Handheld-Impedance-Measurement System with seven-decade capability and potentiostatic function

This paper describes design and test of a new impedance-measurement system for nonlinear devices that exhibits a seven-decade range and works down to a frequency of 0.01 Hz. The system is specifically designed for electrochemical measurements, but the proposed architecture can be employed in many other fields where flexible signal generation and analysis are required. The system employs an unconventional signal generator based on two pulsewidth modulation (PWM) oscillators and an autocalibration system that allows uncertainties of less than 3% to be obtained over a range of 1 kΩ to 100 GΩ. A synchronous demodulation processing allows the noise superimposed to the low-amplitude input signals to be made negligibl

Effect of TiO2 and Al2O3 Addition on the Performance of Chitosan/Phosphotungstic Composite Membranes for Direct Methanol Fuel Cells

Composite chitosan/phosphotungstic acid (CS/PTA) with the addition of TiO2 and Al2O3 particles were synthesized to be used as proton exchange membranes in direct methanol fuel cells (DMFCs). The influence of fillers was assessed through X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, liquid uptake, ion exchange capacity and methanol permeability measurements. The addition of TiO2 particles into proton exchange membranes led to an increase in crystallinity and a decrease in liquid uptake and methanol permeability with respect to pristine CS/PTA membranes, whilst the effect of the introduction of Al2O3 particles on the characteristics of membranes is almost the opposite. Membranes were successfully tested as proton conductors in a single module DMFC of 1 cm(2) as active area, operating at 50 degrees C fed with 2 M methanol aqueous solution at the anode and oxygen at the cathode. Highest performance was reached by using a membrane with TiO2 (5 wt.%) particles, i.e., a power density of 40 mW cm(-2), almost doubling the performance reached by using pristine CS/PTA membrane (i.e., 24 mW cm(-2))

Decipher the glioblastoma microenvironment: The first milestone for new groundbreaking therapeutic strategies

Glioblastoma (GBM) is the most common primary malignant brain tumour in adults. Despite the combination of novel therapeutical approaches, it remains a deadly malignancy with an abysmal prognosis. GBM is a polymorphic tumour from both molecular and histological points of view. It consists of different malignant cells and various stromal cells, contributing to tumour initiation, progression, and treatment response. GBM’s microenvironment is multifaceted and is made up of soluble factors, extracellular matrix components, tissue-resident cell types (e.g., neurons, astrocytes, endothelial cells, pericytes, and fibroblasts) together with resident (e.g., microglia) or recruited (e.g., bone marrow-derived macrophages) immune cells. These latter constitute the so-called immune microenvironment, accounting for a substantial GBM’s tumour volume. Despite the abundance of immune cells, an intense state of tumour immunosuppression is promoted and developed; this represents the significant challenge for cancer cells’ immune-mediated destruction. Though literature data suggest that distinct GBM’s subtypes harbour differences in their microenvironment, its role in treatment response remains obscure. However, an in-depth investigation of GBM’s microenvironment may lead to novel therapeutic opportunities to improve patients’ outcomes. This review will elucidate the GBM’s microenvironment composition, highlighting the current state of the art in immunotherapy approaches. We will focus on novel strategies of active and passive immunotherapies, including vaccination, gene therapy, checkpoint blockade, and adoptive T-cell therapies

A new colony of Olrog's Gull (Larus Atlanticus) in the Bahía Blanca estuary, Argentina

