A health data led approach for assessing potential health benefits of green and blue spaces: Lessons from an Irish case study.

Research producing evidence-based information on the health benefits of green and blue spaces often has within its design, the potential for inherent or implicit bias which can unconsciously orient the outcomes of such studies towards preconceived hypothesis. Many studies are situated in proximity to specific or generic green and blue spaces (hence, constituting a green or blue space led approach), others are conducted due to availability of green and blue space data (hence, applying a green or blue space data led approach), while other studies are shaped by particular interests in the association of particular health conditions with presence of, or engagements with green or blue spaces (hence, adopting a health or health status led approach). In order to tackle this bias and develop a more objective research design for studying associations between human health outcomes and green and blue spaces, this paper discussed the features of a methodological framework suitable for that purpose after an initial, year-long, exploratory Irish study. The innovative approach explored by this study (i.e., the health-data led approach) first identifies sample sites with good and poor health outcomes from available health data (using data clustering techniques) before examining the potential role of the presence of, or engagement with green and blue spaces in creating such health outcomes. By doing so, we argue that some of the bias associated with the other three listed methods can be reduced and even eliminated. Finally, we infer that the principles and paradigm adopted by the health data led approach can be applicable and effective in analyzing other sustainability problems beyond associations between human health outcomes and green and blue spaces (e.g., health, energy, food, income, environment and climate inequality and justice etc.). The possibility of this is also discussed within this paper.

An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning.

Electronic properties and absorption spectra are the grounds to investigate molecular electronic states and their interactions with the environment. Modeling and computations are required for the molecular understanding and design strategies of photo-active materials and sensors. However, the interpretation of such properties demands expensive computations and dealing with the interplay of electronic excited states with the conformational freedom of the chromophores in complex matrices (i.e., solvents, biomolecules, crystals) at finite temperature. Computational protocols combining time dependent density functional theory and ab initio molecular dynamics (MD) have become very powerful in this field, although they require still a large number of computations for a detailed reproduction of electronic properties, such as band shapes. Besides the ongoing research in more traditional computational chemistry fields, data analysis and machine learning methods have been increasingly employed as complementary approaches for efficient data exploration, prediction and model development, starting from the data resulting from MD simulations and electronic structure calculations. In this work, dataset reduction capabilities by unsupervised clustering techniques applied to MD trajectories are proposed and tested for the ab initio modeling of electronic absorption spectra of two challenging case studies: a non-covalent charge-transfer dimer and a ruthenium complex in solution at room temperature. The K-medoids clustering technique is applied and is proven to be able to reduce by ∼100 times the total cost of excited state calculations on an MD sampling with no loss in the accuracy and it also provides an easier understanding of the representative structures (medoids) to be analyzed on the molecular scale.

Evaluating the influence of sleep quality and quantity on glycemic control in adults with type 1 diabetes.

Background: Sleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control. Materials and methods: An observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality. Results: A total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure. Conclusions: This study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis.

The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine.

Contributions to Power Grid System Analysis Based on Clustering Techniques.

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters.

Prediction of municipal solid waste generation: an investigation of the effect of clustering techniques and parameters on ANFIS model performance.

The present waste-management system in most developing countries are insufficient to combat the challenge of increasing rate of solid waste generation. Accurate prediction of waste generated through modelling approach will help to overcome the challenge of deficient-planning of sustainable waste-management. In modelling the complexity within a system, a paradigm-shift from classical-model to artificial intelligent model has been necessitated. Previous researches which used Adaptive Neuro-Fuzzy Inference System (ANFIS) for waste generation forecast did not investigate the effect of clustering-techniques and parameters on the performance of the model despite its significance in achieving accurate prediction. This study therefore investigates the impact of the parameters of three clustering-technique namely: Fuzzy c-means (FCM), Grid-Partitioning (GP) and Subtractive-Clustering (SC) on the performance of the ANFIS model in predicting waste generation using South Africa as a case study. Socio-economic and demographic provincial-data for the period 2008-2016 were used as input-variables and provincial waste quantities as output-variable. ANFIS model clustered with GP using triangular input membership-function (tri-MF) and a linear type output membership-function (ANFIS-GP1) is the optimal model with Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Correlation Co-efficient (R2) values of 12.6727, 0.6940, 1.2372 and 0.9392 respectively. Based on the result in this study, ANFIS-GP with a triangular membership-function is recommended for modelling waste generation. The tool presented in this study can be utilized for the national repository of waste generation data by the South Africa Waste Information Centre (SAWIC) in South Africa and in other developing countries.

Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians' Clinical Reasoning by Reducing Patients' Complexity.

Diabetes mellitus type 2 (DM2) is a complex disease associated with chronic inflammation, end-organ damage, and multiple comorbidities. Initiatives are emerging for a more personalized approach in managing DM2 patients. We hypothesized that by clustering inflammatory markers with variables indicating the sociodemographic and clinical contexts of patients with DM2, we could gain insights into the hidden phenotypes and the underlying pathophysiological backgrounds thereof. We applied the k-means algorithm and a total of 30 variables in a group of 174 primary care (PC) patients with DM2 aged 50 years and above and of both genders. We included some emerging markers of inflammation, specifically, neutrophil-to-lymphocyte ratio (NLR) and the cytokines IL-17A and IL-37. Multiple regression models were used to assess associations of inflammatory markers with other variables. Overall, we observed that the cytokines were more variable than the marker NLR. The set of inflammatory markers was needed to indicate the capacity of patients in the clusters for inflammatory cell recruitment from the circulation to the tissues, and subsequently for the progression of end-organ damage and vascular complications. The hypothalamus-pituitary-thyroid hormonal axis, in addition to the cytokine IL-37, may have a suppressive, inflammation-regulatory role. These results can help PC physicians with their clinical reasoning by reducing the complexity of diabetic patients.

Methodological proposal for the identification of tourist routes in a particular region through clustering techniques.

The tourism sector has an essential role in the sustainable development of a country. Therefore, in this research we propose a methodology to identify tourist routes that integrate the most important Points Of Interest in a region taking up as criteria profile characteristics in common between the sites evaluated using clustering techniques. To attain this goal, firstly, a literature review focused on compiled information used for location selection and evaluation in attraction potential sites. Then, clustering techniques are applied to identify similarities between sites, and finally, a layout of tourist routes is presented. We applied this methodology using data from a Region in Colombia. As a result, eight factors are proposed: Natural, Cultural, Tourist Plant, Infrastructure, Superstructure, Accessibility, Human and Tourist Capital and Security. From the second phase, three tourist groups were identified with three tourism factors for each of them; and then, two examples of tourist routes are proposed.

Novel Study of Model-Based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process.

BACKGROUND: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. Identification of similar genes between tissues in different ages may also help to discover the general mechanism of aging or to discover more effective therapeutic decisions. OBJECTIVE: According to the wide application of model-based clustering techniques, the aim is to evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal Distributions (MMVNDs). METHODS: In this study, the expression of aging data from NCBI's Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differentially expressed between different tissues were selected and then clustered together through two methods. Finally, the biological significance of clusters was evaluated, using their ability to find genes in the cell using Enricher. RESULTS: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria, the cancer development incidence, and the chronic kidney failure, respectively. CONCLUSION: Results showed that most biological changes of aging between tissues are related to the specific components of immune cells. Also, the application of MMVNDs can increase the ability to find similar genes.

Cluster Analysis of Microarray Data.

The cluster analysis has been widely applied by researchers from several scientific fields over the last decades. Advances in knowledge of biological phenomena have revived a great interest in cluster analysis due in part to the large amount of microarray data. Traditional clustering algorithms show, apart from the need of user-defined parameters, clear limitations to handle microarray data owing to its inherent characteristics: high-dimensional-low-sample-sized, highly redundant, and noisy. That has motivated the study of clustering algorithms tailored to the task of analyzing microarray data, which currently continue being developed and adapted. The present chapter is devoted to review clustering methods with different cluster analysis approaches in the challenging context of microarray data. Furthermore, the validation of the clustering results is briefly discussed by means of validity indexes used to assess the goodness of the number of clusters and the induced cluster assignments.

