A green strategy for federated and heterogeneous clouds with communicating workloads.

Providers of cloud environments must tackle the challenge of configuring their system to provide maximal performance while minimizing the cost of resources used. However, at the same time, they must guarantee an SLA (service-level agreement) to the users. The SLA is usually associated with a certain level of QoS (quality of service). As response time is perhaps the most widely used QoS metric, it was also the one chosen in this work. This paper presents a green strategy (GS) model for heterogeneous cloud systems. We provide a solution for heterogeneous job-communicating tasks and heterogeneous VMs that make up the nodes of the cloud. In addition to guaranteeing the SLA, the main goal is to optimize energy savings. The solution results in an equation that must be solved by a solver with nonlinear capabilities. The results obtained from modelling the policies to be executed by a solver demonstrate the applicability of our proposal for saving energy and guaranteeing the SLA.

Database constraints applied to metabolic pathway reconstruction tools.

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

Low-dose acetylsalicylic acid for cancer prevention considering risk factors: a retrospective cohort study.

PURPOSE: Aspirin (acetylsalicylic acid) has been reported to protect against certain cancers. However, patient-related risk factors may moderate protective effects, including excess weight, smoking, risky alcohol use, and diabetes. We explore the cancer-risk relationship between aspirin intake and those four factors. METHODS: Retrospective cohort study of cancers, aspirin intake, and four risk factors in persons aged ≥50 years. Participants received medication during 2007-2016, and cancers were diagnosed in 2012-2016. Adjusted hazard ratios (aHR) for 95% confidence intervals (95%CI) were calculated for aspirin intake and risk factors using Cox proportional hazard modeling. RESULTS: Of 118,548 participants, 15,793 consumed aspirin, and 4003 had cancer. Results indicated a significant protective effect of aspirin against colorectal (aHR: 0.7; 95%CI: 0.6-0.8), pancreatic (aHR: 0.5; 95%CI: 0.2-0.9), prostate (aHR: 0.6; 95%CI: 0.5-0.7) cancers and lymphomas (aHR: 0.5; 95%CI: 0.2-0.9), and also, although not significantly, against esophageal (aHR: 0.5; 95%CI: 0.2-1.8), stomach (aHR: 0.7; 95%CI: 0.4-1.3), liver (aHR: 0.7; 95%CI: 0.3-1.5), breast (aHR: 0.8; 95%CI: 0.6-1.0), and lung and bronchial (aHR: 0.9; 95%CI: 0.7-1.2) cancers. Aspirin intake was not significantly protective against leukemia (aHR: 1.0; 95%CI: 0.7-1.4) or bladder cancer (aHR: 1.0; 95%CI: 0.8-1.3). CONCLUSIONS: Our results suggest that aspirin intake is associated with a reduced incidence of colorectal, pancreatic, and prostate cancers and lymphomas.

Acetylsalicylic Acid Effect in Colorectal Cancer Taking into Account the Role of Tobacco, Alcohol and Excess Weight.

Excess weight, smoking and risky drinking are preventable risk factors for colorectal cancer (CRC). However, several studies have reported a protective association between aspirin and the risk of CRC. This article looks deeper into the relationships between risk factors and aspirin use with the risk of developing CRC. We performed a retrospective cohort study of CRC risk factors and aspirin use in persons aged >50 years in Lleida province. The participants were inhabitants with some medication prescribed between 2007 and 2016 that were linked to the Population-Based Cancer Registry to detect CRC diagnosed between 2012 and 2016. Risk factors and aspirin use were studied using the adjusted HR (aHR) with 95% confidence intervals (CI) using a Cox proportional hazard model. We included 154,715 inhabitants of Lleida (Spain) aged >50 years. Of patients with CRC, 62% were male (HR = 1.8; 95% CI: 1.6-2.2), 39.5% were overweight (HR = 2.8; 95% CI: 2.3-3.4) and 47.3% were obese (HR = 3.0; 95% CI: 2.6-3.6). Cox regression showed an association between aspirin and CRC (aHR = 0.7; 95% CI: 0.6-0.8), confirming a protective effect against CRC and an association between the risk of CRC and excess weight (aHR = 1.4; 95% CI: 1.2-1.7), smoking (aHR = 1.4; 95% CI: 1.3-1.7) and risky drinking (aHR = 1.6; 95% CI: 1.2-2.0). Our results show that aspirin use decreased the risk of CRC and corroborate the relationship between overweight, smoking and risky drinking and the risk of CRC.

The Use of Multiple Correspondence Analysis to Explore Associations Between Categories of Qualitative Variables and Cancer Incidence.

BACKGROUND: Previous works have shown that risk factors for some kinds of cancer depend on people's lifestyle (e.g. rural or urban residence). This article looks into this, seeking relationships between cancer, age group, gender and population in the region of Lleida (Catalonia, Spain) using Multiple Correspondence Analysis (MCA). METHODS: The dataset analysed was made up of 3408 cancer episodes between 2012 and 2014, extracted from the Population-based Cancer Registry (PCR) for Lleida province. The cancers studied were colon and rectal (1059 cases), lung (551 cases), urinary bladder (446 cases), prostate (609 cases) and breast (743 cases). The MCA technique was applied and used to search relationships among the main qualitative features. The basic statistics were the percentage explaining (variance), the inertia and the contribution of each qualitative variable. RESULTS: General outcomes showed a low and moderate contribution of living in rural areas to colorectal and male prostate cancer. Males in urban areas were slightly and heavily affected by lung and urinary bladder cancer respectively. The analysis of each cancer provided additional information. Colorectal cancer greatly affected males aged <60, urban residents aged 70-79, and rural females aged ≥ 80. The impact of lung cancer was high among urban females <60, moderate among males aged 70-79 and high among rural females aged ≥ 80. The results for urinary bladder cancer results were similar to those for lung cancer. Prostate cancer affected both the <60 and ≥ 80 age groups significantly in rural areas. Breast cancer hit the 70-79 group significantly and, somewhat less so, rural females aged ≥ 80. CONCLUSIONS: MCA was a significant help for detecting the contributions of qualitative variables and the associations between them. MCA has proven to be an effective technique for analyzing the incidence of cancer. The outcomes obtained help to corroborate suspected trends, as well as detecting and stimulating new hypotheses about the risk factors associated with a specific area and cancer. These findings will be helpful for encouraging new studies and prevention campaigns to highlight observed singularities.

