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Yosemite Valley is renowned for its striking topography, with many sheer granite cliffs carved during past glaciations. At the base of these cliffs many large rock avalanche deposits can be found that were deposited since ice retreated from Yosemite Valley. Cosmogenic 10Be measurements indicate that there are at least 10 different rock avalanche deposits that range in age from 13 to ~1 ka.In this study, we estimate the time-averaged temperatures experienced by rocks from five of these rock avalanche deposits using cosmogenic noble gas and luminescence paleothermometers. These two systems yield independent estimates of valley floor temperatures during the Holocene, information that is useful for reconstructing the local environmental conditions since deglaciation.Cosmogenic noble gas paleothermometry utilizes the fact that cosmogenic noble gases like 3He experience thermally-activated diffusive loss at Earth surface temperatures in minerals like quartz. The concentration of cosmogenic 3He in quartz relative to a cosmogenic nuclide that does not experience diffusive loss should therefore be a function of a rock’s thermal history over the duration of its exposure to cosmic ray particles. Apparent 3He boulder exposure ages from these five rock avalanche deposits are 58 to > 98% younger than the corresponding 10Be exposure ages. Preliminary models that combine these 3He observations and sample-specific diffusion parameters indicate that effective diffusion temperatures (EDTs) recorded by 3He in quartz are similar to or higher than the modern EDT from the instrumental record.Like with the cosmogenic 3He system, thermoluminescence (TL) paleothermometry of K-feldspars also relies upon the balance between steady signal build-up and thermally-activated loss. The difference is that TL derives from trapped electronic charge at defect sites within the feldspar crystal lattice that accumulates in response to natural background radiation. K-feldspar TL signals comprise a range of stabilities. The least stable sites will experience diffusive loss even at temperatures below 0 °C and the most stable sites will accumulate at upper crustal temperatures. By monitoring which sites are occupied and how long those sites have been accumulating charge, we estimate both the ambient temperature and the time spent at that temperature.We compare and discuss the history of rock temperatures estimated from these two systems with implications for the post-glacial climate of Yosemite Valley.

We present new families of thick origami mechanisms that achieve rigid foldability and parallel stacking of panels in the flat-folded state using linkages for some or all of the hinges between panels. A degree-four vertex results in a multiloop eight-bar spatial mechanism that can be analyzed as separate linkages. The individual linkages are designed so that they introduce offsets perpendicular to the panels that are mutually compatible around each vertex. This family of mechanisms offers the unique combination of planar unfolded state, parallel-stacked panels in the flat-folded state and kinematic single-degree-of-freedom motion from the flat-unfolded to the flat-folded state. The paper develops the mathematics defining the necessary offsets, beginning with a symmetric bird’s-foot vertex, and then shows that the joints can be developed for asymmetric flat-foldable systems. Although in the general case there is no guarantee of achieving perfect kinematic motion, we show that for many cases of interest, the deviation is a tiny fraction of the plate thickness. Mechanical realizations of several examples are presented.

Linkage origami is one effective approach for addressing stiffness and accommodating panels of finite size in origami models and tessellations. However, successfully implementing linkage origami in tessellations can be challenging. In this work, multiple theorems are presented that provide criteria for designing origami units or cells that can be assembled into arbitrarily large tessellations. The application of these theorems is demonstrated through examples of tessellations in two and three dimensions.

This book has been an effort to cover the many various aspects of artificial intelligence as it is applied to drug discovery. While many new methods and approaches have been introduced, and their impact or potential impact in drug discovery, has been presented, it is also important to reflect on the various advances that have been made in all fields as a whole to optimising drug discovery and thereby improving success rates in this endeavour. A grand challenge of artificial intelligence in drug discovery is to anticipate the potential benefits and adverse effects prior to testing these potential drugs experimentally, but realising that judicious use of experimentation is essential to enable these advances. We are experiencing a step-change in the ability of algorithms to apply available data in helping design the next generation of therapeutics. It is unlikely that any one innovation will be the dominant method going forward, but it is likely however that novel combinations of both new and old methods, with appropriate enhancements will lead to significant advances in these methods in drug discovery efforts.

