Browsing by Subject "classification"

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  • Leppäniemi, A.; Tolonen, M.; Tarasconi, A.; Segovia-Lohse, H.; Gamberini, E.; Kirkpatrick, A.W.; Ball, C.G.; Parry, N.; Sartelli, M.; Wolbrink, D.; Van Goor, H.; Baiocchi, G.; Ansaloni, L.; Biffl, W.; Coccolini, F.; Di Saverio, S.; Kluger, Y.; Moore, E.; Catena, F. (2019)
    Although most patients with acute pancreatitis have the mild form of the disease, about 20-30% develops a severe form, often associated with single or multiple organ dysfunction requiring intensive care. Identifying the severe form early is one of the major challenges in managing severe acute pancreatitis. Infection of the pancreatic and peripancreatic necrosis occurs in about 20-40% of patients with severe acute pancreatitis, and is associated with worsening organ dysfunctions. While most patients with sterile necrosis can be managed nonoperatively, patients with infected necrosis usually require an intervention that can be percutaneous, endoscopic, or open surgical. These guidelines present evidence-based international consensus statements on the management of severe acute pancreatitis from collaboration of a panel of experts meeting during the World Congress of Emergency Surgery in June 27-30, 2018 in Bertinoro, Italy. The main topics of these guidelines fall under the following topics: Diagnosis, Antibiotic treatment, Management in the Intensive Care Unit, Surgical and operative management, and Open abdomen. © 2019 The Author(s).
  • Kotamäki, Niina; Järvinen, Marko; Kauppila, Pirkko; Korpinen, Samuli; Lensu, Anssi; Malve, Olli; Mitikka, Sari; Silander, Jari; Kettunen, Juhani (Springer, 2019)
    Environmental Monitoring Assessment 191, 318 (2019)
    The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying the uncertainties in the status class estimation. We estimated the temporal and spatial variance components, as well as the effect of sampling allocation to the precision and confidence of chlorophyll-a and total phosphorus. Our results suggest that almost 70% of the lake and coastal waterbodies, and 27% of the river waterbodies, were classified without sufficient confidence in these variables. On the other hand, many of the waterbodies produced unnecessary precise metric means. Thus, reallocation of sampling effort is needed. Our results show that, even though the studied variables are among the most monitored status metrics, the unexplained variation is still high. Combining multiple data sets and using fixed covariates would improve the modeling performance. Our study highlights that ongoing monitoring programs should be evaluated more systematically, and the information from the statistical uncertainty analysis should be brought concretely to the decision-making process.
  • Mandelblum, Jorge; Fischer, Naomi; Achiron, Asaf; Goldberg, Mordechai; Tuuminen, Raimo; Zunz, Eran; Spierer, Oriel (2020)
    Background: The aim of this study was to evaluate whether a simplified pre-operative nuclear classification score (SPONCS) was valid, both for clinical trials and real-world settings. Methods: Cataract classification was based on posterior nuclear color: 0 (clear), 1 (subcapsular/posterior cataract with clear nucleus), 2 (mild "green nucleus" with plus sign for yellow reflection of the posterior cortex), 3 (medium "yellow nucleus" with plus sign for brown/red posterior cortex reflection), 4 (advanced with 4 being "red/brown nucleus" and 4+ white nucleus), and 5 (hypermature/Morgagnian nucleus). Inter- and intra-observer validity was assessed by 30 Ophthalmologists for 15 cataract cases. The reliability of the cataract grading score in a surgical setting was evaluated. Correlation of nuclear scores was compared with phacoemulsification cumulative dissipated energy (CDE) in 596 patients. Results: Analysis of mean intra-observer Cohen kappa agreement was 0.55 with an inter-observer score of 0.54 for the first assessment and 0.49 for the repeat assessment one week later. When evaluating results by nuclear color alone, there was a substantial agreement for both the intra-observer (0.70) and inter-observer parameters: 0.70 for the first test, and 0.66 on repetition with randomization of the cases after a week. CDE levels were found to be significantly different between all SPONCS score groups (p <0.001), with a lower CDE related to a lower SPONCS score. A strong correlation was found between the SPONCS score and CDE (Spearman ' s rho = 0.8, p <0.001). Conclusion: This method of grading cataract hardness is both simple and repeatable. This system can be easily incorporated in randomized controlled trials to lower bias and confounding effects regarding nuclear density along with application in the clinical setting.