Cloud based framework for diagnosis of diabetes mellitus using K-means clustering.

Diabetes mellitus is a serious health problem affecting the entire population all over the world for many decades. It is a group of metabolic disorder characterized by chronic disease which occurs due to high blood sugar, unhealthy foods, lack of physical activity and also hereditary. The sorts of diabetes mellitus are type1, type2 and gestational diabetes. The type1 appears during childhood and type2 diabetes develop at any age, mostly affects older than 40. The gestational diabetes occurs for pregnant women. According to the statistical report of WHO 79% of deaths occurred in people under the age of 60, due to diabetes. With a specific end goal to deal with the vast volume, speed, assortment, veracity and estimation of information a scalable environment is needed. Cloud computing is an interesting computing model suitable for accommodating huge volume of dynamic data. To overcome the data handling problems this work focused on Hadoop framework along with clustering technique. This work also predicts the occurrence of diabetes under various circumstances which is more useful for the human. This paper also compares the efficiency of two different clustering techniques suitable for the environment. The predicted result is used to diagnose which age group and gender are mostly affected by diabetes. Further some of the attributes such as hyper tension and work nature are also taken into consideration for analysis.

Modelling background air pollution exposure in urban environments: Implications for epidemiological research.

Background pollution represents the lowest levels of ambient air pollution to which the population is chronically exposed, but few studies have focused on thoroughly characterizing this regime. This study uses clustering statistical techniques as a modelling approach to characterize this pollution regime while deriving reliable information to be used as estimates of exposure in epidemiological studies. The background levels of four key pollutants in five urban areas of Andalusia (Spain) were characterized over an 11-year period (2005-2015) using four widely-known clustering methods. For each pollutant data set, the first (lowest) cluster representative of the background regime was studied using finite mixture models, agglomerative hierarchical clustering, hidden Markov models (hmm) and k-means. Clustering method hmm outperforms the rest of the techniques used, providing important estimates of exposures related to background pollution as its mean, acuteness and time incidence values in the ambient air for all the air pollutants and sites studied.

Automatic image classification for the urinoculture screening.

Urinary tract infections (UTIs) are considered to be the most common bacterial infection and, actually, it is estimated that about 150 million UTIs occur world wide yearly, giving rise to roughly $6 billion in healthcare expenditures and resulting in 100,000 hospitalizations. Nevertheless, it is difficult to carefully assess the incidence of UTIs, since an accurate diagnosis depends both on the presence of symptoms and on a positive urinoculture, whereas in most outpatient settings this diagnosis is made without an ad hoc analysis protocol. On the other hand, in the traditional urinoculture test, a sample of midstream urine is put onto a Petri dish, where a growth medium favors the proliferation of germ colonies. Then, the infection severity is evaluated by a visual inspection of a human expert, an error prone and lengthy process. In this paper, we propose a fully automated system for the urinoculture screening that can provide quick and easily traceable results for UTIs. Based on advanced image processing and machine learning tools, the infection type recognition, together with the estimation of the bacterial load, can be automatically carried out, yielding accurate diagnoses. The proposed AID (Automatic Infection Detector) system provides support during the whole analysis process: first, digital color images of Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate classification of the infections and their severity evaluation are performed. The AID system speeds up the analysis, contributes to the standardization of the process, allows result repeatability, and reduces the costs. Moreover, the continuous transition between sterile and external environments (typical of the standard analysis procedure) is completely avoided.

A validation of the spectral power clustering technique (SPCT) by using a Rogowski coil in partial discharge measurements.

Both in industrial as in controlled environments, such as high-voltage laboratories, pulses from multiple sources, including partial discharges (PD) and electrical noise can be superimposed. These circumstances can modify and alter the results of PD measurements and, what is more, they can lead to misinterpretation. The spectral power clustering technique (SPCT) allows separating PD sources and electrical noise through the two-dimensional representation (power ratio map or PR map) of the relative spectral power in two intervals, high and low frequency, calculated for each pulse captured with broadband sensors. This method allows to clearly distinguishing each of the effects of noise and PD, making it easy discrimination of all sources. In this paper, the separation ability of the SPCT clustering technique when using a Rogowski coil for PD measurements is evaluated. Different parameters were studied in order to establish which of them could help for improving the manual selection of the separation intervals, thus enabling a better separation of clusters. The signal processing can be performed during the measurements or in a further analysis.

Model selection for degree-corrected block models.

The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks. We illustrate these challenges, and show one way to resolve them, by considering the key network-analysis problem of dividing a graph into communities or blocks of nodes with homogeneous patterns of links to the rest of the network. The standard tool for undertaking this is the stochastic block model, under which the probability of a link between two nodes is a function solely of the blocks to which they belong. This imposes a homogeneous degree distribution within each block; this can be unrealistic, so degree-corrected block models add a parameter for each node, modulating its overall degree. The choice between ordinary and degree-corrected block models matters because they make very different inferences about communities. We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs. We also develop linear-time approximations for log-likelihoods under both the stochastic block model and the degree-corrected model, using belief propagation. Applications to simulated and real networks show excellent agreement with our approximations. Our results thus both solve the practical problem of deciding on degree correction and point to a general approach to model selection in network analysis.

EVALUACIÓN DE LAS SOLICITUDES DE MANTENIMIENTO CORRECTIVO USANDO TÉCNICAS DE AGRUPAMIENTO Y REGLAS DE ASOCIACIÓN

En este trabajo se exponen los resultados obtenidos de la aplicación de técnicas de descubrimiento de asociaciones y de agrupamiento para resolver el problema de la baja eficiencia presentado en un servicio de esterilización de un hospital en estudio. El objetivo fue detectar y discriminar las causas fundamentales que contribuyeron al surgimiento del problema presentado para luego solucionarlo. Para realizar esta investigación se recabó la información contenida en las solicitudes de servicio de mantenimiento correctivo y las órdenes de trabajos durante el período 2002-2004. Primeramente se segmentó la información contenida en el indicador en estudio: razón de las solicitudes de servicio de mantenimiento correctivo vs. cantidad de equipos por tipos de equipos, por servicios, por fabricante (OEM, del inglés Original Equipment Manufacturer) y por modelos. Luego con las técnicas de descubrimiento de asociaciones aplicadas se encontraron las causas fundamentales por las cuales se solicitaban los reportes de servicios. Éstas fueron: falta de entrenamiento en usuarios, fallos intrínsecos en los dispositivos médicos y malas políticas en el establecimiento de la frecuencia del mantenimiento programado. Las técnicas de agrupamientos pudieron discriminar las causas fundamentales por las cuales los dispositivos médicos del servicio de esterilización fallaban. Éstas fueron debido a fallos en el sistema de suministro de vapor y agua que alimenta las unidades de esterilización (en un 75% de los casos). Se tomaron medidas correctoras durante el período 2005-2006, que contribuyeron a que el indicador bajo estudio disminuyera de 6,4 a 0,4 unidades.

In this research association discovering and clustering techniques for the resolution of the low efficiency problem in the sterilization service in a hospital under study were used. The aim was to find and to discriminate the main causes of the problem under study and then to apply corrective solutions. To conduct this research the information contained in corrective maintenance work orders and service requests in the period under study (2002-2004) was collected. First a segmentation of the information was carried out using the indicator: corrective service request versus number of medical devices. The levels of the information segmentation were: equipment types, services or cost centre, original equipment manufacturer and models. Then the association discovery technique was used. It revealed that the main causes of low efficiency in sterilization service were: users' training (errors in operation procedures), intrinsic failures in medical devices, and bad scheduled maintenance policies. Clustering technique uncovered the main causes of failures: malfunctioning of the power supply system (steam and water, in 75% of all cases). With the evidence obtained corrective actions were taken. The service requests dropped dramatically from 6.4 to 0.4 during the period 2005-2006.