Artificial intelligence today is a very broad church covering many different areas and disciplines and has had impact over a number of years. In drug discovery, although there have been very many new innovations in applying artificial intelligence to challenges here and in chemistry in general, methods from AI have been applied over decades in various forms to contribute to the progress to challenges in developing new therapies and new scientific methods.

In the UK, Acute Oak Decline (AOD) has caused much concern, due to its distinctive symptoms and its potential to impact oak species that form the largest component of native broadleaf woodland. Decline complexes involve multiple biotic and abiotic factors, which combine to reduce host vigor. In order to investigate forest decline, it is necessary to take a systems approach by considering biotic agents and their interactions with environmental factors, as unfavorable conditions may predispose host trees to pests and diseases. AOD affected trees have lesions in the phloem caused by necrogenic bacteria associated with galleries of the two-spotted oak buprestid (Agrilus biguttatus). Here, we test the extent to which AOD is influenced by environmental predisposition factors traditionally associated with oak decline. These are often factors that reduce water availability. During 2013 and 2014 extensive surveys were undertaken, which systematically visited oak woodlands across England and Wales. These locations were used in conjunction with reports from landowners, which have been collected from 2006 onwards. In total 544 locations have been used to assess relationships with soil type, climatic factors and pollutant deposition, notably atmospheric nitrogen, using logistic regression models. The resulting model has been used to produce a detailed risk map for England and Wales and predictions have been validated using the locations of an independent dataset of A. biguttatus sightings collected by entomologists. This spatial study re-emphasises the importance of predisposition factors in decline syndromes and suggests avenues for future management and mitigation.

Dear Editor, We read with interest the report by Giron-Arango and colleagues on the novel pericapsular nerve group (PENG) block and its use for postoperative analgesia in five patients with hip fractures.[1][1] Regional anesthesia practitioners have long awaited a primarily sensory block for

Environment-assisted cracking (EAC) of aluminum alloys in corrosive atmospheres is a significant maintenance and safety issue. EAC is influenced by the interaction of stress, environment, and microstructure. Atmospheric conditions and corrosion kinetics are dynamic due to diurnal cycles and changing operating conditions. Temperature, relative humidity, and surface contaminants interact to control thin-film electrolyte properties. Within a crack, the separation of the anode and cathode may occur due to concentration gradients between the crack tip, mouth, and external surface. Conventional immersion testing is not well suited to study factors and interactions leading to atmospheric EAC because the bulk electrolyte conditions for immersion testing are different from the thin-film properties. Additionally, standard three-electrode immersion measurements are not well suited to directly investigate the variation and distribution of cathodic and anodic currents on a sample surface and within an EAC crack. In this work, atmospheric electrochemical tests have been conducted using a segmented, multielectrode sensor with an artificial crevice to quantify local, dynamic anodic and cathodic current distributions. These tests are compared to EAC growth rate measurements. Maximum EAC growth rates are observed when high cathodic current is measured at the tip of artificial crevices, suggesting a hydrogen embrittlement mechanism.

