Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability and validity of measures taken during the Chester step test to predict aerobic power and to prescribe aerobic exercise

Objectives: To evaluate the reliability and validity of measures taken during the Chester step test (CST) used to predict VO2max and prescribe subsequent exercise. Methods: The CST was performed twice on separate days by 7 males and 6 females aged 22.4 (SD 4.6) years. Heart rate (HR), ratings of perceived exertion (RPE), and oxygen uptake (VO2) were measured at each stage of the CST. Results: RPE, HR, and actual VO2 were the same at each stage for both trials but each of these measures was significantly different between CST stages (p<0.0005). Intertrial bias ±95% limits of agreement (95% LoA) of HR reached acceptable limits at CST stage IV (−2±10 beats/min) and for RPE at stages III (0.2±1.4) and IV (0.5±1.9). Age estimated HRmax significantly overestimated actual HRmax of 5 beats/min (p = 0.016) and the 95% LoA showed that this error could range from an underestimation of 17 beats/min to an overestimation of 7 beats/min. Estimated versus actual VO2 at each CST stage during both trials showed errors ranging between 11% and 19%. Trial 1 underestimated actual VO2max by 2.8 ml/kg/min (p = 0.006) and trial 2 by 1.6 ml/kg/min (not significant). The intertrial agreement in predicted VO2max was relatively narrow with a bias ±95% LoA of −0.8±3.7 ml/kg/min. The RPE and %HRmax (actual) correlation improved with a second trial. At all CST stages in trial 2 RPE:%HRmax coefficients were significant with the highest correlations at CST stages III (r = 0.78) and IV (r = 0.84). Conclusion: CST VO2max prediction validity is questioned but the CST is reliable on a test-retest basis. VO2max prediction error is due more to VO2 estimation error at each CST stage compared with error in age estimated HRmax. The HR/RPE relation at >50% VO2max reliably represents the recommended intensity for developing cardiorespiratory fitness, but only when a practice trial of the CST is first performed.
Estimating the ability of plants to plastically track temperature‐mediated shifts in the spring phenological optimum

One consequence of rising spring temperatures is that the optimum timing of key life‐history events may advance. Where this is the case, a population's fate may depend on the degree to which it is able to track a change in the optimum timing either via plasticity or via adaptation. Estimating the effect that temperature change will have on optimum timing using standard approaches is logistically challenging, with the result that very few estimates of this important parameter exist. Here we adopt an alternative statistical method that substitutes space for time to estimate the temperature sensitivity of the optimum timing of 22 plant species based on >200 000 spatiotemporal phenological observations from across the United Kingdom. We find that first leafing and flowering dates are sensitive to forcing (spring) temperatures, with optimum timing advancing by an average of 3 days °C−1 and plastic responses to forcing between −3 and −8 days °C−1. Chilling (autumn/winter) temperatures and photoperiod tend to be important cues for species with early and late phenology, respectively. For most species, we find that plasticity is adaptive, and for seven species, plasticity is sufficient to track geographic variation in the optimum phenology. For four species, we find that plasticity is significantly steeper than the optimum slope that we estimate between forcing temperature and phenology, and we examine possible explanations for this countergradient pattern, including local adaptation.
Improved Local Coordinate Coding using Local Tangents

Local Coordinate Coding (LCC), introduced in (Yu et al., 2009), is a high dimensional nonlinear learning method that explicitly takes advantage of the geometric structure of the data. Its successful use in the winning system of last year's Pascal image classification Challenge (Everingham, 2009) shows that the ability to integrate geometric information is critical for some real world machine learning applications. This paper further develops the idea of integrating geometry in machine learning by extending the original LCC method to include local tangent directions. These new correction terms lead to better approximation of high dimensional nonlinear functions when the underlying data manifold is locally relatively flat. The method significantly reduces the number of anchor points needed in LCC, which not only reduces computational cost, but also improves prediction performance. Experiments are included to demonstrate that this method is more effective than the original LCC method on some image classification tasks.
GermOnline, a cross-species community knowledgebase on germ cell differentiation

GermOnline provides information and microarray expression data for genes involved in mitosis and meiosis, gamete formation and germ line development across species. The database has been developed, and is being curated and updated, by life scientists in cooperation with bioinformaticists. Information is contributed through an online form using free text, images and the controlled vocabulary developed by the GeneOntology Consortium. Authors provide up to three references in support of their contribution. The database is governed by an international board of scientists to ensure a standardized data format and the highest quality of GermOnline's information content. Release 2.0 provides exclusive access to microarray expression data from Saccharomyces cerevisiae and Rattus norvegicus, as well as curated information on approximately 700 genes from various organisms. The locus report pages include links to external databases that contain relevant annotation, microarray expression and proteome data. Conversely, the Saccharomyces Genome Database (SGD), S.cerevisiae GeneDB and Swiss-Prot link to the budding yeast section of GermOnline from their respective locus pages. GermOnline, a fully operational prototype subject-oriented knowledgebase designed for community annotation and array data visualization, is accessible at http://www.germonline. org. The target audience includes researchers who work on mitotic cell division, meiosis, gametogenesis, germ line development, human reproductive health and comparative genomics.
A Theoretical Approach to Predict the Fatigue Life of Flexible Pipes

This paper focuses on a theoretical approach to access the fatigue life of flexible pipes. This methodology employs functions that convert forces and moments obtained in time-domain global analyses into stresses in their tensile armors. The stresses are then processed by well-known cycle counting methods, and S-N curves are used to evaluate the fatigue damage at several points in the pipe’s cross-section. Finally, Palmgren-Miner linear damage hypothesis is assumed in order to calculate the accumulated fatigue damage. A study on the fatigue life of a flexible pipe employing this methodology is presented. The main points addressed in the study are the influence of friction between layers, the effect of the annulus conditions, the importance of evaluating the fatigue life in various points of the pipe’s cross-section, and the effect of mean stresses. The results obtained suggest that the friction between layers and the annulus conditions strongly influences the fatigue life of flexible pipes. Moreover, mean stress effects are also significant, and at least half of the wires in each analyzed section of the pipe must be considered in a typical fatigue analysis.
Secure localization with defense against selective forwarding attacks in wireless sensor networks

In selective forwarding attack, malicious nodes may refuse to forward certain messages and simply drop them, ensuring that they are not propagated any more. Similarly, in sensor localization, a malicious node may refuse to forward the location information or drops them, with confirmation that they are not propagated further. In this paper, we propose a defensive technique for selective forwarding attack in localization. It consists of 3 phases for secure information delivery. First phase is secret sharing of information, secondly randomized propagation of each information share, and finally minhop routing towards the anchor nodes. Each time an information packet needs to be sent, the packet is broke into t shares according to threshold secret sharing algorithm. When the anchor nodes collect at least t shares, original packet are reconstructed. Through simulation results, it is shown that this is effective defensive technique for selective forwarding attack.
A Visual Web Query System for NeuronBank Ontology

Ontologies have recently been heavily developed in the life sciences. Scientists retrieve life science ontologies and references to them (e.g. annotations) for aiding their research. As users usually have little or no knowledge about the query languages such as Algernon or SPARQL to access these ontologies, designing flexible and powerful Web query interfaces will make this information more accessible. In this paper we describe a Web query system for NeuronBank 1 , a web-based tool for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. We aim to assist users in searching within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The query interface ideas are general enough that they can be adapted to work with other ontologies.
Growing and Analyzing Biofilms in Flow Chambers

This unit describes the setup of flow chamber systems for the study of microbial biofilms, and methods for the analysis of structural biofilm formation. Use of flow chambers allows direct microscopic investigation of biofilm formation. The biofilms in flow chambers develop under hydrodynamic conditions, and the environment can be carefully controlled and easily changed. The protocols in this unit include construction of the flow chamber and the bubble trap, assembly and sterilization of the flow chamber system, inoculation of the flow chambers, running of the system, image capture and analysis, and disassembly and cleaning of the system. In addition, embedding and fluorescent in situ hybridization of flow chamber–grown biofilms are addressed. Curr. Protoc. Microbiol. 21:1B.2. 1‐1B.2. 17. © 2011 by John Wiley & Sons, Inc.
Stochastic Simulation of Coupled Reaction-Diffusion Processes

The stochastic time evolution method has been used previously to study non-linear chemical reaction processes in well-stirred homogeneous systems. We present the first treatment of diffusion, in the stochastic method, for non-linear reaction? diffusion processes. The derivation introduces mesoscopic rates of diffusion that are formally analogous to reaction rates. We map, using Green's function, the bulk diffusion coefficient D in Fick's differential law to the corresponding transition rate probability for diffusion of a particle between finite volume elements. This generalized stochastic algorithm enables us to numerically calculate the time evolution of a spatially inhomogeneous mixture of reaction? diffusion species in a finite volume. The algorithm is equivalent to solving the time evolution of the spatially inhomogeneous master equation. A unique feature of our method is that the time step is stochastic and is generated by a probability distribution determined by the intrinsic reaction kinetics and diffusion dynamics. To demonstrate the method, we consider the biologically important nonlinear reaction? diffusion process of calcium wave propagation within living cells.
A Process Theory of Non-monotonic Inference

