Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Strategy selection: An introduction to the modeling challenge

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.

Maca - a configurable tool to integrate Polish morphological data

There are a number of morphological analysers for Polish. Most of these, however, are non-free resources. What is more, different analysers employ different tagsets and tokenisation strategies. This situation calls for a simpleand universal framework to join different sources of morphological information, including the existing resources as well as user-provided dictionaries. We present such a configurable framework that allows to write simple configuration files that define tokenisation strategies and the behaviour of morphologicalanalysers, including simple tagset conversion.

An Italian to Catalan RBMT system reusing data from existing language pairs

This paper presents an Italian to CatalanRBMT system automatically built bycombining the linguistic data of theexisting pairs Spanish-Catalan andSpanish-Italian. A lightweight manualpostprocessing is carried out in order tofix inconsistencies in the automaticallyderived dictionaries and to add very frequentwords that are missing accordingto a corpus analysis. The system isevaluated on the KDE4 corpus and outperformsGoogle Translate by approximatelyten absolute points in terms ofboth TER and GTM.

Pulp Fractionation in a Pilot Paper Machine

Tämän diplomityön tarkoituksena on tutkia paperimassan jakautumiseen vaikuttavia tekijöitä paperinvalmistusprosessissa. Työn empiirisen osan tavoitteena on analysoida perälaatikon hienoainepitoisuuden ja paperimassan virtausnopeuden vaikutusta paperimassan jakautumiseen pilottipaperikoneessa, sekä selvittää voidaanko näitä prosessiparametreja optimoimalla saavuttaa merkittävää retention, vedenpoiston ja kaksipuolisuuden parantumista. Työn teoreettinen osa sisältää kirjallisuuskatsauksen märänpään kemiasta ja yhteenvedon aikaisemmasta tutkimuksesta koskien paperimassan jakautumista paperinvalmistusprosessissa. Työn empiirisessä osassa on tutkittu perälaatikon hienoainepitoisuuden ja paperimassan virtausnopeuden vaikutusta retentioon, vedenpoistoon ja paperimassan jakautumiseen Papricanin pilottipaperikoneessa. Analyysissä on käytetty yhteensovitettua dataa, joka on saatu kattavien pilottipaperikonekokeiden ja taulukkolaskentaohjelmalla toteutettujen staattisten simulointimallien avulla. Simulointimalleissa perälaatikon hienoainepitoisuus on 30-55%, sekä paperimassan virtausnopeudet ovat 2470 L/min, 3870 L/min ja 5230 L/min. Muut prosessiparametrit on vakioitu, eikä retentioainetta käytetty. Retentio pilottipaperikoneessa oli 55-82% riippuen perälaatikon hienoainepitoisuudesta ja paperimassan virtausnopeudesta. Perälaatikon hienoainepitoisuuden ja retention välillä oli voimakas negatiivinen korrelaatio. Myös paperimassan virtausnopeuden ja retention välillä oli negatiivinen korrelaatio. Mitä alhaisempi retentio, sitä enemmän hienoainesta kerääntyi systeemiin. Hienoaineen huuhtoutuminen paperirainasta korreloi vedenpoistoon: pienemmällä paperimassan virtausnopeudella enemmän sekä vettä että hienoainetta poistui viirapuolelta, ja suuremmalla paperimassan virtausnopeudella saman verran sekä vettä että hienoainetta poistui rainan molemmilta puolilta. Paras paperirainan kaksipuolisuus saavutettiin korkeilla perälaatikon hienoainepitoisuuksilla (50% ja 55%) suurilla paperimassan virtausnopeuksilla (3870 L/min ja 5230 L/min).

