Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonance Frequency Analysis in Assessing Implant Stability: A Retrospective Analysis.

PURPOSE To retrospectively assess implant stability quotient (ISQ) values in patients who were followed up between 1998 and 2014 and to evaluate any correlations between ISQ and clinical parameters, such as change in marginal bone level (MBL). MATERIALS AND METHODS A total of 173 participants (65 men and 108 women; age range 21 to 85 years) and 383 implants were included. Implant location, MBL, and ISQ were recorded at surgery and at various recall times for statistical analysis. Mixed-model analysis was applied to evaluate the impact of clinical and demographic variables (time, implant location, patient gender) on ISQ and the correlation between ISQ and MBL. The level of significance was set at P < . 05. RESULTS Of the 21 failed implants, 20 failed within 1 year of functional loading, resulting in a 10-year cumulative implant survival estimate of 95%. The failed implants had lower ISQs at surgery (52.3 ± 7.03) and baseline (52.5 ± 4.20) when compared to surviving implants (63.0 ± 10.74 at surgery and 62.3 ± 8.30 at baseline), and the difference was statistically significant at surgery (P < .05). The mean ISQs generally increased over time, but there were various patterns of changes between implants when grouped according to patient gender and implant location. There was no statistically significant correlation between the changes in ISQ and MBL (P = .211), despite an inverse relationship. CONCLUSION Low initial ISQ values may help to identify implants at higher risk of failure. There may be various patterns of change over time in addition to an overall increase in ISQ values. Both similar and contradictory findings were found when compared to earlier literature, and a correlation between resonance frequency analysis and MBL change could not be identified. Despite limitations, the present study provides an overview of the clinical performance of RFA based on long-term clinical data.
High CCND1 amplification identifies a group of poor prognosis women with estrogen receptor positive breast cancer

CCND1 encodes for the cyclin D1 protein involved in G1/S cell cycle transition. In breast cancer the mechanism of CCND1 amplification, relationship between cyclin D1 protein expression and the key clinical markers estrogen receptor (ER) and HER2 requires elucidation. Tissue microarrays of primary invasive breast cancer from 93 women were evaluated for CCND1 amplification by fluorescent in‐situ hybridization and cyclin D1 protein overexpression by immunohistochemistry. CCND1 amplification was identified in 27/93 (30%) cancers and 59/93 (63%) cancers had overexpression of cyclin D1. CCND1 amplification was significantly associated with cyclin D1 protein overexpression (p < 0.001; Fisher's exact test) and both CCND1 amplification and cyclin D1 protein expression with oestrogen receptor (ER) expression (p = 0.003 and p < 0.001; Fishers exact test). Neither CCND1 amplification nor cyclinD1 expression was associated with tumor size, pathological node status or HER2 amplification, but high CCND1 amplification (Copy Number Gain (CNG) ≥ 8) was associated with high tumor grade (p = 0.005; chi square 7.915, 2 df) and worse prognosis by Nottingham Prognostic Index (p = 0.001; 2 sample t‐test). High CCND1 amplification (CNG ≥ 8) may identify a subset of patients with poor prognosis ER‐positive breast cancers who should be considered for additional therapy.
Tachykinins Stimulate a Subset of Mouse Taste Cells

The tachykinins substance P (SP) and neurokinin A (NKA) are present in nociceptive sensory fibers expressing transient receptor potential cation channel, subfamily V, member 1 (TRPV1). These fibers are found extensively in and around the taste buds of several species. Tachykinins are released from nociceptive fibers by irritants such as capsaicin, the active compound found in chili peppers commonly associated with the sensation of spiciness. Using real-time Ca2+-imaging on isolated taste cells, it was observed that SP induces Ca2+ -responses in a subset of taste cells at concentrations in the low nanomolar range. These responses were reversibly inhibited by blocking the SP receptor NK-1R. NKA also induced Ca2+-responses in a subset of taste cells, but only at concentrations in the high nanomolar range. These responses were only partially inhibited by blocking the NKA receptor NK-2R, and were also inhibited by blocking NK-1R indicating that NKA is only active in taste cells at concentrations that activate both receptors. In addition, it was determined that tachykinin signaling in taste cells requires Ca2+-release from endoplasmic reticulum stores. RT-PCR analysis further confirmed that mouse taste buds express NK-1R and NK-2R. Using Ca2+-imaging and single cell RT-PCR, it was determined that the majority of tachykinin-responsive taste cells were Type I (Glial-like) and umami-responsive Type II (Receptor) cells. Importantly, stimulating NK-1R had an additive effect on Ca2+ responses evoked by umami stimuli in Type II (Receptor) cells. This data indicates that tachykinin release from nociceptive sensory fibers in and around taste buds may enhance umami and other taste modalities, providing a possible mechanism for the increased palatability of spicy foods.
Motion estimation in beating heart surgery