Oak decline syndromes such as Acute Oak Decline (AOD) and Chronic Oak Decline (COD) are becoming increasingly prevalent and with this comes the need for more quantitative, sensitive and standardised visual oak health monitoring. Phenotyping protocols were developed to specifically measure oak decline severity and were based on a comprehensive set of simple to measure phenotypic descriptors. A total of 36 phenotypic measurements describing oak decline status included aspects of tree size, crown condition, the presence of biotic agents and a number of derived composite descriptors. Phenotypic measurements were collected from a total of 174 Quercus robur, surveyed from 9 sites across England and included healthy, AOD, COD and AOD trees in remission. Using these data, the Phenotypic Decline Index (PDI) and the Decline Acuteness Index (DAI) were developed to quantitatively describe and differentiate the acute and chronic oak decline severity spectrum. These decline indexes were derived from unsupervised random forest machine learning models, trained using the collected phenotypic data. The suitability of the decline indexes for describing decline severity and type were assessed by comparing decline index scores to manual decline status classifications along with an assessment of descriptor importance and contribution to the decline index models. Crown condition and trees size descriptors such as ‘composite crown volume’ contributed positively to the PDI. Trees with smaller crowns in poor condition had greater PDI values. Tree stature and the presence of stem bleeding contributed highly to the DAI, allowing differentiation between trees with AOD and COD syndromes. AOD trees had relatively larger stature and the presence of stem bleeding where as COD trees had small stature and stem bleeding was absent. The oak decline indexes are simple but sensitive measures of tree decline severity and allow easy comparisons of oak trees both spatially and temporally. These have the potential to provide useful tools for forest monitoring and management as well application to remote sensing and omics research.

A known drawback using chemical absorbents in post-combustion CO2 capture is degradation of solvents, which increases the capture cost and produces unwanted compounds from a health and environmental perspective. Validation of lab results is necessary to determine how relevant data produced in a laboratory is to predict degradation in a real process environment. In this work, lean samples from SINTEF's SDR rig during two solvent campaigns (S1 and MEA) have been compared with samples from real pilots (comparison depends on available data from pilots). For S1, larger amounts of nitrosamines were observed in the real samples than from the SDR rig. The nitrosamine balance for the proprietary solvent in the SDR rig is closed while a deviation for the MEA solvent is observed. A thorough characterization of degradation compounds was also conducted for the MEA samples from the SDR rig, the evaluation involved 32 degradations compounds. Analytical methods for 31 of these compounds are available, however only 24 of these compounds were observed above the lower limit of quantification in the lean MEA samples. HEPO, HEGly and MEA urea were major degradation products in MEA samples. MEA samples from pilot and SDR rig show that nitrogen containing degradation compounds are well accounted for in the lean samples.

ION Engineering, a leading developer of carbon dioxide capture technology recently demonstrated its leading amine-based solvent system at the National Carbon Capture Center (NCCC) as part of DE-FE0013303. Objectives of ION – NCCC testing were to evaluate the ION solvent over the course of >1,000 hr continuous testing in a real process environment. The pilot solvent test unit (PSTU) at NCCC was utilized with minimal modification to evaluate the ION solvent with coal-fired flue gas, which is a slipstream of an 880 MW. In a combined effort, engineers and operators from ION and NCCC successfully operated the ION solvent in the PSTU for the duration of ION's 1,100 hr test window, providing the necessary information for evaluation of ION's solvent technology.

Comparison of ProTreat ® simulations to data obtained from testing ION's advanced solvent at the NCCC facility demonstrated excellent agreement between process simulations and process telemetry. ProTreat ® simulations created and executed to resemble test conditions during ION's testing campaign at NCCC converged with good accuracy. The models predicted temperature profiles for absorption and stripping columns the were directly compared with real data and served to evaluate the accuracy of the simulations. Lean and rich solvent loading predictions using ProTreat ® were within 6% (mass/mass) of measured results. The software-generated temperature profiles in the absorber and stripper yielded variances of less than 5 °F (2 °C) at any height of the packing across all periods of evaluation with real data. Comparing ProTreat ® simulations with actual process data has provided confidence in using ProTreat ® as a viable method for predicting ION's proprietary solvent's gas treating capabilities.

