No / Aim: A diagnostic test that could detect Treponema pallidum antigens in urine would facilitate the prompt diagnosis of syphilis. Materials & methods: Urine from 54 individuals with various clinical stages of syphilis and 6 controls were pooled according to disease stage and interrogated with complementary mass spectrometry techniques to uncover potential syphilis biomarkers. Results & conclusion: In total, 26 unique peptides were uncovered corresponding to four unique T. pallidum proteins that have low genetic sequence similarity to other prokaryotes and human proteins. This is the first account of direct T. pallidum protein detection in human clinical samples using mass spectrometry. The implications of these findings for future diagnostic test development is discussed. Data are available via ProteomeXchange with identifier PXD009707.

No / Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitors' retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. In order for this process to yield good results it has to rely on some good quality input data. Therefore, more focus in this process should be on data cleaning and pre-processing. On the other hand, one of the challenges facing online prediction is scalability. As a result any improvement in the efficiency of online prediction solutions is more than necessary. As a response to the aforementioned concerns we are proposing an enhancement to the web log mining process and to the online navigational pattern prediction. Our contribution contains three different components. First, we are proposing a refined time-out based heuristic for session identification. Second, we are suggesting the usage of a specific density based algorithm for navigational pattern discovery. Finally, a new approach for efficient online prediction is also suggested. The conducted experiments demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.

Background and Aims Gliomas account for over half of all primary brain tumours and have a very poor prognosis, with a median survival of less than two years. There is an urgent and unmet clinical need to develop new therapies against glioma. Recent reports have indicated the overexpression of FPR1 in gliomas particularly in high grade gliomas. The aim of this project was to identify and synthesise small molecule FPR1 antagonists, and to demonstrate a proof of principle in preclinical in vitro and in vivo models that small molecule FPR1 antagonism can retard expansion of glioma. Methods A number of small molecule FPR1 antagonists were identified by in silico design, or from the literature and then were prepared using chemical synthesis. FPR1 antagonists were evaluated in vitro for their ability to abrogate FPR1-induced cellular responses in a range of models including calcium mobilisation, cell migration, and invasion. The efficacy of FPR1 antagonist ICT12035 in vivo was assessed in a U-87 MG subcutaneous xenograft model. Results Virtual high throughput screening using a homology model of FPR1 led to the identification of two small molecule FPR1 antagonists. At the same time chemical synthesis of two other antagonists, ICT5100 and ICT12035 as well as their analogues were carried out. The FPR1 antagonists were assessed in calcium flux assay which gave an insight into their structure-activity relationship. Further investigation of both ICT5100 and ICT12035 demonstrated that both small molecule FPR1 antagonists were effective at abrogating FPR1-induced calcium mobilisation, migration, and invasion in U- 87 MG in vitro models in a dose-dependent manner. ICT12035 is a particularly selective and potent inhibitor of FPR1 with an IC50 of 37.7 nM in calcium flux assay. Additionally, it was shown that the FPR1 antagonist ICT12035 was able to arrest the growth rate of U-87 MG xenografted tumours in mice. Conclusion The results demonstrate that targeting FPR1 by a small molecule antagonist such as ICT12035, could provide a potential new therapy for the treatment of glioblastoma. / Yorkshire Cancer Research

<p dir="ltr">Deep learning models have demonstrated remarkable success across diverse domains, including computer vision and natural language processing. These models heavily rely on resources, encompassing annotated data, computational power, and storage. However, mobile devices, particularly in scenarios like medical or multilingual contexts, often face constraints with computing power, making ample data annotation prohibitively expensive. Developing deep learning models for such resource-constrained scenarios presents a formidable challenge. Our primary goal is to enhance the efficiency of state-of-the-art neural network models tailored for resource-limited scenarios. Our commitment lies in crafting algorithms that not only mitigate annotation requirements but also reduce computational complexity and alleviate storage demands. Our dissertation focuses on two key areas: Parameter-efficient Learning and Data-efficient Learning. In Part 1, we present our studies on parameter-efficient learning. This approach targets the creation of lightweight models for efficient storage or inference. The proposed solutions are tailored for diverse tasks, including text generation, text classification, and text/image retrieval. In Part 2, we showcase our proposed methods for data-efficient learning, concentrating on cross-lingual and multi-lingual text classification applications. </p>

Over the last decade, chronic inflammatory enteropathies (CIE) in dogs have received great attention in the basic and clinical research arena. The 2010 ACVIM Consensus Statement, including guidelines for the diagnostic criteria for canine and feline CIE, was an important milestone to a more standardized approach to patients suspected of a CIE diagnosis. Great strides have been made since understanding the pathogenesis and classification of CIE in dogs, and novel diagnostic and treatment options have evolved. New concepts in the microbiome-host-interaction, metabolic pathways, crosstalk within the mucosal immune system, and extension to the gut-brain axis have emerged. Novel diagnostics have been developed, the clinical utility of which remains to be critically evaluated in the next coming years. New directions are also expected to lead to a larger spectrum of treatment options tailored to the individual patient. This review offers insights into emerging concepts and future directions proposed for further CIE research in dogs for the next decade to come.

