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Background Centre hospitalier de l’Université de Montréal in Canada introduced accompanying patients (APs) into the breast cancer care trajectory. APs are patients who have been treated for breast cancer and have been integrated into the clinical team to expand the services offered to people affected by cancer. This study describes the profiles of the people who received the support and explores whether one-offs vs ongoing encounters with APs influence their experience of care, on self-efficacy in coping with cancer, and on their level of psychological distress. Methods An exploratory cross-sectional study was carried out among patients to compare patients who had one encounter with an AP (G1) with those who had had several encounters (G2). Five questionnaires were administered on socio-demographic characteristics, care pathway, evaluation of the support experience, self-efficacy in coping with cancer, and level of psychological distress. Logbooks, completed by the APs, determined the number of encounters. Linear regression models were used to evaluate the associations between the number of encounters, patient characteristics, care pathway, number of topics discussed, self-efficacy measures in coping with cancer, and level of psychological distress. Results Between April 2020 and December 2021, 60% of 535 patients who were offered support from an AP accepted. Of these, one hundred and twenty-four patients participated in the study. The study aimed to recruit a minimum of 70 patients with the expectation of obtaining at least 50 participants, assuming a response rate of 70%. There were no differences between G1 and G2 in terms of sociodemographic data and care pathways. Statistical differences were found between G1 and G2 for impacts on and the return to daily life (p = 0.000), the return to the work and impacts on professional life (p = 0.044), announcement of a diagnosis to family and friends (p = 0.033), and strategies for living with treatment under the best conditions (p = 0.000). Significant differences were found on the topics of cancer (p = 0.000), genetic testing (p = 0.023), therapeutic options (p = 0.000), fatigue following treatment (p = 0.005), pain and discomfort after treatment or surgery (p = 0.000), potential emotions and their management (p = 0.000) and the decision-making processes (p = 0.011). A significant relationship was found between the two groups for patients’ ability to cope with cancer (p = 0.038), and their level of psychological distress at different stages of the care pathway (p = 0.024). Conclusions This study shows differences between one-time and ongoing support for cancer patients. It highlights the potential for APs to help patients develop self-efficacy and cope with the challenges of cancer treatment.

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted., 33 pages, 10 figures

Public Perspectives on Exposure Notification Apps: A Patient and Citizen Co-Designed Study

The objective was to determine the mean morphometric characteristics of the rectovaginal septum (RVS) and its variations in correlation with the number of pregnancies, method of delivery, parity, and estrogenic exposure. An observational, cross-sectional, retrospective, descriptive, and comparative study was carried out. Pelvic MRI of Hispanic women (≥15 years of age) from the northeast of Mexico were obtained. Age and obstetric and gynecological history were registered and the sample women were categorized by their variables. Length and thickness measurements were standardized. A total of 102 MRI studies were included, with a mean age of 41; 24.5% were nulligravida, the rest primi- or multigravida. Vaginal delivery was the most common type (49.35%), 16.88% had a cesarean section, and 31.17% had mixed delivery. 74.5% of the women were premenopausal. The mean RVS length was 73.2 ± 15.3 mm, with a thickness of 2.8 ± 1.7, 2.2 ± 1.2, and 2.5 ± 1.3 mm for the upper, middle, and lower thirds respectively. There were tendencies to increase the length of the RVS, and the thickness of the upper and middle thirds in the non-pregnancy and the at-least-one-pregnancy groups; to increase the length and middle-third thickness in those with mixed delivery, and increased upper- and lower-third thickness in those with only a cesarean section. Multiparous women with vaginal delivery had significantly longer and thicker RVS than primiparous. Premenopausal women had significantly longer RVS with a tendency to lose thickness toward postmenopause. The morphology of RVS can be modified by different factors such as age, number of pregnancies, number of births, and estrogenic exposure. This structure should be evaluated and taken into account in preoperative management and surgical technique planning.

The use of cloud computing for delivering application services over the Internet has gained rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home scheme and increased business presence online have created more demand for computing resources. Many enterprises and organizations are expanding their private data centres and utilizing hybrid or multi-cloud environments for their IT infrastructure. Because of the ever-increasing demand for computing resources, energy consumption and carbon emission have become a pressing issue. Renewable energy sources have been recognized as clean and sustainable alternatives to fossil-fuel based brown energy. However, due to the intermittent nature of availability of renewable energy sources, it brings many challenges to automatically and efficiently schedule tasks under renewable energy constraints and deadlines. Task scheduling with traditional heuristic algorithms are not able to adapt quickly with changing energy availability and stochastic task arrival. In this regard, this work aims at building a novel scheduling policy with deep reinforcement learning, which automatically applies scheduling techniques like workload shifting and cloud -bursting in a geographically distributed hybrid multi-cloud environment consists of multiple private and public clouds. Our primary goals are maximizing renewable energy utilization and avoiding deadline constraint violations. We also introduce user configurable hyper-parameters to enable multi-objective scheduling on cloud cost, makespan and utilization. Our experiment results show that the proposed scheduling approach can achieve the aforementioned objectives dynamically to varying renewable energy availability.

Serverless computing is an event-driven cloud computing architecture for processing requests on-demand, using light weight function containers and a micro-services model. A variety of applications like Internet of Things (IoT) services, edge computing, and stream processing have been introduced to the serverless paradigm. These applications are characterized by their stringent response time requirements, therefore expecting a quick and fault tolerant feedback from the application. The serverless, or Function-as-a-Service (FaaS), paradigm suffers from function ‘cold start’ challenges, where the serverless platform takes time to set up the dependencies, prepare the runtime environment and code for execution before serving the incoming workload. Most of the current works address the problem of cold start by (1) reducing the start-up or preparation time of function containers, or (2) reducing the frequency of function cold starts on the platform. Recent industrial research has identified that factors such as runtime environment, CPU and memory settings, invocation concurrency, and networking requirements, affect the cold start of a function. Therefore, we propose a Reinforcement Learning (Q-Learning) agent setting, to analyze the identified factors such as function CPU utilization, to ascertain the function-invocation patterns and reduce the function cold start frequency by preparing the function instances in advance. The proposed Q-Learning agent interacts with the Kubeless serverless platform by discretizing the environment states, actions and rewards with the use of per-instance CPU utilization, available function instances and success or failure rate of response, respectively. The workload is replicated using the Apache JMeter non-GUI toolkit and our agent is evaluated against the baseline default auto-scale feature of Kubeless. The agent demonstrates the capability of learning the invocation pattern, make informed decisions by preparing the optimal number of function instances over the period of learning, under controlled environment settings.

Streaming applications have become widespread with the advent of big data and IoT. They are latency-sensitive applications that aim to process vast amounts of data in near real time. They are usually modelled as directed graphs and their deployment and orchestration in a cluster of nodes are managed by distributed stream processing systems. These systems are responsible for placing the graph components within the cluster nodes, which determines the application’s communication overhead and ultimately affects performance metrics such as latency and throughput. This work presents an adaptive, heuristic-based, scheduling algorithm for distributed stream processing systems that aims to minimize the latency and maximize the throughput of streaming applications deployed in heterogeneous clusters, while considering the resource constraints of the available nodes. The proposed approach uses a graph partitioning algorithm and real time traffic data monitored from a deployed application to derive a near-optimal operator placement plan that minimizes inter-node communication and balances the overall communication load distribution. We evaluated our approach using three micro-benchmark and two practical applications, and the results demonstrate that our scheduler outperforms the default scheduler of a popular stream processing system and a state-of-the-art algorithm, improving throughput by up to 106% and reducing complete latency by up to 58% for most applications.

Software Defined Networking (SDN) has emerged as a programmable approach for provisioning and managing network resources by defining a clear separation between the control and data forwarding planes. Nowadays SDN has gained significant attention in the military domain. Its use in the battlefield communication facilitates the end-to-end interactions and assists the exploitation of edge computing resources for processing data in the proximity. However, there are still various challenges related to the security and interoperability among several heterogeneous, dynamic, intermittent, and data packet technologies like multi-bearer network (MBN) that need to be addressed to leverage the benefits of SDN in tactical environments. In this chapter, we explicitly analyse these challenges and review the current research initiatives in SDN-enabled tactical networks. We also present a taxonomy on SDN-based tactical network orchestration according to the identified challenges and map the existing works to the taxonomy aiming at determining the research gaps and suggesting future directions.

COVID-19 global pandemic is an unprecedented health crisis. Since the outbreak, many researchers around the world have produced an extensive collection of literatures. For the research community and the general public to digest, it is crucial to analyse the text and provide insights in a timely manner, which requires a considerable amount of computational power. Clouding computing has been widely adopted in academia and industry in recent years. In particular, hybrid cloud is gaining popularity since its two-fold benefits: utilising existing resource to save cost and using additional cloud service providers to gain assess to extra computing resources on demand. In this paper, we developed a system utilising the Aneka PaaS middleware with parallel processing and multi-cloud capability to accelerate the ETL and article categorising process using machine learning technology on a hybrid cloud. The result is then persisted for further referencing, searching and visualising. Our performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.

