Application of the Water Poverty Index at Different Scales: A Cautionary Tale

Abstract

The Water Poverty Index is an integrated tool developed on the basis of extensive consultation with a range of scientists, practitioners and policymakers. It is primarily designed for use at the community level to enable more holistic water-resource assessments on a site-specific basis. It can however be applied at different scales to suit different needs. One of the motivations to design such a tool was an attempt to move away from the conventional, purely deterministic, approaches to water assessment, relying primarily on models and large-scale data. In today's world such an approach is inappropriate, ill representing the complexities of modern water-allocation decisions where economic, political and social issues all have a powerful role to play. This paper highlights some applications of the Water Poverty Index at different spatial scales and discusses the implications of applying indicators at these different scales.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Including sorption to black carbon in modeling bioaccumulation of polycyclic aromatic hydrocarbons: uncertainty analysis and comparison to field data

Model estimations of bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) have been higher than field or laboratory data. This has been explained by strong sorption to black carbon (BC). In this paper, eight previously published bioaccumulation datasets were reinterpreted in terms of additional BC sorption. Biota--Solids Accumulation Factors (BSAFs) of PAHs typically decreased by 1-2 orders of magnitude and were better in line with field data in marine, fresh water, and terrestrial ecosystems. Probabilistic BC-inclusive modeling showed that if BC content is not accurately known, uncertainty in BSAFs is 2-3 orders of magnitude (90 percentile confidence interval) due to uncertainty in the BC sorption term. When BC contents are measured, the deviation between model estimations and field measurements reduces to about a factor of 3. This implies that including routine measurements of BC contents is crucial in improving risk estimations of PAHs.     

Multiwavelength imaging of defects in ultraviolet optical materials

Laser-induced damage in bare glass substrates and thin films has long been widely acknowledged as a localized phenomenon associated with the presence of micrometer and submicrometer scale defects. The scanning of both optical absorption and scattering allows us to discriminate between absorbing and nonabsorbing defects and can give specific information about the origin of the defects. We investigate the spectral properties of defects in thin films and fused-silica surfaces. Absorbing and scattering defects are studied at different wavelengths in the ultraviolet, visible, and infrared ranges. Absorbing defects are shown to be highly wavelength dependent, whereas we have observed significant correlation between scattering defects.     

Negotiating the hybrid: art, theory and genetic technologies

Over the past decade artists have increasingly turned to science in order to investigate technology’s effect. The move from hardware-based technologies to live organisms as media, raises ethical issues that the broader art community is addressing. This paper tracks the history of instrumental disengagement to determine when and how the gradual codification of life contributed to the eventual use of live organisms in art practice.     

Optimal control law for classical and multiconjugate adaptive optics

Classical adaptive optics (AO) is now a widespread technique for high-resolution imaging with astronomical ground-based telescopes. It generally uses simple and efficient control algorithms. Multiconjugate adaptive optics (MCAO) is a more recent and very promising technique that should extend the corrected field of view. This technique has not yet been experimentally validated, but simulations already show its high potential. The importance for MCAO of an optimal reconstruction using turbulence spatial statistics has already been demonstrated through open-loop simulations. We propose an optimal closed-loop control law that accounts for both spatial and temporal statistics. The prior information on the turbulence, as well as on the wave-front sensing noise, is expressed in a state-space model. The optimal phase estimation is then given by a Kalman filter. The equations describing the system are given and the underlying assumptions explained. The control law is then derived. The gain brought by this approach is demonstrated through MCAO numerical simulations representative of astronomical observation on a 8-m-class telescope in the near infrared. We also discuss the application of this control approach to classical AO. Even in classical AO, the technique could be relevant especially for future extreme AO systems.     

The Emotional Wardrobe

Since the industrial revolution, fashion and technology have been linked through the textile and manufacturing industries, a relationship that has propelled technical innovation and aesthetic and social change. Today, a new alliance is emerging through the integration of electronic technology and smart materials on the body. This study addresses the integration of technology with clothing from a fashion perspective, and examines its expressive and interactive potential. It proposes the concept of The Emotional Wardrobe: clothing that represents and stimulates emotional response through the interface of technology. It asks if fashion can offer a more personal and provocative definition of self, which actively involves the wearer in a mutable aesthetic identity. A multi-disciplinary framework combines fashion, material science and the real-time, affective computing platform, called AffectiveWare. By merging technology and fashion, The Emotional Wardrobe becomes a poetic interface, shifting emphasis from human–computer interaction to computer-aided, human–human communication.