The purpose of the study was to investigate and analyze the role of school counselors in detecting and preventing juvenile delinquency. To address this purpose, a qualitative method was used in the form of an interview study, where eight middle school counselors were interviewed. The data was processed through thematic analysis. Subsequently, the results were analyzed in relation to system theory and collaboration theory. The results indicate that school counselors currently work on crime detection and prevention, albeit not to the extent they desire. The lack of preventive work is a consequence of resource scarcity, weak school structure, and ambiguity regarding the role of school counselors in legislation. Additionally, the sense of professional isolation experienced by school counselors and the resulting heavy workload hinder crime prevention efforts. To effectively detect and prevent juvenile delinquency, school counselors collaborate with other stakeholders. Building relationships with students also allows school counselors to work on detecting and preventing juvenile delinquency. The study concludes that school counselors have a significant, but often overlooked role in crime preventative work. / Syftet med studien var att undersöka och analysera skolkuratorns roll för att upptäcka och förebygga ungdomsbrottslighet. För att besvara syftet användes en kvalitativ metod i form av en intervjustudie, där åtta högstadiekuratorer intervjuades. Empirin bearbetades genom en tematisk analys. Därefter analyserades resultatet i relation till systemteori och samverkansteori. Av resultatet framkommer att skolkuratorer idag arbetar brottsupptäckande och förebyggande, dock inte i den utsträckning de önskar. Bristen på förebyggande arbete är en konsekvens av resursbrist, svag struktur i skolan samt en otydlighet gällande skolkuratorns roll i skollagen. Vidare visar resultatet att skolkuratorerna upplever en ensamhet i professionen och har därmed en stor arbetsbelastning vilket hämmar det brottsförebyggande arbetet. För att på bästa sätt upptäcka och förebygga ungdomsbrottslighet arbetar skolkuratorer i samverkan med andra aktörer. Även genom relationsskapande med eleverna kan skolkuratorn arbeta för att upptäcka och förebygga ungdomsbrottslighet. Studiens slutsats är att skolkuratorer har en betydelsefull men ofta förbisedd roll i det brottsförebyggande arbetet.

京都大学 / 新制・論文博士 / 博士(医科学) / 乙第13611号 / 論医科博第12号 / 新制||医科||11(附属図書館) / 九州大学大学院薬学府創薬科学専攻 / (主査)教授 井上 治久, 教授 松田 秀一, 教授 萩原 正敏 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Na área de inteligência artificial existem algoritmos de aprendizado, notavelmente aqueles pertencentes à área de aprendizado de máquina AM , capazes de automatizar a extração do conhecimento implícito de um conjunto de dados. Dentre estes, os algoritmos de AM simbólico são aqueles que extraem um modelo de conhecimento inteligível, isto é, que pode ser facilmente interpretado pelo usuário. A utilização de AM simbólico é comum no contexto de classificação, no qual o modelo de conhecimento extraído é tal que descreve uma correlação entre um conjunto de atributos denominados premissas e um atributo particular denominado classe. Uma característica dos algoritmos de classificação é que, em geral, estes são utilizados visando principalmente a maximização das medidas de cobertura e precisão, focando a construção de um classificador genérico e preciso. Embora essa seja uma boa abordagem para automatizar processos de tomada de decisão, pode deixar a desejar quando o usuário tem o desejo de extrair um modelo de conhecimento que possa ser estudado e que possa ser útil para uma melhor compreensão do domínio. Tendo-se em vista esse cenário, o principal objetivo deste trabalho é pesquisar métodos de computação evolutiva multiobjetivo para a construção de regras de conhecimento individuais com base em critérios definidos pelo usuário. Para isso utiliza-se a biblioteca de classes e ambiente de construção de regras de conhecimento ECLE, cujo desenvolvimento remete a projetos anteriores. Outro objetivo deste trabalho consiste comparar os métodos de computação evolutiva pesquisados com métodos baseado em composição de rankings previamente existentes na ECLE. É mostrado que os métodos de computação evolutiva multiobjetivo apresentam melhores resultados que os métodos baseados em composição de rankings, tanto em termos de dominância e proximidade das soluções construídas com aquelas da fronteira Pareto-ótima quanto em termos de diversidade na fronteira de Pareto. Em otimização multiobjetivo, ambos os critérios são importantes, uma vez que o propósito da otimização multiobjetivo é fornecer não apenas uma, mas uma gama de soluções eficientes para o problema, das quais o usuário pode escolher uma ou mais soluções que apresentem os melhores compromissos entre os objetivos / Machine Learning algorithms are notable examples of Artificial Intelligence algorithms capable of automating the extraction of implicit knowledge from datasets. In particular, Symbolic Learning algorithms are those which yield an intelligible knowledge model, i.e., one which a user may easily read. The usage of Symbolic Learning is particularly common within the context of classification, which involves the extraction of knowledge such that the associated model describes correelation among a set of attributes named the premises and one specific attribute named the class. Classification algorithms usually target into creating knowledge models which maximize the measures of coverage and precision, leading to classifiers that tend to be generic and precise. Althought this constitutes a good approach to creating models that automate the decision making process, it may not yield equally good results when the user wishes to extract a knowledge model which could assist them into getting a better understanding of the domain. Having that in mind, it has been established as the main goal of this Masters thesis the research of multi-objective evolutionary computing methods to create individual knowledge rules maximizing sets of arbitrary user-defined criteria. This is achieved by employing the class library and knowledge rule construction environment ECLE, which had been developed during previous research work. A second goal of this Masters thesis is the comparison of the researched evolutionary computing methods against previously existing ranking composition methods in ECLE. It is shown in this Masters thesis that the employment of multi-objective evolutionary computing methods produces better results than those produced by the employment of ranking composition-based methods. This improvement is verified both in terms of solution dominance and proximity of the solution set to the Pareto-optimal front and in terms of Pareto-front diversity. Both criteria are important for evaluating the efficiency of multi-objective optimization algorithms, for the goal of multi-objective optimization is to provide a broad range of efficient solutions, so the user may pick one or more solutions which present the best trade-off among all objectives

Finally, by basing its analysis on theories of institutional change, this study provides explanation for two phenomena that emerge from the process of curriculum implementation: formalism in the implementation strategies that attempt to cope with policy requirements and the lack of change in the curriculum deal to difficulties encountered. In light of the aforesaid phenomena, policy recommendations are given for the advancement of curriculum reform. / Secondly, the said pattern of behavior can be explained by institutional factors that have played a substantial role in the implementation process. Guided by a theoretical framework proposed by Scott (2001), this study uses its concepts and propositions to illustrate the institutional resources that are present in the practices of sampled schools and illuminates the institutional mechanisms that are at work. / Since 2001, a new round of curriculum reform was initiated in the Chinese Mainland. In the process of implementation, the reform endeavor has encountered a lot of difficulties. For those scholars who are interested in the reform of Chinese education, achieving an understanding of these difficulties has become their common pursuit. / Thirdly, the nature of the institution that influences the implementation of school curriculum is constituted by the interaction of stakeholders, namely, schools, government, higher institutions, parents, and the media. Through interaction, these stakeholders reach compromises in an "organization field" which in turn contribute to the formation of the institution. / This study approaches education reform from the perspective of policy implementation. It adopts the theoretical lens of neo-institutionalism and examines the implementation of "research based learning" in the schools of District A. As a new form of learning, "research based learning" is heralded as one of the major theme of the new school curriculum. Through the analysis of data collected through interviews and field observation, this study yields results that are presented in the paragraphs below. / To begin with, research has unearthed an obvious "isomorphism" in the reform practices in different schools. The study finds that there is a stable pattern of behavior that is discernible in the implementation process of curriculum reform. This pattern of behavior is commonly shared by different schools engaged in the reform process. / 柯政. / Advisers: Leslie Lo; Wing-kwong Tsang. / Source: Dissertation Abstracts International, Volume: 70-06, Section: A, page: 1903. / Thesis (doctoral)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 243-261). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in Chinese and English. / School code: 1307. / Ke Zheng.

Data dependencies, or integrity constraints, are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. In the last years conditional dependencies have been introduced to analyze and improve data quality. In short, a conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs). We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we define quality measures for conditions inspired by precision and recall. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case. / Datenabhängigkeiten (wie zum Beispiel Integritätsbedingungen), werden verwendet, um die Qualität eines Datenbankschemas zu erhöhen, um Anfragen zu optimieren und um Konsistenz in einer Datenbank sicherzustellen. In den letzten Jahren wurden bedingte Abhängigkeiten (conditional dependencies) vorgestellt, die die Qualität von Daten analysieren und verbessern sollen. Eine bedingte Abhängigkeit ist eine Abhängigkeit mit begrenztem Gültigkeitsbereich, der über Bedingungen auf einem oder mehreren Attributen definiert wird. In diesem Bericht betrachten wir bedingte Inklusionsabhängigkeiten (conditional inclusion dependencies; CINDs). Wir generalisieren die Definition von CINDs anhand der Unterscheidung von überdeckenden (covering) und vollständigen (completeness) Bedingungen. Wir stellen einen Anwendungsfall für solche CINDs vor, der den Nutzen von CINDs bei der Lösung komplexer Datenqualitätsprobleme aufzeigt. Darüber hinaus definieren wir Qualitätsmaße für Bedingungen basierend auf Sensitivität und Genauigkeit. Wir stellen effiziente Algorithmen vor, die überdeckende und vollständige Bedingungen innerhalb vorgegebener Schwellwerte finden. Unsere Algorithmen wählen nicht nur die Werte der Bedingungen, sondern finden auch die Bedingungsattribute automatisch. Abschließend zeigen wir, dass unser Ansatz effizient sinnvolle und hilfreiche Ergebnisse für den vorgestellten Anwendungsfall liefert.