be turned on with the command setOption, at the beginning of a Bi

be turned on with the command setOption, at the beginning of a BioNetGen input file. The default for BioNetGen is to calculate pseudo canonical labels that do not distinguish all isomorphic graphs but are much faster to generate than HNauty. Then any two graphs which share pseudo canonical labels are checked for iso morphism using Ullmanns algorithm. The genera tion of pseudo canonical labels followed by applying Ullmanns algorithm to graphs with the same label always produces correct results, though it can be much slower than HNauty if a chemical species graph is composed of many isomorphic subgraphs. The HNauty code can be run as stand alone code separate from Bio NetGen. The Python version of HNauty uses the graph structures defined in the freely available package Net workX.

The Perl Entinostat version of HNauty takes as input the graph adjacency matrix together with an initial par tition of the vertices of a graph. The adjacency matrix should be in the form of a dictionary of dictionaries. The keys of the first dictionary are the vertices of a graph. Each vertex i points to a second dictionary whose keys are the neighbors of vertex i in the graph. In this second dictionary, a vertex j points to an array contain ing the edge types between vertices i and j. The initial partition of the vertices should be given in the form of an array of arrays, each of the smaller arrays being a set in the partition. HNauty returns as output a permutation of the vertices of the input graph. Permuting the input graph under this per mutation gives the canonical label of the graph.

Testing Both the Python and Perl versions of HNauty were exten sively checked using a database of isomorphic graphs. The Perl version was further checked against ran domly generated graphs with two types of edge, directed and undirected. These graphs were generated using the Erd?s R��nyi model for random graphs, the edges were chosen independently with uniform probability. Edges were selected to be undirected with probability 0. 1 and directed with probability 0. 05. With probability 0. 85 an edge was not in the graph. One thousand graphs, each on two hundred nodes, were produced in this way. Each was given as input to HNauty and then a random permu tation of the vertices was applied to each graph, the result was also given as input to HNauty. A test was successful if the two isomorphic inputs resulted in the same canoni cal label.

All of the tests were successful. Discussion In the section above, we discussed the significance of our results as the results were presented. Thus, this sec tion will be brief. Hierarchical graphs can be powerful visual aids in understanding complex molecular struc tures. For rule based models of cell signaling systems, hierarchical graphs provide more natural representations of proteins than the regular flat graphs of BNGL or Kappa and thus promote clarity in building and annotat ing models. Regular flat graphs can obscure the struc tural properties of molecules and m

plasmid was a generous gift from Dr Jack Dixon The plasmid pGL

plasmid was a generous gift from Dr. Jack Dixon. The plasmid pGL EPM2A containing the gene for Mm laforin was a kind gift from Dr. Kazuhiro Yamakawa. Mm laforin was subcloned into pET21a that includes a C terminal His6 tag. Expressed sequence tags of Xt laforin and Gg laforin were purchased from Open Biosystems and Delaware Biotech nology Institute, respectively, and cloned into ppSUMO according to standard protocols. ppSUMO encodes a small Ub like modifier fusion tag that includes an amino terminal His6 tag to aid purification. Sequences were verified by DNA sequencing. pET21a Vaccinia H1 related phosphatase and pET21a Hs laforin constructs have been described previously. Protein expression and purification All proteins were expressed in BL21 CodonPlus E.

coli cells and purified using an IMAC column on a Profinia purification system followed by size exclusion chromatography. Bacterial cultures were grown in 1 L 2xYT or Terrific Broth with 1 mM kanamyacin and 1 mM chloramphenicol at 37 C until OD600 reached 0. 8. Cultures were chilled on ice for 20 minutes, and isopropyl thio B D galactopyra noside was added for a final concentration of 0. 4 mM to induce protein expression. After growth for approximately 12 16 hours, cells were harvested by cen trifugation and stored at 20 C. Bacterial pellets express ing Hs laforin were resuspended in buffer A, 50 mM Tris HCl, 300 mM NaCl, and 2 mM dithiothreitol. Pellets expressing Mm laforin were resuspended in buffer B, 50 mM Tris HCl, 300 mM NaCl, and 0. 05% B mercaptoethanol.