Although cloud computing became a mainstream industrial computing paradigm, low resource utilization remains a common problem that most warehouse-scale datacenters suffer from. This leads to a significant waste of hardware resources, infrastructure investment, and energy consumption. As the diversity in application workloads grows into an essential characteristic in modern datacenters, task co-location of different workloads to the same compute cluster has gained immense popularity as a heuristic solution for resource utilization optimization. Although the existing co-location methodologies manage to improve resource efficiency to a certain degree, application QoS is usually sacrificed as a trade-off when dealing with resource interference between different applications. This paper proposes a containerized task co-location (CTCL) scheduler to improve resource utilization and minimize task eviction rate. Our CTCL scheduler (1) applies an elastic task co-location strategy to improve resource utilization; and (2) supports a dynamic task rescheduling mechanism to prevent severe QoS degradation from frequent task evictions. We evaluate our approach in terms of resource efficiency and rescheduling cost through the ContainerCloudSim simulator. Our experiments with the Alibaba 2018 workload traces demonstrate that CTCL could improve overall resource efficiency and reduce rescheduling rate by 38% and 99% respectively.

Canada deployed a digital exposure notification app (COVID Alert) as a strategy to support manual contact tracing. Our aims are to (1) assess the use, knowledge, and concerns of the COVID Alert app, (2) identify predictors of app downloads, and (3) develop strategies to promote social acceptability. A 36-item questionnaire was co-designed by 12 citizens and patients partnered with 16 academic researchers and was distributed in the province of Québec, Canada, from May 27 to 28 June 2021. Of 959 respondents, 43% had downloaded the app. Messaging from government sources constituted the largest influence on app download. Infrequent social contacts and perceived app inefficacy were the main reasons not to download the app. Cybersecurity, data confidentiality, loss of privacy, and geolocation were the most frequent concerns. Nearly half of the respondents inaccurately believed that the app used geolocation. Most respondents supported citizen involvement in app development. The identified predictors for app uptake included nine characteristics. In conclusion, this project highlights four key themes on how to promote the social acceptability of such tools: (1) improved communication and explanation of key app characteristics, (2) design features that incentivize adoption, (3) inclusive socio-technical features, and (4) upstream public partnership in development and deployment.

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.

Technological advances and the emergence of the Internet of Things have lead to the collection of vast amounts of scientific data from increasingly powerful scientific instruments and a growing number of distributed sensors. This has not only exacerbated the significance of the analyses performed by scientific applications but has also increased their complexity and scale. Hence, emerging extreme-scale scientific workflows are becoming widespread and so is the need to efficiently automate their deployment on a variety of platforms such as high performance computers, dedicated clusters, and cloud environments. Performance anomalies can considerably affect the execution of these applications. They may be caused by different factors including failures and resource contention and they may lead to undesired circumstances such as lengthy delays in the workflow runtime or unnecessary costs in cloud environments. As a result, it is essential for modern workflow management systems to enable the early detection of this type of anomalies, to identify their cause, and to formulate and execute actions to mitigate their effects. In this work, we propose the use of Hierarchical Temporal Memory (HTM) to detect performance anomalies on real-time infrastructure metrics collected by continuously monitoring the resource consumption of executing workflow tasks. The framework is capable of processing a stream of measurements in an online and unsupervised manner and is successful in adapting to changes in the underlying statistics of the data. This allows it to be easily deployed on a variety of infrastructure platforms without the need of previously collecting data and training a model. We evaluate our approach by using two real scientific workflows deployed in Microsoft Azure’s cloud infrastructure. Our experiment results demonstrate the ability of our model to accurately capture performance anomalies on different resource consumption metrics caused by a variety of competing workloads introduced into the system. A performance comparison of HTM to other online anomaly detection algorithms is also presented, demonstrating the suitability of the chosen algorithm for the problem presented in this work.

