A prospective study on pregnancy complicated with jaundice with special emphasis on fetomaternal outcome

Background: Jaundice defined as yellow discolouration of skin, sclera and mucus membrane resulting from increased serum bilirubin concentration. It is usually clinical visible when plasma bilirubin exceeds 3mg/dl. This study aimed at determining maternal and foetal outcome in women with jaundice complicating pregnancy.Methods: This prospective study was conducted on 58 cases of pregnant women with jaundice (serum bilirubin ≥3 mg%) admitted at Panna Dhaya Zanana Hospital (PDZH), RNT Medical College, Udaipur, Rajasthan from January 2016 December 2016.Results: The incidence observed in this study was 0.28%. 77.59% cases in this study were in third trimester of pregnancy. Serum bilirubin was >20 mg% in 5.18% cases. Haemolysis, elevated liver enzymes, low platelets (HELLP) syndrome, acute fatty liver of pregnancy, intrahepatic cholestasis of pregnancy, viral hepatitis and malaria were the causes of jaundice. HELLP syndrome was the most common cause of jaundice. Of 58 women 38 delivered vaginally and 12 were LSCS for obstetrical indication and 8 were undelivered. The disease is associated with high incidence of preterm labour, IUGR, birth asphyxia and foetal distress. Perinatal mortality was 38%. Maternal mortality in 11 cases i.e. 18.96%. Main causes of maternal mortality were hepatic encephalopathy, DIC followed by shock due to PPH, DIC followed by multiple organ dysfunction syndrome.Conclusion: Jaundice and pregnancy having a grave prognosis, resulting in a very high perinatal as well as maternal morbidity and mortality, and requires an early diagnosis and careful management

Damage detection in tensegrity under varying temperature using interacting Particle-Ensemble Kalman Filter

International audienceTensegrities are structural mechanisms, with dedicated compression (struts/bars) and tension members (cables). The compression members float inside the network of tension members. Tensegrities are characterized by the presence of at least one infinitesimal mechanism, which is stabilized by the pre-stress present in the members, to ensure the equilibrium of the structure. Under external load, tensegrity may change its form by altering its member pre-stress, thereby affecting its global stiffness even in the absence of damage. Moreover, tensegrities can have different stiffness properties under the same structural configuration in the absence of any damage or external load, if the pre-stress levels of the members are different. However, the changes in dynamic characteristics of tensegrities are not limited to the aforementioned causes only and is also affected by ambient uncertainties. A variation in temperature may alter the dynamic characteristics of a tensegrity by influencing its material (Young's modulus, etc.) and structural (boundary conditions, structural dimensions, etc.) properties. This can potentially lead to a false impression of tensegrity damage/health. Meanwhile, the prolonged usage of tensegrity may lead to loss of pre-stress in the cables, buckling of the bars, corrosion, and damage of the members, etc. Thus affecting the structural stiffness which leads to change in the measured dynamic properties of the tensegrity. To account for this actual damage in the tensegrity, all the mentioned major challenges that could lead to a false alarm need to be dealt with. The present study develops a vibration-based time-domain approach for tensegrity health monitoring in the presence of uncertainties due to ambient force, measurement noise, and varying temperature. An interacting filtering technique has been used, where the state variables are estimated by the Ensemble Kalman filter that resides inside the Particle filter which computes the health parameters

Switching Kalman filter for damage estimation in the presence of sensor faults

International audienceBayesian filtering based approaches for diagnosis of structural damage have been widely employed in structural health monitoring (SHM) research. The approach however may lead to an inaccurate alarm/decision due to the presence of faulty sensor/s. Nevertheless, sensor faults are inevitable during real field SHM in which sensor may malfunction or get detached from the structural surface, registering completely irrelevant information as measurement. Eventually, such erroneous information induce error in the estimation which leads to an inaccurate, sometimes divergent and impractical solution. The current study deals with Bayesian filtering based structural damage detection in the presence of one or multiple (consecutive) sensor faults. The damage detection is addressed with joint state-parameter estimation approach while a switching filtering strategy is employed for sensor fault detection. Switching approach employs multiple possible sensor fault models which are subsequently integrated to the measurement model of the joint estimation approach. The selection of the competent model (/switching between model ensembles) is undertaken recursively based on their likelihood against measured response. The proposed approach is tested on a numerical lumped mass model of a shear frame building, followed by a laboratory experiment on a cantilever beam. It has been perceived that estimation of health for structures measured with faulty sensors can actually lead to a false (positive and negative) alarm which can, however, be avoided by the employment of the proposed approach. The performance of the proposed approach is further established for healthy and damaged system with pre-existing and sudden sensor faults