Pellets expressing VHR, Xt laforin or Gg laforin were resuspended in buffer C, 20 mM Tris HCl, 100 mM NaCl and 2 mM DTT. 15% maltose or 10 mM B cyclodextrin was added to some preparations. Resuspended cells were lysed with a microfluidizer, and soluble fractions were separated by high speed centrifugation. His6 SUMO GSK-3 tagged Xt laforin and Gg laforin were purified using a Profinia IMAC column with a Profinia protein purification system and dialyzed into buffer C in the presence of the SUMO specific protease ULP1 that also contains a His6 tag. Re verse purification over the Profinia IMAC column was used to remove ULP1 His6 and the fusion tag. Each protein was then purified using a HiLoad 16 60 Superdex 200 size exclusion column and AKTA FPLC. Fractions containing the Gg laforin monomer species were collected and put back over the same column.

Mm laforin, Hs laforin and VHR were also expressed as His6 tagged recombinant proteins and purified in a similar manner. Protein gel electrophoresis, quantitation of stability, and dynamic light scattering Protein purity was assessed by sodium dodecyl sulfate polyacrylamide gel electrophoresis. Gels were stained with Coomassie brilliant blue to visualize proteins. To quantify stability of Hs laforin and Gg laforin, elution fractions were concentrated using centrifugal filter units. Volume and concentration were monitored throughout centrifugation at 3,220 �� g, and protein concentration was measured u

B gets less degraded in presence of ACHP, and that canonical NF �

B gets less degraded in presence of ACHP, and that canonical NF ��B signals are blocked. In summary, these data show that Fascin is regulated by canonical NF ��B signals not only in LMP1 transfected cells, but also in LMP1 e pressing, EBV transformed lymphoblastoid B cells. Fascin contributes to invasion of cancer cells and HTLV 1 transformed T lymphocytes, however, the relative contribution of Fascin to the motility of EBV transformed lymphocytes has not been investigated. To analyse whether inhibition of NF ��B, which leads to re duction of Fascin, also affects invasion of EBV transformed lymphocytes, LCL B cells were incubated in the presence of ACHP and serum starved for 4 h. Subsequently, invasion assays were performed util izing basement membrane coated inserts which separate the cells from medium with 20% fetal calf serum in the lower well.

Invasive cells are able to degrade the matri Cilengitide , pass through the pores of the polycarbonate mem brane, and attach either to the bottom of the membrane, or they migrate to the lower well after invasion. We did not detect different numbers of cells attached to the bottom of the membrane. This suggests that inhibition of NF ��B does not affect ad hesion of invaded LCLs to the membranes used in our assay. However, we observed that the number of invaded and non attached LCLs in the lower well was significantly Fascin protein, Western blot analysis was performed upon treatment of LCLs with low doses of ACHP. These data revealed that also Fascin protein is re duced upon treatment of LCLs with ACHP, despite the reduced to appro imately 11% in presence of ACHP com pared to the solvent control.

We observed slight reduction of cell vital ity in presence of the inhibitor, but we measured significant impairment of NF ��B activity and Fascin e pression. Therefore, we conclude that inhibition of NF ��B significantly reduces the migratory rate of LCLs subsequent to invasion of the e tracellular matri , and Fascin might contribute to this phenotype. Knockdown of Fascin reduces the invasive capacity of LMP1 e pressing lymphocytes. In studies focusing on NPC and cells of epithelial origin, LMP1 has been described as a potent regulator of cellular migration and invasion. To test, whether sole e pression of LMP1 induces invasion of lymphocytes, too, and whether this specifically depends on Fascin, invasion assays were performed in transiently transfected cells.