Environmentally assisted cracking (EAC) of aluminum alloys in corrosive atmospheres is an important maintenance and safety issue for U.S. Department of Defense assets. EAC initiation and propagation of cracks is influenced by the complex interactions of load, environment, and alloy properties. Traditional environmental fracture testing conducted under immersion or constant humidity conditions may produce results that are different than measurements collected under thin electrolyte layers or droplets formed during atmospheric exposure. In addition, most standard methods do not provide instantaneous measures of crack velocity that can be used to identify specific environmental conditions that promote cracking. Improved assessment of EAC susceptibility and the conditions that promote cracking of aluminum alloys has been accomplished with an autonomous, in situ measurement system that can be used in accelerated corrosion test chambers and outdoor exposure sites. Continuous measurements of crack length through...

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades. As a consequence, integrative data-driven approaches are nowadays on the rise in pharmaceutical research, making use of an inter-connected (network) view on diseases. In addition, evidence-based decisions are alleviated by studying the time evolution of innovation trends in drug discovery.In this paper a new approach leveraging data mining and data integration for inspecting target innovation trends protein family-wise is presented. The study highlights protein families which are receiving emerging interest in the drug discovery community (mainly kinases and G protein coupled receptors) and those with areas of interest in target space that have just emerged in the scientific literature (mainly kinases and transporters) highlighting novel opportunities for drug intervention.In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology (GO) and disease annotations from DisGeNet for major target families are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of disease annotations, targets associated to e.g., cancer-related pathologies as well as to intellectual disability and schizophrenia are increasingly investigated nowadays.Can this knowledge be used to study the “movement of targets” in a network view and unravel new links between diseases and biological processes? We tackled this question by creating dynamic network representations considering data from different time periods. The dynamic network for immune system process-associated targets suggest that e.g. breast cancer as well as schizophrenia are linked to the same targets (cannabinoid receptor CB2 and VEGFR2) thus suggesting similar treatment options which could be confirmed by literature search. The methodology has the potential to identify other drug repurposing candidates and enables researchers to capture trends in research attention in target space at an early stage.The KNIME workflows and R scripts used in this study are publicly available fromhttps://github.com/BZdrazil/Moving_Targets.Author summaryIn this study we have investigated target innovation in drug discovery over a period of 22 years (1995-2016) by extracting time trends of research interest (as published in the scientific literature and stored in the ChEMBL database) in certain protein classes inspecting different measures (numbers of pharmacological measurements, targets, papers, and drugs). Focusing on the most relevant protein classes in drug discovery (G protein-coupled receptors, kinases, ion channels, nuclear receptors, proteases, and transporters), we further linked single targets to Gene Ontology (GO) biological process annotations and inspected steep increasing or decreasing trends of GO annotations within target families over time. We also tracked trends in disease annotations from DisGeNET by filtering out diseases linked to targets with emerging trends in research interest. Finally, targets, GO terms, and diseases are interconnected in network representations and shifts in research foci are investigated over time. This new methodology which utilizes data mapping and data analysis can be used to explore trends in research attention target family-wise, to uncover previously unknown links between diseases and biological processes and to identify potential candidates for drug repurposing.

Despite a wealth of knowledge on Salmonella phages worldwide, little is known about poultry-associated Salmonella phages from Thailand. Here, we isolated 108 phages from Thai poultry farms that infect Salmonella enterica serovar Typhimurium. Phages STm101 and STm118 were identified as temperate Siphoviridae phages. Genome sequencing and analyses revealed these phages share approximately 96% nucleotide sequence similarity to phage SPN19, a member of the Chi-like virus genus. PCR amplification of the gene encoding capsid protein E of the Chi-like phage was positive for 50% of phage isolates, suggesting a predominance of this phage type among the sampled poultry farms. In addition to the flagella, two phages required the lipopolysaccharide to infect and lyse Salmonella. Furthermore, phylogenomic analysis demonstrated that phages STm101 and STm118 formed a monophyletic clade with phages isolated from Western countries, but not from closer isolated phages from Korea. However, further investigation and more phage isolates are required to investigate possible causes for this geographic distribution., Viruses, 11 (6), ISSN:1999-4915