Estimation of local failure in large tensegrity structures via substructuring using Interacting Particle-Ensemble Kalman Filter

International audienceTensegrity is a network of bars and cables that maintains its structural integrity with tension present in its cables. Other than typical structural failure mechanisms, tensegrity may fail due to slacking of cables or buckling of bars. Reallife tensegrities are an assemblage of component modules. Large tensegrities require excessive computation for model-based structural health monitoring (SHM), which may sometimes make the problem ill-posed. Instead of the entire domain, only a substructure can be investigated explicitly. Substructures decouple the structure into independent components that can be monitored individually, provided the sub-domain interface is measured. Yet the integration of substructures within predictor-corrector model-based SHM algorithms needs special investigation from consistency, stability, and accuracy perspectives. To consider system uncertainties Bayesian filtering-based SHM approaches have been employed in this study. The need for interface measurement has been circumvented through an output injection approach. To increase computational efficiency, the domain decomposition approach is coupled with an interacting filtering-based approach that employs Ensemble Kalman filter (for state estimation) within an envelope of Particle filter (for health parameter estimation). This facilitates simultaneous estimation of state and parameters while enabling full parallelization capability. The proposed approach is tested on a six-stage tensegrity tower made of component simplex modules

Estimation of local failure in tensegrity using Interacting Particle-Ensemble Kalman Filter

International audienceTensegrities form a special case of truss, wherein compression members (struts/bars) float within a network of tension members (cables). Tensegrities are characterized by the presence of at least one infinitesimal mechanism stabilized with member pre-stress to ensure equilibrium. Over prolonged usage, the cables may lose their pre-stress while the bars may buckle, get damaged, or corrode, affecting the structural stiffness leading to change in the measured dynamic properties. Upon loading, a tensegrity structure may change its form through altering its member pre-stress affecting its global stiffness, even in the absence of damage. This can potentially mask the effect of damage leading to a false impression of tensegrity health. This poses the major challenge in tensegrity health monitoring especially when the load is stochastic and unknown. Present study proposes an output-only time-domain method that makes use of tensegrity vibrational responses within a Bayesian filtering-based approach to monitor the tensegrity health in the presence of uncertainties due to ambient force, model inaccuracy, and measurement noise. For this, an interacting strategy combining Particle Filter (PF) and Ensemble Kalman Filter (EnKF) has been adopted (Interacting particle-Ensemble Kalman Filter, IP-EnKF) in which the EnKF estimates the response states as ensembles while running within a PF envelop that estimates a set of location-based health parameters as particles. Furthermore, for a cheaper damage detection procedure, strain responses are used as measurements. The efficiency of the proposed methodology in terms of accuracy, computational cost, and robustness against noise contamination has been demonstrated using numerical experiments performed on two tensegrity modules: a simplex tensegrity and an extended-octahedron tensegrity

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

International audienceTo avert catastrophic failure in the structures, joints are typically designed to yield, but not fail, so that energy accumulated under cyclic loading is dissipated. Eventually, this renders the structural joints to be characteristically weaker and more vulnerable than the members. Yet, damage detection research mostly assumes damage in the members only. This article proposes a model-based predictor-corrector algorithm that uses an interacting filtering approach to efficiently estimate joint damage in the presence of input and measurement uncertainties. For the predictor model, a novel strain-displacement relationship specific to semi-rigid frames is developed to map nodal displacements to corresponding strain measurements. The proposed estimation method embeds robustness against non-stationary input (e.g. seismic excitation) in the state filter, itself. For this, an output injection technique is integrated within the state filter. The modified state filter (robust Kalman filter) runs within an enveloping parameter filter (Particle filter) to simultaneously estimate the system states and joint damage parameters, respectively, using the response signal. Strain has been adopted as measurement since it is frame independent (beneficial for seismic activity) and also comparatively cheaper to use. Numerical studies are performed on a two-dimensional three storythree bay shear frame for different joint damage locations and severities. The sensitivity and the stability of the proposed approach are further investigated. Experimental validation of the proposed algorithm is carried out on a 2-D steel frame

Structural health monitoring with non-linear sensor measurements robust to unknown non-stationary input forcing

International audienceBayesian filtering based structural health monitoring algorithms typically assume stationary white Gaussian noise models to represent an unknown input forcing. However, typical structural damages occur mostly under the action of extreme loading conditions, like earthquake or high wind/waves, which are characteristically non-stationary and non-Gaussian. Clearly, this invalidates this basic assumption, causing these algorithms to perform poorly under non-stationary noise conditions. This paper extends an existing interacting filtering algorithm to efficiently estimate structural damages while being robust to unknown non-stationary non-Gaussian input forcing. Furthermore, this approach is generalized beyond linear measurements to encompass the case of non-linear measurements such as strains. The joint estimation of state and parameters is performed by combining Ensemble Kalman filtering, for non-linear system state estimation, and Particle filtering to estimate changes in the structural parameters. The robustness against input forcing is achieved through an output injection approach embedded in the state filter equation. Numerical simulations for two kinds of response measurements (acceleration and strain) are performed on a 3D frame structure under different damage location and severity scenarios. The sensitivity with respect to noise and the impact of different sensor combinations have also been investigated

Robust Interacting Particle-Kalman Filter based structural damage estimation using dynamic strain measurements under non-stationary excitation -an experimental study

International audienceSensor types and their positioning is a major factor in structural health monitoring (SHM) to ensure certainty in estimation. While acceleration has predominantly been employed for damage detection, they are known to be costly and not frame invariant (except for moderately accurate GPS based accelerometers). A thorough monitoring of a real life structure requires dense instrumentation which might become expensive with costly sensor types. Further, damages mostly occur at rare events, like seismic base excitation, for which typical accelerometers are not proper. This study employs strain as a cheaper alternative for damage sensitive measurement that is also frame invariant. An interacting filtering approach with particle and Kalman filters is employed that estimates structural health from measured dynamic strains. Further to account for extreme non-stationary events like seismic excitation, robustness against uncertain inputs is induced in the filtering environment following an output injection approach. The proposed algorithm is tested on a seven story-one bay frame model and a real experimental beam structure

Dichlorido[μ-10,21-dimethyl-2,7,13,18-tetra­phenyl-3,6,14,17-tetra­aza­tricyclo­[17.3.1.18,12]tetra­cosa-1(23),2,6,8,10,12(24),13,17,19,21-deca­ene-23,24-diolato]dicopper(II) ethanol hemisolvate dihydrate

The dinuclear title complex, [Cu2(C46H38N4O2)Cl2]·0.5C2H5OH·2H2O, is located on crystallographic inversion centres with two half-mol­ecules in the asymmetric unit. The two CuII atoms are coordinated by a hexa­dentate dianionic ligand formed in situ from the condensation of two tridentate ligands by four imine N atoms and two bridging phenolate O atoms along with two Cl atoms at axial positions. The coordination geometry around the metal atoms is distorted square-pyramidal (τ = 0.185 and 0.199). The non-bonding Cu⋯Cu distances are 2.9556 (12) and 2.9506 (12) Å in the two dimers. The packing is stabilized through solvent-mediated inter­molecular O—H⋯O and O—H⋯Cl hydrogen bonds. The diamine chain of one of the dimers is disordered over two positions in a 0.680 (5):0.320 (5) ratio

Functional interplay between DEAD-box RNA helicases Ded1 and Dbp1 in preinitiation complex attachment and scanning on structured mRNAs in vivo

RNA structures that impede ribosome binding or subsequent scanning of the 5′-untranslated region (5′-UTR) for the AUG initiation codon reduce translation efficiency. Yeast DEAD-box RNA helicase Ded1 appears to promote translation by resolving 5′-UTR structures, but whether its paralog, Dbp1, performs similar functions is unknown. Furthermore, direct in vivo evidence was lacking that Ded1 or Dbp1 resolves 5′-UTR structures that impede attachment of the 43S preinitiation complex (PIC) or scanning. Here, profiling of translating 80S ribosomes reveals that the translational efficiencies of many more mRNAs are reduced in a ded1-ts dbp1Δ double mutant versus either single mutant, becoming highly dependent on Dbp1 or Ded1 only when the other helicase is impaired. Such ‘conditionally hyperdependent’ mRNAs contain unusually long 5′-UTRs with heightened propensity for secondary structure and longer transcript lengths. Consistently, overexpressing Dbp1 in ded1 cells improves the translation of many such Ded1-hyperdependent mRNAs. Importantly, Dbp1 mimics Ded1 in conferring greater acceleration of 48S PIC assembly in a purified system on mRNAs harboring structured 5′-UTRs. Profiling 40S initiation complexes in ded1 and dbp1 mutants provides direct evidence that Ded1 and Dbp1 cooperate to stimulate both PIC attachment and scanning on many Ded1/Dbp1-hyperdependent mRNAs in vivo.Intramural Research Program of the National Institutes of Health; Australian Research Council Discovery Project grant [DP180100111 to T.P.]; National Health and Medical Research Council of Australia Senior Research Fellowship [APP1135928]. Funding for open access charge: Intramural Research Program of the National Institutes of Health