Minimally invasive beating-heart surgery offers substantial benefits for the patient, compared to conventional open surgery. Nevertheless, the motion of the heart poses increased requirements to the surgeon. To support the surgeon, algorithms for an advanced robotic surgery system are proposed, which offer motion compensation of the beating heart. This implies the measurement of heart motion, which can be achieved by tracking natural landmarks. In most cases, the investigated affine tracking scheme can be reduced to an efficient block matching algorithm allowing for realtime tracking of multiple landmarks. Fourier analysis of the motion parameters shows two dominant peaks, which correspond to the heart and respiration rates of the patient. The robustness in case of disturbance or occlusion can be improved by specially developed prediction schemes. Local prediction is well suited for the detection of single tracking outliers. A global prediction scheme takes several landmarks into account simultaneously and is able to bridge longer disturbances. As the heart motion is strongly correlated with the patient's electrocardiogram and respiration pressure signal, this information is included in a novel robust multisensor prediction scheme. Prediction results are compared to those of an artificial neural network and of a linear prediction approach, which shows the superior performance of the proposed algorithms.
The PRMA-ISA protocol for multiple access of mixed voice and data traffic

A combined multiple access protocol is investigated, based on the ISA algorithm, which was first introduced for a slotted unframed channel, and later modified to fit a cellular wireless network with distributed handoff. ISA manages the access rights at the beginning of each slot in order to maximize the success probability (one step throughput), based on an independence assumption on the presence of packets at the stations. We extend this concept in the context of a framed channel that has the same basic structure as packet reservation multiple access (PRMA). In this case, it is possible to see the frame as a whole bunch of "parallel channels", over which the overall success probability must be maximized at the beginning of the frame for all free slots. The combined PRMA-ISA algorithm has been developed and investigated by means of simulations, using models previously proposed for data and voice (phone calls) traffic. <<ETX>>
Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes

Falls are unpredictable accidents and resulting injuries can be serious to the elderly. A preventative solution can be the use of vibration stimulus of white noise to improve the sense of balance. In this work, a pair of vibration shoes were developed and controlled by a touch-type switch which can generate mechanical vibration noise to stimulate the patient’s feet while wearing the shoes. In order to evaluate the balance stability and treatment effect of vibrating insoles in these shoes, multivariate multiscale entropy (MMSE) algorithm is applied to calculate the relative complexity index of reconstructed center of pressure (COP) signals in antero-posterior and medio-lateral directions by the multivariate empirical mode decomposition (MEMD). The results show that the balance stability of 61.5% elderly subjects is improved after wearing the developed shoes, which is more than 30.8% using multiscale entropy. In conclusion, MEMD-enhanced MMSE is able to distinguish the smaller differences between before and after the use of vibration shoes in both two directions, which is more powerful than the empirical mode decomposition (EMD)-enhanced MSE in each individual direction.
Formalizing Semantic Parsing with Tree Transducers

This paper introduces tree transducers as a unifying theory for semantic parsing models based on tree transformations. Many existing models use tree transformations, but implement specialized training and smoothing methods, which makes it difficult to modify or extend the models. By connecting to the rich literature on tree automata, we show how semantic parsing models can be developed using completely general estimation methods. We demonstrate the approach by reframing and extending one state-of-the-art model as a tree automaton. Using a variant of the inside-outside algorithm with variational Bayesian estimation, our generative model achieves higher raw accuracy than existing generative and discriminative approaches on a standard data set.
Chronic Lower Leg Pain in Athletes

Context: Chronic lower leg pain in athletes can be a frustrating problem for patients and a difficult diagnosis for clinicians. Myriad approaches have been suggested to evaluate these conditions. With the continued evolution of diagnostic studies, evidence-based guidance for a standard approach is unfortunately sparse. Evidence Acquisition: PubMed was searched from January 1980 to May 2011 to identify publications regarding chronic lower leg pain in athletes (excluding conditions related to the foot), including differential diagnosis, clinical presentation, physical examination, history, diagnostic workup, and treatment. Results: Leg pain in athletes can be caused by many conditions, with the most frequent being medial tibial stress syndrome; chronic exertional compartment syndrome, stress fracture, nerve entrapment, and popliteal artery entrapment syndrome are also considerations. Conservative management is the mainstay of care for the majority of causes of chronic lower leg pain; however, surgical intervention may be necessary. Conclusion: Chronic lower extremity pain in athletes includes a wide differential and can pose diagnostic dilemmas for clinicians.
Class Dependent Cluster Refinement

Unsupervised classification is a very common problem in pattern recognition even when the classes are known. In many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. The subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. This paper presents a novel class-dependent approach to the clustering problem. Experiments with online handwriting data show that the novel clustering approach CDCR produces a clustering better suited for the task of pattern recognition. Although only validated for two recognition methods in this paper, the same approach could be applied to other methods as well as to other pattern recognition problems
ApoE and pulse pressure interactively influence level and change in the aging of episodic memory: Protective effects among ε2 carriers.

