Toiminimen kirjanpito

Opinnäytetyön tavoitteena oli selvittää mitä osaamista tarvitaan toiminimen kirjanpidon toteuttamiseen. Työn toimeksiantajana toimi tamperelainen hyvinvointialan yrittäjä, joka ei ole toiminnastaan alv-velvollinen. Työn tarkoituksena oli kouluttaa tekijä peruskirjanpidon hoitamiseen. Tarkoituksena oli, että toimeksiantajan kirjanpito sekä veroilmoituksen täyttäminen ovat vuodesta 2017 eteenpäin opinnäytetyön tekijän vastuulla. Työn teoriaosuudessa keskityttiin kirjanpidollisiin peruskäsitteisiin toimeksiantajan elinkeinotoiminnan luonne huomioiden. Verotuksen osuudessa keskityttiin yritystulon verottamiseen yleensä ja esiteltiin ammatinharjoittajan veroilmoitus siltä osin, kun se toimeksiantajan yritystoiminta huomioiden oli tarpeellista. Sen lisäksi, että tekijä perehdytti itsensä alusta alkaen kirjanpidon maailmaan, teki työstä työlään erityisesti edellisvuosien kirjanpitomateriaaliin tutustuminen, sopivan kirjanpito-ohjelman etsiminen ja juoksevan kirjanpidon suorittaminen. Opinnäyteyön tavoite toteutui, sillä työn tekijällä on nyt tarvittava osaaminen kirjanpito-ohjelman käyttämiseen sekä juoksevan kirjanpidon hoitamiseen. Kirjanpidollisiin perusasioihin keskittyminen oli työn tavoitteen toteutumisen kannalta tärkeää, sillä perustietoja kirjanpidosta tekijällä ei juurikaan ennestään ollut. Toimeksiantajan kirjanpito on tällä hetkellä toteutettuna Tappio kirjanpito-ohjelmaa käyttäen elokuun 2017 loppuun asti. Opinnäytetyön liitteenä on Tappio-ohjelmasta poimitut tuloslaskelma ja tase. Kirjaukset perustavat tapahtumiin, jotka ovat muodostuneet 31.8.2017 mennessä. Opinnäytetyötä varten lukuja on muunnettu. Työ ei suinkaan lopu raportin palauttamiseen. Juoksevaa kirjanpitoa jatketaan loppuvuoden osalta ja tilinpäätös tulee toteuttaa vuoden 2018 alussa todellisia tilikauden lukuja käyttäen. Toimeksiantaja on myös ulkoistanut veroilmoituksen täyttämisen tämän työn tekijälle. Prosessin myötä tekijän osaaminen toiminimen kirjanpidon hoitamiseen on kohonnut ammattimaiselle tasolle, joten kirjanpitopalvelua voi jatkossa tarjota myös muille toiminimiyrittäjille. Tekijä jatkaa osaamisen kartoitusta tutustumalla alv-kirjauksiin.The purpose of this thesis was to find out what skills are required to perform the accounting for a sole trader. The commissioner was a welfare entrepreneur who practices business in Tampere. The business is not liable to pay value added tax. The aim of the thesis was to give the author the capability to perform the commissioner´s accounting independently in the future. The theoretical part of the thesis focused on the basic bookkeeping concepts considering the nature of the commissioner´s business. The taxation section focused on the business income, and the tax return was presented, as it is necessary for the commissioner´s business. An essential part of the work process was to familiarize with the accounting material of the previous years, to find a suitable accounting program, and to perform the current bookkeeping. The author is now capable of using the accounting program and keeping the accounts until preparing the income statement and balance sheet. Thus the goal of the thesis was reached. The routine bookkeeping continues and the financial statements will be prepared at the end of the year. The author will also complete the tax return for the financial year of 2017. Because of the professional development, the author can provide bookkeeping services to other welfare entrepreneurs, too

The ins and outs of vanillyl alcohol oxidase: Identification of ligand migration paths

Vanillyl alcohol oxidase (VAO) is a homo-octameric flavoenzyme belonging to the VAO/PCMH family. Each VAO subunit consists of two domains, the FAD-binding and the cap domain. VAO catalyses, among other reactions, the two-step conversion of p-creosol (2-methoxy-4-methylphenol) to vanillin (4-hydroxy-3-methoxybenzaldehyde). To elucidate how different ligands enter and exit the secluded active site, Monte Carlo based simulations have been performed. One entry/exit path via the subunit interface and two additional exit paths have been identified for phenolic ligands, all leading to the si side of FAD. We argue that the entry/exit path is the most probable route for these ligands. A fourth path leading to the re side of FAD has been found for the co-ligands dioxygen and hydrogen peroxide. Based on binding energies and on the behaviour of ligands in these four paths, we propose a sequence of events for ligand and co-ligand migration during catalysis. We have also identified two residues, His466 and Tyr503, which could act as concierges of the active site for phenolic ligands, as well as two other residues, Tyr51 and Tyr408, which could act as a gateway to the re side of FAD for dioxygen. Most of the residues in the four paths are also present in VAO’s closest relatives, eugenol oxidase and p-cresol methylhydroxylase. Key path residues show movements in our simulations that correspond well to conformations observed in crystal structures of these enzymes. Preservation of other path residues can be linked to the electron acceptor specificity and oligomerisation state of the three enzymes. This study is the first comprehensive overview of ligand and co-ligand migration in a member of the VAO/PCMH family, and provides a proof of concept for the use of an unbiased method to sample this process.Enzymes are bionanomachines, which speed up chemical reactions in organisms. To understand how they achieve that, we need to study their mechanisms. Computational enzymology can show us what happens in the enzyme’s active site during a reaction. But molecules need first to reach the active site before a reaction can start. The process of substrate entry and product exit to the active site is often neglected when studying enzymes. However, these two events are of fundamental importance to the proper functioning of any enzyme. We are interested in these dynamic processes to complete our understanding of the mode of action of enzymes. In our work, we have studied substrate and product migration in vanillyl alcohol oxidase. This enzyme can produce the flavour vanillin and enantiopure alcohols, but also catalyses other reactions. The named products are of interest to the flavour- and fine-chemical industries.This work was supported by: FP7-KBBE- 2013-7-613549 http://cordis.europa.eu/ programme/rcn/851_en.html (see INDOX project (http://indoxproject.eu), funding received by: GG MFL VG WJHvB; and CTQ2016-79138-R, http:// www.mineco.gob.es/portal/site/mineco/?lang_ choosen=en, funding received by: MFL VG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

A Novel Autoencoder-Based Diagnostic System for Early Assessment of Lung Cancer

© 2018 IEEE. A novel framework for the classification of lung nodules using computed tomography (CT) scans is proposed in this paper. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that is modeled using higher-order Markov Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) geometric features that describe the shape geometry of the lung nodules. The novelty of this paper is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium (LIDC). We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 92.20%

A generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules

© The Author(s) 2018. A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%

Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy

Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves 92.55%, 91.70%, and 93.40% for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely