Showing papers by "Iowa State University published in 2018"

An autonomously electrically self-healing liquid metal-elastomer composite for robust soft-matter robotics and electronics.

Abstract: Large-area stretchable electronics are critical for progress in wearable computing, soft robotics and inflatable structures. Recent efforts have focused on engineering electronics from soft materials-elastomers, polyelectrolyte gels and liquid metal. While these materials enable elastic compliance and deformability, they are vulnerable to tearing, puncture and other mechanical damage modes that cause electrical failure. Here, we introduce a material architecture for soft and highly deformable circuit interconnects that are electromechanically stable under typical loading conditions, while exhibiting uncompromising resilience to mechanical damage. The material is composed of liquid metal droplets suspended in a soft elastomer; when damaged, the droplets rupture to form new connections with neighbours and re-route electrical signals without interruption. Since self-healing occurs spontaneously, these materials do not require manual repair or external heat. We demonstrate this unprecedented electronic robustness in a self-repairing digital counter and self-healing soft robotic quadruped that continue to function after significant damage.

Inflammasome inhibition prevents α-synuclein pathology and dopaminergic neurodegeneration in mice

Abstract: Parkinson's disease (PD) is characterized by a profound loss of dopaminergic neurons in the substantia nigra, accompanied by chronic neuroinflammation, mitochondrial dysfunction, and widespread accumulation of α-synuclein-rich protein aggregates in the form of Lewy bodies. However, the mechanisms linking α-synuclein pathology and dopaminergic neuronal death to chronic microglial neuroinflammation have not been completely elucidated. We show that activation of the microglial NLR family pyrin domain containing 3 (NLRP3) inflammasome is a common pathway triggered by both fibrillar α-synuclein and dopaminergic degeneration in the absence of α-synuclein aggregates. Cleaved caspase-1 and the inflammasome adaptor protein apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain (ASC) were elevated in the substantia nigra of the brains of patients with PD and in multiple preclinical PD models. NLRP3 activation by fibrillar α-synuclein in mouse microglia resulted in a delayed but robust activation of the NLRP3 inflammasome leading to extracellular interleukin-1β and ASC release in the absence of pyroptosis. Nanomolar doses of a small-molecule NLRP3 inhibitor, MCC950, abolished fibrillar α-synuclein-mediated inflammasome activation in mouse microglial cells and extracellular ASC release. Furthermore, oral administration of MCC950 in multiple rodent PD models inhibited inflammasome activation and effectively mitigated motor deficits, nigrostriatal dopaminergic degeneration, and accumulation of α-synuclein aggregates. These findings suggest that microglial NLRP3 may be a sustained source of neuroinflammation that could drive progressive dopaminergic neuropathology and highlight NLRP3 as a potential target for disease-modifying treatments for PD.

Probing magnetism in 2D van der Waals crystalline insulators via electron tunneling.

Abstract: Magnetic insulators are a key resource for next-generation spintronic and topological devices. The family of layered metal halides promises varied magnetic states, including ultrathin insulating multiferroics, spin liquids, and ferromagnets, but device-oriented characterization methods are needed to unlock their potential. Here, we report tunneling through the layered magnetic insulator CrI3 as a function of temperature and applied magnetic field. We electrically detect the magnetic ground state and interlayer coupling and observe a field-induced metamagnetic transition. The metamagnetic transition results in magnetoresistances of 95, 300, and 550% for bilayer, trilayer, and tetralayer CrI3 barriers, respectively. We further measure inelastic tunneling spectra for our junctions, unveiling a rich spectrum consistent with collective magnetic excitations (magnons) in CrI3.

The Narcissism Spectrum Model: A Synthetic View of Narcissistic Personality.

Abstract: The narcissism spectrum model synthesizes extensive personality, social-psychological, and clinical evidence, building on existing knowledge about narcissistic grandiosity and vulnerability to reveal a view of narcissism that respects its clinical origins, embraces the diversity and complexity of its expression, and reflects extensive scientific evidence about the continuity between normal and abnormal personality expression. Critically, the proposed model addresses three key, inter-related problems that have plagued narcissism scholarship for more than a century. These problems can be summarized as follows: (a) What are the key features of narcissism? (b) How are they organized and related to each other? and (c) Why are they organized that way, that is, what accounts for their relationships? By conceptualizing narcissistic traits as manifested in transactional processes between individuals and their social environments, the model enables integration of existing theories of narcissism and thus provides a compelling perspective for future examination of narcissism and its developmental pathways.