For this purpose, Jurkat cells were transfected with LMP1 e pression plasmids, two different shRNA constructs tar geting Fascin or unspecific control shRNAs. To increase the sensitivity of our analysis, cells were co transfected with an e pression plasmid for LNGFR, which encodes a cytoplasmic trun cated, low affinity nerve growth factor receptor that is not e pressed on Jurkat cells, and therefore allows positive selection of transfected cells by mag netic separation. Flow cytometry using LNGFR specific antibodies revealed that the amount of LNGFR e pressing cells was enri

The thickness (h) of the resonator varies in the meridional direc

The thickness (h) of the resonator varies in the meridional direction, ?. The thicknesses at the top (? = ?t) and at the bottom (? = ?b) ends of the resonator are denoted by ht and hb, respectively Thus, the resonator is generated by rotating the cross-section of Figure 6 one revolution about the y-axis (0 �� �� �� 2��). The typical point, P, in the resonator is located by giving its meridional and circumferent
Optical sensors for the detection and quantification of hazardous chemicals, investigation of biomolecular interactions or studies on cellular systems have been developed for decades and are still a field of extensive research (reviewed for example in Reference [1]). Label-free optical sensors use mainly surface plasmon resonance and interferometry as transduction methods whose performance complements each other.

A recent study on the sensitivity of localized surface plasmon resonance (LSPR) transducers in comparison to interferometric sensors identified the superiority of LSPR based devices for the analysis of thin (several nm) analyte and recognition interfaces and emphasized the advantage of interferometric sensors for the investigation of thicker layers [2]. The high potential of porous silicon for fabrication of interferometric sensors originates from its easily controllable fabrication process resulting in layers with defined porosity (refractive index), its high surface area, simple surface chemistry, and full compatibility with microprocessing techniques. In the early stages porous silicon based sensors were composed of a single porous layer on the silicon substrate leading to Fabry-P��rot interference.

Here, the reflectivity spectrum shows interference fringes which correspond to constructive and destructive interference from light reflected at the air/porous silicon and porous silicon/crystalline silicon interfaces. Changes in the average refractive index of the porous silicon layer caused by infiltration or adsorption of analytes are detected by spectral Carfilzomib shifts in the reflectivity spectrum [3,4].Photonic crystals are composed of alternating regions of high and low dielectric constants and can be obtained in 1, 2, or 3 dimensional periodic array arrangements (Figure 1). By choosing appropriate dielectric constants and geometry these materials can exhibit a photonic band gap (stop band) which is characterized by the prevention of light propagation at a range of frequencies defined by the internal structure of the photonic crystal.

Hence, photonic crystals can be employed as optical filters and allow for the isolation of narrow reflection bands. Since the conceptual introduction of photonic crystals by Yablonovitch and John in 1987 [5,6] photonic crystals have been fabricated from diverse materials including semiconductors, polymers, oxides, and porous silicon.Figure 1.Photonic crystals (adapted from Reference [7]).

Before arranging the control signal, the Raman pump necessary to

Before arranging the control signal, the Raman pump necessary to feed the switch is removed by other two WDMs with the same characteristics as the previous ones.Figure 1.Structure of the BOTDA sensor network. WDM: wavelength division multiplexer; PPC: photoelectric cell; OC: optical control; LS: Laser Source; 1,445: 1,445 nm Raman pump.We used the photoelectric cell to drive a commercial, low consumption 1 �� 2 fiber optic switch based on MEMS technology and developed by DiCon Fiberoptics Inc. (Richmond, VA, USA) A power converter, working at 1,445 nm, is used to convert 125 mW of optical power into ~60 mW of electrical power. In order to select the channel, two different voltages are applied on the electrical ports of the switch. As the photoelectric cell provides a maximum of 4.

8 V, DC-DC voltage converters are necessary to drive the optical switch as it is shown in Figure 2a. A remote optical control (OC) for the powered by light switch is developed. A laser source (LS) centered in 1,540 nm is used as the control signal. This light is inserted into the network by a 90:10 coupler and another 90:10 coupler is used to extract 10% of the power at the OC location. The control wavelength is filtered and photodetected. Finally, depending on the detected intensity, one of the two switch’s channels is selected.Figure 2.(a) Electronics setup for the remote control of the fiber optic switch; (b) Zoom of the switch rising flank response for both channels.For measuring the switch response, the light coupled at the input port was detected and monitored by an oscilloscope.