MetReS, an Efficient Database for Genomic Applications.

MetReS (Metabolic Reconstruction Server) is a genomic database that is shared between two software applications that address important biological problems. Biblio-MetReS is a data-mining tool that enables the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the processes of interest and their function. The main goal of this work was to identify the areas where the performance of the MetReS database performance could be improved and to test whether this improvement would scale to larger datasets and more complex types of analysis. The study was started with a relational database, MySQL, which is the current database server used by the applications. We also tested the performance of an alternative data-handling framework, Apache Hadoop. Hadoop is currently used for large-scale data processing. We found that this data handling framework is likely to greatly improve the efficiency of the MetReS applications as the dataset and the processing needs increase by several orders of magnitude, as expected to happen in the near future.

A production planning model considering uncertain demand using two-stage stochastic programming in a fresh vegetable supply chain context.

Production planning models are achieving more interest for being used in the primary sector of the economy. The proposed model relies on the formulation of a location model representing a set of farms susceptible of being selected by a grocery shop brand to supply local fresh products under seasonal contracts. The main aim is to minimize overall procurement costs and meet future demand. This kind of problem is rather common in fresh vegetable supply chains where producers are located in proximity either to processing plants or retailers. The proposed two-stage stochastic model determines which suppliers should be selected for production contracts to ensure high quality products and minimal time from farm-to-table. Moreover, Lagrangian relaxation and parallel computing algorithms are proposed to solve these instances efficiently in a reasonable computational time. The results obtained show computational gains from our algorithmic proposals in front of the usage of plain CPLEX solver. Furthermore, the results ensure the competitive advantages of using the proposed model by purchase managers in the fresh vegetables industry.

BPcontrol. A Mobile App to Monitor Hypertensive Patients.

BACKGROUND: Hypertension or high blood pressure is on the rise. Not only does it affect the elderly but is also increasingly spreading to younger sectors of the population. Treating this condition involves exhaustive monitoring of patients. The current mobile health services can be improved to perform this task more effectively. OBJECTIVE: To develop a useful, user-friendly, robust and efficient app, to monitor hypertensive patients and adapted to the particular requirements of hypertension. METHODS: This work presents BPcontrol, an Android and iOS app that allows hypertensive patients to communicate with their health-care centers, thus facilitating monitoring and diagnosis. Usability, robustness and efficiency factors for BPcontrol were evaluated for different devices and operating systems (Android, iOS and system-aware). Furthermore, its features were compared with other similar apps in the literature. RESULTS: BPcontrol is robust and user-friendly. The respective start-up efficiency of the Android and iOS versions of BPcontrol were 2.4 and 8.8 times faster than a system-aware app. Similar values were obtained for the communication efficiency (7.25 and 11.75 times faster for the Android and iOS respectively). When comparing plotting performance, BPcontrol was on average 2.25 times faster in the Android case. Most of the apps in the literature have no communication with a server, thus making it impossible to compare their performance with BPcontrol. CONCLUSIONS: Its optimal design and the good behavior of its facilities make BPcontrol a very promising mobile app for monitoring hypertensive patients.

S-PC: an e-treatment application for management of smoke-quitting patients.

The main objective of this paper is to present a new program that facilitates the management of people who want to quit smoking, implemented through an e-treatment software called S-PC (Smoker Patient Control). S-PC is a web-based application that manages groups of patients, provides a bidirectional communication through mobile text messages and e-mails between patients and clinicians and offers advice and control to keep track of the patients and their status. A total of 229 patients were enrolled in the study, randomly divided into two groups, although some variables were tested to ensure that there were no significant differences between the groups that could have an impact on the outcome of the treatment. There were no significant differences between the two groups regarding the ratio/number of males/females, tobacco dependence, co-oximetry, average cigarette consumption, current age and age when smoking started. The first group was made up of 104 patients (45.4% of the total) and followed a treatment that incorporated the S-PC tool, while the second one had 125 patients without the S-PC tool. S-PC was evaluated for its effectiveness at assisting the patients to give up smoking, and its effect on clinician time management. 74% of the S-PC group completed the treatment without relapses and remained abstinent three months after the completion of the treatment, understanding abstinence as being continuous (with no relapses allowed and co-oximetry below 1 ppm) from the day of stopping. In contrast only 45.6% of the No S-PC group completed the treatment without relapses and remained abstinent three months after completion of the treatment. The rate of admittance to the program has doubled in one year and patients went from having to wait for 3 months to be immediately admitted into the program. This therapeutic e-health program aims at maximizing the number of patients that a professional can effectively help to quit smoking. In addition, the system also detects patients who are not progressing appropriately, allowing the professional to improve their treatment parameters dynamically.