Purification of infectious canine parvovirus from cell culture by affinity chromatography with monoclonal antibodies.

Immuno affinity chromatography with virus neutralizing monoclonal antibodies, directed to the haemagglutinating protein of canine parvovirus (CPV) was used to purify and concentrate CPV from infected cell culture. The procedure was monitored by testing the respective fractions in an infectivity titration system, in an ELISA, in a haemagglutination assay and by negative contrast electron microscopy to quantify CPV or CPV antigen. The degree of purification was further estimated by testing the fractions for total protein content in a colorimetric method, for bovine serum albumin content in an ELISA and by SDS-PAGE. Over 99% of the contaminating proteins proved to be removed, and 20% or 70-90% of infectious CPV or CPV antigen, respectively, was recovered

Urban environmental quality and wellbeing in the context of incomplete urbanization in Brazil: integrating directly experienced ecosystem services into planning

The benefits of urban greenspace to residents are increasingly recognized as important to planning for sustainable and healthy cities. However, the way that people interact with and benefit from urban greenspace is context dependent and conditioned by a range of social and material factors. This paper applies and expands the ecosystems services based approach to understanding urban environmental quality and the way in which greenspace is appropriated by residents in the context of incomplete urbanization in three peri-urban target areas in Brazil. We develop and employ the notion of indirect (scientifically detected) and directly experienced ecosystems services, and undertake a science based ecosystem services assessment and a qualitative analysis of interviews, walking narratives and images captured with a smartphone application to understand what functions urban greenspace serves in the daily life of the studied neighborhoods. Findings demonstrate how elements of urban greenspace and what can be termed ecosystem services serve both material and signifying functions and produce subjective and collective benefits and dis-benefits that hinge on aspects of livability such as quality of urban service delivery, housing status and perceptions of crime and neighborhood character. We identify factors that enable, hinder and motivate both active material and interpretative interactions with urban greenspace. The findings suggest that the relationship between ecosystem service provision and wellbeing is better understood as reciprocal rather than one way. Although at the neighborhood scale, fear of crime and poor access to urban services can hinder positive engagements with urban greenspace and experienced benefits form ES, urban squares and fringe vegetation is also being appropriated to address experienced disadvantages. Presently however these local interactions and ecosystem service benefits are overlooked in formal planning and conservation efforts and are increasingly compromised by growing population density and environmental degradation. We make recommendations for a nuanced assessment of the material and interpretative human-nature interactions and associated ecosystem services in an urban context, and discuss the potential for planning initiatives that could be employed to articulate and nurture these important interactions in our target areas

‘It’s not necessarily a social space’ − Institutions, power and nature’s wellbeing benefits in the context of diverse inner-city neighbourhoods

Urban nature is widely known to provide wellbeing benefits to people and communities, but evidence particularly from diverse and disadvantaged contexts suggests that these benefits are not experienced equally by all. This paper unpacks this complexity by focussing on how urban nature is interacted with to produce relational wellbeing on two diverse inner-city housing estates undergoing regeneration in London, UK. We focus on the role of both formal institutions and the perceptions that people form of spatial features and their meanings and functions, and the manner in which these intermediaries shape human-nature interactions and the co-production of nature’s wellbeing impact. Our findings from quantitative and qualitative data demonstrate that urban nature contributes to all aspects of a five-dimensional notion of wellbeing. But social housing residents’ and young peoples’ ability to experience these benefits is limited. Informal mechanisms of social control such as perceptions of ownership of space and its appropriate uses, and fear of conflict and crime limit the extent to which residents access greenspaces and the activities within them. Together with formal institutions such as tenancy types, housing targets and criteria for optimisation of site allocation, they produce hierarchies of use of public greenspaces and reinforce existing divisions between people of different demographic and socio-economic status. The findings underline the need to facilitate the establishment of shared and inclusive norms concerning access and appropriate uses of natural spaces in housing and greenspace delivery

Beyond agriculture: alternative geographies of rural land investment and place effects across the United Kingdom

Global land ownership patterns have been shifting in recent decades, as institutional and non-traditional investors redirect capital into rural areas. Such investment is a stimulating alternative for innovative profit-driven land uses that move beyond agriculture. This paper explores how ‘new money’ economies have created place effects in three rural case studies across the United Kingdom, through concepts of built, natural, social, and economic capital. The case studies are informed by secondary research, site visits, and interviews, providing snapshots of investment impact. They represent diverse transformations in rural land use via new forms of direct investment, active investment, and processes of financing rather than financialisation, with distinct spatial and temporal characteristics. The case studies include new wine production in Kent, England; transforming the Menie Estate into Trump International Golf Links Scotland (TIGLS); and farm diversification in Northern Ireland. The conclusions tell three investment stories, where place effects reflect the dichotomies, contestation, and symbiosis between investors and local contexts. New land uses create place effects where economic potential often conflicts with natural capital impacts, although they foster knowledge creation and exchange. The underlying values of the investors and their navigation of local politics also have key roles to play in shaping the built, natural, social, and economic place effects