OBJECTIVE We tested independent and interactive effects of Apolipoprotein E (ApoE) and pulse pressure (PP) concurrently and longitudinally across 9 years (3 waves) of episodic (EM) and semantic memory (SM) data from the Victoria Longitudinal Study. METHOD We assembled a sample of older adults (n = 570, baseline M age = 71, age range = 53-95) and used latent growth modeling to test 4 research goals. RESULTS First, the best fitting memory model was 2 single latent variables for EM and SM, each exhibiting configural, metric, and partial scalar invariance. This model was analyzed as a parallel process model. Second, baseline level of PP predicted EM performance at centering age (75) and rate of 9-year EM change. Third, we observed no main effects of ApoE on EM or SM. Fourth, EM was affected by higher PP but differentially less so for carriers of the ApoE ε2 allele than the ε3 or ε4 alleles. CONCLUSIONS PP is confirmed as a risk factor for concurrent and changing cognitive health in aging, but the effects operate differently across risk and protective allelic distribution of the ApoE gene.
Drowsy caches: simple techniques for reducing leakage power

On-chip caches represent a sizable fraction of the total power consumption of microprocessors. Although large caches can significantly improve performance, they have the potential to increase power consumption. As feature sizes shrink the dominant component of this power loss will be leakage. However, during a fixed period of time the activity in a cache is only centered on a small subset of the lines. This behavior can be exploited to cut the leakage power of large caches by putting the cold cache lines into a state preserving, low-power drowsy mode. Moving lines into and out of drowsy state incurs a slight performance loss. In this paper we investigate policies and circuit techniques for implementing drowsy caches. We show that with simple architectural techniques, about 80%-90% of the cache lines can be maintained in a drowsy state without affecting performance by more than 1%. According to our projections, in a 0.07 um CMOS process, drowsy caches will be able to reduce the total energy (static and dynamic) consumed in the caches by 50%-75%. We also argue that the use of drowsy caches can simplify the design and control of low-leakage caches, and avoid the need to completely turn off selected cache lines and lose their state.
An Interactive Tool for Extracting Exons and Snp from Genomic Sequence: Isolation of Hcn1 and Hcn3 Ion Channel Genes

Genome Analyzer (GenoA) with a relational database back-end, was developed to extract information from mammalian genomic sequences. This data mining and visualization tool-set enables laboratory bench scientists to identify and assemble virtual cDNA from genomic exon sequences, and provides a starting point to identify potential alternative splice variants and polymorphisms in silico. The study described in this paper demonstrates the use of GenoA to study human brain hyperpolarization-activated cation channel genes HCN1 and HCN3.
Green chemistry used in the synthesis and characterization of silver and gold nanoparticles

Gold (Au) and silver (Ag) nanoparticles have a diversity of interesting properties between which they emphasize the electrical ones, optical, catalytic and the applications in biomedicine like antibacterial and antiviral, same that depend on their morphology and size. The nanoparticles were synthesized using polyol and green methods. We made a comparison of these methods in order to investigate the influence of reaction parameters on the resulting particle size and its distribution. In the first method we use polyol process with poly (vinylpyrrolidone) (PVP) acting as a stabilizer and ethylenglycol as a reductor. The green method is an ecological synthesis technique. In this article, we made use of chemical compounds of plants in order to obtain ascorbic acid as reductor agent and saponins with surfactant properties. Here, we show that green method allows synthesize metallic nanoparticles and reduces the temperature requirement which is in contrast to the obtained with the polyol method.
Knowledge based Analysis of Software Defined Radio for Wireless Communication: A Preliminary Survey

The need of wireless access in many applications like voice and high data rate multi-media is rapidly growing and latest wireless communication systems serving this need through advanced resource management, and improved transmission technologies resulting in very small communication devices. During this optimization, communication systems must be designed for transparent insertion of new technologies at virtually every stage. The upgradation should not disturb the communication between upgraded devices. To satisfy future needs in spectrum access and spectrum efficiency, Software Defined Radios (SDR) was introduced as a new technology in 1970s. Software Defined Radio (SDR) has become significant in research since it substitutes conventional implementation on wireless communication system. The technical progress and conception of SDR has led to the evolution of high-performance digital signal processors and required software to become key enabling technologies. This survey paper identifies the enabling technologies and research areas resulting in development of Software Defined Radios (SDR). Transmitter and Receiver architectures of SDR are also discussed and their feasibility for a reconfigurable radio application is investigated.
Online The Open University ’ s repository of research publications and other research outputs Learning rules from user behaviour