Artificial Intelligence needs a formal theory of the process of non-monotonic reasoning. Ideally, such a theory would decide, for every proposition and state of the process, whether the program should believe the proposition in that state, or remain agnostic. Without non-monotonic inference rules, nonmonotonic inferences cannot be explained in the same relational, rule-based fashion as other inferences. But with such rules, theoremhood is often formally undecidable and thus a useless criterion for our purpose. So how could any system be a "non-monotonic logic programming language"? Our method uses the language, inference rules and proofs of non-monotonic logics, but ignores theoremhood. Instead, it defines states of the reasoning process, and focuses on current proof as the criterion for belief. It defines "admissible beliefs" and "valid proof" for given states, and we prove in [5] that a belief is currently admissible iff it is currently proven. The primitive nonmonotonic condition is "currently unproven". The theory, Logical Process Theory, can accept a range of non-monotonic logics. It was inspired by Doyle's RMS [3] and is similar to his more recent theory in [4|. A model implementation, WATSON, exists and has been used to write a small diagnostic reasoner, which reasons non-monotonic ally using violation of expectations and an abstraction hierarchy.
Three-Dimensional Collagen I Promotes Gemcitabine Resistance In Vitro in Pancreatic Cancer Cells through HMGA2-Dependent Histone Acetyltransferase Expression

Pancreatic ductal adenocarcinoma (PDAC) is associated with a pronounced collagen-rich stromal reaction that has been shown to contribute to chemo-resistance. We have previously shown that PDAC cells are resistant to gemcitabine chemotherapy in the collagen microenvironment because of increased expression of the chromatin remodeling protein high mobility group A2 (HMGA2). We have now found that human PDAC tumors display higher levels of histone H3K9 and H3K27 acetylation in fibrotic regions. We show that relative to cells grown on tissue culture plastic, PDAC cells grown in three-dimensional collagen gels demonstrate increased histone H3K9 and H3K27 acetylation, along with increased expression of p300, PCAF and GCN5 histone acetyltransferases (HATs). Knocking down HMGA2 attenuates the effect of collagen on histone H3K9 and H3K27 acetylation and on collagen-induced p300, PCAF and GCN5 expression. We also show that human PDAC tumors with HMGA2 demonstrate increased histone H3K9 and H3K27 acetylation. Additionally, we show that cells in three-dimensional collagen gels demonstrate increased protection against gemcitabine. Significantly, down-regulation of HMGA2 or p300, PCAF and GCN5 HATs sensitizes the cells to gemcitabine in three-dimensional collagen. Overall, our results increase our understanding of how the collagen microenvironment contributes to chemo-resistance in vitro and identify HATs as potential therapeutic targets against this deadly cancer.
Evaluation of the Risk Factors for a Rotator Cuff Retear After Repair Surgery

Background: A retear is a significant clinical problem after rotator cuff repair. However, no study has evaluated the retear rate with regard to the extent of footprint coverage. Purpose: To evaluate the preoperative and intraoperative factors for a retear after rotator cuff repair, and to confirm the relationship with the extent of footprint coverage. Study Design: Cohort study; Level of evidence, 3. Methods: Data were retrospectively collected from 693 patients who underwent arthroscopic rotator cuff repair between January 2006 and December 2014. All repairs were classified into 4 types of completeness of repair according to the amount of footprint coverage at the end of surgery. All patients underwent magnetic resonance imaging (MRI) after a mean postoperative duration of 5.4 months. Preoperative demographic data, functional scores, range of motion, and global fatty degeneration on preoperative MRI and intraoperative variables including the tear size, completeness of rotator cuff repair, concomitant subscapularis repair, number of suture anchors used, repair technique (single-row or transosseous-equivalent double-row repair), and surgical duration were evaluated. Furthermore, the factors associated with failure using the single-row technique and transosseous-equivalent double-row technique were analyzed separately. Results: The retear rate was 7.22%. Univariate analysis revealed that rotator cuff retears were affected by age; the presence of inflammatory arthritis; the completeness of rotator cuff repair; the initial tear size; the number of suture anchors; mean operative time; functional visual analog scale scores; Simple Shoulder Test findings; American Shoulder and Elbow Surgeons scores; and fatty degeneration of the supraspinatus, infraspinatus, and subscapularis. Multivariate logistic regression analysis revealed patient age, initial tear size, and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the single-row group revealed patient age and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the transosseous-equivalent double-row group revealed a frozen shoulder as an independent risk factor for a rotator cuff retear. Conclusion: Our results suggest that patient age, initial tear size, and fatty degeneration of the supraspinatus are independent risk factors for a rotator cuff retear, whereas the completeness of rotator cuff repair based on the extent of footprint coverage and repair technique are not.
Efficient index computation for array based structured data

