Removal of Chromium (VI) cation metal using Poly (N-vinyl-2-pyrrolidone)/Magnetite nanocomposite from aqueous media

Groundwater contamination with heavy metals is considered as serious environmental hazard that affect the human society. Nano adsorbents incorporating magnetite nanoparticles provides promising alternative to facilitate removal of heavy metal ions from wastewater. The present work focuses on removal of chromium (VI) cationic metals from aqueous media using Polyvinyl Pyrrolidone (PVP)/Magnetite (Fe3O4) Nanocomposite (MNC). Magnetite nanoparticles are synthesized using chemical co-precipitation and grafted using polyvinyl pyrrolidone to form a magnetite nanocomposite. MNC were characterized with X-ray diffraction (XRD) and Infrared absorption spectrum (FT-IR) studies to affirm the formation and presence of polymeric functional groups of PVP/Magnetite nanocomposite. Batch experiments are carried out at exclusive concentration intervals to study about the adsorption efficiency of MNC on chromium (VI) cationic metal using U-Vis spectroscopy. The results obtained through adsorption studies shows the synthesized PVP/Magnetite nanocomposites has a removal efficiency of 94%

Pivotal Role of the α2A-Adrenoceptor in Producing Inflammation and Organ Injury in a Rat Model of Sepsis

Background: Norepinephrine (NE) modulates the responsiveness of macrophages to proinflammatory stimuli through the activation of adrenergic receptors (ARs). Being part of the stress response, early increases of NE in sepsis sustain adverse systemic inflammatory responses. The intestine is an important source of NE release in the early stage of cecal ligation and puncture (CLP)-induced sepsis in rats, which then stimulates TNF-a production in Kupffer cells (KCs) through the activation of the a2-AR. It is important to know which of the three a2-AR subtypes (i.e., a2A, a2B or a2C) is responsible for the upregulation of TNF-a production. The aim of this study was to determine the contribution of a2A-AR in this process. Methodology/Principal Findings: Adult male rats underwent CLP and KCs were isolated 2 h later. Gene expression of a2A-AR was determined. In additional experiments, cultured KCs were incubated with NE with or without BRL-44408 maleate, a specific a2A-AR antagonist, and intraportal infusion of NE for 2 h with or without BRL-44408 maleate was carried out in normal animals. Finally, the impact of a2A-AR activation by NE was investigated under inflammatory conditions (i.e., endotoxemia and CLP). Gene expression of the a2A-AR subtype was significantly upregulated after CLP. NE increased the release of TNF-a in cultured KCs, which was specifically inhibited by the a2A-AR antagonist BRL-44408. Equally, intraportal NE infusion increased TNF-a gene expression in KCs and plasma TNF-a which was also abrogated by co-administration of BRL-44408. NE also potentiated LPS-induced TNF-a release via the a2A-AR in vitro and in vivo. This potentiation of TNF-a release by NE was mediated through the a2A-AR coupled Gai protein and the activation of the p38 MAP kinase. Treatment of septic animals with BRL-44408 suppressed TNF-a, prevented multiple organ injury and significantly improved survival from 45% to 75%. Conclusions/Significance: Our novel finding is that hyperresponsiveness to a2-AR stimulation observed in sepsis is primarily due to an increase in a2A-AR expression in KCs. This appears to be in part responsible for the increased proinflammatory response and ensuing organ injury in sepsis. These findings provide important feasibility information for further developing the a2A-AR antagonist as a new therapy for sepsis

Utilization of Novel Overlap Functions in Wireless Sensor Fusion

With the emergence of high-speed wireless networks and with their increased computational capabilities, distributed sensor networks (DSNs) have a wide range of real time applications in automation, defense, medical imaging, robotics, whether predictions, etc. Since the early 1990s, DSNs have been an active research area. In this research paper, various well known overlap functions utilized in wireless sensor fusion are briefly summarized. Novel overlap functions, namely the dual of W, N Functions are defined. By associating a overlap probability distribution function with the W Function, novel performance measures related to the sensor fusion problem are discussed. Other interesting approaches for defining novel overlap functions are discussed. The relationship between existing theories (such as rough set theory, detection theory and fuzzy set theory) and wireless sensor fusion problem is discussed

A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection

A robust medical decision support system for classifying skin lesions from dermoscopy images is a crucial instrument for determining skin cancer prognosis. In recent years, full resolution convolutional network has made significant progress in recognizing skin cancer types from Dermoscopic images despite their fine-grained changes in appearance. Recently, full resolution convolutional network have gained popularity as a solution to semantic segmentation issues. However, the hyper-parameters it chooses are what determine how well it performs, and manually fine-tuning these hyper-parameters takes time. Therefore, a hyper-parameter optimized full resolution convolutional network is suggested for dermoscopy picture segmentation in this research. The network’s hyper-parameters are optimized by a brand-new dynamic graph cut algorithm method. Hyper-parameters emphasize the proper balance between exploration and exploitation by combining the wolves’ individual haunting tactics with their global haunting strategies to generate a neighborhood-based searching strategy. The fundamental objective of this study is to develop a hyper-parameter-optimized Full resolution convolutional network-based model capable of reliably diagnosing skin cancer types using dermoscopy images. The computer-aided diagnosis could be more efficient and precise. The segmentation approach is the primary way to identify cancerous tumors with precision. This study introduces a dynamic graph cut algorithm -based method for accurate segmentation and improved skin cancer classification using a full resolution convolutional network. Experiments reveal that the proposed model effectively addresses the frequent over-segmentation and under-segmentation issues in graph cut and the subject of wrongly segmented small sections in the grab cut method. In addition, the results illustrate the utility of data augmentation in training and testing, with enhanced performance compared to the usage of fresh images. Multiple experiments were done using various transferring models, and the results of our recommended model showed superior performance in skin lesion categorization tasks relative to other architectures with an accuracy of 97.986%