Showing papers by "Oregon State University published in 2017"

Normalization and microbial differential abundance strategies depend upon data characteristics

Abstract: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets ( 20 samples per group) but also critically the only method tested that has a good control of false discovery rate. These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Single Atomic Iron Catalysts for Oxygen Reduction in Acidic Media: Particle Size Control and Thermal Activation.

Abstract: It remains a grand challenge to replace platinum group metal (PGM) catalysts with earth-abundant materials for the oxygen reduction reaction (ORR) in acidic media, which is crucial for large-scale deployment of proton exchange membrane fuel cells (PEMFCs). Here, we report a high-performance atomic Fe catalyst derived from chemically Fe-doped zeolitic imidazolate frameworks (ZIFs) by directly bonding Fe ions to imidazolate ligands within 3D frameworks. Although the ZIF was identified as a promising precursor, the new synthetic chemistry enables the creation of well-dispersed atomic Fe sites embedded into porous carbon without the formation of aggregates. The size of catalyst particles is tunable through synthesizing Fe-doped ZIF nanocrystal precursors in a wide range from 20 to 1000 nm followed by one-step thermal activation. Similar to Pt nanoparticles, the unique size control without altering chemical properties afforded by this approach is able to increase the number of PGM-free active sites. The best O...

World Scientists’ Warning to Humanity: A Second Notice

Abstract: Twenty-five years ago, the Union of Concerned Scientists and more than 1700 independent scientists, including the majority of living Nobel laureates in the sciences, penned the 1992 "World Scientists’ Warning to Humanity" (see supplemental file S1). These concerned professionals called on humankind to curtail environmental destruction and cautioned that "a great change in our stewardship of the Earth and the life on it is required, if vast human misery is to be avoided." In their manifesto, they showed that humans were on a collision course with the natural world. They expressed concern about current, impending, or potential damage on planet Earth involving ozone depletion, freshwater availability, marine life depletion, ocean dead zones, forest loss, biodiversity destruction, climate change, and continued human population growth. They proclaimed that fundamental changes were urgently needed to avoid the consequences our present course would bring.

2017 European League Against Rheumatism/American College of Rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups

Abstract: Objective To develop and validate new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIM) and their major subgroups. Methods Candidate variables were assembled from published criteria and expert opinion using consensus methodology. Data were collected from 47 rheumatology, dermatology, neurology and paediatric clinics worldwide. Several statistical methods were used to derive the classification criteria. Results Based on data from 976 IIM patients (74% adults; 26% children) and 624 non-IIM patients with mimicking conditions (82% adults; 18% children), new criteria were derived. Each item is assigned a weighted score. The total score corresponds to a probability of having IIM. Subclassification is performed using a classification tree. A probability cut-off of 55%, corresponding to a score of 5.5 (6.7 with muscle biopsy) ‘probable IIM’, had best sensitivity/specificity (87%/82% without biopsies, 93%/88% with biopsies) and is recommended as a minimum to classify a patient as having IIM. A probability of ≥90%, corresponding to a score of ≥7.5 (≥8.7 with muscle biopsy), corresponds to ‘definite IIM’. A probability of Conclusions The European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for IIM have been endorsed by international rheumatology, dermatology, neurology and paediatric groups. They employ easily accessible and operationally defined elements, and have been partially validated. They allow classification of ‘definite’, ‘probable’ and ‘possible’ IIM, in addition to the major subgroups of IIM, including juvenile IIM. They generally perform better than existing criteria.

Potassium Secondary Batteries.

Abstract: Potassium may exhibit advantages over lithium or sodium as a charge carrier in rechargeable batteries. Analogues of Prussian blue can provide millions of cyclic voltammetric cycles in aqueous electrolyte. Potassium intercalation chemistry has recently been demonstrated compatible with both graphite and nongraphitic carbons. In addition to potassium–ion batteries, potassium–O2 (or −air) and potassium–sulfur batteries are emerging. Additionally, aqueous potassium–ion batteries also exhibit high reversibility and long cycling life. Because of potentially low cost, availability of basic materials, and intriguing electrochemical behaviors, this new class of secondary batteries is attracting much attention. This mini-review summarizes the current status, opportunities, and future challenges of potassium secondary batteries.

A human-driven decline in global burned area

Abstract: Fire is an essential Earth system process that alters ecosystem and atmospheric composition. Here we assessed long-term fire trends using multiple satellite data sets. We found that global burned area declined by 24.3 ± 8.8% over the past 18 years. The estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas. Agricultural expansion and intensification were primary drivers of declining fire activity. Fewer and smaller fires reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the terrestrial carbon sink. Fire models were unable to reproduce the pattern and magnitude of observed declines, suggesting that they may overestimate fire emissions in future projections. Using economic and demographic variables, we developed a conceptual model for predicting fire in human-dominated landscapes.

Critical Assessment of Metagenome Interpretation - A benchmark of metagenomics software

Abstract: Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Research priorities for harnessing plant microbiomes in sustainable agriculture.

Abstract: Feeding a growing world population amidst climate change requires optimizing the reliability, resource use, and environmental impacts of food production. One way to assist in achieving these goals is to integrate beneficial plant microbiomes-i.e., those enhancing plant growth, nutrient use efficiency, abiotic stress tolerance, and disease resistance-into agricultural production. This integration will require a large-scale effort among academic researchers, industry researchers, and farmers to understand and manage plant-microbiome interactions in the context of modern agricultural systems. Here, we identify priorities for research in this area: (1) develop model host-microbiome systems for crop plants and non-crop plants with associated microbial culture collections and reference genomes, (2) define core microbiomes and metagenomes in these model systems, (3) elucidate the rules of synthetic, functionally programmable microbiome assembly, (4) determine functional mechanisms of plant-microbiome interactions, and (5) characterize and refine plant genotype-by-environment-by-microbiome-by-management interactions. Meeting these goals should accelerate our ability to design and implement effective agricultural microbiome manipulations and management strategies, which, in turn, will pay dividends for both the consumers and producers of the world food supply.

vcfr: a package to manipulate and visualize variant call format data in R.

Abstract: Software to call single-nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with VCF files. While an increasing number of software exists to read VCF data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the r package vcfr to address this issue. We developed a VCF file exploration tool implemented in the r language because r provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write VCF files into r as well as functions to extract portions of the data and to plot summary statistics of the data are implemented. vcfr further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (fasta) and annotation data (GFF) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the vcfr data structure to formats used by other r genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate VCF data exploration, including intuitive methods for data quality control and easy export to other r packages for further analysis. vcfr thus provides essential, novel tools currently not available in r.

Stress and stability: applying the Anna Karenina principle to animal microbiomes.

Abstract: All animals studied to date are associated with symbiotic communities of microorganisms. These animal microbiotas often play important roles in normal physiological function and susceptibility to disease; predicting their responses to perturbation represents an essential challenge for microbiology. Most studies of microbiome dynamics test for patterns in which perturbation shifts animal microbiomes from a healthy to a dysbiotic stable state. Here, we consider a complementary alternative: that the microbiological changes induced by many perturbations are stochastic, and therefore lead to transitions from stable to unstable community states. The result is an ‘Anna Karenina principle’ for animal microbiomes, in which dysbiotic individuals vary more in microbial community composition than healthy individuals—paralleling Leo Tolstoy's dictum that “all happy families look alike; each unhappy family is unhappy in its own way”. We argue that Anna Karenina effects are a common and important response of animal microbiomes to stressors that reduce the ability of the host or its microbiome to regulate community composition. Patterns consistent with Anna Karenina effects have been found in systems ranging from the surface of threatened corals exposed to above-average temperatures, to the lungs of patients suffering from HIV/AIDs. However, despite their apparent ubiquity, these patterns are easily missed or discarded by some common workflows, and therefore probably underreported. Now that a substantial body of research has established the existence of these patterns in diverse systems, rigorous testing, intensive time-series datasets and improved stochastic modelling will help to explore their importance for topics ranging from personalized medicine to theories of the evolution of host–microorganism symbioses. This Perspective argues that Anna Karenina effects (that is, changes resulting in increased variation in community composition under stress) are a common and important response of animal microbiomes that have been under-reported.

2017 European League Against Rheumatism/American College of Rheumatology Classification Criteria for Adult and Juvenile Idiopathic Inflammatory Myopathies and Their Major Subgroups.