Traditional computing techniques widely vary over large scale computing. Some techniques are useful for structured data while some other techniques are useful for non-structured data. To store and operation on the structured data, multidimensional array is widely used as basic data structure. But the performance of multidimensional array is very poor when the number of dimension is very high i.e. 32-dimension. Data scientist suggests linearization of dimensions for higher dimensional data. But linearized data are highly compute intensive for retrieving into original data format as well as operations are very expensive on that data. It suffers from the higher index computational cost and lower data locality. In this paper, we propose an index computation algorithm based on generalized two-dimensional structure. We showed that the accessing cost of array elements is higher for traditional multidimensional array over generalized two-dimensional representation. Though the most sequential access of main memory is itself very simple for any compiler yet experimental results showed a better performance. Both theoretical analysis and experiment are done to show its' effectiveness of the proposed algorithm.
Scalability analysis of declustering methods for Cartesian product files

Efficient storage and retrieval of multi-attributedatasets have become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multi-attribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Though the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. In this paper we derive formulas describing the scalability of two popular declustering methods Disk Modulo and Fieldwise Xor for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods and are corroborated by extensive simulation experiments. From the practical point of view, the formulas given in this paper provide a simple measure which can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions.
A Non-Intrusive and Context-Based Vulnerability Scoring Framework for Cloud Services

Understanding the severity of vulnerabilities within cloud services is particularly important for today service administrators.Although many systems, e.g., CVSS, have been built to evaluate and score the severity of vulnerabilities for administrators, the scoring schemes employed by these systems fail to take into account the contextual information of specific services having these vulnerabilities, such as what roles they play in a particular service. Such a deficiency makes resulting scores unhelpful. This paper presents a practical framework, NCVS, that offers automatic and contextual scoring mechanism to evaluate the severity of vulnerabilities for a particular service. Specifically, for a given service S, NCVS first automatically collects S contextual information including topology, configurations, vulnerabilities and their dependencies. Then, NCVS uses the collected information to build a contextual dependency graph, named CDG, to model S context. Finally, NCVS scores and ranks all the vulnerabilities in S by analyzing S context, such as what roles the vulnerabilities play in S, and how critical they affect the functionality of S. NCVS is novel and useful, because 1) context-based vulnerability scoring results are highly relevant and meaningful for administrators to understand each vulnerability importance specific to the target service; and 2) the workflow of NCVS does not need instrumentation or modifications to any source code. Our experimental results demonstrate that NCVS can obtain more relevant vulnerability scoring results than comparable system, such as CVSS.
Delay analysis for ethernet long-reach passive optical networks

Designing low latency polling schemes is one of the most important parts for passive optical networks (PONs), particularly for long-reach PONs (LR-PON) which suffer from long propagation delays. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed transmission delay in LR-PONs. In this work, we evaluate three dynamic bandwidth allocation methods in terms of transmission delay. Namely, we consider conventional or interleaved polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We examine various flavors of each scheduling method and investigate their shortcomings and advantages in a LR-PON setting. Furthermore, we provide an analytical framework for obtaining packet delay in an enhanced version of RT-P method. The simulation results highly match the analysis for this framework. Also, our results indicate that RT-P method significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling methods.
Anomaly Detection Support Vector Machine and Its Application to Fault Diagnosis

We address the issue of classification problems in the following situation: test data include data belonging to unlearned classes. To address this issue, most previous works have taken two-stage strategies where unclear data are detected using an anomaly detection algorithm in the first stage while the rest of data are classified into learned classes using a classification algorithm in the second stage. In this study, we propose anomaly detection support vector machine (ADSVM) which unifies classification and anomaly detection. ADSVM is unique in comparison with the previous work in that it addresses the two problems simultaneously. We also propose a multiclass extension of ADSVM that uses a pairwise voting strategy. We empirically present that ADSVM outperforms two-stage algorithms in application to an real automobile fault dataset, as well as to UCI benchmark datasets.