Safety and occlusion rates of surgical treatment of unruptured intracranial aneurysms: a systematic review and meta-analysis of the literature from 1990 to 2011.

BACKGROUND AND PURPOSE: Surgical clipping of unruptured intracranial aneurysms (UIAs) has recently been challenged by the emergence of endovascular treatment. We performed an updated systematic review and meta-analysis on the surgical treatment of UIAs, in an attempt to determine the aneurysm occlusion rates and safety of surgery in the modern era. METHODS: A detailed protocol was developed prior to conducting the review according to the Cochrane Collaboration guidelines. Electronic databases spanning January 1990-April 2011 were searched, complemented by hand searching. Heterogeneity was assessed using I(2), and publication bias with funnel plots. Surgical mortality and morbidity were analysed with weighted random effect models. RESULTS: 60 studies with 9845 patients harbouring 10 845 aneurysms were included. Mortality occurred in 157 patients (1.7%; 99% CI 0.9% to 3.0%; I(2)=82%). Unfavourable outcomes, including death, occurred in 692 patients (6.7%; 99% CI 4.9% to 9.0%; I(2)=85%). Morbidity rates were significantly greater in higher quality studies, and with large or posterior circulation aneurysms. Reported morbidity rates decreased over time. Studies were generally of poor quality; funnel plots showed heterogeneous results and publication bias, and data on aneurysm occlusion rates were scant. CONCLUSIONS: In studies published between 1990 and 2011, clipping of UIAs was associated with 1.7% mortality and 6.7% overall morbidity. The reputed durability of clipping has not been rigorously documented. Due to the quality of the included studies, the available literature cannot properly guide clinical decisions.

Genomic and transcriptomic analysis of the streptomycin-dependent Mycobacterium tuberculosis strain 18b.

The ability of Mycobacterium tuberculosis to establish a latent infection (LTBI) in humans confounds the treatment of tuberculosis. Consequently, there is a need to discover new therapeutic agents that can kill M. tuberculosis both during active disease and LTBI. The streptomycin-dependent strain of M. tuberculosis, 18b, provides a useful tool for this purpose since upon removal of streptomycin (STR) it enters a non-replicating state that mimics latency both in vitro and in animal models. The 4.41 Mb genome sequence of M. tuberculosis 18b was determined and this revealed the strain to belong to clade 3 of the ancient ancestral lineage of the Beijing family. STR-dependence was attributable to insertion of a single cytosine in the 530 loop of the 16S rRNA and to a single amino acid insertion in the N-terminal domain of initiation factor 3. RNA-seq was used to understand the genetic programme activated upon STR-withdrawal and hence to gain insight into LTBI. This revealed reconfiguration of gene expression and metabolic pathways showing strong similarities between non-replicating 18b and M. tuberculosis residing within macrophages, and with the core stationary phase and microaerophilic responses. The findings of this investigation confirm the validity of 18b as a model for LTBI, and provide insight into both the evolution of tubercle bacilli and the functioning of the ribosome.

International Trade and Cochin Port A Historical. and statistical Analysis -- 1881-1980

The overall focus of the thesis involves the International trade and cochin port a historical and statistical analysis 1881-1980.Analysing the trend of exports and imports through cochin port during the course of the last hundred years .This analysis has brought to light some very pertinent facts which , in our opinion,deserve serious consideration of the policy makers,the partise involved in trade and those who are interested in the development of the cochin port.Our study is restricted to twelve commodities -ten commodities of exports and two commodities of imports.The study reveals that the commodities that were exported from cochin are subjected to fluctuations -some mild and others wild. The projections only indicate the potential and unless we are very cautious the chance will be taken away by our competitors .With reference to the development of the port in particular and the states economy in general we would like to make a suggestion .This suggestion relates to declaring cochin as a free port .This will go a long way in the develppment of the port and the state's economy.The sooner it is done the better for the port and the state.

Marine yeast isolates from Arabian Sea and Bay of Bengal :Biochemical, Molecular and Phylogenetic Analysis

the present study was undertaken with the following objectives: 1. Isolation and identification of yeasts from Arabian Sea and Bay of Bengal. 2. Molecular characterization of yeast isolates and phylogenetic analysis 3. Physiological and biochemical characterization of the isolates. 4. Proximate composition of yeast biomass and bioactive compounds. The Thesis is comprised of six chapters. A general introduction to the topic is given in Chapter1. Isolation and identification of marine yeasts are presented in Chapter 2. Chapter 3 deals with molecular identification and physiological characterization of Non- pigmented yeasts. Molecular identification and physiological characterization of pigmented yeast is presented in Chapter 4. Proximate composition of yeast biomass of various genera and their bioactive compounds are illustrated in Chapter 5. A summary of the results of the present study is given in Chapter 6. References and Appendices are followed

Conceptual knowledge discovery and data analysis

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.

Mining, grievance and conflict in rural Tanzania

This paper provides an extended analysis of the tensions that have surfaced between large-scale mine operators and artisanal miners in gold-rich areas of rural Tanzania. The literature on grievance is used to contextualise, these disputes, the underlying cause of which is artisanal miners' mounting frustration over not being able to secure viable concessions to work. Newly implemented legislation has, for the most part, empowered foreign large-scale mine operators, while simultaneously disempowering indigenous small-scale miners. In many cases, the former have addressed mounting security and community problems on their own. Until the country's major mine operators extend assistance to marginalised small-scale mining groups, the likelihood of violent conflict unfolding between these parties will increase.

The economics of enhanced landfill mining: private and societal performance drivers

This paper addresses the economics of Enhanced Landfill Mining (ELFM) both from a private point of view as well as from a society perspective. The private potential is assessed using a case study for which an investment model is developed to identify the impact of a broad range of parameters on the profitability of ELFM. We found that especially variations in Waste-to-Energy (WtE efficiency, electricity price, CO2-price, WtE investment and operational costs) and ELFM support explain the variation in economic profitability measured by the Internal Rate of Return. To overcome site-specific parameters we also evaluated the regional ELFM potential for the densely populated and industrial region of Flanders (north of Belgium). The total number of potential ELFM sites was estimated using a 5-step procedure and a simulation tool was developed to trade-off private costs and benefits. The analysis shows that there is a substantial economic potential for ELFM projects on the wider regional level. Furthermore, this paper also reviews the costs and benefits from a broader perspective. The carbon footprint of the case study was mapped in order to assess the project’s net impact in terms of greenhouse gas emissions. Also the impacts of nature restoration, soil remediation, resource scarcity and reduced import dependence were valued so that they can be used in future social cost-benefit analysis. Given the complex trade-off between economic, social and environmental issues of ELFM projects, we conclude that further refinement of the methodological framework and the development of the integrated decision tools supporting private and public actors, are necessary.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Advanced pattern mining for complex sata analysis

The thesis has researched a set of critical problems in data mining and has proposed four advanced pattern mining algorithm to discover the most interesting and useful data patterns highly relevant to the user’s application targets from the data is represented in complex structures.

Economic evaluation of adult rehabilitation: a systematic review and meta-analysis of randomized controlled trials in a variety of settings

Objectives:
To report if there is a difference in costs from a societal perspective between adults receiving rehabilitation in an inpatient rehabilitation setting versus an alternative setting. If there are cost differences, to report whether opting for the least expensive program setting adversely affects patient outcomes.

Data Sources:
Electronic databases from the earliest possible date until May 2011. All languages were included.

Study Selection
Multiple reviewers identified randomized controlled trials with a full economic evaluation that compared adult inpatient rehabilitation with an alternative. There were 29 included trials with 6746 participants.

Data Extraction
Multiple observers extracted data independently. Trial appraisal included a risk of bias assessment and a checklist to report the strength of the economic evaluation.

Data Synthesis:
Results were synthesized using standardized mean differences (SMDs) and meta-analyses for the primary outcome of cost. The Grading of Recommendations Assessment, Development, and Evaluation was applied to assess for risk of bias across studies for meta-analyses. There was high-quality evidence that cost was significantly reduced for rehabilitation in the home versus inpatient rehabilitation in a meta-analysis of 732 patients poststroke (pooled SMD [δ]=−.28; 95% confidence interval [CI], −.47 to −.09), without compromise to patient outcomes. Results of individual trials in other patient groups (orthopedic, rheumatoid arthritis, and geriatric) receiving rehabilitation in the home or community were generally consistent with the meta-analysis. There was moderate quality evidence that cost was significantly reduced for inpatient rehabilitation (stroke unit) versus general acute care in a meta-analysis of 463 patients poststroke (δ=.31; 95% CI, .15–.48), with improvement to patient outcomes. These results were not replicated in 2 individual trials with a geriatric and a mixed cohort, where costs did not differ between general acute care and inpatient rehabilitation. Three of the 4 individual trials, inclusive of a stroke or orthopedic population, reported less cost for an intensive inpatient rehabilitation program compared with usual inpatient rehabilitation. Sensitivity analysis included a health service perspective and varied inflation rates with no change to the significant findings of the meta-analyses.

Conclusions:
Based on this systematic review and meta-analyses, a single rehabilitation service may not provide health economic benefits for all patient groups and situations. For some patients, inpatient rehabilitation may be the most cost-effective method of providing rehabilitation; yet, for other patients, rehabilitation in the home or community may be the most cost-effective model of care. To achieve cost-effective outcomes, the ideal combination of rehabilitation services and patient inclusion criteria, as well as further data for nonstroke populations, warrants further research.

Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.

Data-mining twitter and the autism spectrum disorder: a pilot study

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

Community factors influencing child and adolescent depression: A systematic review and meta-analysis

OBJECTIVE: Depression has been identified as a priority disorder among children and adolescents. While numerous reviews have examined the individual and family factors that contribute to child and adolescent depressive symptoms, less is known about community-level risk and protective factors. The aim of this study was to complete a systematic review to identify community risk and protective factors for depression in school-aged children (4-18 years). METHOD: The review adopted the procedures recommended by the Cochrane Non-Randomised Studies Methods Working Group and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted to identify both observational and intervention study designs in both peer-reviewed and non-peer reviewed publications. RESULTS: A total of 21 studies met the inclusion criteria. Seventeen of the 18 community association studies and 2 of the 3 intervention studies reported one or more significant effects. Results indicated that community safety and community minority ethnicity and discrimination act as risk factors for depressive symptoms in school-aged children. Community disadvantage failed to achieve significance in meta-analytic results but findings suggest that the role of disadvantage may be influenced by other factors. Community connectedness was also not directly associated with depressive symptoms. CONCLUSION: There is evidence that a number of potentially modifiable community-level risk and protective factors influence child and adolescent depressive symptoms suggesting the importance of continuing research and intervention efforts at the community-level.

Business Plan, financial and risk analysis from the start-up mathrix

Mathrix is an e-learning math website that will be launched in March 2016. This master thesis offered a unique chance to interact with experienced supervisors in venture capitalism and project investment. It could serve as guidelines for entrepreneurs who intend to raise funds. Starting with the company’s business plan, the thesis focuses on estimating the company’s value with its return on investment using three scenarios and taking into consideration the risks evolved.