| 1. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
- Arndt, D. S., et al.
(författare)
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STATE OF THE CLIMATE IN 2017
- 2018
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Ingår i: Bulletin of The American Meteorological Society - (BAMS). - : American Meteorological Society. - 0003-0007 .- 1520-0477. ; 99:8, s. S1-S310
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Forskningsöversikt (refereegranskat)
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| 3. |
- Tinetti, G., et al.
(författare)
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A chemical survey of exoplanets with ARIEL
- 2018
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Ingår i: Experimental Astronomy. - : Springer Science and Business Media LLC. - 0922-6435 .- 1572-9508. ; 46:1, s. 135-209
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Tidskriftsartikel (refereegranskat)abstract
- Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.
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| 4. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 6. |
- Achberger, Christine, 1968, et al.
(författare)
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State of the Climate in 2011
- 2012
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Ingår i: Bulletin of the American Meteorological Society. - 0003-0007. ; 93:7
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale climate patterns influenced temperature and weather patterns around the globe in 2011. In particular, a moderate-to-strong La Nina at the beginning of the year dissipated during boreal spring but reemerged during fall. The phenomenon contributed to historical droughts in East Africa, the southern United States, and northern Mexico, as well the wettest two-year period (2010-11) on record for Australia, particularly remarkable as this follows a decade-long dry period. Precipitation patterns in South America were also influenced by La Nina. Heavy rain in Rio de Janeiro in January triggered the country's worst floods and landslides in Brazil's history. The 2011 combined average temperature across global land and ocean surfaces was the coolest since 2008, but was also among the 15 warmest years on record and above the 1981-2010 average. The global sea surface temperature cooled by 0.1 degrees C from 2010 to 2011, associated with cooling influences of La Nina. Global integrals of upper ocean heat content for 2011 were higher than for all prior years, demonstrating the Earth's dominant role of the oceans in the Earth's energy budget. In the upper atmosphere, tropical stratospheric temperatures were anomalously warm, while polar temperatures were anomalously cold. This led to large springtime stratospheric ozone reductions in polar latitudes in both hemispheres. Ozone concentrations in the Arctic stratosphere during March were the lowest for that period since satellite records began in 1979. An extensive, deep, and persistent ozone hole over the Antarctic in September indicates that the recovery to pre-1980 conditions is proceeding very slowly. Atmospheric carbon dioxide concentrations increased by 2.10 ppm in 2011, and exceeded 390 ppm for the first time since instrumental records began. Other greenhouse gases also continued to rise in concentration and the combined effect now represents a 30% increase in radiative forcing over a 1990 baseline. Most ozone depleting substances continued to fall. The global net ocean carbon dioxide uptake for the 2010 transition period from El Nino to La Nina, the most recent period for which analyzed data are available, was estimated to be 1.30 Pg C yr(-1), almost 12% below the 29-year long-term average. Relative to the long-term trend, global sea level dropped noticeably in mid-2010 and reached a local minimum in 2011. The drop has been linked to the La Nina conditions that prevailed throughout much of 2010-11. Global sea level increased sharply during the second half of 2011. Global tropical cyclone activity during 2011 was well-below average, with a total of 74 storms compared with the 1981-2010 average of 89. Similar to 2010, the North Atlantic was the only basin that experienced above-normal activity. For the first year since the widespread introduction of the Dvorak intensity-estimation method in the 1980s, only three tropical cyclones reached Category 5 intensity level-all in the Northwest Pacific basin. The Arctic continued to warm at about twice the rate compared with lower latitudes. Below-normal summer snowfall, a decreasing trend in surface albedo, and above-average surface and upper air temperatures resulted in a continued pattern of extreme surface melting, and net snow and ice loss on the Greenland ice sheet. Warmer-than-normal temperatures over the Eurasian Arctic in spring resulted in a new record-low June snow cover extent and spring snow cover duration in this region. In the Canadian Arctic, the mass loss from glaciers and ice caps was the greatest since GRACE measurements began in 2002, continuing a negative trend that began in 1987. New record high temperatures occurred at 20 m below the land surface at all permafrost observatories on the North Slope of Alaska, where measurements began in the late 1970s. Arctic sea ice extent in September 2011 was the second-lowest on record, while the extent of old ice (four and five years) reached a new record minimum that was just 19% of normal. On the opposite pole, austral winter and spring temperatures were more than 3 degrees C above normal over much of the Antarctic continent. However, winter temperatures were below normal in the northern Antarctic Peninsula, which continued the downward trend there during the last 15 years. In summer, an all-time record high temperature of -12.3 degrees C was set at the South Pole station on 25 December, exceeding the previous record by more than a full degree. Antarctic sea ice extent anomalies increased steadily through much of the year, from briefly setting a record low in April, to well above average in December. The latter trend reflects the dispersive effects of low pressure on sea ice and the generally cool conditions around the Antarctic perimeter.
