Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of a Cardiac Specific Protein Transduction Domain by In Vivo Biopanning Using a M13 Phage Peptide Display Library in Mice

Background A peptide able to transduce cardiac tissue specifically, delivering cargoes to the heart, would be of significant therapeutic potential for delivery of small molecules, proteins and nucleic acids. In order to identify peptide(s) able to transduce heart tissue, biopanning was performed in cell culture and in vivo with a M13 phage peptide display library. Methods and Results A cardiomyoblast cell line, H9C2, was incubated with a M13 phage 12 amino acid peptide display library. Internalized phage was recovered, amplified and then subjected to a total of three rounds of in vivo biopanning where infectious phage was isolated from cardiac tissue following intravenous injection. After the third round, 60% of sequenced plaques carried the peptide sequence APWHLSSQYSRT, termed cardiac targeting peptide (CTP). We demonstrate that CTP was able to transduce cardiomyocytes functionally in culture in a concentration and cell-type dependent manner. Mice injected with CTP showed significant transduction of heart tissue with minimal uptake by lung and kidney capillaries, and no uptake in liver, skeletal muscle, spleen or brain. The level of heart transduction by CTP also was greater than with a cationic transduction domain. Conclusions Biopanning using a peptide phage display library identified a peptide able to transduce heart tissue in vivo efficiently and specifically. CTP could be used to deliver therapeutic peptides, proteins and nucleic acid specifically to the heart.
The influence of human factors on vulnerability to information security breaches

Within the context of information security (InfSec), human aspects have been receiving particular attention in research studies and business practices because of the fundamental role of the users. The objective of this research-in-progress is to develop a theoretical model of how human factors such as behavior with InfSec, familiarity with policies and procedures, awareness, organizational environment, and work conditions, contribute to InfSec breaches. The proposed model will be applied through interviews with CIO, multiple case studies and a survey of information system users in some South American countries and the USA. The sample will consist of end users of integrated web information systems (IS) in large organizations. An instrument will be designed and validated using qualitative and multivariate techniques. This research is intended to contribute towards identifying and managing the impact of human aspects on InfSec breaches.
Deep Learning Architectures for Soil Property Prediction

Advances in diffuse reflectance infra-red spec-cryoscopy measurements have made it possible to estimate number of functional properties of soil inexpensively and accurately. Core to such techniques are machine learning methods that can map high-dimensional spectra to real-valued outputs. While previous works have considered predicting each property individually using simple regression methods, the correlation structure present in the output variables prompts us to consider methods that can leverage this structure to make more accurate predictions. In this paper, we leverage advances in deep learning architectures, specifically convolution neural networks and conditional restricted Boltzmann machines for structured output prediction for soil property prediction. We evaluate our methods on two recent spectral datasets, where output soil properties are shown to have a measurable degree of correlation.
Histone H2B Monoubiquitination Facilitates the Rapid Modulation of Gene Expression during Arabidopsis Photomorphogenesis

Profiling of DNA and histone modifications has recently allowed the establishment of reference epigenomes from several model organisms. This identified a major chromatin state for active genes that contains monoubiquitinated H2B (H2Bub), a mark linked to transcription elongation. However, assessment of dynamic chromatin changes during the reprogramming of gene expression in response to extrinsic or developmental signals has been more difficult. Here we used the major developmental switch that Arabidopsis thaliana plants undergo upon their initial perception of light, known as photomorphogenesis, as a paradigm to assess spatial and temporal dynamics of monoubiquitinated H2B (H2Bub) and its impact on transcriptional responses. The process involves rapid and extensive transcriptional reprogramming and represents a developmental window well suited to studying cell division–independent chromatin changes. Genome-wide H2Bub distribution was determined together with transcriptome profiles at three time points during early photomorphogenesis. This revealed de novo marking of 177 genes upon the first hour of illumination, illustrating the dynamic nature of H2Bub enrichment in a genomic context. Gene upregulation was associated with H2Bub enrichment, while H2Bub levels generally remained stable during gene downregulation. We further report that H2Bub influences the modulation of gene expression, as both gene up- and downregulation were globally weaker in hub1 mutant plants that lack H2Bub. H2Bub-dependent regulation notably impacted genes with fast and transient light induction, and several circadian clock components whose mRNA levels are tightly regulated by sharp oscillations. Based on these findings, we propose that H2B monoubiquitination is part of a transcription-coupled, chromatin-based mechanism to rapidly modulate gene expression.
Optimized Surface Acoustic Wave-based Pressure Sensor Using Equivalent Circuit Model

