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

Generation of correlated Rayleigh fading channels for accurate simulationof promising wireless communication systems

In this paper, a generalized method is proposed for the accurate simulation of equal/ unequal power correlated Rayleigh fading channels to overcome the shortcomings of existing methods. Spatial and spectral correlations are also considered in this technique for different transmission conditions. It employs successive coloring for the inphase and quadrature components of successive signals using real correlation vector of successive signal envelopes rather than complex covariance matrix of the Gaussian signals which is utilized in conventional methods. Any number of fading signals with any desired correlations of successive envelope pairs in the interval [0, 1] can be generated with high accuracy. Moreover, factorization of the desired covariance matrix is avoided to overcome the shortcomings and high computational complexity of conventional methods. Extensive simulations of different representative scenarios demonstrate the effectiveness of the proposedtechnique. The simplicity and accuracy of this method will help the researchers to study and simulate the impact of fading correlation on the performance evaluation of various multi-antenna and multicarrier communication systems. Moreover, it enables the engineers for efficient design and deployment of new schemes for feasible wireless application

Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM

Channel estimation algorithms and their implementations for mobile receivers are considered in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The decision directed (DD) space alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The performance is improved with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering is also used in estimating the channel in between pilot symbols. The pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent increase in data throughput. Complexity reduction and latency issues are considered in the architecture design. The pilot based LS, MMSE and the SAGE channel estimators are implemented with a high level synthesis tool, synthesized with the UMC 0.18 μm CMOS technology and the performance-complexity trade-offs are studied. The MMSE estimator improves the performance from the simple LS estimator with LTE pilot structure and has low power consumption. The SAGE estimator has high power consumption but can be used with reduced pilot density to increase the data rate.National Science FoundationTekesElektrobitRenesas Mobile EuropeAcademy of FinlandNokia Siemens NetworksXilin

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

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

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

Islet Transplantation to the Anterior Chamber of the Eye—A Future Treatment Option for Insulin-Deficient Type-2 Diabetics? A Case Report from a Nonhuman Type-2 Diabetic Primate

10.1177/0963689720913256Cell Transplantation2

BVDV and BHV-1 Infections in Dairy Herds in Northern and Northeastern Thailand

Bulk milk samples from 220 dairy herds were collected at 9 public milk collection centres in the northeastern and northern Thailand, and a subset of 11 herds was selected for individual testing. The samples were tested for presence of antibodies to BVDV and BHV-1 using an indirect ELISA. The results from the bulk milk testing demonstrated a moderate level of exposure to BVDV and BHV-1 (73% and 67%, respectively). However, the low proportion of herds with high BVDV antibody-levels (13%) and the low within-herd seroprevalence of BVDV and BHV-1 in the 11 herds (24% and 5%, respectively), particularly among the young stock (15% and 0%, respectively), demonstrated a low prevalence of active BVDV infection and a low rate of reactivation of latent BHV-1. The presence of a self-clearance process was also indicated by the results from the individual testing. Moreover, a surprisingly low prevalence of BVDV and BHV-1 antibody-positive herds at one of the milk centres was found. This centre was established 5–10 years before the others. Our impression is that this reflects the self-clearance process, where consecutive replacement of imported infected animals without further spread has resulted in a nearly total elimination of the infections. Based on our experiences and on these results we are convinced that this process can continue if there is awareness of herd biosecurity. This is especially important in the context of a future intensification of the dairy production

Fatal drug poisonings in a Swedish general population

ABSTRACT: BACKGROUND: Pharmaceutical drug poisonings have previously been reported using single sources of information, either hospital data or forensic data, which might not reveal the true incidence. We therefore aimed to estimate the incidence of suspected fatal drug poisonings, defined as poisonings by pharmaceutical agents, by using all relevant case records from various sources in a Swedish population. METHODS: Every seventh randomly selected deceased in three counties in southeastern Sweden during a one-year period was identified in the Cause of Death Register. Relevant case records (death certificates, files from hospitals and/or primary care centres and medico-legal files) were reviewed for all study subjects. RESULTS: Of 1574 deceased study subjects, 12 cases were classified as pharmaceutical drug poisonings according to the death certificates and 10 according to the medico-legal files. When reviewing all available data sources, 9 subjects (0.57%; 95% confidence interval: 0.20-0.94%) were classified as drug poisonings, corresponding to an incidence of 6.5 (95% confidence interval: 2.3-10.7) per 100 000 person-years in the general population. The drug groups most often implicated were benzodiazepines (33%), antihistamines (33%) and analgesics (22%). CONCLUSIONS: Fatal drug poisonings is a relatively common cause of death in Sweden. By using multiple sources of information when investigating the proportion of fatal poisonings in a population, more accurate estimates may be obtained.Original Publication:Anna Jonsson, Olav Spigset, Micaela Tjäderborn, Henrik Druid and Staffan Hägg, Fatal drug poisonings in a Swedish general population., 2009, BMC clinical pharmacology, (9), 7, 1-5.http://dx.doi.org/10.1186/1472-6904-9-7Licensee: BioMed Centralhttp://www.biomedcentral.com

Association of an APOC3 promoter variant with type 2 diabetes risk and need for insulin treatment in lean persons

Aims/hypothesis: An APOC3 promoter haplotype has been previously associated with type 1 diabetes. In this population-based study, we investigated whether APOC3 polymorphisms increase type 2 diabetes risk and need for insulin treatment in lean participants. Methods: In the Rotterdam Study, a population-based prospective cohort (n = 7,983), Cox and logistic regression models were used to analyse the associations and interactive effects of APOC3 promoter variants (-482C > T, -455T > C) and BMI on type 2 diabetes risk and insulin treatment. Analyses were followed by replication in an independent case-control sample (1,817 cases, 2,292 controls) and meta-analysis. Results: In lean participants, the -482T allele was associated with increased risk of prevalent and incident type 2 diabetes: OR -482CT 1.47 (95% CI 1.13-1.92), -482TT 1.40 (95% CI 0.83-2.35), p = 0.009 for trend; HR -482CT 1.35 (95% CI 0.96-1.89), -482TT 1.68 (95% CI 0.91-3.1), p = 0.03 for trend, respectively. These results were confirmed by replication. Meta-analysis was highly significant (-482T meta-analysis p = 1.1 × 10-4). A borderline significant interaction was observed for insulin use among participants with type 2 diabetes (-482CT*BMI p = 0.06, -455TC*BMI p = 0.02). Conclusions/interpretation: At a population-based level, the influence of APOC3 promoter variants on type 2 diabetes risk varies with the level of adiposity. Lean carriers of the -482T allele had increased type 2 diabetes risk, while such an effect was not observed in overweight participants. Conversely, in overweight participants the -455C allele seemed protective against type 2 diabetes. The interaction of the variants with need for insulin treatment may indicate beta cell involvement in lean participants. Our findings suggest overlap in the genetic backgrounds of type 1 diabetes and type 2 diabetes in lean patients