Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Doping the holographic Mott insulator

Mott insulators form because of strong electron repulsions, being at the heart of strongly correlated electron physics. Conventionally these are understood as classical "traffic jams" of electrons described by a short-ranged entangled product ground state. Exploiting the holographic duality, which maps the physics of densely entangled matter onto gravitational black hole physics, we show how Mott-insulators can be constructed departing from entangled non-Fermi liquid metallic states, such as the strange metals found in cuprate superconductors. These "entangled Mott insulators" have traits in common with the "classical" Mott insulators, such as the formation of Mott gap in the optical conductivity, super-exchange-like interactions, and form "stripes" when doped. They also exhibit new properties: the ordering wave vectors are detached from the number of electrons in the unit cell, and the DC resistivity diverges algebraically instead of exponentially as function of temperature. These results may shed light on the mysterious ordering phenomena observed in underdoped cuprates.Comment: 27 pages, 9 figures. Accepted in Nature Physic

Election proximity and representation focus in party-constrained environments

Do elected representatives have a time-constant representation focus or do they adapt their focus depending on election proximity? In this article, we examine these overlooked theoretical and empirical puzzles by looking at how reelection-seeking actors adapt their legislative behavior according to the electoral cycle. In parliamentary democracies, representatives need to serve two competing principals: their party and their district. Our analysis hinges on how representatives make a strategic use of parliamentary written questions in a highly party-constrained institutional context to heighten their reselection and reelection prospects. Using an original data set of over 32,000 parliamentary questions tabled by Portuguese representatives from 2005 to 2015, we examine how time interacts with two key explanatory elements: electoral vulnerability and party size. Results show that representation focus is not static over time and, in addition, that electoral vulnerability and party size shape strategic use of parliamentary questions

Dynamical Mean-Field Theory

The dynamical mean-field theory (DMFT) is a widely applicable approximation scheme for the investigation of correlated quantum many-particle systems on a lattice, e.g., electrons in solids and cold atoms in optical lattices. In particular, the combination of the DMFT with conventional methods for the calculation of electronic band structures has led to a powerful numerical approach which allows one to explore the properties of correlated materials. In this introductory article we discuss the foundations of the DMFT, derive the underlying self-consistency equations, and present several applications which have provided important insights into the properties of correlated matter.Comment: Chapter in "Theoretical Methods for Strongly Correlated Systems", edited by A. Avella and F. Mancini, Springer (2011), 31 pages, 5 figure

Bone mineral density and the subsequent risk of cancer in the NHANES I follow-up cohort

BACKGROUD: Bone mineral density (BMD) is a marker of long-term estrogen exposure. BMD measurement has been used in this context to investigate the association of estrogen with breast cancer risk in three cohorts. In order to assess further BMD as a predictor of estrogen related cancer risk, the association of BMD with colorectal and corpus uteri cancer was investigated in the NHANES I Epidemiologic Followup Study (NHEFS) cohort along with breast cancer and prostate cancer. METHODS: Participants were members of the NHEFS cohort who had BMD measurement in 1974–1975. Age, race, and BMI adjusted rate ratios and 95% confidence intervals were calculated for incidence of cancers of the corpus uterus, breast, colorectum, prostate, and of osteoporosis and hip fracture related to baseline BMD. RESULTS: Data were available for 6046 individuals. One hundred cases of breast cancer, 94 prostate cancers, 115 colorectal cancers, 29 uterine cancers, 110 cases of hip fracture and 103 cases of osteoporosis were reported between 1974 and 1993. Hip fracture and osteoporosis were both significantly inversely associated with BMD. Uterine cancer was positively associated (p = 0.005, test for linear trend) and colorectal cancer negatively associated (p = 0.03) with BMD. No association was found between elevated BMD and incidence of breast cancer (p = 0.74) or prostate cancer (p = 0.37) in the overall cohort, although a weak association was seen between BMD and subsequent breast cancer incidence when BMD was measured in post-menopausal women (p = 0.04). CONCLUSION: The findings related to cancers of the uterus and colorectum as well as the weak association of BMD with breast cancer strengthen the use of BMD as a marker of estrogen exposure and cancer risk

