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in Architecture & Planning
Periodicals

Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability

The Psychiatric Genomics Consortium-Posttraumatic Stress Disorder group (PGC-PTSD) combined genome-wide case–control molecular genetic data across 11 multiethnic studies to quantify PTSD heritability, to examine potential shared genetic risk with schizophrenia, bipolar disorder, and major depressive disorder and to identify risk loci for PTSD. Examining 20 730 individuals, we report a molecular genetics-based heritability estimate ( h 2 SNP ) for European-American females of 29% that is similar to h 2 SNP for schizophrenia and is substantially higher than h 2 SNP in European-American males (estimate not distinguishable from zero). We found strong evidence of overlapping genetic risk between PTSD and schizophrenia along with more modest evidence of overlap with bipolar and major depressive disorder. No single-nucleotide polymorphisms (SNPs) exceeded genome-wide significance in the transethnic (overall) meta-analysis and we do not replicate previously reported associations. Still, SNP-level summary statistics made available here afford the best-available molecular genetic index of PTSD—for both European- and African-American individuals—and can be used in polygenic risk prediction and genetic correlation studies of diverse phenotypes. Publication of summary statistics for ∼ 10 000 African Americans contributes to the broader goal of increased ancestral diversity in genomic data resources. In sum, the results demonstrate genetic influences on the development of PTSD, identify shared genetic risk between PTSD and other psychiatric disorders and highlight the importance of multiethnic/racial samples. As has been the case with schizophrenia and other complex genetic disorders, larger sample sizes are needed to identify specific risk loci.

Architecture & Planning• 2017• Springer Nature
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An Update on the Impact of Climate Change in Viticulture and Potential Adaptations

Climate change will impose increasingly warm and dry conditions on vineyards. Wine quality and yield are strongly influenced by climatic conditions and depend on complex interactions between temperatures, water availability, plant material, and viticultural techniques. In established winegrowing regions, growers have optimized yield and quality by choosing plant material and viticultural techniques according to local climatic conditions, but as the climate changes, these will need to be adjusted. Adaptations to higher temperatures include changing plant material (e.g., rootstocks, cultivars and clones) and modifying viticultural techniques (e.g., changing trunk height, leaf area to fruit weight ratio, timing of pruning) such that harvest dates are maintained in the optimal period at the end of September or early October in the Northern Hemisphere. Vineyards can be made more resilient to drought by planting drought resistant plant material, modifying training systems (e.g., goblet bush vines, or trellised vineyards at wider row spacing), or selecting soils with greater soil water holding capacity. While most vineyards in Europe are currently dry-farmed, irrigation may also be an option to grow sustainable yields under increasingly dry conditions but consideration must be given to associated impacts on water resources and the environment.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Financial literacy, financial advice, and financial behavior

In this survey, we review the voluminous body of literature on the measurement and the determinants of financial literacy. Wherever possible, we supplement existing findings with recent descriptive evidence of German households’ financial literacy levels based on the novel Panel on Household Finances dataset, a large-scale survey administered by the Deutsche Bundesbank and representative of the financial situation of households in Germany. Prior research not only documents generally low levels of financial literacy but also finds large heterogeneity in financial literacy across the population, suggesting that economically vulnerable groups are placed at further disadvantage by their lack of financial knowledge. In addition, we assess the literature evaluating financial education as a means to improve financial literacy and financial behavior. Our survey suggests that the evidence with respect to the effectiveness of the programs is rather disappointing. We also review the role of individuals’ financial literacy for the use of professional financial advice and assess whether expert intervention can serve as a substitute to financial literacy. We conclude by discussing several directions for future research.

Architecture & Planning• 2017• Springer Science+Business Media
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RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.

Architecture & Planning• 2020• Frontiers Media
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MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

Architecture & Planning• 2019• BioMed Central
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Measuring sleep quality in older adults: a comparison using subjective and objective methods

Sleep quality decreases with aging and thus sleep complaints are prevalent in older adults, particularly for those with cognitive impairment and dementia. For older adults, emerging evidence suggests poor sleep quality increases risk of developing cognitive impairment and dementia. Given the aging population-and the impending economic burden associated with increasing numbers of dementia patients-there is pressing need to improve sleep quality among older adults. As such, research efforts have increased focus on investigating the association between age-related sleep changes and cognitive decline in older adults. Sleep quality is a complex construct to evaluate empirically, and yet the Pittsburg Sleep Quality Index (PSQI) is commonly used in studies as their only measure of sleep quality. Furthermore, the PSQI may not be the best sleep quality measure for older adults, due to its reliance on the cognitive capacity to reflect on the past month. Further study is needed to determine the PSQI's validity among older adults. Thus, the current study examined sleep quality for 78 community dwelling adults 55+ to determine the PSQI's predictive validity for objective sleep quality (as measured by actigraphy). We compared two subjective measures of sleep quality-the PSQI and Consensus Sleep Diary (CSD)-with actigraphy (MotionWatch 8©; camntech). Our results suggest perceived sleep quality is quite different from objective reality, at least for adults 55+. Importantly, we show this difference is unrelated to age, gender, education, or cognitive status (assessed using standard screens). Previous studies have shown the PSQI to be a valuable tool for assessing subjective sleep quality; however, our findings indicate for older adults the PSQI should not be used as a substitute for actigraphy, or vice versa. Hence, we conclude best practice is to include both subjective and objective measures when examining sleep quality in older adults (i.e., the PSQI, CSD, and actigraphy).

Architecture & Planning• 2015• Frontiers Media
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Complete classification of reflexive polyhedra in four dimensions

Four dimensional reflexive polyhedra encode the data for smooth Calabi-Yau threefolds that are hypersurfaces in toric varieties, and have important applications both in perturbative and in non-perturbative string theory. We describe how we obtained all 473,800,776 reflexive polyhedra that exist in four dimensions and the 30,108 distinct pairs of Hodge numbers of the resulting Calabi-Yau manifolds. As a byproduct we show that all these spaces (and hence the corresponding string vacua) are connected via a chain of singular transitions.

Architecture & Planning• 2000
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Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits

Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.

Architecture & Planning• 2013• Public Library of Science
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Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed

Large scale distributed systems such as Grids are difficult to study from theoretical models and simulators only. Most Grids deployed at large scale are production platforms that are inappropriate research tools because of their limited reconfiguration, control and monitoring capabilities. In this paper, we present Grid'5000, a 5000 CPU nation-wide infrastructure for research in Grid computing. Grid'5000 is designed to provide a scientific tool for computer scientists similar to the large-scale instruments used by physicists, astronomers, and biologists. We describe the motivations, design considerations, architecture, control, and monitoring infrastructure of this experimental platform. We present configuration examples and performance results for the reconfiguration subsystem.

Architecture & Planning• 2006• SAGE Publishing
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A Review on Virtual Reality Skill Training Applications

This study aimed to discuss the research efforts in developing virtual reality (VR) technology for different training applications. To begin with, we describe how VR training experiences are typically created and delivered using the current software and hardware. We then discuss the challenges and solutions of applying VR training to different application domains, such as first responder training, medical training, military training, workforce training, and education. Furthermore, we discuss the common assessment tests and evaluation methods used to validate VR training effectiveness. We conclude the article by discussing possible future directions to leverage VR technology advances for developing novel training experiences.

Architecture & Planning• 2021• Frontiers Media
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Periodicals

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

SUMMARY We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

Architecture & Planning• 2014• Cambridge University Press
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Neonatal Morbidities of Fetal Growth Restriction: Pathophysiology and Impact

Being born small lays the foundation for short-term and long-term implications for life. Intrauterine or fetal growth restriction describes the pregnancy complication of pathological reduced fetal growth, leading to significant perinatal mortality and morbidity, and subsequent long-term deficits. Placental insufficiency is the principal cause of FGR, which in turn underlies a chronic undersupply of oxygen and nutrients to the fetus. The neonatal morbidities associated with FGR depend on the timing of onset of placental dysfunction and growth restriction, its severity, and the gestation at birth of the infant. In this review, we explore the pathophysiological mechanisms involved in the development of major neonatal morbidities in FGR, and their impact on the health of the infant. Fetal cardiovascular adaptation and altered organ development during gestation are principal contributors to postnatal consequences of FGR. Clinical presentation, diagnostic tools and management strategies of neonatal morbidities are presented. We also present information on the current status of targeted therapies. A better understanding of neonatal morbidities associated with FGR will enable early neonatal detection, monitoring and management of potential adverse outcomes in the newborn period and beyond.

Architecture & Planning• 2019• Frontiers Media
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Long-Read Sequencing Emerging in Medical Genetics

The wide implementation of next-generation sequencing (NGS) technologies has revolutionized the field of medical genetics. However, the short read lengths of currently used sequencing approaches pose a limitation for identification of structural variants, sequencing repetitive regions, phasing alleles and distinguishing highly homologous genomic regions. These limitations may significantly contribute to the diagnostic gap in patients with genetic disorders who have undergone standard NGS, like whole exome or even genome sequencing. Now, the emerging long-read sequencing (LRS) technologies may offer improvements in the characterization of genetic variation and regions that are difficult to assess with the currently prevailing NGS approaches. LRS has so far mainly been used to investigate genetic disorders with previously known or strongly suspected disease loci. While these targeted approaches already show the potential of LRS, it remains to be seen whether LRS technologies can soon enable true whole genome sequencing routinely. Ultimately, this could allow the de novo assembly of individual whole genomes used as a generic test for genetic disorders. In this article, we summarize the current LRS-based research on human genetic disorders and discuss the potential of these technologies to facilitate the next major advancements in medical genetics.

Architecture & Planning• 2019• Frontiers Media
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An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Architecture & Planning• 2023• Frontiers Media
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Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

Architecture & Planning• 2023• Frontiers Media
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Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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The International Limits and Population at Risk of Plasmodium vivax Transmission in 2009

BACKGROUND: A research priority for Plasmodium vivax malaria is to improve our understanding of the spatial distribution of risk and its relationship with the burden of P. vivax disease in human populations. The aim of the research outlined in this article is to provide a contemporary evidence-based map of the global spatial extent of P. vivax malaria, together with estimates of the human population at risk (PAR) of any level of transmission in 2009. METHODOLOGY: The most recent P. vivax case-reporting data that could be obtained for all malaria endemic countries were used to classify risk into three classes: malaria free, unstable (<0.1 case per 1,000 people per annum (p.a.)) and stable (> or =0.1 case per 1,000 p.a.) P. vivax malaria transmission. Risk areas were further constrained using temperature and aridity data based upon their relationship with parasite and vector bionomics. Medical intelligence was used to refine the spatial extent of risk in specific areas where transmission was reported to be absent (e.g., large urban areas and malaria-free islands). The PAR under each level of transmission was then derived by combining the categorical risk map with a high resolution population surface adjusted to 2009. The exclusion of large Duffy negative populations in Africa from the PAR totals was achieved using independent modelling of the gene frequency of this genetic trait. It was estimated that 2.85 billion people were exposed to some risk of P. vivax transmission in 2009, with 57.1% of them living in areas of unstable transmission. The vast majority (2.59 billion, 91.0%) were located in Central and South East (CSE) Asia, whilst the remainder were located in America (0.16 billion, 5.5%) and in the Africa+ region (0.10 billion, 3.5%). Despite evidence of ubiquitous risk of P. vivax infection in Africa, the very high prevalence of Duffy negativity throughout Central and West Africa reduced the PAR estimates substantially. CONCLUSIONS: After more than a century of development and control, P. vivax remains more widely distributed than P. falciparum and is a potential cause of morbidity and mortality amongst the 2.85 billion people living at risk of infection, the majority of whom are in the tropical belt of CSE Asia. The probability of infection is reduced massively across Africa by the frequency of the Duffy negative trait, but transmission does occur on the continent and is a concern for Duffy positive locals and travellers. The final map provides the spatial limits on which the endemicity of P. vivax transmission can be mapped to support future cartographic-based burden estimations.