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

Abstract: The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

Global assessment of agricultural system redesign for sustainable intensification

Abstract: The sustainable intensification of agricultural systems offers synergistic opportunities for the co-production of agricultural and natural capital outcomes. Efficiency and substitution are steps towards sustainable intensification, but system redesign is essential to deliver optimum outcomes as ecological and economic conditions change. We show global progress towards sustainable intensification by farms and hectares, using seven sustainable intensification sub-types: integrated pest management, conservation agriculture, integrated crop and biodiversity, pasture and forage, trees, irrigation management and small or patch systems. From 47 sustainable intensification initiatives at scale (each >104 farms or hectares), we estimate 163 million farms (29% of all worldwide) have crossed a redesign threshold, practising forms of sustainable intensification on 453 Mha of agricultural land (9% of worldwide total). Key challenges include investment to integrate more forms of sustainable intensification in farming systems, creating agricultural knowledge economies and establishing policy measures to scale sustainable intensification further. We conclude that sustainable intensification may be approaching a tipping point where it could be transformative.

Search for dark matter and other new phenomena in events with an energetic jet and large missing transverse momentum using the ATLAS detector

Abstract: Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 36.1 fb−1 at a centre-of-mass energy of 13 TeV collected in 2015 and 2016 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons (e or μ). Several signal regions are considered with increasing requirements on the missing transverse momentum above 250 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.

Stealing Hyperparameters in Machine Learning

Abstract: Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.

An explainable deep machine vision framework for plant stress phenotyping.

Abstract: Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar stresses in soybean [Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress), classification (low, medium, or high stress), and quantification (stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar stresses would have significant implications in scientific research, plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of stress by farmers and researchers.

RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization

Abstract: The ability to analyse multidimensional traits and other multivariate data has become a requisite skill for evolutionary biologists and ecologists over the last few decades. This reality is important, as highdimensional data are not just encumbrance, but often necessary for addressing important research questions. Advanced computing technology has spurred the quick collection of “highdimensional” data (e.g., genomics, imaging, remote sensing) and some characteristics of research subjects just cannot be described with scant information. Highdimensional (continuous) data are multivariateresponse data that require strict attention to using all variables (p) in analyses, even if they exceed the number of observations (n), as they are needed to describe complex multidimensional traits of research subjects. For example, a comparative morphometric analysis might include many anatomical landmarks (large p, relative to n) whose twoor threedimensional Cartesian coordinates comprise a single trait, organism “shape” (Adams, Rohlf, & Slice, 2013). It is not uncommon to have anatomical landmarks per specimen exceed the number of specimens, especially if such information is obtained by 3D laser scanners. The general analytical problem highdimensional data pose is that when p exceeds the error degrees of freedom of linear models (n minus the number of model parameters), parametric analyses Received: 22 January 2018 | Accepted: 30 April 2018 DOI: 10.1111/2041-210X.13029

Genetic Mechanisms of Immune Evasion in Colorectal Cancer

Abstract: To understand the genetic drivers of immune recognition and evasion in colorectal cancer (CRC), we analyzed 1,211 CRC primary tumor samples, including 179 classified as microsatellite instability-high (MSI-high). This set includes The Cancer Genome Atlas CRC cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of CRC, had a high rate of significantly mutated genes in important immune modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy number alterations and copy-neutral loss of heterozygosity (CN-LOH). WNT/β-catenin signaling genes were significantly mutated in all CRC subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of CRC demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration, and furthermore, that CRC tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.

heatmaply: an R package for creating interactive cluster heatmaps for online publishing.

Abstract: Summary heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. Availability and implementation The heatmaply package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=heatmaply. Contact tal.galili@math.tau.ac.il. Supplementary information Supplementary data are available at Bioinformatics online.

A systematic review of research on the flipped learning method in engineering education

Abstract: The purpose of this article is to describe the current state of knowledge and practice in the flipped learning approach in engineering education and to provide guidance for practitioners by critically appraising and summarizing existing research. This article is a qualitative synthesis of quantitative and qualitative research investigating the flipped learning approach in engineering education. Systematic review was adopted as the research methodology and article selection and screening process are described. Articles published between 2000 and May 2015 were reviewed, and 62 articles were included for a detailed analysis and synthesis. The results indicated that flipped learning gained popularity amongst engineering educators after 2012. The review revealed that research in engineering education focused on documenting the design and development process and sharing preliminary findings and student feedback. Future research examining different facets of a flipped learning implementation, framed around sound theoretical frameworks and evaluation methods, is still needed to establish the pedagogy of flipped learning in teaching engineering. [ABSTRACT FROM AUTHOR]

Production and integration of the ATLAS Insertable B-Layer

Abstract: During the shutdown of the CERN Large Hadron Collider in 2013-2014, an additional pixel layer was installed between the existing Pixel detector of the ATLAS experiment and a new, smaller radius beam pipe. The motivation for this new pixel layer, the Insertable B-Layer (IBL), was to maintain or improve the robustness and performance of the ATLAS tracking system, given the higher instantaneous and integrated luminosities realised following the shutdown. Because of the extreme radiation and collision rate environment, several new radiation-tolerant sensor and electronic technologies were utilised for this layer. This paper reports on the IBL construction and integration prior to its operation in the ATLAS detector.

