In Virto

Seven new 3-alkyl(aryl)-4-(2-thienymethylenamino)-4,5-dihydro-1H-1,2,4-triazol-5-ones (2) were synthesized by the reactions of 3-alkyl(aryl)-4-amino-4,5-dihydro-1H-1,2,4-triazol-5-ones (1) with thiophene-2-carbaldehyde. In addition, N-acetyl derivatives of compounds 2d-2g were also prepared. The structures of eleven new compounds synthesized were determined by elemental analysis as well as IR, NMR and UV spectral data. In addition, compounds 2a-g and 3a, 3b, 3d-f were also screened for their antioxidant activities and 2a-g were potentiometrically titrated with tetrabutylammonium hydroxide (TBAH) in four nonaqueous solvents (isopropyl alcohol, t-butyl alcohol, acetonitrile and N,N-dimethyl formamide). Also half-neutralization potential values and the corresponding pKa values were determined in all cases

Efficient channel estimation for reconfigurable MIMO antennas: Training techniques and performance analysis

Multifunctional and reconfigurable multiple-input multiple-output (MR-MIMO) antennas are capable of dynamically changing the operation frequencies, polarizations, and radiation patterns, and can remarkably enhance system capabilities. However, in coherent communication systems, using MR-MIMO antennas with a large number of operational modes may incur prohibitive complexity due to the need for channel state estimation for each mode. To address this issue, we derive an explicit relation among the radiation patterns for the antenna modes and the resulting channel gains. We propose a joint channel estimation/prediction scheme where only a subset of all the antenna modes is trained for estimation, and then, the channels associated with the modes that are not trained are predicted using the correlations among the different antenna modes. We propose various training mechanisms with reduced overhead and improved estimation performance, and study the impact of channel estimation error and training overhead on the MR-MIMO system performance. We demonstrate that one can achieve significantly improved data rates and lower error probabilities utilizing the proposed approaches. For instance, under practical settings, we observe about 25% throughput increase or about 3-dB signal-to-noise ratio improvement under the same training overhead with respect to non-reconfigurable antenna systems. © 2002-2012 IEEE

CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration

Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is typically performed via electronically controllable spatial light modulators (SLM). An HR image is then reconstructed from the encoded measurements by solving an inverse problem that involves the forward model of the imaging system. To capture system non-idealities such as optical aberrations, a mainstream approach is to conduct an offline calibration scan to measure the system response for a point source at each spatial location on the imaging grid. However, it is challenging to run calibration scans when using structured SLMs as they cannot encode individual grid locations. In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that locomotes a pre-printed fixed coded aperture. A deep neural network is then leveraged to correct for the influences of system non-idealities in multiplexed measurements without the need for offline calibration scans. Finally, a deep plug-and-play algorithm is used to reconstruct images from corrected measurements. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity

Making waves: Lessons learned from the COVID-19 anthropause in the Netherlands on urban aquatic ecosystem services provisioning and management

Contains fulltext : 284054.pdf (Publisher’s version ) (Open Access

A Multifaceted Analysis of Immune-Endocrine-Metabolic Alterations in Patients with Pulmonary Tuberculosis

Our study investigated the circulating levels of factors involved in immune-inflammatory-endocrine-metabolic responses in patients with tuberculosis with the aim of uncovering a relation between certain immune and hormonal patterns, their clinical status and in vitro immune response. The concentration of leptin, adiponectin, IL-6, IL-1β, ghrelin, C-reactive protein (CRP), cortisol and dehydroepiandrosterone (DHEA), and the in vitro immune response (lymphoproliferation and IFN-γ production) was evaluated in 53 patients with active untreated tuberculosis, 27 household contacts and 25 healthy controls, without significant age- or sex-related differences. Patients had a lower body mass index (BMI), reduced levels of leptin and DHEA, and increased concentrations of CRP, IL-6, cortisol, IL-1β and nearly significant adiponectin values than household contacts and controls. Within tuberculosis patients the BMI and leptin levels were positively correlated and decreased with increasing disease severity, whereas higher concentrations of IL-6, CRP, IL-1β, cortisol, and ghrelin were seen in cases with moderate to severe tuberculosis. Household contacts had lower DHEA and higher IL-6 levels than controls. Group classification by means of discriminant analysis and the k-nearest neighbor method showed that tuberculosis patients were clearly different from the other groups, having higher levels of CRP and lower DHEA concentration and BMI. Furthermore, plasma leptin levels were positively associated with the basal in vitro IFN-γ production and the ConA-driven proliferation of cells from tuberculosis patients. Present alterations in the communication between the neuro-endocrine and immune systems in tuberculosis may contribute to disease worsening

Getting ‘Smad' about obesity and diabetes

Recent findings on the role of transforming growth factor (TGF)-β/Smad3 signaling in the pathogenesis of obesity and type 2 diabetes have underscored its importance in metabolism and adiposity. Indeed, elevated TGF-β has been previously reported in human adipose tissue during morbid obesity and diabetic neuropathy. In this review, we discuss the pleiotropic effects of TGF-β/Smad3 signaling on metabolism and energy homeostasis, all of which has an important part in the etiology and progression of obesity-linked diabetes; these include adipocyte differentiation, white to brown fat phenotypic transition, glucose and lipid metabolism, pancreatic function, insulin signaling, adipocytokine secretion, inflammation and reactive oxygen species production. We summarize the recent in vivo findings on the role of TGF-β/Smad3 signaling in metabolism based on the studies using Smad3−/− mice. Based on the presence of a dual regulatory effect of Smad3 on peroxisome proliferator-activated receptor (PPAR)β/δ and PPARγ2 promoters, we propose a unifying mechanism by which this signaling pathway contributes to obesity and its associated diabetes. We also discuss how the inhibition of this signaling pathway has been implicated in the amelioration of many facets of metabolic syndromes, thereby offering novel therapeutic avenues for these metabolic conditions

Oncogenic Signaling Pathways in The Cancer Genome Atlas.

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy