Data Visualization on Global Trends on Cancer Incidence An Application of IBM Watson Analytics

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across over a hundred of cancer registries worldwide. In this study, we present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. We included 26 cancers from different geographic regions. An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, we can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is assessable by any computer connected to the Internet

Improving Potato Stress Tolerance and Tuber Yield Under a Climate Change Scenario – A Current Overview

Global climate change in the form of extreme heat and drought poses a major challenge to sustainable crop production by negatively affecting plant performance and crop yield. Such negative impact on crop yield is likely to be aggravated in future because continued greenhouse gas emissions will cause further rise in temperature leading to increased evapo-transpiration and drought severity, soil salinity as well as insect and disease threats. This has raised a major challenge for plant scientists on securing global food demand, which urges an immediate need to enhance the current yield of major food crops by two-fold to feed the increasing population. As a fourth major food crop, enhancing potato productivity is important for food security of an increasing population. However, potato plant is highly prone to high temperature, drought, soil salinity, as well as insect and diseases. In order to maintain a sustainable potato production, we must adapt our cultivation practices and develop stress tolerant potato cultivars that are appropriately engineered for changing environment. Yet the lack of data on the underlying mechanisms of potato plant resistance to abiotic and biotic stress and the ability to predict future outcomes constitutes a major knowledge gap. It is a challenge for plant scientists to pinpoint means of improving tuber yield under increasing CO2, high temperature and drought stress including the changing patterns of pest and pathogen infestations. Understanding stress-related physiological, biochemical and molecular processes is crucial to develop screening procedures for selecting crop cultivars that can better adapt to changing growth conditions. Elucidation of such mechanism may offer new insights into the identification of specific characteristics that may be useful in breeding new cultivars aimed at maintaining or even enhancing potato yield under changing climate. This paper discusses the recent progress on the mechanism by which potato plants initially sense the changes in their surrounding CO2, temperature, water status, soil salinity and consequently respond to these changes at the molecular, biochemical and physiological levels. We suggest that future research needs to be concentrated on the identification and characterization of signaling molecules and target genes regulating stress tolerance and crop yield potential

Modelling ocean acidification effects with life stage-specific responses alters spatiotemporal patterns of catch and revenues of American lobster, Homarus americanus

Ocean acidification (OA) affects marine organisms through various physiological and biological processes, yet our understanding of how these translate to large-scale population effects remains limited. Here, we integrated laboratory-based experimental results on the life history and physiological responses to OA of the American lobster, Homarus americanus, into a dynamic bioclimatic envelope model to project future climate change effects on species distribution, abundance, and fisheries catch potential. Ocean acidification effects on juvenile stages had the largest stage-specific impacts on the population, while cumulative effects across life stages significantly exerted the greatest impacts, albeit quite minimal. Reducing fishing pressure leads to overall increases in population abundance while setting minimum size limits also results in more higher-priced market-sized lobsters (> 1 lb), and could help mitigate the negative impacts of OA and concurrent stressors (warming, deoxygenation). However, the magnitude of increased effects of climate change overweighs any moderate population gains made by changes in fishing pressure and size limits, reinforcing that reducing greenhouse gas emissions is most pressing and that climate-adaptive fisheries management is necessary as a secondary role to ensure population resiliency. We suggest possible strategies to mitigate impacts by preserving important population demographics

Annex 6: Changing Ocean Impacts on the Key Forage Fish Species Arctic Cod in the Western Canadian Arctic – Linking Climate Model Projections to Subsistence Fisheries

This annex highlights the results of a study focusing on the potential impacts of ocean acidification and other climate- related stressors on marine species relevant for subsistence fisheries in the Western Arctic Bioregion. The study uses a knowledge co-production approach developed in the form of a multi-step process based on a combination of modelling and analysis tools including the Scientific Method and Indigenous Traditional Knowledge (Figure A6.1). Once all steps have been completed, uncertainties can be estimated and improvements can be made either with respect to the individual steps or to the linkages between them. The process can then be repeated, including those improvements to provide a revised assessment with reduced uncertainty ranges. The steps can be summarized as follows: (1) analyze past observed trends; (2) perform projection simulations with global and regional climate models, allowing trend estimates on 20–50 year timescales; (3) assess physiological responses and thresholds in marine species via literature research, Indigenous Traditional Knowledge, observations and focused laboratory experiments; (4) add trends, climate model projections and physiological response data to species distribution / habitat suitability and higher trophic level Ecosim/ Ecopath (see Section A6.3) models; (5) assess socio-economic impacts by applying bio-economic models, evaluating current fishery-economic activities, and discussion with communities/ community representatives; and (6) review law and governance. The latter addresses adaptation measures on global, regional and national scales. This annex describes the first application of the multi-step framework in the Western Arctic Bioregion. At this point in time all the required tools have been developed, but not all components have been adequately linked. For example, while higher resolution model projections are available for the area the habitat suitability and economic models are still driven by global climate models, the Ecopath model (see Section on The Beaufort Sea food web model) has not yet been run into the future and physiological responses are reflected in the higher trophic level models to a limited extent, if at all. In addition, while collaboration with local communities has been established (see Section on Community interests), Indigenous Traditional Knowledge has been included to a very limited extent. To summarize, this case study provides an assessment that includes all required tools, but limited linkages. It has a strong focus on uncertainty analyses and the identification of gaps in knowledge. Particular emphasis is given to the key forage fish species Arctic cod (Boreogadus saida), since climate model projections can be linked more directly to key forage species than to the (mostly) higher trophic level species harvested