Load Modeling—A Review

Abstract: Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: 1) static and 2) dynamic models, while there are two types of approaches to identify model parameters: 1) measurement-based and 2) component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this paper, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.

Extensive intraspecific gene order and gene structural variations between Mo17 and other maize genomes.

Abstract: Maize is an important crop with a high level of genome diversity and heterosis. The genome sequence of a typical female line, B73, was previously released. Here, we report a de novo genome assembly of a corresponding male representative line, Mo17. More than 96.4% of the 2,183 Mb assembled genome can be accounted for by 362 scaffolds in ten pseudochromosomes with 38,620 annotated protein-coding genes. Comparative analysis revealed large gene-order and gene structural variations: approximately 10% of the annotated genes were mutually nonsyntenic, and more than 20% of the predicted genes had either large-effect mutations or large structural variations, which might cause considerable protein divergence between the two inbred lines. Our study provides a high-quality reference-genome sequence of an important maize germplasm, and the intraspecific gene order and gene structural variations identified should have implications for heterosis and genome evolution. The de novo genome assembly of maize line Mo17 and comparative analysis with other sequenced maize lines show extensive gene-order variations. This study provides insights into maize evolution and has implications for improving maize hybrid lines.

Water-dispersible PEG-curcumin/amine-functionalized covalent organic framework nanocomposites as smart carriers for in vivo drug delivery.

Abstract: Covalent organic frameworks (COFs) as drug-delivery carriers have been mostly evaluated in vitro due to the lack of COFs nanocarriers that are suitable for in vivo studies. Here we develop a series of water-dispersible polymer-COF nanocomposites through the assembly of polyethylene-glycol-modified monofunctional curcumin derivatives (PEG-CCM) and amine-functionalized COFs (APTES-COF-1) for in vitro and in vivo drug delivery. The real-time fluorescence response shows efficient tracking of the COF-based materials upon cellular uptake and anticancer drug (doxorubicin (DOX)) release. Notably, in vitro and in vivo studies demonstrate that PEG-CCM@APTES-COF-1 is a smart carrier for drug delivery with superior stability, intrinsic biodegradability, high DOX loading capacity, strong and stable fluorescence, prolonged circulation time and improved drug accumulation in tumors. More intriguingly, PEG350-CCM@APTES-COF-1 presents an effective targeting strategy for brain research. We envisage that PEG-CCM@APTES-COF-1 nanocomposites represent a great promise toward the development of a multifunctional platform for cancer-targeted in vivo drug delivery.

Bismuth ferrite-based lead-free ceramics and multilayers with high recoverable energy density

Abstract: Lead-free ceramics with high recoverable energy density (Wrec) and energy storage efficiency (η) are attractive for advanced pulsed power capacitors to enable greater miniaturization and integration In this work, dense bismuth ferrite (BF)-based, lead-free 075(Bi1−xNdx)FeO3-025BaTiO3 (BNxF-BT) ceramics and multilayers were fabricated A transition from a mixed pseudocubic and R3c to a purely pseudocubic structure was observed as x increased with the optimum properties obtained for mixed compositions The highest energy densities, W ∼ 41 J cm−3 and Wrec ∼ 182 J cm−3, were achieved for BN15F-BT, due to the enhanced breakdown field strength (BDS ∼ 180 kV cm−1) and large maximum polarization (Pmax ∼ 40 μC cm−2) The multilayers of this composition possessed both a high Wrec of 674 J cm−3 and η of 77% and were stable up to 125 °C Nd doped BF-based ceramics with enhanced BDS and large Wrec are therefore considered promising candidates for lead-free energy storage applications

Resilience Enhancement Strategy for Distribution Systems Under Extreme Weather Events

Abstract: This paper proposes an optimal hardening strategy to enhance the resilience of power distribution networks to protect against extreme weather events. Different grid hardening techniques are considered, such as upgrading poles and vegetation management. The problem is formulated as a tri-level optimization problem to minimize grid hardening investment and load shedding in extreme weather events. The first level is to identify vulnerable distribution lines and select hardening strategies, the second level is to determine the set of out-of-service distribution lines so that the damage caused by extreme weather events is maximized, and the third level is to minimize load shedding costs according to load priorities and the set of damaged lines. Since the selection of hardening strategies is coupled with the uncertainty set of out-of-service lines, the original tri-level model is transformed to be an equivalent bi-level problem, which is subsequently solved by a greedy searching algorithm. Case studies demonstrate the effectiveness of the proposed method under multiple severe weather events and different simulation settings.

