High glucose-induced hyperosmolarity contributes to COX-2 expression and angiogenesis: Implications for diabetic retinopathy

Background: We tested the hypothesis that glucose-induced hyperosmolarity, occurring in diabetic hyperglycemia, promotes retinal angiogenesis, and that interference with osmolarity signaling ameliorates excessive angiogenesis and retinopathy in vitro and in vivo. Methods and Results: We incubated human aortic (HAECs) and dermal microvascular endothelial cells (HMVECs) with glucose or mannitol for 24 h and tested them for protein levels and in vitro angiogenesis. We used the Ins2 Akita mice as a model of type 1 diabetes to test the in vivo relevance of in vitro observations. Compared to incubations with normal (5 mmol/L) glucose concentrations, cells exposed to both high glucose and high mannitol (at 30.5 or 50.5 mmol/L) increased expression of the water channel aquaporin-1 (AQP1) and cyclooxygenase (COX)-2. This was preceded by increased activity of the osmolarity-sensitive transcription factor Tonicity enhancer binding protein (TonEBP), and enhanced endothelial migration and tubulization in Matrigel, reverted by treatment with AQP1 and TonEBP siRNA. Retinas of Ins2 Akita mice showed increased levels of AQP1 and COX-2, as well as angiogenesis, all reverted by AQP1 siRNA intravitreal injections. Conclusions: Glucose-related hyperosmolarity seems to be able to promote angiogenesis and retinopathy through activation of TonEBP and possibly increasing expression of AQP1 and COX-2. Osmolarity signaling may be a target for therapy

A systematic approach to diverse, lead-like scaffolds from α,α-disubstituted amino acids.

A powerful strategy for the efficient lead-oriented synthesis of novel molecular scaffolds is demonstrated. Twenty two scaffolds were prepared from just four α-amino acid-derived building blocks and a toolkit of six connective reactions. Importantly, each individual scaffold has the ability to specifically target lead-like chemical space

Are soybean models ready for climate change food impact assessments?

Abstract. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models

Morphological and functional heterogeneity of adipose tissue: Regulatory mechanisms and therapeutic relevance(Article) [L'eterogeneità morfo-funzionale del tessuto adiposo: Meccanismi di regolazione e rilevanza terapeutica]

Adipose tissue is mostly comprised of specific cells, the adipocytes, and of vascular stroma, and plays a key role in energy balance. Two main varieties of adipose tissue have classically been described: white adipose tissue (WAT), mainly involved in energy storing and energy utilization through the synthesis and degradation of triglycerides; and brown adipose tissue (BAT), specialized in energy dissipation as heat. The presence of an additional, special type of adipocyte in the WAT, defined as “beige/brite”, with structural and metabolic features that are inbetween those of the WAT and the BAT, has been recently described. In response to cold, WAT adipocytes may take on an “intermediate” cell morphology and function that resemble those of the brown adipocyte (a process termed “browning”, or catabolic remodeling of white fat). Promoting the browning of the WAT may be a new strategy in the treatment of obesity, aimed at reducing its expansion. Several recently identified molecules play a key role in the pathophysiology of adipocytes, and can be potentially useful targets in the treatment of obesity and type 2 diabete

Hardware Solutions Supporting a Multiscale Approach in Early Vision

Recurrent networks are efficient computational structures for embedded analog filtering operations in smart vision sensors. Still, their use requires adequate control of various network parameters to achieve flexibility without incurring instability. In this paper, we analyze the behaviour of bounded recurrent networks, and we find how to modify interconnections at boundary nodes to avoid artifacts in the profiles of the resulting convolution kernels, particularly when the kernels span over a wide number of pixels. This solution can be implemented at circuital level just by varying the ratio of current mirrors at boundary cells, without modifying their inner arrangement. 1 Introduction Analog VLSI vision chips are expected to provide very efficient solutions for a broad range of early vision tasks [1]. However, even when some specific restricted functionality is addressed (e.g., stereo depth maps for autonomous robot navigation, detection of discontinuities in the optical flow ..