Abstract: Objective To develop and validate new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIM) and their major subgroups. Methods Candidate variables were assembled from published criteria and expert opinion using consensus methodology. Data were collected from 47 rheumatology, dermatology, neurology, and pediatric clinics worldwide. Several statistical methods were utilized to derive the classification criteria. Results Based on data from 976 IIM patients (74% adults; 26% children) and 624 non-IIM patients with mimicking conditions (82% adults; 18% children), new criteria were derived. Each item is assigned a weighted score. The total score corresponds to a probability of having IIM. Subclassification is performed using a classification tree. A probability cutoff of 55%, corresponding to a score of 5.5 (6.7 with muscle biopsy) “probable IIM,” had best sensitivity/specificity (87%/82% without biopsies, 93%/88% with biopsies) and is recommended as a minimum to classify a patient as having IIM. A probability of ≥90%, corresponding to a score of ≥7.5 (≥8.7 with muscle biopsy), corresponds to “definite IIM.” A probability of <50%, corresponding to a score of <5.3 (<6.5 with muscle biopsy), rules out IIM, leaving a probability of ≥50–<55% as “possible IIM.” Conclusion The European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for IIM have been endorsed by international rheumatology, dermatology, neurology, and pediatric groups. They employ easily accessible and operationally defined elements, and have been partially validated. They allow classification of “definite,” “probable,” and “possible” IIM, in addition to the major subgroups of IIM, including juvenile IIM. They generally perform better than existing criteria.

Emerging Non-Aqueous Potassium-Ion Batteries: Challenges and Opportunities

Abstract: The ever-increasing demand for storing renewable energy sources calls for novel battery technologies that are of sustainably low levelized energy cost. Research into battery chemistry has evolved to a stage where a plethora of choices based on earth-abundant elements can be compared during their development. One of the emerging candidates is the nonaqueous potassium-ion battery. K-ion’s unique properties as a charge carrier have aroused intense interest in exploring high-performing cathode and anode materials for this battery. Rapid progress has been made, where leading candidates of electrodes have been proposed, i.e., hard carbon as anode and Prussian white analogues as cathode. In this new battery technology’s infancy, it is our opinion that the focus should be given to potentially scalable, inexpensive electrode materials and the understanding of their cycle-life-property correlations. It may be the ultralong cycle life that differentiates potassium-ion batteries from sodium-ion batteries in the futur...

Discovery of 40 Classes of Per- and Polyfluoroalkyl Substances in Historical Aqueous Film-Forming Foams (AFFFs) and AFFF-Impacted Groundwater

Abstract: Aqueous film-forming foams (AFFFs), containing per- and polyfluoroalkyl substances (PFASs), are released into the environment during response to fire-related emergencies. Repeated historical applications of AFFF at military sites were a result of fire-fighter training exercises and equipment testing. Recent data on AFFF-impacted groundwater indicates that ∼25% of the PFASs remain unidentified. In an attempt to close the mass balance, a systematic evaluation of 3M and fluorotelomer-based AFFFs, commercial products, and AFFF-impacted groundwaters from 15 U.S. military bases was conducted to identify the remaining PFASs. Liquid chromatography quadrupole time-of-flight mass spectrometry was used for compound discovery. Nontarget analysis utilized Kendrick mass defect plots and a “nontarget” R script. Suspect screening compared masses with those of previously reported PFASs. Forty classes of novel anionic, zwitterionic, and cationic PFASs were discovered, and an additional 17 previously reported classes were o...

A Review on Design Strategies for Carbon Based Metal Oxides and Sulfides Nanocomposites for High Performance Li and Na Ion Battery Anodes

Abstract: Carbon-oxide and carbon-sulfide nanocomposites have attracted tremendous interest as the anode materials for Li and Na ion batteries. Such composites are fascinating as they often show synergistic effect compared to their singular components. Carbon nanomaterials are often used as the matrix due to their high conductivity, tensile strength, and chemical stability under the battery condition. Metal oxides and sulfides are often used as active material fillers because of their large capacity. Numerous works have shown that by taking one step further into fabricating nanocomposites with rational structure design, much better performance can be achieved. The present review aims to present and discuss the development of carbon-based nanocomposite anodes in both Li ion batteries and Na ion batteries. The authors introduce the individual components in the composites, i.e., carbon matrices (e.g., carbon nanotube, graphene) and metal oxides/sulfides; followed by evaluating how advanced nanostructures benefit from the synergistic effect when put together. Particular attention is placed on strategies employed in fabricating such composites, with examples such as yolk–shell structure, layered-by-layered structure, and composite comprising one or more carbon matrices. Lastly, the authors conclude by highlighting challenges that still persist and their perspective on how to further develop the technologies.