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Emerging Workflow as a Service (WaaS) platforms offer scientists a simple, easily accessible, and cost-effective way of deploying their applications in the cloud at anytime and from anywhere. They are multi-tenant frameworks and are designed to manage the execution of a continuous workload of heterogeneous workflows. To achieve this, they leverage the compute, storage, and network resources offered by Infrastructure as a Service (IaaS) providers. Hence, at any given point in time, a WaaS platform should be capable of efficiently scheduling an arbitrarily large number of workflows with different characteristics and quality of service requirements. As a result, we propose a resource provisioning and scheduling strategy designed specifically for WaaS environments. The algorithm is scalable and dynamic to adapt to changes in the environment and workload. It leverages containers to address resource utilization inefficiencies and aims to minimize the overall cost of leasing the infrastructure resources while meeting the deadline constraint of each individual workflow. To the best of our knowledge, this is the first approach that explicitly addresses VM sharing in the context of WaaS by modeling the use of containers in the resource provisioning and scheduling heuristics. Our simulation results demonstrate its responsiveness to environmental uncertainties, its ability to meet deadlines, and its cost-efficiency when compared to a state-of-the-art algorithm.

The climate and environmental crises of our times heighten existing inequalities and pose challenges to our very survival. The lives, livelihoods and bodies of women around the world are at the frontlines of its impacts and of the struggles to resist and build alternatives centred around justice and the sustainability of life. The 2016 AWID Forum gathered ecological and women’s rights activists to share experiences and perspectives on how the exploitation of people and planet is interlinked and interdependent and what shapes our resistance does and must take.

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored for the cloud resource model. The majority of these algorithms focus on coarse-grained billing periods that are much larger than the average execution time of individual tasks. Instead, our work focuses on emerging finer-grained pricing schemes (e.g., per-minute billing) that provide users with more flexibility and the ability to reduce the inherent wastage that results from coarser-grained ones. We propose a scheduling algorithm whose objective is to optimize a workflow’s execution time under a budget constraint; quality of service requirement that has been overlooked in favor of optimizing cost under a deadline constraint. Our proposal addresses fundamental challenges of clouds such as resource elasticity, abundance, and heterogeneity, as well as resource performance variation and virtual machine provisioning delays. The simulation results demonstrate our algorithm’s responsiveness to environmental uncertainties and its ability to generate high-quality schedules that comply with the budget constraint while achieving faster execution times when compared to state-of-the-art algorithms.

To determine the association between tobacco consumption and self-reported visual impairment. We performed a cross-sectional study based on an original cohort study. A non-probabilistic sampling was performed to invite 413 patients of 60 years or more from 11 high-altitude Andean communities (altitude higher than 1500 m above sea level) of Peru between 2013 and 2017. Demographic data and information on tobacco consumption were collected. Associations were determined using a Poisson regression model with 95% confidence intervals (CI). Of the 413 participants, 141 (34.14%) were men and 49 (11.86%) were tobacco users. In the adjusted model, tobacco users presented a high probability of visual impairment with a prevalence ratio of 1.32 (95% CI: 1.18–1.97). We also found that having two or more comorbidities 2.19 (95% CI: 1.53–3.15), receiving health assistance in a pharmacy 3.75 (95% CI: 1.97–7.16), and coffee consumption 1.67 (95% CI: 1.26–2.21) were factors significantly associated with self-reported visual impairment. We determined that in Peruvian high-altitude Andean communities, visual impairment was more frequent in individuals reporting tobacco consumption, taking alternative medicine, going directly to a drug store without primary care physician consultation, having more than one comorbidity, and coffee consumption.

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing., Comment: 51 pages, 3 figures

The workflow is a general notion representing the automated processes along with the flow of data. The automation ensures the processes being executed in the order. Therefore, this feature attracts users from various background to build the workflow. However, the computational requirements are enormous and investing for a dedicated infrastructure for these workflows is not always feasible. To cater to the broader needs, multi-tenant platforms for executing workflows were began to be built. In this paper, we identify the problems and challenges in the multiple workflows scheduling that adhere to the platforms. We present a detailed taxonomy from the existing solutions on scheduling and resource provisioning aspects followed by the survey of relevant works in this area. We open up the problems and challenges to shove up the research on multiple workflows scheduling in multi-tenant distributed systems., Several changes has been done based on reviewers' comments after first round review. This is a pre-print for paper (currently under second round review) submitted to ACM Computing Surveys