Shigellosis, caused by Shigella boydii type 1, is understudied and underreported. For 3 years, GEMS study identified 5.4% of all Shigella as S. boydii. We showed the prevalent serotypes of S. boydii in Bangladesh and phage-based diagnosis of S. boydii type 1, a rapid and low-cost approach. Previously typed 793 clinical S. boydii strains were used for serotype distribution. Twenty-eight environmental water samples were collected for isolation of Shigella phages. Forty-eight serotypes of Shigella and other enteric bacteria were used for testing the susceptibility to phage MK-13. Electron microscopy, restriction enzyme analysis, whole genome sequencing (WGS), and annotation were performed for extensive characterization. S. boydii type 1 is the second most prevalent serotype among 20 serotypes of S. boydii in Bangladesh. We isolated a novel phage, MK-13, which specifically lyses S. boydii type 1, but doesn’t lyse other 47 serotypes of Shigella or other enteric bacteria tested. The phage belongs to the Myoviridae family and distinct from other phages indicated by electron microscopy and restriction enzyme analysis, respectively. MK-13 genome consists of 158 kbp of circularly permuted double-stranded DNA with G + C content of 49.45%, and encodes 211 open reading frames including four tRNA-coding regions. The genome has 98% identity with previously reported phage, ΦSboM-AG3, reported to have a broader host range infecting most of the S. boydii and other species of Shigella tested. To our knowledge, MK-13 is the first phage reported to be used as a diagnostic marker to detect S. boydii type 1, especially in remote settings with limited laboratory infrastructure.

Acute Oak Decline (AOD) is devastating thousands of native oaks, Quercus robur and Q. petraea, in the UK and continental Europe. The syndrome is principally caused by multiple, interacting bacterial species that degrade inner bark tissues. The level of host predisposition required prior to AOD infection is unknown. The two spotted oak buprestid Agrilus biguttatus is strongly associated with AOD, although its role remains unclear. To investigate the nature of predisposition in AOD, to explore the role of the beetle in the syndrome, and to examine growth trends after AOD onset, the stem growth of 243 trees with a range of severity of AOD symptoms was analysed at five sites in England. Novel mixed effects dendrochronological modelling methods were developed. The presence/absence of A. biguttatus exit holes in the trees was not specifically linked with reduced stem growth, nor was there evidence of further reductions in growth after the onset of AOD symptoms. Instead, trees with long-term AOD symptoms show significantly reduced growth compared to asymptomatic trees from as far back as the 1930s, following a period of widespread decline in English Oaks. These results suggest that a cohort of oak trees across Britain was permanently damaged in the 1930s and predisposed to develop AOD symptoms decades later. Additionally, correlations with climatic variables suggest that diseased trees are less able to take advantage of good growing conditions in the spring and autumn. This study sheds light on how historical episodes of stress may impact the future resilience of oaks to disturbance, and supports the use of dendrochronological modelling as a technique to study the underlying health status of oak tree populations, and to better understand tree decline episodes.

Forest decline diseases are complex processes driven by biotic and abiotic factors. Although information about host&ndash, microbiome&ndash, environment interactions in agricultural systems is emerging rapidly, similar studies on tree health are still in their infancy. We used acute oak decline (AOD) as a model system to understand whether the rhizosphere physicochemical properties and microbiome are linked to tree health by studying these two factors in healthy and diseased trees located in three sites in different AOD stages&mdash, low, mid and severe. We found significant changes in the rhizosphere properties and microbiome composition across the different AOD sites and between the tree health conditions. Rhizosphere pH correlated with microbiome composition, with the microbial assemblages changing in more acidic soils. At the severe AOD site, the oak trees exhibited the lowest rhizosphere pH and distinct microbiome, regardless of their health condition, whereas, at the low and mid-stage AOD sites, only diseased trees showed lower pH and the microbial composition differed significantly from healthy trees. On these two sites, less extreme soil conditions and a high presence of host-beneficial microbiota were observed in the healthy oak trees. For the first time, this study gathers evidence of associations among tree health conditions, rhizosphere properties and microbiome as well as links aboveground tree decline symptoms to the belowground environment. This provides a baseline of rhizosphere community profiling of UK oak trees and paves the way for these associations to be investigated in other tree species suffering decline disease events.