Figure 2b shows the rising flank of the switch response. A switching time less than 2 ms is observed.2.1. Simplified BOTDAThe BOTDA technique is based on the analysis of the Brillouin interaction between two counter propagating optical waves. One of them is a continuous wave (CW), the probe, while the other is pulsed, the pump. They have to be separated in frequency the value of the Brillouin frequency shift (��B) for the fiber where the process takes place in order to induce stimulated Brillouin scattering (SBS). When this occurs, there is an energy transfer from one wave to the other, giving rise to a Brillouin spectrum that can be measured by tuning the frequency separation between pump and probe. The peak of this spectrum gives the Brillouin frequency shift in the fiber, which depends on temperature and strain.

Moreover, using classic time-domain reflectometric techniques the distribution of these measurants along the fiber can be determined. There are two possible configurations for a BOTDA system: the so-called gain regime, when the pulsed Carfilzomib wave is used as pump wave to amplify the CW (probe), and the so-called loss regime, when the CW is used to amplify the pulsed wave and is attenuated in the process.

Another technique currently under development for early diagnosis

Another technique currently under development for early diagnosis is the study of the voice, both in apnea detection and evaluation of the patient’s risk to develop it. Systems based on piezoelectric sensors integrated into a belt have also been used [10].However, in spite of all efforts, current systems still involve a series of drawbacks that hinder early detection of sleep apnea. The main drawbacks detected so far could be summarized as follows: (1) they are invasive and annoying for patients; (2) their long duration; (3) they are based on algorithms with scarce patient-to-patient calibration; (4) they are costly and (5) complex to use.This work presents the development of a low-cost and non-obstructive sensor to monitor respiratory rate for sleep apnea follow-up and diagnosis purposes.

The paper is organized as follows: Section 2 contributes a preliminary study on the technological feasibility of capacitive sensing. Section 3 describes each of the stages of the proposed sensing system, as well as the necessary materials and methods. Section 4 presents the simulation and experimental results obtained in terms of sensitivity, interferences and respiratory rate detection. Finally, conclusions are drawn in Section 5.2.?Preliminary ConsiderationsThe importance of using reliable, affordable and highly performing sensors for noninvasive medical therapies is progressively growing, since trends forecast a significant increase in patient follow-up and control from home [3].

The technology based on capacitive sensing was chosen for respiratory rate measurement instead of other solutions (see [11�C13]) because it involves the following advantages:�C The sensor’s price would be low, since it is made of standard electronic components;�C Capacitive sensors are widely used in industry and prove rather efficient. Therefore, we consider that adapting them to a different sector (namely, healthcare) could be achievable and beneficial;�C Their inner configuration allows them to meet the requisite of avoiding contact between the electrodes and the patient;�C The resolution of capacitive sensors in short distances is rather high;�C The most relevant parameters for alterations to take place in the operating frequency of capacitive sensors are dielectric variations produced between the electrodes. In our case, the critical dielectric is air inside the lungs.One of the applications equipped with capacitive sensors has been successfully applied to monitor intraocular pressure [14,15] and intracranial pressure [16]. Pressure measurements are the activities in which capacitive sensors have been most extensively used in medicine, although Brefeldin_A these capacitive systems have also been applied in some other relevant projects in the medical field.

(2)A ROI belonging to list RMA(t) has no common pixel with any RO

(2)A ROI belonging to list RMA(t) has no common pixel with any ROI belonging to RTA(t): the ROI from RMA(t) is included as is in the new list of ROIs called RF(t).(3)A ROI belonging to list RTA(t) has some common pixels with a given ROI belonging to RMA(t): the ROIs from RTA(t) and RMA(t) compose a new ROI containing all pixels from the previous ones; this new ROI is included in the new list of ROIs called RF(t).Rules (1) and (2) show the possibilities to sum up the ROIs coming from both Thermal Analysis and Motion Analysis. Rule (3) demonstrates the case when both Thermal Analysis and Motion Analysis have detected the same candidates as pedestrians (or at least part of them).2.4. Blob AnalysisThis part of the algorithm works with the list RF(t). This list was obtained at the end of the previous section.