  • Yu, Xiaowei; Litkey, Paula; Hyyppa, Juha; Holopainen, Markus; Vastaranta, Mikko (2014)
  • Ärje, Johanna; Melvad, Claus; Jeppesen, Mads Rosenhoj; Madsen, Sigurd Agerskov; Raitoharju, Jenni; Rasmussen, Maria Strandgård; Iosifidis, Alexandros; Tirronen, Ville; Gabbouj, Moncef; Meissner, Kristian; Hoye, Toke Thomas (British Ecological Society, 2020)
    Methods in Ecology and Evolution 11 8 (2020)
    1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. 2. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate sample sorting, specimen identification and biomass estimation. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species which is then used to test classification accuracy, that is, how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 convolutional neural networks for the classification task. 3. The results for the initial dataset are very promising as we achieved an average classification accuracy of 0.980. While classification accuracy is high for most species, it is lower for species represented by less than 50 specimens. We found significant positive relationships between mean area of specimens derived from images and their dry weight for three species of Diptera. 4. The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.
  • Kokko, Jan (Helsingin yliopisto, 2019)
    In this thesis we present a new likelihood-free inference method for simulator-based models. A simulator-based model is a stochastic mechanism that specifies how data are generated. Simulator-based models can be as complex as needed, but they must allow exact sampling. One common difficulty with simulator-based models is that learning model parameters from observed data is generally challenging, because the likelihood function is typically intractable. Thus, traditional likelihood-based Bayesian inference is not applicable. Several likelihood-free inference methods have been developed to perform inference when a likelihood function is not available. One popular approach is approximate Bayesian computation (ABC), which relies on the fundamental principle of identifying parameter values for which summary statistics of simulated data are close to those of observed data. However, traditional ABC methods tend have high computational cost. The cost is largely due to the need to repeatedly simulate data sets, and the absence of knowledge of how to specify the discrepancy between the simulated and observed data. We consider speeding up the earlier method likelihood-free inference by ratio estimation (LFIRE) by replacing the computationally intensive grid evaluation with Bayesian optimization. The earlier method is an alternative to ABC that relies on transforming the original likelihood-free inference problem into a classification problem that can be solved using machine learning. This method is able to overcome two traditional difficulties with ABC: it avoids using a threshold value that controls the trade-off between computational and statistical efficiency, and combats the curse of dimensionality by offering an automatic selection of relevant summary statistics when using a large number of candidates. Finally, we measure the computational and statistical efficiency of the new method by applying it to three different real-world time series models with intractable likelihood functions. We demonstrate that the proposed method can reduce the computational cost by some orders of magnitude while the statistical efficiency remains comparable to the earlier method.
  • Agnelli, J. P.; Çöl, A.; Lassas, M.; Murthy, R.; Santacesaria, M.; Siltanen, S. (2020)
    Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).
  • Ma, Liang; Chen, Zehua; Huang, Da Wei; Cisse, Ousmane H.; Rothenburger, Jamie L.; Latinne, Alice; Bishop, Lisa; Blair, Robert; Brenchley, Jason M.; Chabe, Magali; Deng, Xilong; Hirsch, Vanessa; Keesler, Rebekah; Kutty, Geetha; Liu, Yueqin; Margolis, Daniel; Morand, Serge; Pahar, Bapi; Peng, Li; Van Rompay, Koen K. A.; Song, Xiaohong; Song, Jun; Sukura, Antti; Thapar, Sabrina; Wang, Honghui; Weissenbacher-Lang, Christiane; Xu, Jie; Lee, Chao-Hung; Jardine, Claire; Lempicki, Richard A.; Cushion, Melanie T.; Cuomo, Christina A.; Kovacs, Joseph A. (2020)
    Pneumocystis, a major opportunistic pathogen in patients with a broad range of immunodeficiencies, contains abundant surface proteins encoded by a multicopy gene family, termed the major surface glycoprotein (Msg) gene superfamily. This superfamily has been identified in all Pneumocystis species characterized to date, highlighting its important role in Pneumocystis biology. In this report, through a comprehensive and in-depth characterization of 459 msg genes from 7 Pneurnocystis species, we demonstrate, for the first time, the phylogeny and evolution of conserved domains in Msg proteins and provide a detailed description of the classification, unique characteristics, and phylogenetic relatedness of five Msg families. We further describe, for the first time, the relative expression levels of individual msg families in two rodent Pneumocystis species, the substantial variability of the msg repertoires in P. coda from laboratory and wild rats, and the distinct features of the expression site for the classic msg genes in Pneumocystis from 8 mammalian host species. Our analysis suggests multiple functions for this superfamily rather than just conferring antigenic variation to allow immune evasion as previously believed. This study provides a rich source of information that lays the foundation for the continued experimental exploration of the functions of the Msg superfamily in Pneumocystis biology. IMPORTANCE Pneumocystis continues to be a major cause of disease in humans with immunodeficiency, especially those with HIV/AIDS and organ transplants, and is being seen with increasing frequency worldwide in patients treated with immunode-pleting monoclonal antibodies. Annual health care associated with Pneumocystis pneumonia costs similar to$475 million dollars in the United States alone. In addition to causing overt disease in immunodeficient individuals, Pneumocystis can cause subclinical infection or colonization in healthy individuals, which may play an important role in species preservation and disease transmission. Our work sheds new light on the diversity and complexity of the msg superfamily and strongly suggests that the versatility of this superfamily reflects multiple functions, including antigenic variation to allow immune evasion and optimal adaptation to host environmental conditions to promote efficient infection and transmission. These findings are essential to consider in developing new diagnostic and therapeutic strategies.
  • Fleischer, Thomas; Klajic, Jovana; Aure, Miriam Ragle; Louhimo, Riku; Pladsen, Arne V.; Ottestad, Lars; Touleimat, Nizar; Laakso, Marko; Halvorsen, Ann Rita; Alnaes, Grethe I. Grenaker; Riis, Margit L. H.; Helland, Aslaug; Hautaniemi, Sampsa; Lonning, Per Eystein; Naume, Bjorn; Borresen-Dale, Anne-Lise; Tost, Joerg; Kristensen, Vessela N. (2017)
    Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.
  • Krali, Olga; Palle, Josefine; Backlin, Christofer L.; Abrahamsson, Jonas; Noren-Nyström, Ulrika; Hasle, Henrik; Jahnukainen, Kirsi; Jónsson, Olafur Gisli; Hovland, Randi; Lausen, Birgitte; Larsson, Rolf; Palmqvist, Lars; Staffas, Anna; Zeller, Bernward; Nordlund, Jessica (2021)
    Pediatric acute myeloid leukemia (AML) is a heterogeneous disease composed of clinically relevant subtypes defined by recurrent cytogenetic aberrations. The majority of the aberrations used in risk grouping for treatment decisions are extensively studied, but still a large proportion of pediatric AML patients remain cytogenetically undefined and would therefore benefit from additional molecular investigation. As aberrant epigenetic regulation has been widely observed during leukemogenesis, we hypothesized that DNA methylation signatures could be used to predict molecular subtypes and identify signatures with prognostic impact in AML. To study genome-wide DNA methylation, we analyzed 123 diagnostic and 19 relapse AML samples on Illumina 450k DNA methylation arrays. We designed and validated DNA methylation-based classifiers for AML cytogenetic subtype, resulting in an overall test accuracy of 91%. Furthermore, we identified methylation signatures associated with outcome in t(8;21)/RUNX1-RUNX1T1, normal karyotype, and MLL/KMT2A-rearranged subgroups (p < 0.01). Overall, these results further underscore the clinical value of DNA methylation analysis in AML.
  • Silén, Yasmina; Sipilä, Pyry N; Raevuori, Anu; Mustelin, Linda; Marttunen, Mauri; Kaprio, Jaakko; Keski-Rahkonen, Anna (2020)
    OBJECTIVE: We aimed to assess the lifetime prevalence, 10-year incidence, and peak periods of onset for eating disorders as defined by the Fifth Diagnostic and Statistical Manual of Mental Disorders (DSM-5) among adolescents and young adults born in the 1980s in Finland. METHOD: Virtually all Finnish twins born in 1983-1987 (n = 5,600) were followed prospectively from the age of 12 years. A subsample of participants (n = 1,347) was interviewed using a semi-structured diagnostic interview in their early twenties. RESULTS: The prevalence of lifetime DSM-5 eating disorders was 17.9% for females and 2.4% for males (pooled across genders, 10.5%). The estimated lifetime prevalences for females and males, respectively, were 6.2 and 0.3% for anorexia nervosa (AN), 2.4 and 0.16% for bulimia nervosa (BN), 0.6 and 0.3% for binge-eating disorder (BED), 4.5 and 0.16% for other specified feeding or eating disorder (OSFED), and 4.5 and 1.6% for unspecified feeding or eating disorder (UFED). Among females, the prevalence of OSFED subcategories was as follows: atypical AN 2.1%, purging disorder 1.3%, BED of low frequency/limited duration 0.7%, and BN of low frequency/limited duration 0.4%. The 10-year incidence rate of eating disorders was 1,700 per 100,000 person-years among females (peak age of onset 16-19 years) and 220 per 100,000 person-years among males. DISCUSSION: Eating disorders are a common public health concern among youth and young adults, affecting one in six females and one in 40 males. Adequate screening efforts, prevention, and interventions are urgently needed.
  • Pitkänen, Timo P.; Sirro, Laura; Häme, Lauri; Häme, Tuomas; Törmä, Markus; Kangas, Annika (ScienceDirect, 2020)
    International Journal of Applied Earth Observation and Geoinformation 86 (2020)
    The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency.
  • Latva-Käyrä, Petri (Helsingfors universitet, 2012)
    The intensity and frequency of insect outbreaks have increased in Finland in the last decades and they are expected to increase even further in the future due to global climate change. In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multitemporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine defoliation. The study area, Palokangas, is located in Ilomantsi district, Eastern-Finland and consists mainly even-aged Scots pine forests on relatively dry soils. Most of the forests in the area are young or middle-aged managed forests. The study material was comprised of multi-temporal ERS-2 and ENVISAT synthetic aperture radar (SAR) data. The images had been taken between the years 2001 and 2008. The field data consisted 16 sample plots which had been measured seven times between the years 2002 and 2009. In addition, eight sample plots were added afterwards to places which were known to have had cuttings during the study period. Three methods were tested to estimate Scots pine defoliation: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, it was assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, >20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates.
  • Cajander, A. K. (Suomen metsätieteellinen seura, 1949)
  • Kyrö, Minna (Helsingfors universitet, 2011)
    FTIR spectroscopy (Fourier transform infrared spectroscopy) is a fast method of analysis. The use of interferometers in Fourier devices enables the scanning of the whole infrared frequency region in a couple of seconds. There is no need to elaborate sample preparation when the FTIR spectrometer is equipped with an ATR accessory and the method is therefore easy to use. ATR accessory facilitates the analysis of various sample types. It is possible to measure infrared spectra from samples which are not suitable for traditional sample preparation methods. The data from FTIR spectroscopy is frequently combined with statistical multivariate analysis techniques. In cluster analysis the data from spectra can be grouped based on similarity. In hierarchical cluster analysis the similarity between objects is determined by calculating the distance between them. Principal component analysis reduces the dimensionality of the data and establishes new uncorrelated principal components. These principal components should preserve most of the variation of the original data. The possible applications of FTIR spectroscopy combined with multivariate analysis have been studied a lot. For example in food industry its feasibility in quality control has been evaluated. The method has also been used for the identification of chemical compositions of essential oils and for the detection of chemotypes in oil plants. In this study the use of the method was evaluated in the classification of hog's fennel extracts. FTIR spectra of extracts from different plant parts of hog's fennel were compared with the measured FTIR spectra of standard substances. The typical absorption bands in the FTIR spectra of standard substances were identified. The wave number regions of the intensive absorption bands in the spectra of furanocoumarins were selected for multivariate analyses. Multivariate analyses were also performed in the fingerprint region of IR spectra, including the wave number region 1785-725 cm-1. The aim was to classify extracts according to the habitat and coumarin concentration of the plants. Grouping according to habitat was detected, which could mainly be explained by coumarin concentrations as indicated by analyses of the wave number regions of the selected absorption bands. In these analyses extracts mainly grouped and differed by their total coumarin concentrations. In analyses of the wave number region 1785-725 cm-1 grouping according to habitat was also detected but this could not be explained by coumarin concentrations. These groupings may have been caused by similar concentrations of other compounds in the samples. Analyses using other wave number regions were also performed, but the results from these experiments did not differ from previous results. Multivariate analyses of second-order derivative spectra in the fingerprint region did not reveal any noticeable changes either. In future studies the method could perhaps be further developed by investigating narrower carefully selected wave number regions of second-order derivative spectra.