Una nueva colonia de la Gaviota de Olrog (Larus atlanticus) fue hallada en una pequeña isla de la ría de Bahía Blanca, cercana al puerto de Ingeniero White. El número total de nidos hallados fue de 1635, distribuidos en 11 grupos o subcolonias que presentaron entre 25 y 530 nidos. La colonia se encontraba dentro de una colonia de Gaviota Cocinera (Larus dominicanus). Esta colonia es el mayor asentamiento reproductivo de Larus atlanticus descripto hasta el momento. El área de la ría de Bahía Blanca constituye así el sitio de mayor importancia para la reproducción de esta especie vulnerable, pudiendo representar más del 60% de la población reproductiva conocida.A new colony of Olrog’s Gull (Larus atlanticus) was found on a small island in the Bahía Blanca estuary, near Ingeniero White Harbour. The total number of nests was 1635, distributed in 11 groups or sub-colonies ranging from 25 to 530 nests, inside a large Kelp Gull (Larus dominicanus) colony. This colony of Larus atlanticus is the largest reproductive group reported so far. The Bahía Blanca estuary area constitutes therefore the most important reproductive site of this vulnerable species, perhaps comprising more than 60% of the known reproductive population

Yield gap analysis of field crops: Methods and case studies

The challenges of global agriculture have been analysed exhaustively and the need has been established for sustainable improvement in agricultural production aimed at food security in a context of increasing pressure on natural resources. Whereas the importance of R&D investment in agriculture is increasingly recognised, better allocation of limited funding is essential to improve food production. In this context, the common and often large gap between actual and attainable yield is a critical target. Realistic solutions are required to close yield gaps in both small and large scale cropping systems worldwide; to make progress in this direction, we need (1) definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short, long term); (2) identification of the causes of gaps between yield levels; (3) management options to reduce the gaps where feasible and (4) policies to favour adoption of gap-closing technologies. The aim of this publication is to review the methods for yield gap analysis, and to use case studies to illustrate different approaches, hence addressing the first of these four requirements. Theoretical, potential, water-limited, and actual yield are defined. Yield gap is the difference between two levels of yield in this series. Depending on the objectives of the study, different yield gaps are relevant. The exploitable yield gap accounts for both the unlikely alignment of all factors required for achievement of potential or water limited yield and the economic, management and environmental constraints that preclude, for example, the use of fertiliser rates that maximise yield, when growers’ aim is often a compromise between maximising profit and minimising risk at the whole-farm scale, rather than maximising yield of individual crops. The gap between potential and water limited yield is an indication of yield gap that can be removed with irrigation. Spatial and temporal scales for the determination of yield gaps are discussed. Spatially, yield gaps have been quantified at levels of field, region, national or mega-environment and globally. Remote sensing techniques describes the spatial variability of crop yield, even up to individual plots. Time scales can be defined in order to either remove or capture the dynamic components of the environment (soil, climate, biotic components of ecosystems) and technology. Criteria to define scales in both space and time need to be made explicit, and should be consistent with the objectives of the analysis. Satellite measurements can complement in situ measurements. The accuracy of estimating yield gaps is determined by the weakest link, which in many cases is good quality, sub-national scale data on actual yields that farmers achieve. In addition, calculation and interpretation of yield gaps requires reliable weather data, additional agronomic information and transparent assumptions. The main types of methods used in yield benchmarking and gap analysis are outlined using selected case studies. The diversity of benchmarking methods outlined in this publication reflects the diversity of spatial and temporal scales, the questions asked, and the resources available to answer them. We grouped methods in four broad approaches. Approach 1 compares actual yield with the best yield achieved in comparable environmental conditions, e.g. between neighbours with similar topography and soils. Comparisons of this type are spatially constrained by definition, and are an approximation to the gap between actual and attainable yield. With minimum input and greatest simplicity, this allows for limited but useful benchmarks; yield gaps can be primarily attributed to differences in management. This approach can be biased, however, where best management practices are not feasible; modelled yields provide more relevant benchmarks in these cases. Approach 2 is a variation of approach 1, i.e. it is based on comparisons of actual yield, but instead of a single yield benchmark, yield is expressed as a function of one or few environmental drivers in simple models. In common with Approach 1, these methods do not necessarily capture best management practices. The French and Schultz model is the archetype in this approach; this method plots actual yield against seasonal water use, fits a boundary function representing the best yield for a given water use, and calculates yield gaps as the departure between actual yields and the boundary function. A boundary model fitted to the data provides a scaled benchmark, thus partially accounting for seasonal conditions. Boundary functions can be estimated with different statistical methods but it is recommended that the shape and parameters of boundary functions are also assessed on the basis of their biophysical meaning. Variants of this approach use nitrogen uptake or soil properties instead of water. Approach 3 is based on modelling which may range from simple climatic indices to models of intermediate (e.g. AquaCrop) or high complexity (e.g. CERES-type models). More complex models are valuable agronomically because they capture some genetic features of the specific cultivar, and the critical interaction between water and nitrogen. On the other hand, more complex models have requirements of parameters and inputs that are not always available. “Best practice” approaches to model yield in gap analysis are outlined. Importantly, models to estimate potential yield require parameters that capture the physiology of unstressed crops. Approach 4 benchmarking involves a range of approaches combining actual data, remote sensing, GIS and models of varying complexity. This approach is important for benchmarking at and above the regional scale. At these large scales, particular attention needs to be paid to weather data used in modelling yield because significant bias can accrue from inappropriate data sources. Studies that have used gridded weather databases to simulate potential and water-limited yields for a grid are rarely validated against simulated yields based on actual weather station data from locations within the same grid. This should be standard practice, particularly where global scale yield gaps are used for policy decisions or investment in R&D. Alternatively, point-based simulations of potential and water-limited yields, complemented with an appropriate up-scaling method, may be more appropriate for large scale yield gap analysis. Remote sensing applied to yield gap analysis has improved over the last years, mainly through pixel-based biomass production models. Site-specific yield validation, disaggregated in biomass radiation-use-efficiency and harvest index, remains necessary and need to be carried out every 5 to 10 years

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N\u3csub\u3e2\u3c/sub\u3eO Emissions, and Seed Yield

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems

TMS-EEG reveals hemispheric asymmetries in top-down influences of posterior intraparietal cortex on behavior and visual event-related potentials

Clinical data and behavioral studies using transcranial magnetic stimulation (TMS) suggest right-hemisphere dominance for top-down modulation of visual processing in humans. We used concurrent TMS-EEG to directly test for hemispheric differences in causal influences of the right and left intraparietal cortex on visual event related potentials (ERPs). We stimulated the left and right posterior part of intraparietal sulcus (IPS1) while the participants were viewing and rating the visibility of bilaterally presented Gabor patches. Subjective visibility ratings showed that TMS of right IPS shifted the visibility toward the right hemifield, while TMS of left IPS did not have any behavioral effect. TMS of right IPS, but not left one, reduced the amplitude of posterior N1 potential, 180-220 ms after stimulus-onset. The attenuation of N1 occurred bilaterally over the posterior areas of both hemispheres. Consistent with previous TMS-fMRI studies, this finding suggests that the right IPS has top down control on the neural processing in visual cortex. As N1 most probably reflects reactivation of early visual areas, the current findings support the view that the posterior parietal cortex in the right hemisphere amplifies recurrent interactions in ventral visual areas during the time-window that is critical for conscious perception

Sustainable intensification for a larger global rice bowl.

Future rice systems must produce more grain while minimizing the negative environmental impacts. A key question is how to orient agricultural research & development (R&D) programs at national to global scales to maximize the return on investment. Here we assess yield gap and resource-use efficiency (including water, pesticides, nitrogen, labor, energy, and associated global warming potential) across 32 rice cropping systems covering half of global rice harvested area. We show that achieving high yields and high resource-use efficiencies are not conflicting goals. Most cropping systems have room for increasing yield, resource-use efficiency, or both. In aggregate, current total rice production could be increased by 32%, and excess nitrogen almost eliminated, by focusing on a relatively small number of cropping systems with either large yield gaps or poor resource-use efficiencies. This study provides essential strategic insight on yield gap and resource-use efficiency for prioritizing national and global agricultural R&D investments to ensure adequate rice supply while minimizing negative environmental impact in coming decades