Secondary metabolites from the cultures of the dark septate fungal endophyte (DSE) Drechslera sp., isolated from the roots of rye grass (Lollium sp.) and cultured under different experimental conditions, are described here for the first time. The use of suberoylanilidehydroxamic acid (SAHA) and other histone deacetylase inhibitors as epigenetic modifiers in the culture medium was evaluated by LC/MS and LC/MS/MS. Several differences in the metabolite production were detected by means of supervised principal component analysis (PCA) of LC/MS data. The presence of the compounds in the culture medium or in the mycelium was compared. In order to confirm their structure, many of these natural products were isolated from a larger scale culture. These metabolites were characterized as prenylhydroxybenzoic acids and chromans, two compounds, one of each class were previously undescribed, prenylquinoids, diketopiperazines and macrosphelides. Some of the compounds, which were released to the medium, showed good antifungal activity, suggesting that these compounds could protect Lollium from fungal phytopatogens. The use of SAHA as an additive of the cultures also induced the release of hexosylphytosphyngosine to the culture medium. The biotransformation of the inhibitors was observed in addition to the production of antifungal metabolites, showing the ability of this endophytic strain to control xenobiotics. Fil: Siless, Gastón Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina Fil: Gallardo, Gabriela Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina Fil: Rodriguez, Maria Alejandra. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Rincon Galan, Yuliet Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina Fil: Godeas, Alicia Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Cabrera, Gabriela Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina

This paper proposes an architectural framework for the efficient orchestration of containers in cloud environments. It centres around resource scheduling and rescheduling policies as well as autoscaling algorithms that enable the creation of elastic virtual clusters. In this way, the proposed framework enables the sharing of a computing environment between differing client applications packaged in containers, including web services, offline analytics jobs, and backend pre-processing tasks. The devised resource management algorithms and policies will improve utilization of the available virtual resources to reduce operational cost for the provider while satisfying the resource needs of various types of applications. The proposed algorithms will take factors that are previously omitted by other solutions into consideration, including 1) the pricing models of the acquired resources, 2) and the fault-tolerability of the applications, and 3) the QoS requirements of the running applications, such as the latencies and throughputs of the web services and the deadline of the analytical and pre-processing jobs. The proposed solutions will be evaluated by developing a prototype platform based on one of the existing container orchestration platforms., Comment: 11 pages, 4 figures

Dark Septate Endophytes (DSE) is a diverse group of Ascomycetes that colonize the roots of a wide range of plants species. They can be found in all ecosystems, from deserts to the tropics, but also in agroecosystems associated to the crops. Despite the ubiquitous of these fungi, only a few major crops were assessed for this association in field conditions. In general terms, a complex consortium of DSE fungi were observed to colonize the roots of some crops. In this work we study the DSE community associated to soybean in the most productive area of this crop in Argentina. We hypothesized that DSE root colonization level, and the identity/frequency of the isolates switch according to the growth stage of the crop. A total of 34 dark fungal colonies were isolated from two growth stages, being only six isolates asyntomatic in the resynthesis assay. Some taxa as Boeremia sp., Cadophora sp., Coniothyrium sp., Corynespora cassicola, Peyronellaea sp. and Phaeosphaeria sp. were exclusively isolated from seedlings, supporting the hypothesis of DSE fungal consortium switching. The soybean pathogens Corynespora cassicola and Macrophomina phaseolina were isolated from young and mature plants respectively. Additional studies should focus on a fine analysis of the dynamics of these fungi, considering also the driving factors that could determine these changes. To understand these mechanisms may be fundamental for a better sustainable management of the crop. Fil: Rothen, Carolina Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina Fil: Miranda, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina Fil: Aranda Rickert, Adriana Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina Fil: Fracchia, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina Fil: Rodríguez, M.A.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental. Laboratorio de Microbiología del Suelo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina

The use of cloud computing, particularly of Infrastructure as a Service clouds, for the execution of largescale scientific workflows has been a topic of interest in recent years. These environments offer on-demand access to all of the infrastructure required for the deployment of workflows, allowing users to pay only for what they use. This leads to schedulers having to find a trade-off between two conflicting quality of service requirements: time and cost. The majority of research in this area has focused on developing scheduling algorithms that have as objective minimizing the infrastructure cost while meeting a deadline constraint. Few algorithms, however, have addressed the problem of minimizing the execution time of the workflow while meeting a budget constraint. This paper focuses on the latter case. We propose a budget-distribution algorithm that assigns a portion of the overall workflow budget to the individual tasks. This task-level budget then guides the dynamic scheduling process and is continuously refined to reflect any unexpected costs. When compared to the state-of-the-art algorithm, the performance evaluation results demonstrate that in 88% of the cases, our proposal achieves equal or better performance in terms of meeting the budget constraint and achieves lower execution times in 84% of the cases.

