Männyn- ja kuusentaimikoiden kehitys erilaisia metsänuudistamisketjuja käytettäessä

TutkimusartikkeliTutkimuksessa tarkasteltiin mänty- ja kuusitaimikoiden tiheyttä ja pituuskehitystä. Tutkimus perustui käytännön uudistusaloilta koko maan alueelta kerättyyn kertamittausaineistoon, joka käsitti 68 männyn- ja 19 kuusentaimikkoa. Mänty oli uudistettu luontaisesti, kylvetty tai istutettu tuoreelle ja kuivahkolle kankaalle ja kuusi oli istutettu lehtomaiselle ja tuoreelle kankaalle. Uudistusala oli joko muokkaamaton tai muokattu äestämällä, laikuttamalla, mätästämällä tai auraamalla. Joitakin uudistusaloja oli lisäksi kulotettu. Taimikot olivat mittaushetkellä 6–34 vuotta vanhoja ja valtapituudeltaan 2–11 m. §§ Aineistoon sovitettiin lineaarisia malleja selittämään pääpuulajin osalta sekä yksittäisen taimen pituutta, että taimikon keski- ja valtapituutta sekä runkolukua. Lisäksi tarkasteltiin siemen- ja vesasyntyisen lehtipuuston määrää. Erityisen kiinnostavia selittäviä tekijöitä olivat erilaiset muokkausmenetelmät. Keski- ja valtapituutta selittivät taimikon ikä, lämpösumma, pääpuulaji, uudistamismenetelmä ja kasvupaikka. Keskipituuteen ja runkoluun vaikuttivat myös erilaiset muokkausmenetelmät, mutta niillä ei ollut vaikutusta valtapituuteen. Maanmuokkaus lisäsi lehtipuuston määrää, mutta muokkausmenetelmät eivät eronneet merkitsevästi toisistaan. Yksittäisen taimen pituutta selitti syntytapa, asema muokkausjäljessä ja terveydentila. Metsikkötason mallit selittivät 48–66% runkoluvun ja 81–87% pituustunnusten vaihtelusta. Maanpinnan käsittelyistä kulotus, auraus ja mätästys nopeuttivat keskipituuden kehitystä äestetyn uudistusalan perustasoon verrattuna. Vesakon tiheys ei vaikuttanut merkitsevästi kasvatettavien taimien pituuskehitykseen, mutta vesakon pituus suhteessa kasvatettavan havupuun taimen pituuteen oli merkitsevä selittäjä.201

Sámi Relationship with the Land : what Does the Law Fail to Recognize?

The purpose of this article is to make an overview on how UN Human Rights Committee (HRC), a monitoring body of CCPR, articulates and protects Sámi culture and its values. The further aim of this writing is to discuss Sámi people’s relationship with the Land, its ontological basis and the failure of Finnish legislation to recognize crucial aspects of this relationship and inherently connected worldview.publishedVersio

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

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets

Spatial analysis of histology in 3D : quantification and visualization of organ and tumor level tissue environment

Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological assessments are performed from individual tissue sections, leading to the loss of three-dimensional context of the tissue. Yet, the tissue context and spatial determinants are critical in several pathologies, such as in understanding growth patterns of cancer in its local environment. Here, we develop computational methods for visualization and quantitative assessment of histopathological alterations in three dimensions. First, we reconstruct the 3D representation of the whole organ from serial sectioned tissue. Then, we proceed to analyze the histological characteristics and regions of interest in 3D. As our example cases, we use whole slide images representing hematoxylin-eosin stained whole mouse prostates in a Pten+/- mouse prostate tumor model. We show that quantitative assessment of tumor sizes, shapes, and separation between spatial locations within the organ enable characterizing and grouping tumors. Further, we show that 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology. Our results underline the heterogeneity in composition and cellular organization within individual tumors. As an example, we show how prostate tumors have nuclear density gradients indicating areas of tumor growth directions and reflecting varying pressure from the surrounding tissue. The methods presented here are applicable to any tissue and different types of pathologies. This work provides a proof-of-principle for gaining a comprehensive view from histology by studying it quantitatively in 3D.publishedVersionPeer reviewe

The effect of neural network architecture on virtual H&E staining : Systematic assessment of histological feasibility

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demonstrated highly accurate reproduction of histology, especially with increased network capacity, and demonstrated applicability to several tissues. We show that network architecture optimization can improve the image translation accuracy of virtual H&E staining, highlighting the potential of virtual staining in streamlining histopathological analysis.publishedVersionPeer reviewe

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

ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models

Saamelaisten oikeuksien toteutuminen: kansainvälinen oikeusvertaileva tutkimus

Saamelaisten oikeuksien eteenpäin vieminen Suomessa on kohdannut haasteita. Tämä tutkimus pyrkii vastaamaan valtioneuvoston määrittelemään tietotarpeeseen, joka liittyy erityisesti saamelaisten maa- ja osallistumisoikeuksiin sekä saamelaismääritelmään. Kyseessä on kansainvälinen oikeusvertaileva tutkimus, joka pyrkii tarjoamaan uutta tietoa sekä kansainvälisen alkuperäiskansaoikeuden kehityksestä että siitä, miten muissa aiheen kannalta keskeisissä valtioissa on ratkaistu alkuperäiskansojen oikeuksiin liittyviä kysymyksiä. Tutkimusraportti koostuu neljästä pääosiosta. Ensimmäinen osio tarkastelee saamelaisten oikeusasemaa Suomessa sekä siihen liittyviä ehdotuksia, joita tarkastellaan kansainvälisen oikeuden luomien velvoitteiden näkökulmasta. Toinen kokonaisuus pitää sisällään saamelaismääritelmän problematiikan tarkastelua kansainvälisen oikeuden sekä korkeimman hallinto-oikeuden tapauskäytännön valossa, sisältäen myös kuvauksen saamelaisuuden määrittelyyn liittyvien kiistojen taustoista ja syistä. Kolmas pääosio keskittyy alkuperäiskansojen kansainvälisoikeudellisen aseman ja oikeuksien kehitykseen pääfokuksena ns. ennakkosuostumuksen periaate (FPIC). Tämä osio pitää sisällään myös ILO sopimus 169:n sisältämien maaoikeuksien analyysiin. Neljäs pääkokonaisuus on oikeusvertaileva osio, jossa on mukana Norjaa, Ruotsia, Uutta-Seelantia, Kanadaa ja Latinalaisen Amerikan maita koskevat maaraportit. Tämä kappalekokonaisuus sisältää myös yhteenvedon vertailun piirissä olleiden valtioiden oikeuskäytänteiden keskeisistä elementeistä, joista voidaan löytää parhaita käytänteitä Suomen saamelaisten oikeuksien toteuttamiseksi