Beyond Agriculture: Alternative Geographies of Rural Land Investment and Place Effects across the United Kingdom

Global land ownership patterns have been shifting in recent decades, as institutional and non-traditional investors redirect capital into rural areas. Such investment is a stimulating alternative for innovative profit-driven land uses that move beyond agriculture. This paper explores how ‘new money’ economies have created place effects in three rural case studies across the United Kingdom, through concepts of built, natural, social, and economic capital. The case studies are informed by secondary research, site visits, and interviews, providing snapshots of investment impact. They represent diverse transformations in rural land use via new forms of direct investment, active investment, and processes of financing rather than financialisation, with distinct spatial and temporal characteristics. The case studies include new wine production in Kent, England; transforming the Menie Estate into Trump International Golf Links Scotland (TIGLS); and farm diversification in Northern Ireland. The conclusions tell three investment stories, where place effects reflect the dichotomies, contestation, and symbiosis between investors and local contexts. New land uses create place effects where economic potential often conflicts with natural capital impacts, although they foster knowledge creation and exchange. The underlying values of the investors and their navigation of local politics also have key roles to play in shaping the built, natural, social, and economic place effects. View Full-Tex

A new look to 275 to 400 GHz band : Channel model and performance evaluation

AbstractIn this paper, we present a novel two-path channel model for wireless terahertz (THz) systems operating in the range of 275 to 400 GHz, which accommodates both the channel particularities and the transceivers parameters. The channel particularities include the frequency selectivity, path-loss, as well as the atmospheric conditions, namely temperature, relative humidity and pressure, while the transceiver parameters, which are taken into account, are the antenna gains as well as the power of the transmitted signal. Finally, we evaluated the THz system performance in terms of average signal to noise ratio and ergodic capacity.Abstract In this paper, we present a novel two-path channel model for wireless terahertz (THz) systems operating in the range of 275 to 400 GHz, which accommodates both the channel particularities and the transceivers parameters. The channel particularities include the frequency selectivity, path-loss, as well as the atmospheric conditions, namely temperature, relative humidity and pressure, while the transceiver parameters, which are taken into account, are the antenna gains as well as the power of the transmitted signal. Finally, we evaluated the THz system performance in terms of average signal to noise ratio and ergodic capacity

Performance Evaluation of THz Wireless Systems Operating in 275 − 400 GHz Band

AbstractIn this paper, we establish an appropriate system model for the terahertz (THz) wireless links in the range of 275 to 400 GHz, which accommodates the channel particularities and transceivers parameters. The former includes the frequency selectivity, pathloss, as well as the atmospheric conditions, namely temperature and pressure, whereas the latter are assumed to be consisted of the antenna gains as well as the power allocation of the transmitted signal. Moreover, we present analytical expressions of low computational complexity, for the evaluation of the average SNR, and capacity of the line of sight wireless THz links. These expressions are expected to be the key tools for the design of the THz link.Abstract In this paper, we establish an appropriate system model for the terahertz (THz) wireless links in the range of 275 to 400 GHz, which accommodates the channel particularities and transceivers parameters. The former includes the frequency selectivity, pathloss, as well as the atmospheric conditions, namely temperature and pressure, whereas the latter are assumed to be consisted of the antenna gains as well as the power allocation of the transmitted signal. Moreover, we present analytical expressions of low computational complexity, for the evaluation of the average SNR, and capacity of the line of sight wireless THz links. These expressions are expected to be the key tools for the design of the THz link

Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems

AbstractReconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To over-come this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural net-work (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16 × 2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity.Abstract Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To over-come this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural net-work (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16 × 2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity

Leveraging Deep Neural Networks for Massive MIMO Data Detection

AbstractMassive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.Abstract Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers

Evolution of sex-specific pace-of-life syndromes: genetic architecture and physiological mechanisms

Sex differences in life history, physiology, and behavior are nearly ubiquitous across taxa, owing to sex-specific selection that arises from different reproductive strategies of the sexes. The pace-of-life syndrome (POLS) hypothesis predicts that most variation in such traits among individuals, populations, and species falls along a slow-fast pace-of-life continuum. As a result of their different reproductive roles and environment, the sexes also commonly differ in pace-of-life, with important consequences for the evolution of POLS. Here, we outline mechanisms for how males and females can evolve differences in POLS traits and in how such traits can covary differently despite constraints resulting from a shared genome. We review the current knowledge of the genetic basis of POLS traits and suggest candidate genes and pathways for future studies. Pleiotropic effects may govern many of the genetic correlations, but little is still known about the mechanisms involved in trade-offs between current and future reproduction and their integration with behavioral variation. We highlight the importance of metabolic and hormonal pathways in mediating sex differences in POLS traits; however, there is still a shortage of studies that test for sex specificity in molecular effects and their evolutionary causes. Considering whether and how sexual dimorphism evolves in POLS traits provides a more holistic framework to understand how behavioral variation is integrated with life histories and physiology, and we call for studies that focus on examining the sex-specific genetic architecture of this integration