Cations in Octahedral Sites: A Descriptor for Oxygen Electrocatalysis on Transition-Metal Spinels

Abstract: Exploring efficient and low-cost electrocatalysts for the oxygen-reduction reaction (ORR) and oxygen-evolution reaction (OER) is critical for developing renewable energy technologies such as fuel cells, metal-air batteries, and water electrolyzers. A rational design of a catalyst can be guided by identifying descriptors that determine its activity. Here, a descriptor study on the ORR/OER of spinel oxides is presented. With a series of MnCo2 O4 , the Mn in octahedral sites is identified as an active site. This finding is then applied to successfully explain the ORR/OER activities of other transition-metal spinels, including Mnx Co3-x O4 (x = 2, 2.5, 3), Lix Mn2 O4 (x = 0.7, 1), XCo2 O4 (X = Co, Ni, Zn), and XFe2 O4 (X = Mn, Co, Ni). A general principle is concluded that the eg occupancy of the active cation in the octahedral site is the activity descriptor for the ORR/OER of spinels, consolidating the role of electron orbital filling in metal oxide catalysis.

Adapt to more wildfire in western North American forests as climate changes

Abstract: Wildfires across western North America have increased in number and size over the past three decades, and this trend will continue in response to further warming. As a consequence, the wildland–urban interface is projected to experience substantially higher risk of climate-driven fires in the coming decades. Although many plants, animals, and ecosystem services benefit from fire, it is unknown how ecosystems will respond to increased burning and warming. Policy and management have focused primarily on specified resilience approaches aimed at resistance to wildfire and restoration of areas burned by wildfire through fire suppression and fuels management. These strategies are inadequate to address a new era of western wildfires. In contrast, policies that promote adaptive resilience to wildfire, by which people and ecosystems adjust and reorganize in response to changing fire regimes to reduce future vulnerability, are needed. Key aspects of an adaptive resilience approach are (i) recognizing that fuels reduction cannot alter regional wildfire trends; (ii) targeting fuels reduction to increase adaptation by some ecosystems and residential communities to more frequent fire; (iii) actively managing more wild and prescribed fires with a range of severities; and (iv) incentivizing and planning residential development to withstand inevitable wildfire. These strategies represent a shift in policy and management from restoring ecosystems based on historical baselines to adapting to changing fire regimes and from unsustainable defense of the wildland–urban interface to developing fire-adapted communities. We propose an approach that accepts wildfire as an inevitable catalyst of change and that promotes adaptive responses by ecosystems and residential communities to more warming and wildfire.

Unsupervised Video Summarization with Adversarial LSTM Networks

Abstract: This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupervised way. Such a summarizer can then be applied on a new video for estimating its optimal summarization. For learning, we specify a novel generative adversarial framework, consisting of the summarizer and discriminator. The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video. The discriminator is another LSTM aimed at distinguishing between the original video and its reconstruction from the summarizer. The summarizer LSTM is cast as an adversary of the discriminator, i.e., trained so as to maximally confuse the discriminator. This learning is also regularized for sparsity. Evaluation on four benchmark datasets, consisting of videos showing diverse events in first-and third-person views, demonstrates our competitive performance in comparison to fully supervised state-of-the-art approaches.

Hard–Soft Composite Carbon as a Long-Cycling and High-Rate Anode for Potassium-Ion Batteries

Abstract: There exist tremendous needs for sustainable storage solutions for intermittent renewable energy sources, such as solar and wind energy. Thus, systems based on Earth-abundant elements deserve much attention. Potassium-ion batteries represent a promising candidate because of the abundance of potassium resources. As for the choices of anodes, graphite exhibits encouraging potassium-ion storage properties; however, it suffers limited rate capability and poor cycling stability. Here, nongraphitic carbons as K-ion anodes with sodium carboxymethyl cellulose as the binder are systematically investigated. Compared to hard carbon and soft carbon, a hard–soft composite carbon with 20 wt% soft carbon distributed in the matrix phase of hard carbon microspheres exhibits highly amenable performance: high capacity, high rate capability, and very stable long-term cycling. In contrast, pure hard carbon suffers limited rate capability, while the capacity of pure soft carbon fades more rapidly.