Pervasive computing requires infrastructures that adapt to changes in user behaviour while minimising user interactions. Policy-based approaches have been proposed as a means of providing adaptability but, at present, require policy goals and rules to be explicitly defined by users. This paper presents a novel, logic-based approach for automatically learning and updating models of users from their observed behaviour. We show how this task can be accomplished using a nonmonotonic learning system, and we illustrate how the approach can be exploited within a pervasive computing framework.
Real-time chemical sensing of aqueous ethanol glucose mixtures

A new high-frequency approach to monitoring component concentration in dielectric solutions is proposed. After appropriate calibration, the volume percentage of an n-component solution can be uniquely determined by measuring the real part of the solution permittivity at n-1 distinct frequencies. A flow-through microstrip bandpass filter sensor has been fabricated to demonstrate the accuracy of the technique when applied to a water-ethanol mixture. The sensor can discriminate 0.35% relative variations in ethanol volume concentration when solution temperature is independently monitored.
Calcium-dependent Activation and Autolysis of Arabidopsis Metacaspase 2d*

Metacaspases (MCPs) are members of a new family of cysteine proteases found in plants, fungi, and protozoa that are structurally related to metazoan caspases. Recent studies showed that plant MCPs are arginine/lysine-specific cysteine proteases with caspase-like processing activities in vitro and in vivo, and some of the plant type II MCPs exhibit Ca2+ dependence for their endopeptidase activity in vitro. However, the mechanisms and biological relevance of Ca2+ dependence and self-processing of plant MCPs remains unclear. Here we show that recombinant AtMCP2d, the most abundantly expressed member of Arabidopsis type II MCPs at the transcriptional level, exhibits a strict Ca2+ dependence for its catalytic activation that is apparently mediated by intramolecular self-cleavage mechanism. However, rapid inactivation of AtMCP2d activity concomitant with Ca2+-induced self-processing at multiple internal sites was observed. Because active AtMCP2d can cleave its inactive form, intermolecular cleavage (autolysis) of AtMCP2d could also occur under our assay conditions. Ca2+-induced self-processing of recombinant AtMCP2d was found to correlate with the sequential appearance of at least six intermediates, including self-cleaved forms, during the proenzyme purification process. Six of these peptides were characterized, and the cleavage sites were mapped through N-terminal protein sequencing. Mutation analysis of AtMCP2d revealed that cleavage after Lys-225, which is a highly conserved residue among the six Arabidopsis type II MCPs, is critical for the catalytic activation by Ca2+, and we demonstrate that this residue is essential for AtMCP2d activation of H2O2-induced cell death in yeast. Together, our results provide clues to understand the mode of regulation for this class of proteases.
Nucleic Acid Structure Characterization by Small Angle X‐Ray Scattering (SAXS)

Small angle X‐ray scattering (SAXS) is a powerful method for investigating macromolecular structure in solution. SAXS data provide information about the size and shape of a molecule with a resolution of ∼2 to 3 nm. SAXS is particularly useful for the investigation of nucleic acids, which scatter X‐rays strongly due to the electron‐rich phosphate backbone. Therefore, SAXS has become an increasingly popular method for modeling nucleic acid structures, an endeavor made tractable by the highly regular helical nature of nucleic acid secondary structures. Recently, SAXS was used in combination with NMR to filter and refine all‐atom models of a U2/U6 small nuclear RNA complex. In this unit, general protocols for sample preparation, data acquisition, and data analysis and processing are given. Additionally, examples of correctly and incorrectly processed SAXS data and expected results are przovided. Curr. Protoc. Nucleic Acid Chem. 51:7.18. 1‐7.18. 18. © 2012 by John Wiley & Sons, Inc.
Sustainable dewatering of microalgae by centrifugation using Image 4-focus and MATLAB edge detection

In this study, we demonstrate the feasibility of centrifugation for the successful dewatering of microalgae species. Centrifuge experiments were conducted on Scenedesmus quadricauda and Chlorella vulgaris at different centrifugal speeds between 1000-4000 (rpm) and varying time between 5-30 (min). Dewatering efficiency and microalgae cell disruption were evaluated. Image-focus 4 and Matlab edge detection software were used to model the effect of centrifugation on microalgae cell walls and to determine the water removal ratio. Experimental results indicated that centrifugation technique is an effective approach for dewatering microalgae under specific conditions. Scenedesmus quadricauda showed a maximum dewatering efficiency of 82% and Chlorella vulgaris of 91%. Centrifugation under 4000 rpm at 10 minutes did not show any significant cell damage on the algae cell structure for both species. This study provides information on specific impact of centrifugation on Scenedesmus quadricauda and Chlorella vulgaris for the first time, which is, centrifugation technique under specific conditions (4000 rpm for 10 min) is a successful method for dewatering microalgae without damage to the cell wall. This study therefore provides sustainable option for microalgae dewatering technique in the energy industry.