  • Guilbault, Emy Paulette; Renner, Ian; Mahony, Michael; Beh, Eric (2021)
    Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity. In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity. We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best-performing methods to a dataset of three frog species (Mixophyes). These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.
  • Jong, Monica; Jonas, Jost B.; Wolffsohn, James S.; Berntsen, David A.; Cho, Pauline; Clarkson-Townsend, Danielle; Flitcroft, Daniel I.; Gifford, Kate L.; Haarman, Annechien E. G.; Pardue, Machelle T.; Richdale, Kathryn; Sankaridurg, Padmaja; Tedja, Milly S.; Wildsoet, Christine F.; Bailey-Wilson, Joan E.; Guggenheim, Jeremy A.; Hammond, Christopher J.; Kaprio, Jaakko; MacGregor, Stuart; Mackey, David A.; Musolf, Anthony M.; Klaver, Caroline C. W.; Verhoeven, Virginie J. M.; Vitart, Veronique; Smith, Earl L. (2021)
    PURPOSE. The International Myopia Institute (IMI) Yearly Digest highlights new research considered to be of importance since the publication of the first series of IMI white papers. METHODS. A literature search was conducted for articles on myopia between 2019 and mid-2020 to inform definitions and classifications, experimental models, genetics, interventions, clinical trials, and clinical management. Conference abstracts from key meetings in the same period were also considered. RESULTS. One thousand articles on myopia have been published between 2019 and mid-2020. Key advances include the use of the definition of premyopia in studies currently under way to test interventions in myopia, new definitions in the field of pathologicmyopia, the role of new pharmacologic treatments in experimental models such as intraocular pressure-lowering latanoprost, a large meta-analysis of refractive error identifying 336 new genetic loci, new clinical interventions such as the defocus incorporated multisegment spectacles and combination therapy with low-dose atropine and orthokeratology (OK), normative standards in refractive error, the ethical dilemma of a placebo control group when myopia control treatments are established, reporting the physical metric of myopia reduction versus a percentage reduction, comparison of the risk of pediatric OK wear with risk of vision impairment in myopia, the justification of preventing myopic and axial length increase versus quality of life, and future vision loss. CONCLUSIONS. Large amounts of research in myopia have been published since the IMI 2019 white papers were released. The yearly digest serves to highlight the latest research and advances in myopia.
  • Huong Thi Thanh Nguyen; Trung Minh Doan; Tomppo, Erkki; McRoberts, Ronald E. (2020)
    Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.
  • Parsons, Michael T.; Tudini, Emma; Li, Hongyan; Hahnen, Eric; Wappenschmidt, Barbara; Feliubadaló, Lidia; Aalfs, Cora M.; Agata, Simona; Aittomäki, Kristiina; Alducci, Elisa; Alonso-Cerezo, María Concepción; Arnold, Norbert; Auber, Bernd; Austin, Rachel; Azzollini, Jacopo; Balmaña, Judith; Barbieri, Elena; Bartram, Claus R.; Blanco, Ana; Blümcke, Britta; Bonache, Sandra; Bonanni, Bernardo; Borg, Åke; Bortesi, Beatrice; Brunet, Joan; Bruzzone, Carla; Bucksch, Karolin; Cagnoli, Giulia; Caldés, Trinidad; Caliebe, Almuth; Caligo, Maria A.; Calvello, Mariarosaria; Capone, Gabriele L.; Caputo, Sandrine M.; Carnevali, Ileana; Carrasco, Estela; Caux-Moncoutier, Virginie; Cavalli, Pietro; Cini, Giulia; Clarke, Edward