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

BackgroundBasket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials.MethodsInnovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development.ResultsWe found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial.ConclusionInnovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods

Transcriptome Profiles of Contrasting Potato (Solanum tuberosum L.) Genotypes under Water Stress

Indexación: ScopusThe potato is susceptible to water stress at all stages of development. We examined four clones of tetraploid potato, Cardinal, Desirée, Clone 37 FB, and Mije, from the germplasm bank of the National Institute of Agricultural Research (INIA) in Chile. Water stress was applied by suspending irrigation at the beginning of tuberization. Stomatal conductance, and tuber and plant fresh and dry weight were used to categorize water stress tolerance. Cardinal had a high susceptibility to water stress. Desirée was less susceptible than Cardinal and had some characteristics of tolerance. Mije had moderate tolerance and Clone 37 FB had high tolerance. Differential gene expression in leaves from plants with and without water stress were examined using transcriptome sequencing. Water stress-susceptible Cardinal had the fewest differentially expressed genes at 101, compared to Desirée at 1867, Clone 37 FB at 1179, and Mije at 1010. Water stress tolerance was associated with upregulation of the expression of transcription factor genes and genes involved in osmolyte and polyamine biosynthesis. Increased expression of genes encoding late embryogenesis abundant (LEA) and dehydrin proteins along with decreased expression of genes involved in nitrate assimilation and amino acid metabolism were found for clones showing water stress tolerance. The results also show that a water deficit was associated with reduced biotic stress responses. Additionally, heat shock protein genes were differentially expressed in all clones except for highly susceptible Cardinal. Together, the gene expression study demonstrates variation in the molecular pathways and biological processes in response to water stress contributing to tolerance and susceptibility. © 2019 by the authors.https://www.mdpi.com/2073-4395/9/12/84

PiRamid: A compact Raspberry Pi imaging box to automate small-scale time-lapse digital analysis, suitable for laboratory and field use

Digital imaging permits the quantitation of many experiments, such as microbiological growth assays, but laboratory digital imaging systems can be expensive and too specialised. The Raspberry Pi camera platform makes automated, controlled imaging affordable with accessible customisation. When combined with open source software and open-source 3D printed hardware, the control over image quality and capture of this platform permits the rapid development of novel instrumentation. Here we present "PiRamid", a compact, portable, and inexpensive enclosure for autonomous imaging both in the laboratory and in the field. The modular three-piece 3D printed design makes it easy to incorporate different camera systems or lighting configurations (e.g., single wavelength LED for fluorescence). The enclosed design allows complete control of illumination unlike a conventional digital camera or smartphone, on a tripod or handheld, under ambient lighting. The stackable design permits rapid sample addition or camera focus adjustment, with a corresponding change in magnification and resolution. The entire unit is small enough to fit within a microbiological incubator, and cheap enough (∼£100) to scale out for larger parallel experiments. Simply, Python scripts fully automate illumination and image capture for small-scale experiments with an ∼110×85 mm area at 70-90 µm resolution. We demonstrate the versatility of PiRamid by capturing time-resolved, quantitative image data for a wide range of assays. Bacterial growth kinetics was captured for conventional microbiology (agar Petri dishes), 3D printed custom microbiology labware and microfluidic microbiology. To illustrate application beyond microbiology, we demonstrate time-lapse imaging of crystal growth and degradation of salad leaves. Minor modifications permit epi-illumination by addition of a LED ring to the camera module. We conclude that PiRamid permits inexpensive digital capture and quantitation of a wide range of experiments by time-lapse imaging to simplify both laboratory and field imaging

Intraspecific trait variability is a key feature underlying high Arctic plant community resistance to climate warming

In the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. The slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here we compare taxonomic and functional community compositional responses to a long-term (17-year) warming experiment in Svalbard, Norway, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by 12 size- and leaf economics-related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and mass increased and leaf δ15N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting the functional resilience of those communities, dominated by long-lived plants, due to either phenotypic plasticity or genotypic variation, which most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO2 fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long-term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.publishedVersio