Organiziranje podatkov o drobnih koreninah, dobljenih iz minirizotronov in vrastnih mrežic (kako ustvariti operativno podatkovno bazo z uporabo MS Accessa)

Root observation with minirhizotrons is a useful technique to study root system dynamics by means of a transparent tube and a root image acquisition device. It has been in use in root studies for a few decades. The method is best complemented by sequential soil coring for studying root growth in defined soil volume in a time sequence of sampling, or by use of ingrowth soilcores method, which allows measurement of fine root biomass and growth in the exposed soil (substrate) cores during a defined time interval. Most fine root studies techniques are based on picture taking and computerized image analysis. From such analyses, enormous amount of raw data is derived, which ishard to control and manipulate. To enable a friendly and reliable data organization, two MS Access databases were designed, using data from minirhizotron pictures and ingrowth soil cores. These MS Access databases enable the data user to save time and reduce the amount of errors made during data handling (such as extensive copy-paste data routines in and out of numerous Excel files). Our aim was to improve data quality control and allow an easy, friendly and efficient way of manipulation of fine root growth data without a high level of knowledge on database construction. Therefore, in thisstudy, we present an efficient way of handling a large amount of minirhizotron and ingrowth soil cores data, by using MS Access database. To better present the protocols some results and experience on improving data quality are presented.Opazovanje korenin z minirizotroni je uporabna metoda za študij dinamike rastidrobnih korenin s pomočjo prozorne cevi in naprave za zajemanje slik. V uporabi so že nekaj desetletij. Metodo je najbolje dopolniti z zaporednim vzorčenjem znanega volumna tal v določenem časovnem zaporedju ali z uporabo vrastnih mrežic, ki nam omogočajo merjenje koreninske biomase in rasti drobnihkorenin v določenih časovnih intervalih. Večina metod študija korenin sloni na zajemanju slik in računalniško analizo le-teh. Z analizo slik dobimo ogromno količino neobdelanih podatkov, ki jih je težko nadzorovati in obdelovati. Da bi olajšali organiziranje podatkov in natančnost prenosov, smo pripravili dve podatkovni bazi MS Access, ki vsebujeta podatke o koreninah iz minirizotronov in vrastnih mrežic. Ti podatkovni bazi MS Access omogočata prihranek časa in zmanjšanje števila napak, nastalih med obdelavo podatkov (npr. obsežne operacije "copy-paste" v in iz številnih datotek Excel). Cilj jeizboljšati nadzor kakovosti podatkov ter omogočiti enostaven, prijazen in učinkovit način za manipulacijo podatkov iz minirizotronov in vrastnih mrežic brez visoke stopnje poznavanja dela z bazami podatkov. V prispevku predstavljamo učinkovit način za obdelavo velike količine podatkov iz minirizotronov in vrastnih mrežic z uporabo baze podatkov MS Access. Zaradi lažje predstave je pristop podprt s prikazom izbranih rezultatov in izkušenj pri izboljšanju kakovosti podatkov

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction

BACKGROUND: Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. RESULTS: This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. CONCLUSIONS: Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions

Network regression with predictive clustering trees

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Dealing with spatial autocorrelation when learning predictive clustering trees

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The link between the presence of an odontogenic radicular cyst and the body 's immune response (case report)

Odontogenic radicular cysts are the most common odontogenic inflammatory cysts. Immunopathological reactions play a dominant role in their etiopathogenesis. This study aimed to determine the presence of T and B lymphocytes, cells in the inflammatory infiltrate and, the impact of the cystic lesion on the systemic immune response by verifying the changes in the immune system by applying the immunohistochemical method in a patient with a residual cyst in the lower jaw. and one month after surgery. Case Report: A 60-year-old woman with a residual cyst in her lower jaw was admitted to the Oral Surgery Clinic. Immunoassay of blood was performed to determine the values of immunoglobulins IgA, IgG and Ig M before surgery, cyst enucleation in toto, pathohistological and immunohistochemical analysis of CD3, CD4, CD8, CD20, and CD68 markers. Immunological blood tests were performed one month after surgery. Pathohistological and immunohistochemical analysis confirmed the diagnosis of radicular (residual) cyst in the mandible with the presence of multilayered squamous epithelium beneath which is an inflammatory infiltrate with granulation tissue and deposited cholesterol crystals with predominant lymphadenopathy of predominant lymphadenopathy. %), are dominated by macrophages and histiocytes. Serum immunoglobulin IgA, Ig G, and IgM levels were reduced after surgery. The use of pathohistological and immunohistochemical analyzes proves the presence of cellular and humoral immune responses and their role in the etiopathogenesis and development of cysts, while immunoassays confirm the presence of human immunoglobulin this suggests the importance of early detection and therapeutic approach to radicular cysts. Keywords: residual cyst, pathohistological, immunohistochemical and immunological analysis, human immune response

Organization of fine root data obtained from minirhizotrons and ingrowth soil cores (how to construct an operational database using MS Access)

Root observation with minirhizotrons is a useful technique to study root system dynamics by means of a transparent tube and a root image acquisition device. It has been in use in root studies for a few decades. The method is best complemented by sequential soil coring for studying root growth in defined soil volume in a time sequence of sampling, or by use of ingrowth soilcores method, which allows measurement of fine root biomass and growth in the exposed soil (substrate) cores during a defined time interval. Most fine root studies techniques are based on picture taking and computerized image analysis. From such analyses, enormous amount of raw data is derived, which ishard to control and manipulate. To enable a friendly and reliable data organization, two MS Access databases were designed, using data from minirhizotron pictures and ingrowth soil cores. These MS Access databases enable the data user to save time and reduce the amount of errors made during data handling (such as extensive copy-paste data routines in and out of numerous Excel files). Our aim was to improve data quality control and allow an easy, friendly and efficient way of manipulation of fine root growth data without a high level of knowledge on database construction. Therefore, in thisstudy, we present an efficient way of handling a large amount of minirhizotron and ingrowth soil cores data, by using MS Access database. To better present the protocols some results and experience on improving data quality are presented