Anti-apoE immunotherapy inhibits amyloid accumulation in a transgenic mouse model of Aβ amyloidosis

The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for Alzheimer’s disease (AD). The influence of apoE on amyloid β (Aβ) accumulation may be the major mechanism by which apoE affects AD. ApoE interacts with Aβ and facilitates Aβ fibrillogenesis in vitro. In addition, apoE is one of the protein components in plaques. We hypothesized that certain anti-apoE antibodies, similar to certain anti-Aβ antibodies, may have antiamyloidogenic effects by binding to apoE in the plaques and activating microglia-mediated amyloid clearance. To test this hypothesis, we developed several monoclonal anti-apoE antibodies. Among them, we administered HJ6.3 antibody intraperitoneally to 4-mo-old male APPswe/PS1ΔE9 mice weekly for 14 wk. HJ6.3 dramatically decreased amyloid deposition by 60–80% and significantly reduced insoluble Aβ40 and Aβ42 levels. Short-term treatment with HJ6.3 resulted in strong changes in microglial responses around Aβ plaques. Collectively, these results suggest that anti-apoE immunization may represent a novel AD therapeutic strategy and that other proteins involved in Aβ binding and aggregation might also be a target for immunotherapy. Our data also have important broader implications for other amyloidosis. Immunotherapy to proteins tightly associated with misfolded proteins might open up a new treatment option for many protein misfolding diseases

Mycobacterium tuberculosis complex genetic diversity: mining the fourth international spoligotyping database (SpolDB4) for classification, population genetics and epidemiology

BACKGROUND: The Direct Repeat locus of the Mycobacterium tuberculosis complex (MTC) is a member of the CRISPR (Clustered regularly interspaced short palindromic repeats) sequences family. Spoligotyping is the widely used PCR-based reverse-hybridization blotting technique that assays the genetic diversity of this locus and is useful both for clinical laboratory, molecular epidemiology, evolutionary and population genetics. It is easy, robust, cheap, and produces highly diverse portable numerical results, as the result of the combination of (1) Unique Events Polymorphism (UEP) (2) Insertion-Sequence-mediated genetic recombination. Genetic convergence, although rare, was also previously demonstrated. Three previous international spoligotype databases had partly revealed the global and local geographical structures of MTC bacilli populations, however, there was a need for the release of a new, more representative and extended, international spoligotyping database. RESULTS: The fourth international spoligotyping database, SpolDB4, describes 1939 shared-types (STs) representative of a total of 39,295 strains from 122 countries, which are tentatively classified into 62 clades/lineages using a mixed expert-based and bioinformatical approach. The SpolDB4 update adds 26 new potentially phylogeographically-specific MTC genotype families. It provides a clearer picture of the current MTC genomes diversity as well as on the relationships between the genetic attributes investigated (spoligotypes) and the infra-species classification and evolutionary history of the species. Indeed, an independent Naïve-Bayes mixture-model analysis has validated main of the previous supervised SpolDB3 classification results, confirming the usefulness of both supervised and unsupervised models as an approach to understand MTC population structure. Updated results on the epidemiological status of spoligotypes, as well as genetic prevalence maps on six main lineages are also shown. Our results suggests the existence of fine geographical genetic clines within MTC populations, that could mirror the passed and present Homo sapiens sapiens demographical and mycobacterial co-evolutionary history whose structure could be further reconstructed and modelled, thereby providing a large-scale conceptual framework of the global TB Epidemiologic Network. CONCLUSION: Our results broaden the knowledge of the global phylogeography of the MTC complex. SpolDB4 should be a very useful tool to better define the identity of a given MTC clinical isolate, and to better analyze the links between its current spreading and previous evolutionary history. The building and mining of extended MTC polymorphic genetic databases is in progress

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

What to do with all these Bayes factors: How to make Bayesian reports in deception research more informative

Bayes factors quantify the evidence in support of the null (absence of an effect) or the alternative hypothesis (presence of an effect). Based on commonly used cut‐offs, Bayes factors between 1/3 and 3 are interpreted as evidentially weak, and one typically concludes there is an absence of evidence. In this commentary on Warmelink, Subramanian, Tkacheva, and McLatchie (Legal Criminol Psychol 24, 2019, 258), we discuss how a Bayesian report can be made more informative. Firstly, this implies a departure from the labels provided by commonly used cut‐offs when reporting Bayes factors. Instead, we encourage researchers to report the value of the Bayes factors, or to convert these values into nominal support for the hypotheses. Secondly, researchers can provide recommendations to design follow‐up studies by examining the posterior distribution of the magnitude of the effect size. Lastly, we show how individual Bayes factors can be evaluated in the context of large‐scale meta‐analyses

Rule Determination and Process Verification Using Business Capabilities

Part 2: Process ModelingInternational audienceBusiness architectures are an important part of any enterprise architecture containing business processes and business capabilities. High quality business processes are key factors for the success of a company. Hence, the quality and the correctness or compliance have to be verified. We propose to use the business capabilities for an efficient and easily understandable definition of rules to perform this verification. The rule specification is based on rule patterns to define requirements from an operational point of view. These patterns are derived from experience gained in projects for modeling and optimization of business processes with extensive manual checks. For the rule validation we rely on model checking as an established technology to cope with the dynamic properties of processes. We present a tool based approach to automate this verification integrated in a unique system with a common user interface

The Bayesian methodology of Sir Harold Jeffreys as a practical alternative to the p value hypothesis test

Contains fulltext : 226717.pdf (publisher's version ) (Closed access)Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has to address the practitioners' research question, which - for better or for worse- most often concerns the test of a hypothesis, and less often concerns the estimation of a parameter; (2) available: it must have a concrete implementation for practitioners' statistical workhorses such as the t test, regression, and ANOVA; and (3) easy to use: methods that demand practitioners switch to the theoreticians' programming tools will face an uphill struggle for adoption. The above desiderata are fulfilled by Harold Jeffreys's Bayes factor methodology as implemented in the open-source software JASP. We explain Jeffreys's methodology and showcase its practical relevance with two examples.9 p

A tutorial on conducting and interpreting a Bayesian ANOVA in JASP

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics - and Bayesian ANOVA in particular - is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general