Clay mineralogy, chemistry, and diagenesis of late devonian K-bentonite occurrences in northwestern Turkey

Thin beds of tephra (K-bentonites) formed by the diagenesis of volcanic ash are exposed within the limestone-dolomitic limestone successions of the Yilanli formation at Zonguldak and Bartin in northwestern Turkey. They were deposited on the Middle Devonian-Lower Carboniferous shallow carbonate platform of the Zonguldak terrane. In this study, K-bentonite samples collected from Gavurpinari and Yilanli Burnu limestone quarries are investigated in order to reveal their mineralogical and geochemical characteristics and diagenetic evolution. Illite is the major clay mineral in the studied K-bentonites. Additionally, kaolinite and mixed-layer illite-smectite are identified in some samples. The nonclay minerals calcite, dolomite, quartz, gypsum, feldspar, pyrite, and zircon are also found. Crystal-chemical characteristics (Kiibler index, d060 values, and polytypes of illites) from two different sampling locations do not show significant variations. Kiibler index values for the Yilanli Burnu and Gavurpinari sampling locations, 0.47-0.93 (average: 0.71 A°29) and 0.69-0.77 (average: 0.72 A°29), respectively, indicate that illites were affected by high-grade diagenetic conditions. The swelling (or smectite) component (~5%), crystallite size (N = 10-20 nm), and polytype (2M1 > 1Md) data of illites support the same conditions. Illite d060 values of 1.491-1.503 A correspond to a range of octahedral Mg+Fe values of 0.27-0.51 atoms per formula, indicating a composition between end-member muscovite and phengite unit. Trace and rare earth element-based chemical classification of the K-bentonite samples revealed that composition of original volcanic ash is basaltic. Illitization took place by fixation of K from volcanic minerals and ash, and diffusion of elements (Mg+Fe) into and out of the beds during diagenesis. Mineralogical-chemical data point out that these K-bentonites evolved in high-grade diagenetic conditions (approximately 100-150 °C) from the products of volcanic eruptions of disputed sources and distances during the Late Devonian time. © 2015 TÜBİTAK

Building multi-layer social knowledge maps with google maps API

Google Maps is an intuitive online-map service which changes people's way of navigation on Geo-maps. People can explore the maps in a multi-layer fashion in order to avoid information overloading. This paper reports an innovative approach to extend the "power" of Google Maps to adaptive learning. We have designed and implemented a navigator for multi-layer social knowledge maps, namely ProgressiveZoom, with Google Maps API. In our demonstration, the knowledge maps are built from the Interactive System Design (ISD) course at the School of Information Science, University of Pittsburgh. Students can read the textbooks and reflect their individual and social learning progress in a context of pedagogical hierarchical structure

iWAP: ASingle Pass Approach for Web Access Sequential Pattern Mining

With the explosive growth of data availability on the World Wide Web, web usage mining becomes very essential for improving designs of websites, analyzing system performance as well as network communications, understanding user reaction, motivation and building adaptive websites. Web Access Pattern mining (WAP-mine) is a sequential pattern mining technique for discovering frequent web log access sequences. It first stores the frequent part of original web access sequence database on a prefix tree called WAP-tree and mines the frequent sequences from that tree according to a user given minimum support threshold. Therefore, this method is not applicable for incremental and interactive mining. In this paper, we propose an algorithm, improved Web Access Pattern (iWAP) mining, to find web access patterns from web logs more efficiently than the WAP-mine algorithm. Our proposed approach can discover all web access sequential patterns with a single pass of web log databases. Moreover, it is applicable for interactive and incremental mining which are not provided by the earlier one. The experimental and performance studies show that the proposed algorithm is in general an order of magnitude faster than the existing WAP-mine algorithm

Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress

[EN] Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, much data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. One approach for providing health professionals with access to clinical data is to create the infrastructure and interface for a clinical tool to make data accessible in a relevant manner. In this sense, results should be understandable and valuable for cancer experts to evaluate and optimize cancer processes. This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients¿ care processes in the context of the LifeChamps project. Following a co-creation and interactive approach thanks to the Interactive Process Mining paradigm, the tool presents patients¿ progress over time for different clinical models and a graphical and navigable Process Indicator in the context of prostate cancer patients.This work was partially funded by the European Union¿s Horizon 2020 research and innovation program under Grant Agreement No 875329.Valero Ramon, Z.; Fernández Llatas, C.; Collantes, G.; Valdivieso, F.; Billis, A.; Bamidis, P.; Traver Salcedo, V. (2023). Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress. Frontiers in Oncology. 12:1-19. https://doi.org/10.3389/fonc.2022.10434111191

Odanacatib for the treatment of postmenopausal osteoporosis: Development history and design and participant characteristics of LoFT, the Long-term odanacatib Fracture Trial

Summary: Odanacatib is a cathepsin K inhibitor investigated for the treatment of postmenopausal osteoporosis. Phase 2 data indicate that 50 mg once weekly inhibits bone resorption and increases bone mineral density, with only a transient decrease in bone formation. We describe the background, design and participant characteristics for the phase 3 registration trial. Introduction: Odanacatib (ODN) is a selective cathepsin K inhibitor being evaluated for the treatment of osteoporosis. In a phase 2 trial, ODN 50 mg once weekly reduced bone resorption while preserving bone formation and progressively increased BMD over 5 years. We describe the phase III Long-Term ODN Fracture Trial (LOFT), an event-driven, randomized, blinded placebo-controlled trial, with preplanned interim analyses to permit early termination if significant fracture risk reduction was demonstrated. An extension was planned, with participants remaining on their randomized treatment for up to 5 years, then transitioning to open-label ODN. Methods: The three primary outcomes were radiologically determined vertebral, hip, and clinical non-vertebral fractures. Secondary end points included clinical vertebral fractures, BMD, bone turnover markers, and safety and tolerability, including bone histology. Participants were women, 65 years or older, with a BMD T-score ≤−2.5 at the total hip (TH) or femoral neck (FN) or with a prior radiographic vertebral fracture and a T-score ≤−1.5 at the TH or FN. They were randomized to ODN or placebo tablets. All received weekly vitamin D3 (5600 international units (IU)) and daily calcium supplements as needed to ensure a daily intake of approximately 1200 mg. Results: Altogether, 16,713 participants were randomized at 387 centers. After a planned interim analysis, an independent data monitoring committee recommended that the study be stopped early due to robust efficacy and a favorable benefit/risk profile. Following the base study closeout, 8256 participants entered the study extension. Conclusions: This report details the background and study design of this fracture end point trial and describes the baseline characteristics of its participants

Application of data mining techniques using SAS software

Data mining has captured the hearts and minds of business analysts seeking a solution forexploring and modeling vastly larger, more complex and less well-behaved datasets. Exploratorydata analysis, typically consisting of activities like statistical visualization, hypothesis generation,and introductory model fitting is a vital first step in any successful data mining venture.Exploratory data analysis produces direct benefits for data miners in enhanced understanding ofdata, improved clarity and confidence of the modeling results, and avoidance of pitfalls early inthe process. By using data mining techniques to analyze the data that is accumulating and fillingvast data warehouses, organizations can harness more insight from their large data stores to driveproactive decision making. SAS data mining software can surface patterns and trends in yourdata that you may never have thought to look for. This paper will review the usefulness of SAS Tsoftware for exploratory data analysis, interactive regression modeling, and advancedmultidimensional data visualizatio