Different fungal isolates closely related to Zopfiella erostrata and Cercophora caudata have been found to colonise plant root tissues in an asymptomatic way in the Monte Desert biome of La Rioja Province, Argentina. This interaction has been newly discovered, and the role of these fungi in their desert habitat has never been studied in detail. The objective of this study was to evaluate eight fungal isolates by means of specific physiological traits that could have implications for their interaction with the host plant. The selected isolates, four endophytic and four rhizospheric, were characterised for their ability to produce indole acetic acid, solubilise and mineralise phosphorus (P), and utilise different nitrogen sources. In addition, we evaluated organic acid production and phosphatase activities as mechanisms of P recycling. These analyses revealed that most isolates produced indole acetic acid, and that all isolates could solubilise and mineralise P, although to different degrees. Furthermore, the production of organic acids correlated with P solubilisation levels, which may enhance P availability in soils. Nitrogen utilisation was variable among the isolates, without specific patterns concerning the different sources and fungal isolates tested. Our results demonstrated that the fungal isolates had great variability, probably because they complete their teleomorphic phase in their habitats, generating viable meiotic spores, in addition to the active dispersion of these fungi by the underground rodent Ctenomys aff. knightii from which they were isolated. Although the isolates were variable, the endophytic isolates exhibited improvement in all the P parameters measured. This can be explained by the ability of these isolates to intimately colonise root tissues, while the colonisation by rhizospheric isolates is produced superficially in the rhizoplane. The high occurrence of this interaction, as well as the physiological traits detected, suggest that this kind of fungi could play an important ecological role in the Monte Desert environment, benefiting the establishment and growth of new seedlings in areas occupied by the rodents.

Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.

Infrastructure-as-a-Service clouds offer access to a scalable virtualized infrastructure on a pay-per-use basis. This is greatly beneficial for the deployment of scientific workflows, and as a result considerable effort is being made to develop and update existing workflow management systems to support the cloud resource model. The majority of existing systems are designed to work with traditional distributed platforms such as grids and clusters in which the resources are limited and readily-available. In contrast, clouds offer access to elastic and abundant resources that can be provisioned and deprovisioned on-demand. In this chapter, we present our efforts to extend an existing workflow system, the Cloudbus WMS, to enable the deployment of scientific applications in cloud computing environments. We present a case study to demonstrate the added functionality and evaluate the performance and cost of a well-known astronomy application on Microsoft Azure.

In the present study, we obtained in vitro dual cultures between the liverwort Plagiochasma rupestre and two arbuscular mycorrhizal (AM) fungi: Glomus intraradices and Glomus clarum. Four agarized culture media were tested for optimal growth of P. rupestre. Also, a description of the symbiotic association is provided. Plagiochasma rupestre gametophytes profusely grew axenically in MM with sucrose, and thalli were successfully subcultured under these growth conditions. Arbuscular mycorrhizal fungal hyphae colonized P. rupestre thalli through rhizoids or by forming appresoria in the ventral thallus cells. Arbuscules, mycelia and structures resembling intrathallic spores or vesicles were developed in the internal parenchymatic cells. The pattern of AM colonization in P. rupestre was very similar to the Paris-type. After 100 days of dual culture, the external mycelia of both AM fungal strains formed thousands of small viable spores, suggesting that P. rupestre in vitro culture could be a valuable tool for studying the biology of both symbiotic partners and conserving AM fungi in in vitro germplasm collections.

Aims: To establish the modes of action of the antagonistic fungal strain Clonostachys rosea BAFC3874 isolated from suppressive soils against Sclerotinia sclerotiorum and to determine its potential as a biocontrol agent. Methods and Results: The antagonistic activity of C. rosea BAFC3874 was determined in vitro by dual cultures. The strain effectively antagonized S. sclerotiorum in pot-grown lettuce and soybean plants. Antifungal activity assays of C. rosea BAFC3874 grown in culture established that the strain produced antifungal compounds against S. sclerotiorum associated with secondary metabolism. High mycelial growth inhibition coincided with sclerotia production inhibition. The C. rosea strain produced a microheterogeneous mixture of peptides belonging to the peptaibiotic family. Moreover, mycoparasitism activity was observed in the dual culture. Conclusions: Clonostachys rosea strain BAFC3874 was proved to be an effective antagonist against the aggressive soil-borne pathogen S. sclerotiorum in greenhouse experiments. The main mechanisms involve peptaibiotic metabolite production and mycoparasitism activity. Significance and Impact of the Study: Clonostachys rosea BAFC3874 may be a good fungal biological control agent against S. sclerotiorum. In addition, we were also able to isolate and identify peptaibols, an unusual family of compounds in this genus of fungi.