Architecture & Planning• 2010• Public Library of Science
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The RelA/SpoT Homolog (RSH) Superfamily: Distribution and Functional Evolution of ppGpp Synthetases and Hydrolases across the Tree of Life

RelA/SpoT Homologue (RSH) proteins, named for their sequence similarity to the RelA and SpoT enzymes of Escherichia coli, comprise a superfamily of enzymes that synthesize and/or hydrolyze the alarmone ppGpp, activator of the "stringent" response and regulator of cellular metabolism. The classical "long" RSHs Rel, RelA and SpoT with the ppGpp hydrolase, synthetase, TGS and ACT domain architecture have been found across diverse bacteria and plant chloroplasts, while dedicated single domain ppGpp-synthesizing and -hydrolyzing RSHs have also been discovered in disparate bacteria and animals respectively. However, there is considerable confusion in terms of nomenclature and no comprehensive phylogenetic and sequence analyses have previously been carried out to classify RSHs on a genomic scale. We have performed high-throughput sensitive sequence searching of over 1000 genomes from across the tree of life, in combination with phylogenetic analyses to consolidate previous ad hoc identification of diverse RSHs in different organisms and provide a much-needed unifying terminology for the field. We classify RSHs into 30 subgroups comprising three groups: long RSHs, small alarmone synthetases (SASs), and small alarmone hydrolases (SAHs). Members of nineteen previously unidentified RSH subgroups can now be studied experimentally, including previously unknown RSHs in archaea, expanding the "stringent response" to this domain of life. We have analyzed possible combinations of RSH proteins and their domains in bacterial genomes and compared RSH content with available RSH knock-out data for various organisms to determine the rules of combining RSHs. Through comparative sequence analysis of long and small RSHs, we find exposed sites limited in conservation to the long RSHs that we propose are involved in transmitting regulatory signals. Such signals may be transmitted via NTD to CTD intra-molecular interactions, or inter-molecular interactions either among individual RSH molecules or among long RSHs and other binding partners such as the ribosome.

Architecture & Planning• 2011• Public Library of Science
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Estimation uncertainty of direct monetary flood damage to buildings

Abstract. Traditional flood design methods are increasingly supplemented or replaced by risk-oriented methods which are based on comprehensive risk analyses. Besides meteorological, hydrological and hydraulic investigations such analyses require the estimation of flood impacts. Flood impact assessments mainly focus on direct economic losses using damage functions which relate property damage to damage-causing factors. Although the flood damage of a building is influenced by many factors, usually only inundation depth and building use are considered as damage-causing factors. In this paper a data set of approximately 4000 damage records is analysed. Each record represents the direct monetary damage to an inundated building. The data set covers nine flood events in Germany from 1978 to 1994. It is shown that the damage data follow a Lognormal distribution with a large variability, even when stratified according to the building use and to water depth categories. Absolute depth-damage functions which relate the total damage to the water depth are not very helpful in explaining the variability of the damage data, because damage is determined by various parameters besides the water depth. Because of this limitation it has to be expected that flood damage assessments are associated with large uncertainties. It is shown that the uncertainty of damage estimates depends on the number of flooded buildings and on the distribution of building use within the flooded area. The results are exemplified by a damage assessment for a rural area in southwest Germany, for which damage estimates and uncertainty bounds are quantified for a 100-year flood event. The estimates are compared to reported flood damages of a severe flood in 1993. Given the enormous uncertainty of flood damage estimates the refinement of flood damage data collection and modelling are major issues for further empirical and methodological improvements.

Architecture & Planning• 2004• Copernicus Publications
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Educational design and networked learning: Patterns, pattern languages and design practice

&lt;span&gt;There is a growing demand for advice about effective, time efficient ways of using ICT to support student learning in higher education. This paper uses one such area of activity - networked learning - as a context in which to outline a novel approach to educational design. The paper makes two main contributions. It provides a high level view of the educational design problem space. It then introduces the patterns based approach to educational design. While other professional communities, particularly in software engineering, have been developing patterns based approaches to sharing and re-using design experience, this paper goes back to the original conceptions of participatory design that informed Christopher Alexander's early work on patterns and pattern languages. In particular, it makes connections between the technicalities of design and the central place of values. A patterns based approach can help with encoding, sharing and using knowledge for educational design. But it is also a powerful way of connecting educational values and vision to the details of the tasks, tools and resources we offer our students.&lt;/span&gt;

Architecture & Planning• 2005• Australasian Society for Computers in Learning in Tertiary Education
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