteins consist of β-sheets, which form a part of the cupredoxin domain of the CopA copper resistance family of genes. The model for PcoR_29 revealed the presence of a helix turn helix; this forms part of a DNA binding protein, which is part of a heavy metal transcriptional regulator. The bacterial strains were cured using ethidium bromide. The genes encoding for heavy metal resistance and antibiotic resistance were found to be located on the chromosome for both Pseudomonas sp. (KR23) and E. coli (KR29). For Lysinibacillus (KR25) the heavy metal resistance determinants are suspected to be located on a mobile genetic element, which was not detected using gel electrophoresis.
Implementation and Experience of the Online Peer Grading System for Our Real Class

In an online learning course like MOOC (Massive Open Online Course), peer grading is useful for scaling the grades of assignments to the large number of students. Peer grading is also attractive because it reduces the burden of teachers and gives rich feedback to learners. In Kyushu University, we have a demand for using peer grading in some classes that include a large number of students. However, the discussion about operating peer grading in our university has not been discussed sufficiently. We developed the online peer grading system to cope with the large number of students in the class. Students can upload their assignments, grade others, and receive feedback from other students via the system. We operated it in our university's class in order to verify whether peer grading works well and return accurate scores. In this paper, we verify it with an analysis of the data of user behavior, and also comparison between peer and teacher grading scores. We show questionnaire results. As a result, although there are still issues to improve, we find the validity in peer grading to some extent.
A comparison study of different physical treatments on cartilage matrix derived porous scaffolds for tissue engineering applications

Abstract Native cartilage matrix derived (CMD) scaffolds from various animal and human sources have drawn attention in cartilage tissue engineering due to the demonstrable presence of bioactive components. Different chemical and physical treatments have been employed to enhance the micro-architecture of CMD scaffolds. In this study we have assessed the typical effects of physical cross-linking methods, namely ultraviolet (UV) light, dehydrothermal (DHT) treatment, and combinations of them on bovine articular CMD porous scaffolds with three different matrix concentrations (5%, 15% and 30%) to assess the relative strengths of each treatment. Our findings suggest that UV and UV–DHT treatments on 15% CMD scaffolds can yield architecturally optimal scaffolds for cartilage tissue engineering.
Renoprotective antioxidant effect of alagebrium in experimental diabetes.

BACKGROUND Despite the beneficial effects of alagebrium (ALA), a putative advanced glycation end-product (AGE) breaker, on diabetic nephropathy, its renoprotective mechanisms are incompletely understood. Since oxidative stress exacerbates diabetic renal injury through interaction with AGE, the present study examined the antioxidative property of ALA in db/db mice, mesangial cells cultured under high glucose or H(2)O(2) and a test tube. METHODS ALA (2 mg/kg/day) was administered intraperitoneally for 12 weeks to 8-week-old db/m and db/db (D(ALA)E) mice or for 4 weeks to 16-week-old db/db mice (D(ALA)L). Oxidative stress markers (nitrotyrosine accumulation, expression and translocation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunits, cellular DCF-DA fluorescence) together with urinary albumin excretion and histological changes including mesangial expansion were measured. The concentration of H(2)O(2) in the presence and absence of ALA was measured by iodometric analysis in a test tube. RESULTS ALA significantly reduced not only urinary albumin excretion and renal pathological changes but also accumulation of pentosidine and nitrotyrosine and expression of NADPH oxidase subunits in db/db mice regardless of treatment protocol. In mesangial cells, ALA effectively prevented not only high glucose- but also H(2)O(2)-induced membrane translocation of NADPH oxidase subunit (p47 phox, p67 phox and rac1) and protein kinase C isoform (α, βI and βII) and Nox4 messenger RNA expression concomitant with cellular reactive oxygen species. Furthermore, ALA directly decreased H(2)O(2) in a test tube. CONCLUSION ALA has both direct and indirect antioxidant effects that may play important roles in ALA's renoprotective effect in diabetic kidneys.
Learning mixed-state Markov models for statistical motion texture tracking

A motion texture is the instantaneous scalar map of apparent motion values extracted from a dynamic or temporal texture. It is mostly displayed by natural scene elements (fire, smoke, water) but also involves more general textured motion patterns (eg. a crowd of people, a flock). In this work we are interested in the modeling and tracking of motion textures. Experimentally we observe that such motion maps exhibit values of a mixed type: a discrete component at zero and a continuous component of non-null motion values. Thus, we propose a statistical characterization of motion textures based on a mixed-state causal modeling. Next, the problem of tracking is considered. A set of mixed-state model parameters is learned as a descriptive feature of the motion texture to track and displacement estimation is solved using the conditional Kullback-Leibler divergence for statistical window matching. Results and comparisons are presented on real sequences.
Minimum MSE Filter Banks Design Using Correlated Signal Dependent Quantization Noise Model

A theory is proposed for how to design minimum mean square error (MSE) filter banks for lossy source coding when uniform quantizers and ideal entropy coding are used to compress the subband signals. A signal-dependent correlation model is used to find the auto and cross correlation between the additive quantization noise within the same subband and across different subbands. This theory also shows how to find the cross correlation between the original time-series and the additive quantization noise. The simulation results show that the proposed way of designing the filter banks outperforms the minimum MSE performance results found by using the classical signal independent high-rate quantization noise model.