In many cases, an idea for a dance is the first step of a choreographer’s process, followed by music, choreography, and eventually lighting, set, and costume design. What happens when the costumes ...

Prognostics and health management (PHM) is the study of using health information to support decision making to improve maintenance and operations. There are many existing methods for PHM but most solely focus on predictive accuracy and ignore resource constraints. In a real-world application of a PHM system, resources consumed by the predictive algorithm at the core of the PHM system could be a limiting factor. In this study, we propose using a hierarchical classification scheme to break the conventional classification problem into many sub-problems arranged in a hierarchy. By splitting the diagnostic task into many sub-problems, the hierarchical classifier can be constructed to maximize accuracy while minimizing resource consumption. Reinforcement learning is proposed to select the classifiers for each sub-problem. The proposed methodology is applied to condition monitoring of a hydraulic actuator where power is a limiting resource. Numerical experiments demonstrate that the proposed hierarchical classification method can reduce resource consumption compared to a traditional flat classification approach.

Polyethylene terephthalate (PET) sheets show promise for application in origami-based engineering design. Origami-based engineering provides advantages that are not readily available in traditional engineering design methods. Several processing methods were examined to identify trends and determine the effect of processing of PET sheets on the crease properties of origami mechanisms in PET. Various annealing times, temperatures, and cooling rates were evaluated and data collected for over 1000 samples. It was determined that annealing temperature plays the largest role in crease response. An increase in the crystallinity of a PET sheet while in the folded state likely increases the force response of the crease in PET sheets. An annealing time of at least 60 min at 160-180 C with a quick cooling results in a high force response in the crease. The effectiveness of the processing methods was demonstrated in several origami patterns of various complexities.

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Digital Drug Discovery: Guest Editors Andreas Bender (University of Cambridge) and Nathan Brown (BenevolentAI) present the 20 articles included in this Special Issue on Cheminformatics in Drug Design. As they summarize each article, they also discuss the common themes within in silico drug discovery that these papers represent.

Injecting anti-tumor necrosis factor (TNF)α antibodies into patient joints at the site of inflammation, inter-articular (IA) delivery, has demonstrated modest success for treatment of Spondyloarthritis (SpA), Rheumatoid Arthritis (RA), and osteoarthritis. However, IA delivery is not the treatment route of choice due to rapid clearance from the site of administration. Elastin-like polypeptides (ELPs) are reported to undergo phase transition; forming reversible aggregates above their transition temperature, which when injected into IA space have a 25-fold longer half-life compared to the soluble form. Here, we fused an ELP repeat to the C-terminus of each heavy chain of an anti-TNFα monoclonal antibody (mAb) and provide detailed characterization of the fusion IgG molecule. Expression and purification yielded homogenous protein confirmed by gels, hydrophobic-interaction chromatography, Capilary Electrophoresis (CE), Mass Spectrometry (MS), and analytical ultracentrifugation. The ELPs altered hydrophobicity and pI of the parent mAb and new elastic properties were imparted to the molecule; forming large stable complexes with a hydrodynamic radius of 40 nm above 39°C that dissociated into soluble, active monomer below 37°C. The fusion mAb retained its affinity and ability to neutralize TNFα as determined by surface plasmon resonance and cell-based assay, respectively, with equal potency to unmodified anti-TNFα mAb. Differential-scanning calorimetry studies show stabilization of adjacent CH2 and CH3 domains in the fusion molecule and the aggregated molecule was found to be fully functional after 7 days at 37°C. For the first time, we reveal architecture of an ELP-fusion mAb and binding to antigen using single-particle-transmission-electron microscopy. Unstructured ELP was visualized at the C-terminus and binding to antigen was shown at the N-terminus. Collectively, these studies indicate that it is possible to impart elastic properties to a monoclonal antibody while retaining purity, stability, and ability to effectively bind and neutralize antigen.