Aims: To study phosphate solubilization in Penicillium purpurogenum as function of medium pH, and carbon and nitrogen concentrations. Methods and Results: Tricalcium phosphate (CP) solubilization efficiency of P. purpurogenum was evaluated at acid or alkaline pH using different C and N sources. Glucose- and (NH4)2SO4-based media showed the highest P solubilization values followed by fructose. P. purpurogenum solubilizing ability was higher in cultures grown at pH 6·5 than cultures at pH 8·5. Organic acids were detected in both alkaline and neutral media, but the relative percentages of each organic acid differed. Highest P release coincided with the highest organic acids production peak, especially gluconic acid. When P. purpurogenum grew in alkaline media, the nature and concentration of organic acids changed at different N and C concentrations. A factorial categorical experimental design showed that the highest P-solubilizing activity, coinciding with the highest organic acid production, corresponded to the highest C concentration and lowest N concentration. Conclusions: The results described in the present study show that medium pH and carbon and nitrogen concentrations modulate the P solubilization efficiency of P. purpurogenum through the production of organic acids and particularly that of gluconic acid. In the P solubilization optimization studies, glucose and (NH4)2SO4 as C and N sources allowed a higher solubilization efficiency at high pH. Significance and Impact of the Study: This organism is a potentially proficient soil inoculant, especially in P-poor alkaline soils where other P solubilizers fail to release soluble P. Further work is necessary to elucidate whether these results can be extrapolated to natural soil ecosystems, where different pH values are present. Penicillium purpurogenum could be used to develop a bioprocess for the manufacture of phosphatic fertilizer with phosphate calcium minerals.

The fungal diversity structures of soils that are suppressive and non-suppressive to Sclerotinia sclerotiorum were characterised and screened for fungal strains antagonistic to the S. sclerotiorum pathogen. Soil suppressiveness was associated with a particular fungal diversity structure. Principal component analysis showed that antagonism by fungal species in suppressive soils was associated with the occurrence of Fusarium oxysporum, Fusarium solani, Talaromyces flavus var. flavus and Clonostachys rosea f. rosea. In particular, C. rosea f. rosea occurred exclusively in suppressive soil samples, suggesting that this morpho-species plays an important role in suppression of S. sclerotiorum diseases. One strain of C. rosea f. rosea (BAFC1646) was selected for further experiments. Dual-culture assays confirmed the antagonistic behaviour of C. rosea f. rosea BAFC1646 against three different S. sclerotiorum strains. Antifungal activity was corroborated by diffusion assays with metabolite extracts. Greenhouse assays with soybean plants showed that the selected C. rosea f. rosea strain reduced the percentage of dead plants when co-inoculated with S. sclerotiorum. In addition, inclusion of C. rosea f. rosea alone increased shoot lengths significantly. In this work, we established the involvement of fungal species in soil suppressiveness and in further assays confirmed that C. rosea f. rosea BAFC1646 exhibits a bioprotective effect against S. sclerotiorum in soybean plants. Fil: Rodriguez, Maria Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Rothen, Carolina Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Lo, Tai En. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Cabrera, Gabriela Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina Fil: Godeas, Alicia Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina

Scientific workflows are used to process vast amounts of data and to conduct large-scale experiments and simulations. They are time consuming and resource intensive applications that benefit from running in distributed platforms. In particular, scientific workflows can greatly leverage the ease-of-access, affordability, and scalability offered by cloud computing. To achieve this, innovative and efficient ways of orchestrating the workflow tasks and managing the compute resources in a cost-conscious manner need to be developed. We propose an adaptive, resource provisioning and scheduling algorithm for scientific workflows deployed in Infrastructure as a Service clouds. Our algorithm was designed to address challenges specific to clouds such as the pay-as-you-go model, the performance variation of resources and the on-demand access to unlimited, heterogeneous virtual machines. It is capable of responding to the dynamics of the cloud infrastructure and is successful in generating efficient solutions that meet a user-defined deadline and minimise the overall cost of the used infrastructure. Our simulation experiments demonstrate that it performs better than other state-of-the-art algorithms.

