Future climate projections robustly indicate that the Mediterranean region will experience a significant decrease of mean annual precipitation and an increase in temperature. These changes are expected to seriously affect the hydrologic regime, with a limitation of water availability and an intensification of hydrologic extremes, and to negatively impact local economies. In this study, we quantify the hydrologic impacts of climate change in the Rio Mannu basin (RMB), an agricultural watershed of 472.5 km² in Sardinia, Italy. To simulate the wide range of runoff generation mechanisms typical of Mediterranean basins, we adopted a physically based, distributed hydrologic model. The high-resolution forcings in reference and future conditions (30-year records for each period) were provided by four combinations of global and regional climate models, bias-corrected and downscaled in space and time (from ~25 km, 24 h to 5 km, 1 h) through statistical tools. The analysis of the hydrologic model outputs indicates that the RMB is expected to be severely impacted by future climate change. The range of simulations consistently predict (i) a significant diminution of mean annual runoff at the basin outlet, mainly due to a decreasing contribution of the runoff generation mechanisms depending on water available in the soil; (ii) modest variations in mean annual runoff and intensification of mean annual discharge maxima in flatter sub-basins with clay and loamy soils, likely due to a higher occurrence of infiltration excess runoff; (iii) reduction of soil water content and actual evapotranspiration in most areas of the basin; and (iv) a drop in the groundwater table. Results of this study are useful to support the adoption of adaptive strategies for management and planning of agricultural activities and water resources in the region.

Every year, flood disasters are responsible for widespread destruction and loss of human life. Remote sensing data are capable of providing valuable, synoptic coverage of flood events but are not always available because of satellite revisit limitations, obstructions from cloud cover or vegetation canopy, or expense. In addition, knowledge of road accessibility is imperative during all phases of a flood event. In June 2013, the City of Calgary experienced sudden and extensive flooding but lacked comprehensive remote sensing coverage. Using this event as a case study, this work illustrates how data from non-authoritative sources are used to augment traditional data and methods to estimate flood extent and identify affected roads during a flood disaster. The application of these data, which may have varying resolutions and uncertainities, provide an estimation of flood extent when traditional data and methods are lacking or incomplete. When flooding occurs over multiple days, it is possible to construct an estimate of the advancement and recession of the flood event. Non-authoritative sources also provide flood information at the micro-level, which can be difficult to capture from remote sensing data; however, the distibution and quantity of data collected from these sources will affect the quality of the flood estimations.

Software Defined Network (SDN) architecture has been widely used in various application domains. Aiming at the authentication and security issues of SDN architecture in autonomous decentralized system (ADS) applications, securing the mutual trust among the autonomous controllers, we combine trusted technology and SDN architecture, and we introduce an authentication protocol based on SDN architecture without any trusted third party between trusted domains in autonomous systems. By applying BAN predicate logic and AVISPA security analysis tool of network interaction protocol, we can guarantee protocol security and provide complete safety tests. Our work fills the gap of mutual trust between different trusted domains and provides security foundation for interaction between different trusted domains.

Stimuli-responsive polymers or so-called “smart polymers” are macromolecules that are sensitive to certain triggers from the external environment, including temperature, light, electrical or magnetic fields, and chemicals. The activated polymers produce observable or detectable micro- or nanoscale changes, such as morphology, molecular bond rearrangement/cleavage, and molecular motion, which can induce changes in their macroscopic properties such as color, shape, and functionality. Due to the versatile selection of backbone and functional groups, stimuli-responsive polymers can be tailored to have a variety of specific mechanical, chemical, electrical, optical, biological, or other properties and can be engineered into different forms, including bulk, thin film, micro/nanoparticles, and composites. Over the years, many multidisciplinary efforts have been conducted and reported optimizing the functionality of stimuli-responsive polymers and exploring new and innovative applications. However, as shown below, original and exciting research in emerging sectors continues to drive the evolution of and interest in this class of polymer.

Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.

There is an urgent need for the development of alternative strategies for effective drug delivery to improve the outcome of patients suffering from deadly diseases such as cancer. Nanoparticles, in particular layered double hydroxide (LDH) nanoparticles, have great potential as nanocarriers of chemotherapeutic molecules. In this study, we synthesized (Zn, Al)-LDH nanoparticles and report their enhanced pH-dependent stability in comparison to the commonly used (Mg, Al)-LDH nanoparticles. Fluorescein isothiocyanate (FITC) and valproate (VP) were intercalated into (Zn, Al)-LDH nanoparticles to study cellular uptake, biocompatibility, and drug delivery capabilities using cultured pancreatic adenocarcinoma BxPC3 cells. Fluorescence measurements indicated that FITC-intercalated LDH nanoparticles showed a greater degree of energy-dependent uptake rather than passive uptake by BxPC3 cells, especially at high concentrations of nanoparticles. Tetrazolium-based colorimetric assays indicated that BxPC3 cells treated with VP-intercalated LDH nanoparticles showed a significant reduction in cell viability along with about 30-fold reduction in IC[subscript 50] compared to the drug alone. In contrast, the non-drug-intercalated LDH nanoparticles did not affect the cell viability indicating very low innate cytotoxicity. Our research indicates that the superior properties of (Zn, Al)-LDH nanoparticles make them ideal candidates for further development as in vivo chemotherapy drug delivery agents.

With the ability to perform a multitude of unique and complex chemical transformations, microorganisms have long been the “workhorses” of many industrial processes. However, in addition to exploiting the utility of naturally evolved phenotypes, the principles, strategies, and tools of synthetic biology are now being applied to facilitate the engineering of tailor-made microbes capable of tackling some of society's most important and toughest challenges. Fueled in part by exponentially increasing reservoirs of bioinformatic data and coupled with more robust and powerful tools for its processing, research in the past decade has brought about new and broadened perspectives of fundamental biological phenomena. The application of said insight is now beginning to unlock the unprecedented potential of synthetic biology in biotechnology, as well as its considerable promise for addressing previously unsolved global challenges. For example, within the realm of industrial microbiology, progress in the field of synthetic biology has enabled the development of new biosynthetic pathways for the production of renewable fuels and chemicals, programmable logic controls to regulate and optimize complex cellular functions, and robust microbes for the destruction of harmful environmental contaminants. In this Research Topic, a collection of articles—including original research, reviews, and mini-reviews—from leading investigators in the synthetic biology community are presented to capture the current state, recent progress, and over-arching challenges associated with integrating synthetic biology with industrial microbiology and biotechnology.

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.