This paper presents an optimized design on surface acoustic wave (SAW)-based pressure sensor, which is composed of a broadband reflective delay line and a bond substrate underneath the diaphragm. Using the equivalent circuit model (ECM), the SAW device was simulated, and the effect of inter-digital transducer structure, acoustic aperture and number of finger pairs on the performance of the system was studied. To determine the geometry and configuration of the sensor, Finite Element Method (FEM) was used to calculate the diaphragm bending and stress/strain distribution. From the ECM simulation and FEM analysis, the optimal design parameters were determined, and a new 440 MHz reflective delay line on 41deg YX LiNbO3 was developed, the measured reflection coefficient S11 results in time domain shows a good agreement with simulated one, low loss, sharp reflected peaks and high dynamic separation between the peaks were observed. The SAW device was successfully applied to pressure measurement, and the experiment results approve it is working satisfactorily
Coarse-to-fine texture images retrieval method

By introducing the feature of tree-structured wavelet transform, a novel texture image retrieval method is proposed in this paper. The method can produce eigenfeature at different scales precisely by decomposing the texture at multi-scales and multi-directions adaptively under the energy rule. In terms of these image eigenvalues, the method also suggested a modified algorithm, named Principal Eigenvalues Analysis (PEA), which can cut down eigenfeature dimensions effectively. It was confirmed that on the capability of the hierarchical way provided by this method the use-oriented application processing can allow users to carry out different retrieval on accord to users' requirements, which is called a coarse to fine retrieval. It was indicated by experimental results that the modified texture retrieval way have powerful practical merits for it can improve the retrieval accuracy efficiently and speed up the retrieval processing.
Cetylated fatty acids improve knee function in patients with osteoarthritis.

OBJECTIVE To determine the benefit of cetylated fatty acids (CFA) on knee range of motion and function in patients with osteoarthritis (OA). METHODS Sixty-four patients with chronic knee OA were evaluated at baseline and at 30 and 68 days after consuming either placebo (vegetable oil; n = 31) or CFA (Celadrin; n = 33). Evaluations included physician assessment, knee range of motion with goniometry, and the Lequesne Algofunctional Index (LAI). RESULTS After 68 days, patients treated with CFA exhibited significant (p < 0.001) increase in knee flexion (10.1 degrees) compared to patients given placebo (1.1 degrees). Neither group reported improvement in knee extension. Patient responses to the LAI indicated a significant (p < 0.001) shift towards functional improvement for the CFA group (-5.4 points) after 68 days compared to a modest improvement in the placebo group (-2.1 points). CONCLUSION Compared to placebo, CFA provides an improvement in knee range of motion and overall function in patients with OA of the knee. CFA may be an alternative to the use of nonsteroidal antiinflammatory drugs for the treatment of OA.
Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study

As advanced metering infrastructure (AMI) is responsible for collecting, measuring, and analyzing energy usage data, as well as transmitting this information from a smart meter to a data concentrator and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of smart grid. In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an intrusion detection system (IDS), which is a second line of defense after the primary security methods of encryption, authentication, authorization, etc. We propose a realistic and reliable IDS architecture for the whole AMI system, which consists of individual IDSs for three different levels of AMI's components: smart meter, data concentrator, and AMI headend. We also explore the performances of various existing state-of-the-art data stream mining algorithms on a publicly available IDS data set, namely, the KDD Cup 1999 data set. Then, we conduct a feasibility analysis of using these existing data stream mining algorithms, which exhibit varying levels of accuracies, memory requirements, and running times, for the distinct IDSs at AMI's three different components. Our analysis identifies different candidate algorithms for the different AMI components' IDSs, respectively.
A transfer learning approach to cross-database facial expression recognition

Despite the high facial expression recognition accuracy reported on individual databases, cross-database facial expression recognition is still a challenging problem. This is essentially a problem of generalizing a facial expression recognizer trained with data of certain subjects under certain conditions to different subjects and/or different conditions. Such generalization capability is crucial in real-world applications. However, little attention has been focused on this problem in the literature. Transfer learning, a domain adaptation approach, provides effective techniques for transferring knowledge from source (training) data to target (testing) data when they are characterized by different properties. This paper makes the first attempt to apply transferring learning to cross-database facial expression recognition. It proposes a transfer learning based cross-database facial expression recognition approach, in which two training stages are involved: One for learning knowledge from source data, and the other for adapting the learned knowledge to target data. This approach has been implemented based on Gabor features extracted from facial images, regression tree classifiers, the AdaBoosting algorithm, and support vector machines. Evaluation experiments have been done on the JAFFE, FEED, and extended Cohn-Kanade databases. The results demonstrate that using the proposed transferring learning approach the cross-database facial expression recognition accuracy can be improved by more than 20%.
From UML sequence diagrams and statecharts to analysable petri net models