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

Abstract: Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

Snowball Earth climate dynamics and Cryogenian geology-geobiology

Abstract: Geological evidence indicates that grounded ice sheets reached sea level at all latitudes during two long-lived Cryogenian (58 and ≥5 My) glaciations. Combined uranium-lead and rhenium-osmium dating suggests that the older (Sturtian) glacial onset and both terminations were globally synchronous. Geochemical data imply that CO2 was 102 PAL (present atmospheric level) at the younger termination, consistent with a global ice cover. Sturtian glaciation followed breakup of a tropical supercontinent, and its onset coincided with the equatorial emplacement of a large igneous province. Modeling shows that the small thermal inertia of a globally frozen surface reverses the annual mean tropical atmospheric circulation, producing an equatorial desert and net snow and frost accumulation elsewhere. Oceanic ice thickens, forming a sea glacier that flows gravitationally toward the equator, sustained by the hydrologic cycle and by basal freezing and melting. Tropical ice sheets flow faster as CO2 rises but lose mass and become sensitive to orbital changes. Equatorial dust accumulation engenders supraglacial oligotrophic meltwater ecosystems, favorable for cyanobacteria and certain eukaryotes. Meltwater flushing through cracks enables organic burial and submarine deposition of airborne volcanic ash. The subglacial ocean is turbulent and well mixed, in response to geothermal heating and heat loss through the ice cover, increasing with latitude. Terminal carbonate deposits, unique to Cryogenian glaciations, are products of intense weathering and ocean stratification. Whole-ocean warming and collapsing peripheral bulges allow marine coastal flooding to continue long after ice-sheet disappearance. The evolutionary legacy of Snowball Earth is perceptible in fossils and living organisms.

Marine reserves can mitigate and promote adaptation to climate change.

Abstract: Strong decreases in greenhouse gas emissions are required to meet the reduction trajectory resolved within the 2015 Paris Agreement. However, even these decreases will not avert serious stress and damage to life on Earth, and additional steps are needed to boost the resilience of ecosystems, safeguard their wildlife, and protect their capacity to supply vital goods and services. We discuss how well-managed marine reserves may help marine ecosystems and people adapt to five prominent impacts of climate change: acidification, sea-level rise, intensification of storms, shifts in species distribution, and decreased productivity and oxygen availability, as well as their cumulative effects. We explore the role of managed ecosystems in mitigating climate change by promoting carbon sequestration and storage and by buffering against uncertainty in management, environmental fluctuations, directional change, and extreme events. We highlight both strengths and limitations and conclude that marine reserves are a viable low-tech, cost-effective adaptation strategy that would yield multiple cobenefits from local to global scales, improving the outlook for the environment and people into the future.

Active and intelligent packaging in meat industry

Abstract: Background Microbial contamination and lipid and protein oxidation are major concerns for meat and meat products in terms of food safety and quality deterioration. The meat quality and safety properties are highly dependent on packaging materials and technologies. Scope and approach To achieve longer shelf life, active packaging and intelligent packaging have been developed to change the conditions of the package, impart information, monitor the product supply chain, and provide anti-counterfeit functionality. This will effectively enhance food safety and quality and consequently increase the product value, convenience, and consumer satisfactions. This review analyzes the recent developments in active and intelligent packaging in the meat industry, in both research and commercial domains. The global patents and future research trends are also discussed. Key findings and conclusions Active and intelligent packaging offer great opportunities for enhancing meat safety, quality, and convenience, and consequently decrease the number of retailer and consumer complaints. Some important factors such as legislation concerns (e.g. migration of active substances from packaging materials, labelling), economics and consumers' preferences should be considered to successfully implement antimicrobial and intelligent packaging solutions in the meat industry.

Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics

Abstract: Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. This paper focuses on detecting those adversarial examples by analyzing whether they come from the same distribution as the normal examples. Instead of directly training a deep neural network to detect adversarials, a much simpler approach was proposed based on statistics on outputs from convolutional layers. A cascade classifier was designed to efficiently detect adversarials. Furthermore, trained from one particular adversarial generating mechanism, the resulting classifier can successfully detect adversarials from a completely different mechanism as well. The resulting classifier is non-subdifferentiable, hence creates a difficulty for adversaries to attack by using the gradient of the classifier. After detecting adversarial examples, we show that many of them can be recovered by simply performing a small average filter on the image. Those findings should lead to more insights about the classification mechanisms in deep convolutional neural networks.

The city as a refuge for insect pollinators.

Abstract: Urban ecology research is changing how we view the biological value and ecological importance of cities. Lagging behind this revised image of the city are natural resource management agencies’ urban conservation programs that historically have invested in education and outreach rather than programs designed to achieve high-priority species conservation results. This essay synthesizes research on urban bee species diversity and abundance to suggest how urban conservation can be repositioned to better align with a newly unfolding image of urban landscapes. We argue that pollinators put high-priority and high-impact urban conservation within reach. In a rapidly urbanizing world, transforming how environmental managers view the city can improve citizen engagement while exploring more sustainable practices of urbanization.