Automaattinen rakenteiden etsintä kaukokartoitusdatasta

An objective method for determining the natural state of a mire is needed to preserve mire biodiversity in Finland. Ditches and roads are important descriptors of the natural state, so the objective of this thesis is to detect these structures in mire surroundings. A digital terrain model created from LiDAR data is used for ditch classification and orthophotographs and various LiDAR data are used to detect roads. The classification is done with logistic regression classifier which selects best features for classification from a large feature set. In ditch detection polynomial modeling is used to connect broken segments. Artificial drainage networks were detected well with the method and polynomial modeling improved the results. The percentage of found ditch points from all ditch points was 90.51 before polynomial modeling and 97.27 after. The road detection accuracy did not correspond to values obtained from ditch detection. Yet the ditch detection results indicate that logistic regression classification is a suitable method for this application. For successful classification the feature set needs to be large enough and the training set has to be comprehensive. Artificial drainage network information will later be used in determining the extent of mire drainage and modeling waterflow patterns

Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology. Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology. In this interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins. Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment

Yield, Quality and Nitrogen Use of Forage Maize under Different Nitrogen Application Rates in Two Boreal Locations

Research focusing on the nitrogen (N) application and N use of forage maize (Zea mays L.) in the boreal region is either limited or non-existent. The aim of this study was to investigate the response of yield, quality and N recovery efficiency (NRE) of forage maize to an increase in the N application rate and different climatic conditions in two locations in Finland. The field experiment was conducted in southern (Helsinki; 60° N) and central (Maaninka; 63° N) Finland in 2019 and 2020. Dry matter (DM) yield, forage quality and NRE were determined for N application rates of 100, 150 and 200 N kg ha−1. The DM yield was similar to all studied N application rates. Moreover, there were no marked differences in the studied forage quality traits or the NRE following the N application rates. However, the NRE of maize was generally low at 45%. The current study recommends a N application rate of 100–150 N kg ha−1 for forage maize in the boreal region. There is no need to increase the N application from current recommendations since climate conditions seem to limit the growth, development and NRE of forage maize. The observed low NRE of forage maize warrants further research in the future

Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Background Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology to aid cancer research. Methods Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology. Results In our interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects, which is not possible in the traditional 2D-image view used in digital pathology. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model. Conclusions Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins.</p

Yield, Quality and Nitrogen Use of Forage Maize under Different Nitrogen Application Rates in Two Boreal Locations

Research focusing on the nitrogen (N) application and N use of forage maize (Zea mays L.) in the boreal region is either limited or non-existent. The aim of this study was to investigate the response of yield, quality and N recovery efficiency (NRE) of forage maize to an increase in the N application rate and different climatic conditions in two locations in Finland. The field experiment was conducted in southern (Helsinki; 60° N) and central (Maaninka; 63° N) Finland in 2019 and 2020. Dry matter (DM) yield, forage quality and NRE were determined for N application rates of 100, 150 and 200 N kg ha−1. The DM yield was similar to all studied N application rates. Moreover, there were no marked differences in the studied forage quality traits or the NRE following the N application rates. However, the NRE of maize was generally low at 45%. The current study recommends a N application rate of 100–150 N kg ha−1 for forage maize in the boreal region. There is no need to increase the N application from current recommendations since climate conditions seem to limit the growth, development and NRE of forage maize. The observed low NRE of forage maize warrants further research in the future

Vaikuttavuuden tiekartta käytännön johtamiseen

Pirkanmaan sairaanhoitopiirissä laadittiin tiekartta, jonka avulla vaikuttavuuden tavoite viedään käytännön johtamiseen.publishedVersio

New national and regional biological records for Finland 5. Contributions to agaricoid and ascomycetoid taxa of fungi 4

One genera of agaricoid fungi (Basidiomycota): Romagnesiella and 12 species are reported as new to Finland: Agaricus macrocarpus, Arrhenia obatra, Arrhenia obscurata, Arrhenia rigidipes, Coprinellus brevisetulosus, Coprinus candidatus, Entoloma plebejum, Hydnum vesterholtii, Inocybe phaeocystidiosa, Mycena clavata, Omphalina arctica and Romagnesiella clavus. Two genera of ascomycetoid fungi (Ascomycota): Strossmayeria, Phaeomollisia and 8 species are reported as new to Finland: Arachnopeziza delicatula, Hyaloscypha diabolica, Hyalopeziza cf. tianschanica, Phaeomollisia piceae, Phialina pseudopuberula, Sphaeropezia hepaticarum, Strossmayeria basitricha and Trichopeziza subsulphurea. Information of species recently published elsewhere: Cortinarius angustisporus, C. cacaodiscus, C. caesioarmeniacus, C. centrirufus, C. crassisporus, C. cruentiphyllus, C. davemallochii, C. ferrugineovelatus, C. furvus, C. fuscescens, C. murinascens, C. privignipallens, C. pseudofervidus, C. roseivelatus, C. roseocastaneus, C. subbrunneoideus, C. subexitiosus, C. squamivenetus, C. uraceisporus, Hebeloma eburneum, H. salicicola, Hygrophorus exiguus and Psathyrella fennoscandica is brought here together. New records of little collected and rare taxa Coprinopsis patouillardii, Cuphophyllus cinerellus, Galerina perplexa, Galerina pruinatipes, Gorgoniceps hypothallosa, Inocybe boreocarelica, Marasmius setosus, Psathyrella potteri, Psathyrella tenuicula, Russula adulterina, Russula pyriodora, Scutellinia trechispora, Sowerbyella imperialis and Volvariella murinella are also listed. Corrections of previous information are given on: Cortinarius angulosus (under C. duristipes), Coprinopsis patouillardii and Psathyrella potteri

Outcome from Complicated versus Uncomplicated Mild Traumatic Brain Injury

Objective. To compare acute outcome following complicated versus uncomplicated mild traumatic brain injury (MTBI) using neurocognitive and self-report measures. Method. Participants were 47 patients who presented to the emergency department of Tampere University Hospital, Finland. All completed MRI scanning, self-report measures, and neurocognitive testing at 3-4 weeks after injury. Participants were classified into the complicated MTBI or uncomplicated MTBI group based on the presence/absence of intracranial abnormality on day-of-injury CT scan or 3-4 week MRI scan. Results. There was a large statistically significant difference in time to return to work between groups. The patients with uncomplicated MTBIs had a median of 6.0 days (IQR = 0.75–14.75, range = 0–77) off work compared to a median of 36 days (IQR = 13.5–53, range = 3–315) for the complicated group. There were no significant differences between groups for any of the neurocognitive or self-report measures. There were no differences in the proportion of patients who (a) met criteria for ICD-10 postconcussional disorder or (b) had multiple low scores on the neurocognitive measures. Conclusion. Patients with complicated MTBIs took considerably longer to return to work. They did not perform more poorly on neurocognitive measures or report more symptoms, at 3-4 weeks after injury compared to patients with uncomplicated MTBIs.Hindaw

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting