Curcumin Prevents Cerebellar Hypoplasia and Restores the Behavior in Hyperbilirubinemic Gunn Rat by a Pleiotropic Effect on the Molecular Effectors of Brain Damage

Bilirubin toxicity to the central nervous system (CNS) is responsible for severe and permanent neurologic damage, resulting in hearing loss, cognitive, and movement impairment. Timely and effective management of severe neonatal hyperbilirubinemia by phototherapy or exchange transfusion is crucial for avoiding permanent neurological consequences, but these therapies are not always possible, particularly in low-income countries. To explore alternative options, we investigated a pharmaceutical approach focused on protecting the CNS from pigment toxicity, independently from serum bilirubin level. To this goal, we tested the ability of curcumin, a nutraceutical already used with relevant results in animal models as well as in clinics in other diseases, in the Gunn rat, the spontaneous model of neonatal hyperbilirubinemia. Curcumin treatment fully abolished the landmark cerebellar hypoplasia of Gunn rat, restoring the histological features, and reverting the behavioral abnormalities present in the hyperbilirubinemic rat. The protection was mediated by a multi-target action on the main bilirubin-induced pathological mechanism ongoing CNS damage (inflammation, redox imbalance, and glutamate neurotoxicity). If confirmed by independent studies, the result suggests the potential of curcumin as an alternative/complementary approach to bilirubin-induced brain damage in the clinical scenario.     

The effect of synthesis gas composition on the Fischer–Tropsch synthesis over Co/γ-Al2O3 and Co–Re/γ-Al2O3 catalysts

The Fischer–Tropsch synthesis over Co/γ-Al₂O₃ and Co–Re/γ-Al₂O₃ was investigated in a fixed-bed reactor at 20 bar and 483 K using feed gases with molar H₂/CO ratios of 2.1, 1.5 and 1.0 simulating synthesis gas derived from biomass. With lower H₂/CO ratios in the feed, the CO conversion and the CH₄ selectivity decreased, while the C₅₊ selectivity and olefin/paraffin ratio for C₂–C₄ increased slightly. The water–gas shift activity was low for both catalysts, resulting in high molar usage ratios of H₂/CO (close to 2.0), even at the lower inlet ratios (i.e. 1.5 and 1.0). For both catalysts, the drop in the production rate of hydrocarbons when shifting from an inlet ratio of 2.1 to 1.5 was significant mainly because the H₂/CO usage ratio did not follow the change in the inlet ratio. The hydrocarbon selectivities were rather similar for inlet H₂/CO ratios of 2.1 and 1.5, while significantly deviating from those for an inlet ratio of 1.0. With the studied catalysts, it is possible to utilize the advantages of an inlet ratio of 1.0 (higher selectivity to C₅₊, lower selectivity to CH₄, no water–gas shifting of the bio-syngas needed prior to the FT reactor) if a low syngas conversion is accepted.     

Combining the evidences of temporal and spectral enhancement techniques for improving the performance of Indian language identification system in the presence of background noise

Language Identification has gained significant importance in recent years, both in research and commercial market place, demanding an improvement in the ability of machines to distinguish between languages. Although methods like Gaussian mixture models, hidden Markov models and neural networks are used for identifying languages the problem of language identification in noisy environments could not be addressed so far. This paper addresses the performance of automatic language identification system in noisy environments. A comparative performance analysis of speech enhancement techniques like minimum mean squared estimation, spectral subtraction and temporal processing, with different types of noise at different SNRs, is presented here. Though these individual enhancement techniques may not yield good performance with different types of noise at different SNRs, it is proposed to combine the evidences of all these techniques to improve the overall performance of the system significantly. The language identification studies are performed using IITKGP-MLILSC (IIT Kharagpur-Multilingual Indian Language Speech Corpus) databases which consists of 27 languages.     