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Decline-diseases are complex and becoming increasingly problematic to tree health globally. Acute Oak Decline (AOD) is characterized by necrotic stem lesions and galleries of the bark-boring beetle, Agrilus biguttatus, and represents a serious threat to oak. Although multiple novel bacterial species and Agrilus galleries are associated with AOD lesions, the causative agent(s) are unknown. The AOD pathosystem therefore provides an ideal model for a systems-based research approach to address our hypothesis that AOD lesions are caused by a polymicrobial complex. Here we show that three bacterial species, Brenneria goodwinii, Gibbsiella quercinecans and Rahnella victoriana, are consistently abundant in the lesion microbiome and possess virulence genes used by canonical phytopathogens that are expressed in AOD lesions. Individual and polyspecies inoculations on oak logs and trees demonstrated that B. goodwinii and G. quercinecans cause tissue necrosis and, in combination with A. biguttatus, produce the diagnostic symptoms of AOD. We have proved a polybacterial cause of AOD lesions, providing new insights into polymicrobial interactions and tree disease. This work presents a novel conceptual and methodological template for adapting Koch’s postulates to address the role of microbial communities in disease.

We report a Dalitz plot analysis of charmless hadronic decays of charged $B$ mesons to the final state $K_{\rm S}^{0}\pi^{+}\pi^{0}$ using the full BaBar dataset of $470.9\pm2.8$ million $B\bar{B}$ events collected at the $\Upsilon(4S)$ resonance. We measure the overall branching fraction and $CP$ asymmetry to be ${\cal B}\left(B^{+}\to K^{0}\pi^{+}\pi^{0}\right) = \left(45.9 \pm 2.6 \pm 3.0^{+8.6}_{-0.0}\right)\times10^{-6}$ and $A_{CP}\left(B^{+}\to K^{0}\pi^{+}\pi^{0}\right) = 0.07 \pm 0.05 \pm 0.03^{+0.02}_{-0.03}$, where the uncertainties are statistical, systematic, and due to the signal model, respectively. This is the first measurement of the branching fraction for $B^{+} \to K^{0}\pi^{+}\pi^{0}$. We find first evidence of a $CP$ asymmetry in $B^{+}\to K^{*}(892)^{+}\pi^{0}$ decays: $A_{CP}\left(B^{+}\to K^{*}(892)^{+}\pi^{0}\right)=-0.52 \pm 0.14 \pm 0.04^{+0.04}_{-0.02}$. The significance of this asymmetry, including systematic and model uncertainties, is $3.4$ standard deviations. We also measure the branching fractions and $CP$ asymmetries for three other intermediate decay modes.

The number of emerging tree diseases has increased rapidly in recent times, with severe environmental and economic consequences. Systematic regulatory surveys to detect and establish the distribution of pests are crucial for successful management efforts, but resource-intensive and costly. Volunteers who identify potential invasive species can form an important early warning network in tree health; however, what these data can tell us and how they can be best used to inform and direct official survey effort is not clear. Here, we use an extensive dataset on acute oak decline (AOD) as an opportunity to ask how verified data received from the public can be used. Information on the distribution of AOD was available as (i) systematic regulatory surveys conducted throughout England and Wales, and (ii) ad hoc sightings reported by landowners, land managers and members of the public (i.e. ‘self-reported’ cases). By using the available self-reported cases at the design stage, the systematic survey could focus on defining the boundaries of the affected area. This maximized the use of available resources and highlights the benefits to be gained by developing strategies to enhance volunteer efforts in future programmes.