18/4/20, [avenue du Palais à] St Cloud, concours de patinettes à moteur, Myrtho Laverne [sur la machine] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 58923

18/4/20, [avenue du Palais à] St Cloud, concours de patinettes à moteur, Mistinguette [sur la machine] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 58924

The use of branched ketenedimers in solving the deposit problems related to the internal sizing of uncoated fine paper

Alkyl ketene dimers (AKD) are effective and highly hydrophobic sizing agents for the internal sizing of alkaline papers, but in some cases they may form deposits on paper machines and copiers. In addition, alkenyl succinic anhydrides (ASA)- based sizing agents are highly reactive, producing on-machine sizing, but under uncontrolled wet end conditions the hydrolysis of ASA may cause problems. This thesis aims at developing an improved ketene dimer based sizing agent that would have a lower deposit formation tendency on paper machines and copiers than a traditional type of AKD. The aim is also to improve the ink jet printability of a AKD sized paper. The sizing characteristics ofketene dimers have been compared to those of ASA. A lower tendency of ketene dimer deposit formation was shown in paper machine trials and in printability tests when branched fatty acids were used in the manufacture of a ketene dimer basedsizing agent. Fitting the melting and solidification temperature of a ketene dimer size to the process temperature of a paper machine or a copier contributes to machine cleanliness. A lower hydrophobicity of the paper sized with branched ketene dimer compared to the paper sized with traditional AKD was discovered. However, the ink jet print quality could be improved by the use of a branched ketene dimer. The branched ketene dimer helps in balancing the paper hydrophobicity for both black and color printing. The use of a high amount of protective colloidin the emulsification was considered to be useful for the sizing performance ofthe liquid type of sizing agents. Similar findings were indicated for both the branched ketene dimer and ASA.

Development of a shelling method to recover whole kernels of the Cutia nut (Couepia edulis)

The kernel of the cutia nut (castanha-de-cutia, Couepia edulis (Prance) Prance) of the western Amazon, which is consumed by the local population, has traditionally been extracted from the nut with a machete, a dangerous procedure that only produces kernels cut in half. A shelling off machine prototype, which produces whole kernels without serious risks to its operator, is described and tested. The machine makes a circular cut in the central part of the fruit shell, perpendicular to its main axis. Three ways of conditioning the fruits before cutting were compared: (1) control; (2) oven drying immediately prior to cutting; (3) oven drying, followed by a 24-hour interval before cutting. The time needed to extract and separate the kernel from the endocarp and testa was measured. Treatment 3 produced the highest output: 63 kernels per hour, the highest percentage of whole kernels (90%), and the best kernel taste. Kernel extraction with treatment 3 required 50% less time than treatment 1, while treatment 2 needed 38% less time than treatment 1. The proportion of kernels attached to the testa was 93%, 47%, and 8% for treatments 1, 2, and 3, respectively, and was the main reason for extraction time differences.

Methods of Jet to Wire Speed Ratio Determination and Its Effect in Paper and Board Manufacture

Suihku/viira-nopeussuhde on perälaatikon huulisuihkun ja viiran välinen nopeusero. Se vaikuttaa suuresti paperin ja kartongin loppuominaisuuksiin, kuten formaatioon sekä kuituorientaatioon ja näin ollen paperin lujuusominaisuuksiin. Tämän johdosta on erityisen tärkeää tietää todellinen suihku/viira-nopeussuhde paperin- ja kartonginvalmistuksessa. Perinteinen suihku/viira-nopeussuhteen määritysmenetelmä perustuu perälaatikon kokonaispaineeseen. Tällä menetelmällä kuitenkin todellinen huulisuihkun nopeus saattaa usein jäädä tietämättä johtuen mahdollisesta virheellisestä painemittarin kalibroinnista sekä laskuyhtälön epätarkkuuksista. Tämän johdosta on kehitetty useita reaaliaikaisia huulisuihkun mittausmenetelmiä. Perälaatikon parametrien optimaaliset asetukset ovat mahdollista määrittää ja ylläpitää huulisuihkun nopeuden “on-line” määrityksellä. Perälaatikon parametrejä ovat mm. huulisuihku, huuliaukon korkeusprofiili, reunavirtaukset ja syöttövirtauksen tasaisuus. Huulisuihkun nopeuden on-line mittauksella paljastuu myös muita perälaatikon ongelmakohtia, kuten mekaaniset viat, joita on perinteisesti tutkittu aikaa vievillä paperin ja kartongin lopputuoteanalyyseillä.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.