Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits : A Multi-Ethnic Meta-Analysis of 45,891 Individuals

J. Kaprio, S. Ripatti ja M.-L. Lokki työryhmien jäseniä.Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Modelling and Estimation of Spatiotemporal Surface Dynamics applied to a Middle Himalayan Region

Accurate and timely estimation of the spatiotemporal surface dynamics is very important for natural resource planning and disaster mitigation. This paper discusses a novel technique to assess the patterns of the surfaces of a particular severe landslide susceptible zone (Kullu-Larji-Rampur geological window, near Aut village, district Mandi, Himachal Pradesh, India; N 31°44’34.78’’ E 77°12’29.02’’). The spatiotemporal surface dynamics of this region, spanning over last 20 years (1989 - 2009), has been modelled using Landsat TM images acquired during summers of 1989, 2000 and 2009. The proposed technique uses image processing to derive regression models of selected area segments, these models are then used to measure area under the curve to estimate the surface area changes. The surface area changes thus obtained have also been validated by standard method of pixel counting. Principal component analysis has been done in order to understand the correlations amongst the estimated parameters, namely; segment lengths, percentage area change and the area change in the first (1989-2000) and second (2000-2009) decades. The results obtained show a fair degree of accuracy as compared to the standard method of pixel counting

Measurement of changes in glacier extent in the Rimo glacier, a sub-range of the Karakoram Range, determined from Landsat imagery

Accurate estimation of the spatiotemporal surface dynamics is very important for natural resource planning. This paper discusses a novel approach for the study of the surface patterns of a particular glacier Rimo located at 35°21′21″N77°22′05″E, about 20 km northeast of the snout of Siachen. Change detection in multiple images of the same location taken at different time intervals are of widely circulated use due to a large number of applications in various disciplines such as climate change, remote sensing and so on. The proposed technique uses image processing to derive regression models of selected glacier segments, these models are then used to measure area under the curve to estimate the surface area changes of the glacier. The surface area changes thus obtained have also been validated by standard method of pixel counting. With the rise in the global warming, the net change in the surface area of the concerned glacier is estimated using statistical analysis from 1998 to 2011. The results obtained show a fair degree of accuracy as compared to the standard method of pixel counting. We also discuss important pre-processing methods used in extracting the final concerned region of interest from a large satellite imagery of fairly average resolution

Human opinion dynamics: An inspiration to solve complex optimization problems

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making

Performance Evaluation of a Novel iTongue for Indian Black Tea Discrimination

In this work, the multi-electrode-single-frequency (MESF), multi-frequency-single-electrode (MFSE), and multi-frequency- multi-electrode (MFME) impedance responses of an impedance-Tongue reported previously, are evaluated for their discriminability of Indian Black Teas. Principal component analysis (PCA) in conjunction with a cluster validity measure, Davis–Bouldin Index (DBI), has been used for discriminability evaluation. The discriminabilities of electrode specific frequency segments chosen by optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have also been evaluated. The results show that the MFSE impedance response of Gold electrode gives the best discriminability without compromising the system complexity as against MESF, MFSE, MFME, and GA/PSO-optimized response. The results also suggest that the cross-sensitivity of electrodes may be enhanced by selecting optimum frequencies and/or electrodes, paralleling the practice of modifying the electrodes. This opens up a new approach towards qualitative and quantitative analysis of complex liquids

A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue)

This paper presents a novel multiobjective wrapper approach using Dynamic Social Impact Theory based optimizer (SITO). A Fuzzy Inference System in conjunction with support vector machines classifier has been used for the optimization of an impedance-Tongue for the classification of samples collected from single batch production of Kangra orthodox black tea. Impedance spectra of the tea samples have been measured in the range of 20 Hz to 1 MHz using a two electrode setup employing platinum and gold electrodes. The proposed approach has been compared, for its robustness and validity using various intra and inter measures, against Genetic Algorithm and binary Particle Swarm Optimization. Feature subset selection methods based on the first and second order statistics have also been employed for comparisons. The proposed approach outperforms the Genetic Algorithm and binary Particle Swarm Optimization

Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)

This paper presents a novel multiobjective wrapper approach using dynamic social impact theory based optimizer (SITO) and moving window time slicing (MWTS) for the performance enhancement of an electronic nose (EN). SITO, in conjunction with principal component analysis (PCA) and support vector machines (SVMs) classifier, has been used for the classification of samples collected from the single batch production of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). The work employs a novel SITO assisted MWTS (SITO-MWTS) technique for identifying the optimum time intervals of the EN sensor array response, which give the maximum classification rate. Results show that, by identifying the optimum time slicing window positions for each sensor response, the performance of an EN can be improved. Also, the sensor response variability is time dependent in a sniffing cycle, and hence good classification can be obtained by selecting different time intervals for different sensors. The proposed method has also been compared with other established techniques for EN feature extraction. The work not only demonstrates the efficacy of SITO for feature selection owing to its simplicity in terms of few control parameters, but also the capability of an EN to differentiate Kangra orthodox black tea samples at different production stages

Development and optimisation of an HPLC method for the routine analysis of catechins, caffeine and gallic acid in tea (Camellia Sinesis)

An efficient and rapid high performance liquid chromatographic (HPLC) assay was developed for the quantification of catechins [(+) – catechin, (−) – epigallocatechin, (−) – epigallocatechingallate, (−) – epicatechin, and (−) – epicatechingallate], caffeine, and gallic acid in tea (Camellia Sinesis var. sinesis). The assay was optimized by varying sample strength, column temperature, gradient type, and detection wavelength. A curved gradient using a Thermo Hypersil ODS column with 0.05% orthophosphoric acid and methanol as mobile phase A and B, respectively, and UV detection at 277.5 nm was employed. It was observed that a curved gradient along with an optimal temperature, dramatically improves the signal to noise ratio and separation profile. The limit of detection (LOD) and limit of quantification (LOQ) were found to be in the range of 1.04–22.81 µg mL−1 and 3.47–76.05 µg mL−1 respectively. The overall precision values obtained from the analysis of Kangra and Darjeeling orthodox tea samples were within the range of 0–0.34 (standard error)