A Hybrid RF and Vision Aware Fusion Scheme for Multi-Sensor Wireless Capsule Endoscopic Localization

A huge torrent of data traffic is generated from various heterogeneous applications and services at the Internet backbone. In general, at the backbone, all such applications and services are allocated spectral resources under a shared spectrum environment within elastic optical networks (EONs). In such a fully shared environment, connection requests (CRs) belonging to different traffic profiles compete for spectral resources. Hence, it is very challenging for network operators to resolve resource conflict that occur at the time of provisioning resources to such CRs. The heterogeneous traffic profile (HTP) considered in this work includes permanent lightpath demands (PLDs) and scheduled lightpath demands (SLDs). We propose various distance adaptive routing and spectrum assignment (DA-RSA) heuristics to resolve resource conflict among these two traffic profiles in EONs under a full sharing environment. Conventionally, preemption was the only technique to resolve such resource conflict among HTPs. Since preemption involves the overhead of selecting CRs to be preempted and then deallocating the resources given to those CRs, excessive preemption adversely affects the performance of the network. Therefore, in this work, we utilized bandwidth splitting as a solution to resolve resource conflict among HTPs under such a shared environment in EONs. Moreover, an integrated solution consisting of splitting and preemption is also proposed. We refer to this new integration as flow-based preemption. Our simulation results demonstrate that bandwidth splitting-based heuristics yield significant improvement in terms of the amount of bandwidth accepted in the network, link and node utilization ratio, number of transponders utilized and the amount of bandwidth dropped due to preemption. Moreover, the flow-based preemption approach is proved to be superior in performance amongst all proposed strategies.

     

Autonomous Magnetic Microrobots by Navigating Gates for Multiple Biomolecules Delivery

The precise delivery of biofunctionalized matters is of great interest from the fundamental and applied viewpoints. In spite of significant progress achieved during the last decade, a parallel and automated isolation and manipulation of rare analyte, and their simultaneous on‐chip separation and trapping, still remain challenging. Here, a universal micromagnet junction for self‐navigating gates of microrobotic particles to deliver the biomolecules to specific sites using a remote magnetic field is described. In the proposed concept, the nonmagnetic gap between the lithographically defined donor and acceptor micromagnets creates a crucial energy barrier to restrict particle gating. It is shown that by carefully designing the geometry of the junctions, it becomes possible to deliver multiple protein‐functionalized carriers in high resolution, as well as MCF‐7 and THP‐1 cells from the mixture, with high fidelity and trap them in individual apartments. Integration of such junctions with magnetophoretic circuitry elements could lead to novel platforms without retrieving for the synchronous digital manipulation of particles/biomolecules in microfluidic multiplex arrays for next‐generation biochips.     

Credible sets for risk ratios in over-reported two-sample binomial data using the double-sampling scheme

We consider point and interval estimation for risk ratios based on two independent samples of binomial data subject to false positive misclassification. For such data it is well known that the model is unidentifiable. We consider incorporating training data obtained by using a double-sampling scheme to make the model identifiable. In this identifiable model, we propose a Bayesian method to make statistical inferences. In particular, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. The algorithm is illustrated using a real data example and further examined via Monte Carlo simulation studies.     

The Added Value of Understanding Informal Social Networks in an Adaptive Capacity Assessment: Explorations of an Urban Water Management System in Indonesia

Social networks play an important role in environmental governance regimes, and they are a key to the adaptive capacity of systems that deal with complex, contextual and multi-faceted issues. Urban water systems are typical examples of complex systems facing many pressures, such as increased population, water quality deterioration, and climate change. This paper explores social networks of the key stakeholders engaged in urban water management, in Makassar City, Indonesia, in the context of exploring ways to improve management of an increasingly complex urban water system. Three social networks were explored; those constituted by formal and informal interactions and networks perceived by stakeholders to be “ideal”. Formal networks were identified through an examination of the legislative instruments and government agencies’ documents relating to water provision in Makassar, while the informal and “ideal” networks were investigated in collaboration with the stakeholders. The research found that the informal social network was more extensive than were the formally required networks, and the investigation of informal networks created a potentially more robust and adaptive water management system than would have occurred through inclusion of formal institutional arrangements. We suggest that in examination of the adaptive capacity of an urban water system, one also considers the informal arrangements and linkages, as this additional information about the system is necessary to enhance our understanding of potential adaptation of water management and improved urban water systems.