M.; Concolino, Paola; Cops, Elisa J.; Cortesi, Laura; Couch, Fergus J.; Darder, Esther; de la Hoya, Miguel; Dean, Michael; Debatin, Irmgard; del Valle, Jesús; Delnatte, Capucine; Derive, Nicolas; Diez, Orland; Ditsch, Nina; Domchek, Susan M.; Dutrannoy, Véronique; Eccles, Diana M.; Ehrencrona, Hans; Enders, Ute; Evans, D. Gareth; Faust, Ulrike; Felbor, Ute; Feroce, Irene; Fine, Miriam; Galvao, Henrique C.R.; Gambino, Gaetana; Gehrig, Andrea; Gensini, Francesca; Gerdes, Anne-Marie; Germani, Aldo; Giesecke, Jutta; Gismondi, Viviana; Gómez, Carolina; Gómez Garcia, Encarna B.; González, Sara; Grau, Elia; Grill, Sabine; Gross, Eva; Guerrieri-Gonzaga, Aliana; Guillaud-Bataille, Marine; Gutiérrez-Enríquez, Sara; Haaf, Thomas; Hackmann, Karl; Hansen, Thomas V.O.; Harris, Marion; Hauke, Jan; Heinrich, Tilman; Hellebrand, Heide; Herold, Karen N.; Honisch, Ellen; Horvath, Judit; Houdayer, Claude; Hübbel, Verena; Iglesias, Silvia; Izquierdo, Angel; James, Paul A.; Janssen, Linda A.M.; Jeschke, Udo; Kaulfuß, Silke; Keupp, Katharina; Kiechle, Marion; Kölbl, Alexandra; Krieger, Sophie; Kruse, Torben A.; Kvist, Anders; Lalloo, Fiona; Larsen, Mirjam; Lattimore, Vanessa L.; Lautrup, Charlotte; Ledig, Susanne; Leinert, Elena; Lewis, Alexandra L.; Lim, Joanna; Loeffler, Markus; López-Fernández, Adrià; Lucci-Cordisco, Emanuela; Maass, Nicolai; Manoukian, Siranoush; Marabelli, Monica; Matricardi, Laura; Meindl, Alfons; Michelli, Rodrigo D.; Moghadasi, Setareh; Moles-Fernández, Alejandro; Montagna, Marco; Montalban, Gemma; Monteiro, Alvaro N.; Montes, Eva; Mori, Luigi; Moserle, Lidia; Müller, Clemens R.; Mundhenke, Christoph; Naldi, Nadia; Nathanson, Katherine L.; Navarro, Matilde; Nevanlinna, Heli; Nichols, Cassandra B.; Niederacher, Dieter; Nielsen, Henriette R.; Ong, Kai-ren; Pachter, Nicholas; Palmero, Edenir I.; Papi, Laura; Pedersen, Inge Sokilde; Peissel, Bernard; Pérez-Segura, Pedro; Pfeifer, Katharina; Pineda, Marta; Pohl-Rescigno, Esther; Poplawski, Nicola K.; Porfirio, Berardino; Quante, Anne S.; Ramser, Juliane; Reis, Rui M.; Revillion, Françoise; Rhiem, Kerstin; Riboli, Barbara; Ritter, Julia; Rivera, Daniela; Rofes, Paula; Rump, Andreas; Salinas, Monica; Sánchez de Abajo, Ana María; Schmidt, Gunnar; Schoenwiese, Ulrike; Seggewiß, Jochen; Solanes, Ares; Steinemann, Doris; Stiller, Mathias; Stoppa-Lyonnet, Dominique; Sullivan, Kelly J.; Susman, Rachel; Sutter, Christian; Tavtigian, Sean V.; Teo, Soo H.; Teulé, Alex; Thomassen, Mads; Tibiletti, Maria Grazia; Tognazzo, Silvia; Toland, Amanda E.; Tornero, Eva; Törngren, Therese; Torres-Esquius, Sara; Toss, Angela; Trainer, Alison H.; van Asperen, Christi J.; van Mackelenbergh, Marion T.; Varesco, Liliana; Vargas-Parra, Gardenia; Varon, Raymonda; Vega, Ana; Velasco, Ángela; Vesper, Anne-Sophie; Viel, Alessandra; Vreeswijk, Maaike P.G.; Wagner, Sebastian A.; Waha, Anke; Walker, Logan C.; Walters, Rhiannon J.; Wang-Gohrke, Shan; Weber, Bernhard H.F.; Weichert, Wilko; Wieland, Kerstin; Wiesmüller, Lisa; Witzel, Isabell; Wöckel, Achim; Woodward, Emma R.; Zachariae, Silke; Zampiga, Valentina; Zeder-Göß, Christine; Investigators, KConFab; Lázaro, Conxi; De Nicolo, Arcangela; Radice, Paolo; Engel, Christoph; Schmutzler, Rita K.; Goldgar, David E.; Spurdle, Amanda B. (2019)
    Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.
  • Jaakkola, A (Aalto University, 2015)
    Aalto University publication series DOCTORAL DISSERTATIONS