Affective–Reflective Theory of physical inactivity and exercise

Abstract: This article introduces a new theory, the Affective–Reflective Theory (ART) of physical inactivity and exercise. ART aims to explain and predict behavior in situations in which people either remain in a state of physical inactivity or initiate action (exercise). It is a dual-process model and assumes that exercise-related stimuli trigger automatic associations and a resulting automatic affective valuation of exercise (type-1 process). The automatic affective valuation forms the basis for the reflective evaluation (type-2 process), which can follow if self-control resources are available. The automatic affective valuation is connected with an action impulse, whereas the reflective evaluation can result in action plans. The two processes, in constant interaction, direct the individual towards or away from changing behavior. The ART of physical inactivity and exercise predicts that, when there is an affective–reflective discrepancy and self-control resources are low, behavior is more likely to be governed by the affective type-1 process. This introductory article explains the underlying concepts and main theoretical roots from which the ART of physical inactivity and exercise was developed (field theory, affective responses to exercise, automatic evaluation, evaluation-behavior link, dual-process theorizing). We also summarize the empirical tests that have been conducted to refine the theory in its present form.

Effects of Cold Plasma on Food Quality: A Review

Abstract: Cold plasma (CP) technology has proven very effective as an alternative tool for food decontamination and shelf-life extension. The impact of CP on food quality is very crucial for its acceptance as an alternative food processing technology. Due to the non-thermal nature, CP treatments have shown no or minimal impacts on the physical, chemical, nutritional and sensory attributes of various products. This review also discusses the negative impacts and limitations posed by CP technology for food products. The limited studies on interactions of CP species with food components at the molecular level offers future research opportunities. It also highlights the need for optimization studies to mitigate the negative impacts on visual, chemical, nutritional and functional properties of food products. The design versatility, non-thermal, economical and environmentally friendly nature of CP offers unique advantages over traditional processing technologies. However, CP processing is still in its nascent form and needs further research to reach its potential.

The General Aggression Model.

Abstract: The General Aggression Model (GAM) is a comprehensive, integrative, framework for understanding aggression. It considers the role of social, cognitive, personality, developmental, and biological factors on aggression. Proximate processes of GAM detail how person and situation factors influence cognitions, feelings, and arousal, which in turn affect appraisal and decision processes, which in turn influence aggressive or nonaggressive behavioral outcomes. Each cycle of the proximate processes serves as a learning trial that affects the development and accessibility of aggressive knowledge structures. Distal processes of GAM detail how biological and persistent environmental factors can influence personality through changes in knowledge structures. GAM has been applied to understand aggression in many contexts including media violence effects, domestic violence, intergroup violence, temperature effects, pain effects, and the effects of global climate change.

Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

Abstract: Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA). For long data sequences, if one tries to use a single lower-dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of RSL and tracking.

Supply chain disruption propagation: a systemic risk and normal accident theory perspective

Abstract: When a disruption occurs in a firm, its effects are often felt throughout the supply chain. As supply chains expand globally and companies pursue velocity and efficiency, the probability of disruptions propagating throughout a chain grows. In this paper, we employ a qualitative, grounded theory case study approach to help understand what drives supply chain disruption propagation and to provide theoretical insights into this emerging area. For a more complete perspective, we study three interconnected tiers in seven unique supply chains. Each supply chain triad consists of (1) a focal firm (a manufacturer), (2) a supplier to the focal firm and (3) a customer of the focal firm allowing us to gain perspective from three levels in multiple supply chains. Three aggregate dimensions are defined which help explain the propagation of supply chain disruptions: the nature of the disruption, structure and dependence, and managerial decision-making. Within these dimensions, six themes are identified giving an increa...

Strengths and limitations of Nitrogen rate recommendations for corn and opportunities for improvement

Abstract: 1 The goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors. Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Mining Halophytes for Plant Growth-Promoting Halotolerant Bacteria to Enhance the Salinity Tolerance of Non-halophytic Crops.

Abstract: Salinity stress is one of the major abiotic stresses limiting crop production in arid and semi-arid regions. Interest is increasing in the application of PGPRs (plant growth promoting rhizobacteria) to ameliorate stresses such as salinity stress in crop production. The identification of salt-tolerant, or halophilic, PGPRs has the potential to promote saline soil-based agriculture. Halophytes are a useful reservoir of halotolerant bacteria with plant growth-promoting capabilities. Here, we review recent studies on the use of halophilic PGPRs to stimulate plant growth and increase the tolerance of non-halophytic crops to salinity. These studies illustrate that halophilic PGPRs from the rhizosphere of halophytic species can be effective bio-inoculants for promoting the production of non-halophytic species in saline soils. These studies support the viability of bioinoculation with halophilic PGPRs as a strategy for the sustainable enhancement of non-halophytic crop growth. The potential of this strategy is discussed within the context of ensuring sustainable food production for a world with an increasing population and continuing climate change. We also explore future research needs for using halotolerant PGPRs under salinity stress.