The Hubbard model within the equations of motion approach

The Hubbard model has a special role in Condensed Matter Theory as it is considered as the simplest Hamiltonian model one can write in order to describe anomalous physical properties of some class of real materials. Unfortunately, this model is not exactly solved except for some limits and therefore one should resort to analytical methods, like the Equations of Motion Approach, or to numerical techniques in order to attain a description of its relevant features in the whole range of physical parameters (interaction, filling and temperature). In this manuscript, the Composite Operator Method, which exploits the above mentioned analytical technique, is presented and systematically applied in order to get information about the behavior of all relevant properties of the model (local, thermodynamic, single- and two- particle ones) in comparison with many other analytical techniques, the above cited known limits and numerical simulations. Within this approach, the Hubbard model is shown to be also capable to describe some anomalous behaviors of the cuprate superconductors.Comment: 232 pages, more than 300 figures, more than 500 reference

Intermatrix synthesis: easy technique permitting preparation of polymer-stabilized nanoparticles with desired composition and structure

The synthesis of polymer-stabilized nanoparticles (PSNPs) can be successfully carried out using intermatrix synthesis (IMS) technique, which consists in sequential loading of the functional groups of a polymer with the desired metal ions followed by nanoparticles (NPs) formation stage. After each metal-loading-NPs-formation cycle, the functional groups of the polymer appear to be regenerated. This allows for repeating the cycles to increase the NPs content or to obtain NPs with different structures and compositions (e.g. core-shell or core-sandwich). This article reports the results on the further development of the IMS technique. The formation of NPs has been shown to proceed by not only the metal reduction reaction (e.g. Cu0-NPs) but also by the precipitation reaction resulting in the IMS of PSNPs of metal salts (e.g. CuS-NPs)

Exploiting Nucleotide Composition to Engineer Promoters

The choice of promoter is a critical step in optimizing the efficiency and stability of recombinant protein production in mammalian cell lines. Artificial promoters that provide stable expression across cell lines and can be designed to the desired strength constitute an alternative to the use of viral promoters. Here, we show how the nucleotide characteristics of highly active human promoters can be modelled via the genome-wide frequency distribution of short motifs: by overlapping motifs that occur infrequently in the genome, we constructed contiguous sequence that is rich in GC and CpGs, both features of known promoters, but lacking homology to real promoters. We show that snippets from this sequence, at 100 base pairs or longer, drive gene expression in vitro in a number of mammalian cells, and are thus candidates for use in protein production. We further show that expression is driven by the general transcription factors TFIIB and TFIID, both being ubiquitously present across cell types, which results in less tissue- and species-specific regulation compared to the viral promoter SV40. We lastly found that the strength of a promoter can be tuned up and down by modulating the counts of GC and CpGs in localized regions. These results constitute a “proof-of-concept” for custom-designing promoters that are suitable for biotechnological and medical applications

Progestogens to prevent preterm birth in twin pregnancies: an individual participant data meta-analysis of randomized trials

<p>Abstract</p> <p>Background</p> <p>Preterm birth is the principal factor contributing to adverse outcomes in multiple pregnancies. Randomized controlled trials of progestogens to prevent preterm birth in twin pregnancies have shown no clear benefits. However, individual studies have not had sufficient power to evaluate potential benefits in women at particular high risk of early delivery (for example, women with a previous preterm birth or short cervix) or to determine adverse effects for rare outcomes such as intrauterine death.</p> <p>Methods/design</p> <p>We propose an individual participant data meta-analysis of high quality randomized, double-blind, placebo-controlled trials of progestogen treatment in women with a twin pregnancy. The primary outcome will be adverse perinatal outcome (a composite measure of perinatal mortality and significant neonatal morbidity). Missing data will be imputed within each original study, before data of the individual studies are pooled. The effects of 17-hydroxyprogesterone caproate or vaginal progesterone treatment in women with twin pregnancies will be estimated by means of a random effects log-binomial model. Analyses will be adjusted for variables used in stratified randomization as appropriate. Pre-specified subgroup analysis will be performed to explore the effect of progestogen treatment in high-risk groups.</p> <p>Discussion</p> <p>Combining individual patient data from different randomized trials has potential to provide valuable, clinically useful information regarding the benefits and potential harms of progestogens in women with twin pregnancy overall and in relevant subgroups.</p