At this point, there is a need to validate the content of each ROI to find out if it contains one single human candidate or more than one. Therefore, each detected ROI is individually proces
Rapid developments have recently occurred in minimally invasive surgery, which has become a practical reality, especially after the advent of rod optics, optical fibres and the first solid-state cameras. The distinct advantages offered by MIS over conventional operations include reductions in the following: intraoperative blood loss, tissue trauma, risk of post-operative infection, pain experienced by the patient and recovery time [1].

However, there are two major drawbacks to such surgeries: the constrained spaces (only key-hole incisions are used), which lead to a reduction in the degree-of-freedom (DOF) during manipulation, and the absence of haptic feedback (including tactile forces) during the tool-tissue interactions [2,3]. Surgeons in MIS, including microsurgeries, must accurately and carefully manipulate delicate tissues using customised surgical tools (ranging from simple freehand to sophisticated tools) in constrained spaces. As a result, the surgeons may perform inappropriate tool movements and may suffer from premature fatigue during MIS [4,5]. Advances in robotic systems have made their use possible in the operating room, and minimally invasive robotic surgery (MIRS) systems are now common [6�C8]. Consequently, robots in master-slave configurations, such as the ZEUS? Surgical System [9] and the da Vinci? Surgical System (DVSS) [10], have been introduced to solve motion-constraint problems in MIS.

These systems have increased the attainable DOF of tool-tissue manipulation. This helps AV-951 surgeons perform a variety of MIS operations more effectively for different types of abdominal interventions [11�C15]. Nonetheless, the performance of the surgeons during MIRS or MIS manipulation is still severely limited by their having little to no tactile information compared with the rich tactile feedback of the human hand [16].

In polarimetric SAR interferometry (PolInSAR), since the scatteri

In polarimetric SAR interferometry (PolInSAR), since the scattering element data of each pixel are composed of two scattering matrices or scattering vectors corresponding to two spatially separated antennae, it is possible to enhance the coherence and improve the phase between the signals received by both the antennae. In recent years, several algorithms have been proposed, such as the coherence optimization method with two vectors (CO2) [6, 7], the coherence optimization method with one vector (CO1) [8] and so on. The CO2 method is important for vegetation characteristics analysis. In addition, these methods can be used for phase improvement by interferometric coherence optimization.In this paper, a novel method is proposed.

First we provide a mathematical model to maximize the lower of the two amplitudes from the interferometric complex signal pair.

Then the optimal solution is obtained in closed-form. Comparing with the CO2 method, we demonstrate that the proposed method has better performance.This paper is organized as follows. In Section 2, the coherence optimization method proposed by Cloude et al. [6, 7] is reviewed. Section 3 describes the relationship between the amplitude and the phase of a complex signal. In general, weak signals Anacetrapib with low amplitudes have unreliable phases. To improve the phase quality, one should augment the amplitude of the signal. For each scattering element, the amplitudes of both the receiving signals should be both as large as possible.

The proposed method is introduced in detail in Section 4, where the optimal solution is obtained by an eigendecomposition.

In Section 5, a physical explanation is presented. The improved phase is proved to be equivalent to the weighted average Brefeldin_A of phases in each polarimetric channel in the single-look case, which provides a good intuitive explanation for the proposed approach. Section 6 provides the experimental results, which demonstrate the performance of the proposed method. Finally, some conclusions are given in Section 7.2.?Review of coherence optimization (CO2) methodIn SAR interferometry, for each scattering element, two complex scalar signals s1 and s2 are received from two spatially separated antennae. A 2��2 Hermitian semi-definite coherency matrix [J] is defined as:[J]=?[s1s2][s1*s1*]?=[?s1s1*??s1s2*??s2s1*??s2s2*?](1)where * means the complex conjugation and �� indicates the expectation value. From [J], the interferometric phase can be obtained by?=arg(s1s2*)(2)where arg( ) indicates the argument of a complex number.