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| 7. |
- Ding, Ming, et al.
(författare)
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Dairy consumption, systolic blood pressure, and risk of hypertension : Mendelian randomization study
- 2017
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Ingår i: The BMJ. - : BMJ Publishing Group Ltd. - 1756-1833 .- 0959-8138. ; 356
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE To examine whether previous observed inverse associations of dairy intake with systolic blood pressure and risk of hypertension were causal. DESIGN Mendelian randomization study using the single nucleotide polymorphism rs4988235 related to lactase persistence as an instrumental variable. SETTING CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium. PARTICIPANTS Data from 22 studies with 171 213 participants, and an additional 10 published prospective studies with 26 119 participants included in the observational analysis. MAIN OUTCOME MEASURES The instrumental variable estimation was conducted using the ratio of coefficients approach. Using metaanalysis, an additional eight published randomized clinical trials on the association of dairy consumption with systolic blood pressure were summarized. RESULTS Compared with the CC genotype (CC is associated with complete lactase deficiency), the CT/TT genotype (TT is associated with lactose persistence, and CT is associated with certain lactase deficiency) of LCT-13910 (lactase persistence gene) rs4988235 was associated with higher dairy consumption (0.23 (about 55 g/day), 95% confidence interval 0.17 to 0.29) serving/day; P<0.001) and was not associated with systolic blood pressure (0.31, 95% confidence interval -0.05 to 0.68 mm Hg; P=0.09) or risk of hypertension (odds ratio 1.01, 95% confidence interval 0.97 to 1.05; P=0.27). Using LCT-13910 rs4988235 as the instrumental variable, genetically determined dairy consumption was not associated with systolic blood pressure (beta=1.35, 95% confidence interval -0.28 to 2.97 mm Hg for each serving/day) or risk of hypertension (odds ratio 1.04, 0.88 to 1.24). Moreover, meta-analysis of the published clinical trials showed that higher dairy intake has no significant effect on change in systolic blood pressure for interventions over one month to 12 months (intervention compared with control groups: beta=-0.21, 95% confidence interval -0.98 to 0.57 mm Hg). In observational analysis, each serving/day increase in dairy consumption was associated with -0.11 (95% confidence interval -0.20 to -0.02 mm Hg; P=0.02) lower systolic blood pressure but not risk of hypertension (odds ratio 0.98, 0.97 to 1.00; P=0.11). CONCLUSION The weak inverse association between dairy intake and systolic blood pressure in observational studies was not supported by a comprehensive instrumental variable analysis and systematic review of existing clinical trials.
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| 8. |
- Galozy, Alexander, 1991-
(författare)
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Mobile Health Interventions through Reinforcement Learning
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario. The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.
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| 9. |
- Ghoshal, Biraja, et al.
(författare)
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DeepHistoClass : A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning
- 2021
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Ingår i: Molecular & Cellular Proteomics. - : Elsevier. - 1535-9476 .- 1535-9484. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.
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| 10. |
- Ghoshal, Biraja, et al.
(författare)
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Estimating Uncertainty in Deep Learning for Reporting Confidence : An Application on Cell Type Prediction in Testes Based on Proteomics
- 2020
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Ingår i: Advances In Intelligent Data Analysis XVIII, IDA 2020. - Cham : Springer Nature. - 9783030445843 - 9783030445836 ; , s. 223-234
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Konferensbidrag (refereegranskat)abstract
- Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches.
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