In this paper we study the use of Sequence Diagrams and Statecharts of the Unified Modeling Language (UML) for the validation and the performance evaluation of systems. We assume that the system is specified as a set of Statecharts and that Sequence Diagrams are used to represent "executions of interest". UML lacks a formal semantics and hence it is not possible to apply, directly, mathematical techniques on UML models for system validation. To reach this goal we propose an automatic translation of Statecharts and Sequence Diagrams into Generalized Stochastic Petri Nets, and a composition of the resulting net models suitable for reaching a given analysis goal. The translation into Petri nets is based on the abstract syntax of the UML Collaborations and of the State Machines packages (from which Sequence Diagrams and Statecharts are derived) and the construction of the complete model heavily uses compositionality.
Systematic Literature Review and Meta-Analysis of the Association of Sarcopenia With Mortality.

BACKGROUND The World Health Organization (2002) indicates that the prevention of frailty is a key indicator of successful aging, and the assessment of the muscular system is the core for determining the frailty syndrome in older adults. Sarcopenia in older adults will affect more than 50 million people and will affect more than 200 million people after 40 years. Systematic literature reviews have yet to focus on the association between sarcopenia and mortality. AIMS The aims of this study were to determine the correlation between sarcopenia and mortality by using various evaluation methods and to analyze any differences among the resulting correlations of the different assessment tools. METHODS This study conducted a systematic review of relevant studies in four databases (Cochrane Library, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, and Ovid). The inclusion criteria were prospective research, the results included mortality or death, and risk estimates included risk ratio or hazard ratio estimates and 95% confidence intervals. Exclusion criteria included studies that provided no sarcopenia data, the risk of mortality or death, or data indicating deaths from cancers and in hospitals. RESULTS This study identified 10 studies that have investigated the association between sarcopenia and mortality with an average follow up of 4.17 years. In total, these studies have sampled 3,797 people and recorded 1,010 deaths. The result suggested that the risk of mortality in the sarcopenia group was higher than that in the nonsarcopenia group. LINKING EVIDENCE TO ACTION Although the results of evidence-based studies have suggested that sarcopenia is a geriatric syndrome associated with increased mortality, few studies have focused on decreasing sarcopenia mortality. Therefore, nurse practitioners must be aware of the correlation between sarcopenia and the mortality rate and become actively involved in developing intervention methods for reducing the mortality risk of sarcopenia.
High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden-angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space data for more accurate motion representation. The <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2-D and 3-D synthetic and <italic>in vivo</italic> data sets. On the synthetic data sets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the <italic>in vivo</italic> data, compared with a state-of-the-art approach based on the center of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.
An Approach of Implementing General Learning Companions for Problem Solving

Adding a learning companion, a computer simulated social agent, to a computer based learning system can enhance its educational value by enriching the way in which the computer and the user interact. This paper presents a novel simulation approach, named General Companion Modeling (referred to hereinafter as GCM), to implement learning companions in a general problem-solving domain. DwestAgent, a learning companion system, is also implemented using the GCM approach to demonstrate the feasibility of the proposed approach. In addition, the approach can help developers of learning companions clarify implementation issues and requirements involved in simulating: (1) domain competencies, (2) learning competencies, (3) behaviors as a peer tutor, and (4) behaviors as a peer tutee of a learning companion. Using GCM, one can simulate learning companions with various characteristics by adjusting parameters within the proposed simulation framework.
An acoustic emission method for on-line monitoring the contamination-causing flashover of insulator

This paper presents an acoustic emission (AE) monitoring method. When a partial discharge occurs, there is an instantaneous release of energy, some of which is emitted as acoustic energy. The pollution severities of insulators in-service can be monitored and accordingly threaten to the insulation of power grid is evaluated by detecting the sound signals. To prove this AE monitoring method, artificial contaminations experiments are carried out. The essential information about the relationship of AE signal and PD are obtained in different moments of the whole process of flashover. The studied method is a new way of AE technique in monitoring and characterizing defects of electrical power equipments in-service.
-Enhanced Capacity 802.11 Hotspots