This study utilized energy simulation in support of a forensic pathology time-of-death analysis for a corpse discovered in a single-family residence two years prior to the study. In order to produce an accurate estimate of the interior temperature profile at the time of death, a thermal model was constructed using EnergyPlus and calibrated using environmental monitoring data from the site. The calibration methods used in the study draw from several precedents and are presented in detail. The thermal model was able to predict the temperature in the room of interest within 1.4°C (2.5°F) with 90% confidence. This model was then altered to account for known differences between the monitoring period and the period of interest, and used to predict what the temperature profile had been at the time of death. This study adds to a small body of work that compares simulated to measured performance data for unconditioned spaces, which should have a growing relevance as building energy performance simulation tools are used to model passive strategies.

Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene Oncotype DX approximation algorithm was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER)-positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P

Virtual reality simulation is a promising alternative to training surgical residents outside the operating room. It is also a useful aide to anatomic study, residency training, surgical rehearsal, credentialing, and recertification. Surgical simulation is based on a virtual reality with varying degrees of immersion and realism. Simulators provide a no-risk environment for harmless and repeatable practice. Virtual reality has three main components of simulation: graphics/volume rendering, model behavior/tissue deformation, and haptic feedback. The challenge of accurately simulating the forces and tactile sensations experienced in neurosurgery limits the sophistication of a virtual simulator. The limited haptic feedback available in minimally invasive neurosurgery makes it a favorable subject for simulation. Virtual simulators with realistic graphics and force feedback have been developed for ventriculostomy, intraventricular surgery, and transsphenoidal pituitary surgery, thus allowing preoperative study of the individual anatomy and increasing the safety of the procedure. The authors also present experiences with their own virtual simulation of endoscopic third ventriculostomy.

BreastPRS is a suite of complimentary gene signatures that resolves several of the significant limitations of current gene expression signatures in breast cancer. Notably, BreastPRS can accurately provide prognosis prediction, histological grade resolution and molecular subtyping of invasive breast cancer. The 200-gene prognostic component was previously validated in an independent retrospective series of 1,016 previously published fresh-frozen breast cancers and has now been applied to data from over 6,000 patients. In part one of this study, a meta-analysis of 890 previously published neoadjuvant treated breast cancer patients was performed. A highly significant association was observed between prognosis risk group (high vs low), continuous risk score (0–100), and a patient's probability of achieving complete pathologic response with neoadjuvant chemotherapy. Data from both fine-needle-aspirate and biopsy tissue were included in the cohort. Notably, fewer than 10% of patients with the lowest (most favorable) pre-treatment BreastPRS prognosis scores achieve pCR. Probability of pCR was observed to increase steadily to over 40% for individuals with the highest prognosis scores. In part two of the analysis, 50 paired fresh-frozen and FFPE tumors were profiled on whole genome microarrays containing all three signatures, in a CLIA-certified high throughput genomics laboratory. Results will be presented that highlight the consistency of the prognosis, grade and subtype signatures in clinically relevant FFPE tissue. This suggests that BreastPRS, in addition to identifying 10-year recurrence risk, histologic grade and molecular subtype, may enable physicians to determine a patients benefit from receiving neoadjuvant chemotherapy with more granularity than current clinical or genomic methods. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-05.

Many medicinal chemistry-relevant structures and core scaffolds tend toward geometric planarity, which hampers the optimization of physicochemical properties desirable in drug-like molecules. As challenging drug target classes emerge, the exploitation of molecular three-dimensionality in lead optimization is becoming increasingly important. While recent interest has emphasized the importance of enhanced three-dimensionality in molecular fragment designs, the extent to which this is required in core scaffolds remains unclear.Three computational methods, Scaffold Tree deconstruction, Synthetic Disconnection Rules retrosynthetic deconstruction and virtual library enumeration, are applied, together with the descriptors plane of best fit and principal moments of inertia, to investigate the origins of three-dimensionality in drug-like molecules.This study informs on the stage at which molecular three-dimensionality should be considered in drug design.