Cities are hotspots of commodity consumption, with implications for both local and systemic water resources. Water flows “virtually” into and out of cities through the extensive cross-boundary exchange of goods and services. Both virtual and real water flows are affected by water supply investments and urban planning decisions, which influence residential, commercial, and industrial development. This form of water “teleconnection” is being increasingly recognized as an important aspect of water decision-making. The role of trade and virtual water flows as an alternative to expanding a city’s “real” water supply is rarely acknowledged, with an emphasis placed instead on monotonic expansion of engineering potable water supplies. We perform a literature review of water footprint studies to evaluate the potential and importance of taking virtual flows into account in urban planning and policy. We compare and contrast current methods to assess virtual water flows. We also identify and discuss priorities for future research in urban water footprint analysis.

Background: Photosynthetic oleaginous microalgae are considered promising feedstocks for biofuels. The marine microalga, Nannochloropsis oceanica, has been attracting ever-increasing interest because of its fast growth, high triacylglycerol (TAG) content, and available genome sequence and genetic tools. Diacylglycerol acyltransferase (DGAT) catalyzes the last and committed step of TAG biosynthesis in the acyl-CoA-dependent pathway. Previous studies have identified 13 putative DGAT-encoding genes in the genome of N. oceanica, but the functional role of DGAT genes, especially type-I DGAT (DGAT1), remains ambiguous.

Results: Nannochloropsis oceanica IMET1 possesses two DGAT1 genes: NoDGAT1A and NoDGAT1B. Functional complementation demonstrated the capability of NoDGAT1A rather than NoDGAT1B to restore TAG synthesis in a TAG-deficient yeast strain. In vitro DGAT assays revealed that NoDGAT1A preferred saturated/monounsaturated acyl-CoAs and eukaryotic diacylglycerols (DAGs) for TAG synthesis, while NoDGAT1B had no detectable enzymatic activity. Assisted with green fluorescence protein (GFP) fusion, fluorescence microscopy analysis indicated the localization of NoDGAT1A in the chloroplast endoplasmic reticulum (cER) of N. oceanica. NoDGAT1A knockdown caused ~25% decline in TAG content upon nitrogen depletion, accompanied by the reduced C16:0, C18:0, and C18:1 in TAG sn-1/sn-3 positions and C18:1 in the TAG sn-2 position. NoDGAT1A overexpression, on the other hand, led to ~39% increase in TAG content upon nitrogen depletion, accompanied by the enhanced C16:0 and C18:1 in the TAG sn-1/sn-3 positions and C18:1 in the TAG sn-2 position. Interestingly, NoDGAT1A overexpression also promoted TAG accumulation (by ~2.4-fold) under nitrogen-replete conditions without compromising cell growth, and TAG yield of the overexpression line reached 0.49 g L[superscript −1] at the end of a 10-day batch culture, 47% greater than that of the control line.

Conclusions: Taken together, our work demonstrates the functional role of NoDGAT1A and sheds light on the underlying mechanism for the biosynthesis of various TAG species in N. oceanica. NoDGAT1A resides likely in cER and prefers to transfer C16 and C18 saturated/monounsaturated fatty acids to eukaryotic DAGs for TAG assembly. This work also provides insights into the rational genetic engineering of microalgae by manipulating rate-limiting enzymes such as DGAT to modulate TAG biosynthesis and fatty acid composition for biofuel production.

The projected changes in the downward solar radiation at the surface over North America for late 21st century are deduced from global climate model simulations with greenhouse-gas (GHG) forcing. A robust trend is found in winter over the United States, which exhibits a simple pattern of a decrease of sunlight over Northern USA. and an increase of sunlight over Southern USA. This structure was identified in both the seasonal mean and the mean climatology at different times of the day. It is broadly consistent with the known poleward shift of storm tracks in winter in climate model simulations with GHG forcing. The centennial trend of the downward shortwave radiation at the surface in Northern USA. is on the order of 10% of the climatological value for the January monthly mean, and slightly over 10% at the time when it is midday in the United States. This indicates a nonnegligible influence of the GHG forcing on solar energy in the long term. Nevertheless, when dividing the 10% by a century, in the near term, the impact of the GHG forcing is relatively minor such that the estimate of solar power potential using present-day climatology will remain useful in the coming decades.

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.

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.

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.

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.

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.

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.

Background: Magnetic Resonance Spectroscopic Imaging (MRSI) has wide applicability for non-invasive biochemical assessment in clinical and pre-clinical applications but suffers from long scan times. Compressed sensing (CS) has been successfully applied to clinical H-1 MRSI, however a detailed evaluation of CS for conventional chemical shift imaging is lacking. Here we evaluate the performance of CS accelerated MRSI, and specifically apply it to accelerate Na-23-MRSI on mouse hearts in vivo at 9.4 T.

Methods: Synthetic phantom data representing a simplified section across a mouse thorax were used to evaluate the fidelity of the CS reconstruction for varying levels of under-sampling, resolution and signal-to-noise ratios (SNR). The amplitude of signals arising from within a compartment, and signal contamination arising from outside the compartment relative to noise-free Fourier-transformed (FT) data were determined. Simulation results were subsequently verified experimentally in phantoms and in three mouse hearts in vivo.

Results: CS reconstructed MRSI data are scaled linearly relative to absolute signal intensities from the fully-sampled FT reconstructed case (R-2 > 0.8, p-value < 0.001). Higher acceleration factors resulted in a denoising of the reconstructed spectra, but also in an increased blurring of compartment boundaries, particularly at lower spatial resolutions. Increasing resolution and SNR decreased cross-compartment contamination and yielded signal amplitudes closer to the FT data. Proof-of-concept high-resolution, 3-fold accelerated Na-23-amplitude maps of murine myocardium could be obtained within similar to 23 mins.

Conclusions: Relative signal amplitudes (i.e. metabolite ratios) and absolute quantification of metabolite concentrations can be accurately determined with up to 5-fold under-sampled, CS-reconstructed MRSI. Although this work focused on murine cardiac Na-23-MRSI, the results are equally applicable to other nuclei and tissues (e.g. H-1 MRSI in brain). Significant reduction in MRSI scan time will reduce the burden on the subject, increase scanner throughput, and may open new avenues for (pre-) clinical metabolic studies.