The total number of hotspot users around the world is expected to grow from 9.3 million at the end of 2003 to 30 million by the end of 2004 according to researcher Gartner. Given the explosive growth in hotspot wireless usage, enhancing capacity of 802.11-based hotspot wireless networks is an important problem. In this paper, we make two important contributions. We first present the AP-CST algorithm that dynamically adjusts the Carrier Sense Threshold (CST) in order to allow more flows to coexist in current 802.11 architectures. We then extend the current hotspot engineering paradigm by allowing every cell and AP access to all available channels. These cells are then managed by the RNC-SC algorithm running in a centralized Radio Network Controller. This algorithm assigns mobile stations to appropriate cells/channels and adjusts transmit power values dynamically, thereby exploiting spatial heterogeneity in distribution of users at the hotspots. Through detailed and extensive simulations, we show that the performance of 802.11-based hotspots can be improved by up to 195% per-cell and 70% overall.
The Huntington's Disease-Related Cardiomyopathy Prevents a Hypertrophic Response in the R6/2 Mouse Model

Huntington's disease (HD) is neurodegenerative disorder for which the mutation results in an extra-long tract of glutamines that causes the huntingtin protein to aggregate. It is characterized by neurological symptoms and brain pathology that is associated with nuclear and cytoplasmic aggregates and with transcriptional deregulation. Despite the fact that HD has been recognized principally as a neurological disease, there are multiple epidemiological studies showing that HD patients exhibit a high rate of cardiovascular events leading to heart failure. To unravel the mechanistic basis of cardiac dysfunction in HD, we employed a wide range of molecular techniques using the well-established genetic R6/2 mouse model that develop a considerable degree of the cardiac atrophy at end stage disease. We found that chronic treatment with isoproterenol, a potent beta-adrenoreceptor agonist, did not change the overall gross morphology of the HD murine hearts. However, there was a partial response to the beta-adrenergenic stimulation by the further re-expression of foetal genes. In addition we have profiled the expression level of Hdacs in the R6/2 murine hearts and found that the isoproterenol stimulation of Hdac expression was partially blocked. For the first time we established the Hdac transcriptional profile under hypertrophic conditions and found 10 out of 18 Hdacs to be markedly deregulated. Therefore, we conclude that R6/2 murine hearts are not able to respond to the chronic isoproterenol treatment to the same degree as wild type hearts and some of the hypertrophic signals are likely attenuated in the symptomatic HD animals.
Design and In Vitro Testing of a Pressure-Sensing Syringe for Endotracheal Tube Cuffs

Endotracheal intubation is a frequently performed procedure in the prehospital setting, intensive care unit, and for patients undergoing surgery. The endotracheal tube cuff must be inflated to a pressure that prevents air leaks without compromising tracheal mucosal blood flow. For simultaneous endotracheal tube cuff inflation and measurement, we designed and tested a novel pressure-sensing syringe in vitro. The prototype was developed using a standard 10-mL polycarbonate syringe body that houses a plunger and a silicone rubber bellows, the pressure-sensing element. Bellow feasibility was determined and modeled using finite element analysis. Repeatability testing at each pressure measurement for each bellows (pressure versus deflection) was within an average standard deviation of 0.3 cm to 1.61 cm (1%–5% error). Using an aneroid manometer for comparison, there was excellent linear correlation with a Spearman rank of 0.99 (P < 0.001), up to 30 cm H2O.
Correlated genetic effects on reproduction define a domestication syndrome in a forest tree

Compared to natural selection, domestication implies a dramatic change in traits linked to fitness. A number of traits conferring fitness in the wild might be detrimental under domestication, and domesticated species typically differ from their ancestors in a set of traits known as the domestication syndrome. Specifically, trade‐offs between growth and reproduction are well established across the tree of life. According to allocation theory, selection for growth rate is expected to indirectly alter life‐history reproductive traits, diverting resources from reproduction to growth. Here we tested this hypothesis by examining the genetic change and correlated responses of reproductive traits as a result of selection for timber yield in the tree Pinus pinaster. Phenotypic selection was carried out in a natural population, and progenies from selected trees were compared with those of control trees in a common garden experiment. According to expectations, we detected a genetic change in important life‐history traits due to selection. Specifically, threshold sizes for reproduction were much higher and reproductive investment relative to size significantly lower in the selected progenies just after a single artificial selection event. Our study helps to define the domestication syndrome in exploited forest trees and shows that changes affecting developmental pathways are relevant in domestication processes of long‐lived plants.