Mathematical theories of tracking error distributions were also d

Mathematical theories of tracking error distributions were also developed to improve the algorithms of determining sun position [14,15].With rapid advances in the computer technology and systems control fields in recent decades, the literature now contains many sophisticated sun tracking systems designed to maximize the efficiency of solar thermal and photovoltaic systems. Broadly speaking, these systems can be classified as either closed-loop or open-loop types, depending on their mode of signal operation (Table 1). The remainder of this paper presents a systematic review of the operational principles and advantages of each of the major closed-loop and open-loop types of sun tracking systems presented in the literature over the past 20 years.Table 1.Performance of sun tracking systems [16-53].

2.?Closed-loop Types of Sun Tracking SystemsClosed-loop types of sun tracking systems are based on feedback control principles. In these systems, a number of inputs are transferred to a controller from sensors which detect relevant parameters induced by the sun, manipulated in the controller and then yield outputs (i.e. sensor-based). In 1986, Akhmedyarov et al. [16] first increased the output power of a solar photoelectric station in Kazakhstan from 357 W to 500 W by integrating the station with an automatic sun tracking system. Several years later, Maish [17] developed a control system called SolarTrak to provide sun tracking, night and emergency storage, communication, and manual drive control functions for one- and two-axis solar trackers in a low-cost, user-friendly package.

The control algorithm used a six-degree self-alignment routine and a self-adjusting motor actuation time in order to improve both the pointing accuracy and the system reliability. The experimental results showed that the control system enabled a full-day pointing accuracy of better than ��0.1�� to be achieved. In 1992, Agarwal [18] presented a two-axis tracking system consisting of worm gear drives and four bar-type kinematic linkages to facilitate the accurate focusing of the reflectors in a solar concentrator system. In the same year, Enslin [19] applied the principles of maximum power point tracking (MPPT) to realize a power electronic converter for transforming the output voltage of a solar panel to the required DC battery bus voltage.

An MPPT system consists of two basic components: a switchmode converter and a control/tracking section. The switchmode Anacetrapib converter is the core of the entire system and allows energy at one potential to be drawn, stored as magnetic energy in an inductor, and then released at a different potential. By setting up the switchmode section in various different topologies, either high-to-low or low-to-high voltage converters can be constructed.

In large-scale systems, the components are interconnected and so

In large-scale systems, the components are interconnected and so the variables are correlated, which constitutes information on system topology with causality. After a fault occurs, it not only shows up as local phenomenon but also propagates to some other components or variables. Hence we should consider the sensor location problem to find the root cause of the fault origin and type from the viewpoint of the whole system.In order to measure the fault detection quality related to sensor location, some criteria are defined in Kawabata et al.’s paper [1]. Firstly, all the faults should be detected when they occur. Secondly, different faults should be identified from each other so that one can differentiate them based on the sensor readings.

The criteria of detectability and identifiability are basic requirements for fault detection [2].

In this reference all the sensors are assumed to be effective, that is, they show exactly whether the process variables are normal or abnormal.In engineering practice, sensors may often be faulty, meaning that they may fail to give adequate readings. For example, the reading may remain unchanged when the true value should be a deviation, which is called a missed alarm; or the sensor may give an alarm for a normal operation state, known as a false alarm. We should therefore allow for some redundancy in sensors in case of failures. More commonly, the measurements may show AV-951 these two kinds of sensor faults because of the choice of the threshold.

Often due to noise there are no real Dacomitinib sensor faults but deviations due to measurement noise, which is inevitable.

If the threshold setting is strict in order to suppress the missed alarm probability, the reading will be sensitive to random noise and temporary deviations, resulting in a high probability of false alarm. If we relax the threshold and accept larger region to be considered as normal, then the number of false alarms will decrease with more missed alarms. Therefore, missed alarms and false alarms are two aspects of reliability and we have to make a trade-off between them. This can be clearly illustrated via a receiver operating characteristics (ROC) curve [3,4].

Sensors in this present paper also include soft sensors that measure some specific variables by soft sensing techniques [5].With increasing complexity in process industrial systems, traditional mathematical models are difficult to obtain. Hence, graph-based models are proposed in the modeling analysis. Based on the signed directed graph (SDG) model, Raghuraj, et al. [2] have discussed the problems of detectability and identifiability in sensor location and presented the corresponding algorithm for locating each sensor.