Aims: The present study was aimed to search for metabolites produced by fungal strains with antifungal activity against phytopathogens. Study Design: The polen of four behives each from two apiaries from two locations in Argentina (Balcarce and Mar del Plata) was studied. Nine be polen samples were randomly colected from each behive. Place and Duration of Study: Study was undertaken during 201 on the Organic Chemistry Department and Biodiversity and Experimental Biology Department, Schol of Exact and Natural Sciences, University of Buenos Aires. Methodology: Al of the 13 isolated strains were tested for antifungal activity. The most bioactive strains, Myrothecium verucaria, Nigrospora sphaerica and a levuliform mycelia, were selected for the isolation and identifcation of compounds responsible for the antifungal activity. Results: Seven mycotoxins, roridin A, verucarin A, α-β-dehydrocurvularin, phomalactones A, B, C and D, were identifed spectroscopicaly and were the responsible the antifungal activity. Conclusion: Mycotoxins were isolated from behive polen mycoflora. This is the first report of the production of these mycotoxins, which are not curently targets for the analysis of polen or honey products. Fil: Cirigliano, Adriana Monica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina Fil: Rodriguez, Maria Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina Fil: Godeas, Alicia Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentina Fil: Cabrera, Gabriela Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina

Exudates of Rhodotorula mucilaginosa, a yeast commonly found in the rhizosphere, increased hyphal length of the arbuscular mycorrhizal (AM) fungi Gigaspora rosea and Gigaspora margarita. Rhodotorulic acid (RA), a siderophore compound obtained from R. mucilaginosa exudates, increased hyphal length and branching. Thus, the increase in the number of entry points and the higher AM root colonization of tomato plants in the presence of RA can at least partially be explained by the positive effect of RA on the pre-symbiotic stages of the AM fungi.

A multi-physics and multi-scale computational approach is proposed in the present work to study the evolution of microcracking in polycrystalline Silicon (Si) solar cells composing photovoltaic (PV) modules. Coupling between the elastic and the electric fields is provided according to an equivalent circuit model for the PV module where the electrically inactive area is determined from the analysis of the microcrack pattern. The structural scale of the PV laminate(the macro-model) is coupled to the scale of the polycrystals (the micro-model) using a multiscale nonlinear finite element approach where the macro-scale displacements of the Si cell borders are used as boundary conditions for the micro-model. Intergranular cracking in the Si cell is simulated using a nonlinear fracture mechanics cohesive zone model (CZM). A case-study shows the potentiality of the method, in particular as regards the analysis of the microcrack orientation and distribution, as well as of the effect of cracking on the electric characteristics of the PV module., 20 pages, 17 figures, post-print of the article published in Composite Structures

Summary Large-scale scientific problems are often modeled as workflows. The ever-growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.

Los helechos presentan dos etapas en su ciclo de vida, una fase esporofitica y una gametofitica. Estos por lo general pueden colonizar diferentes ambientes y frecuentemente presentan raices micorrizadas. Este estudio describe la asociacion experimental entre Pteris vittata, un helecho leptosporangiado y una cepa de Glomus intraradices durante el ciclo de vida del helecho, desde la germinacion de las esporas hasta el desarrollo del esporofito maduro. El objetivo de este estudio fue comparar los patrones de colonizacion de G. intraradices a lo largo de todo el ciclo de vida del helecho con los tipos encontrados en la naturaleza. Las esporas maduras fueron obtenidas de frondes fertiles de P. vittata que crecen sobre las paredes de la ciudad de Buenos Aires, Argentina. Las raices se tineron y fueron observadas bajo microscopio optico para el estudio de la colonizacion micorrizica. Aproximadamente 75 esporas de helecho se cultivaron en macetas con un sustrato esteril y con un inoculo de G. intraradices (N° 51.331 BAFC) en la superficie. Despues de la germinacion, se tomaron muestras cada 15 dias hasta que se completo el ciclo de vida del helecho. Con el fin de determinar la dinamica de la colonizacion, cada muestra se observo con el microscopio 864 optico y el microscopio de confocal luego de la tincion correspondiente. El gametofito fue clasificado como del tipo “Adiantum”. Los gametangios femeninos y masculinos se desarrollaron en la cara inferior del mismo. La micorrizacion comenzo cuando los gametangios estaban ya diferenciados y la colonizacion se produjo a traves de los rizoides. Las esporas y las vesiculas no se encontraron en esta fase del ciclo. La micorrizacion tipo Paris se observo sobre la linea de la nervadura central. El pie del esporofito fue colonizado por el micelio externo. Cuando la raiz se desarrollo, se colonizo “de novo”, y se observo una colonizacion de tipo Arum. Este estudio demuestra que el tipo de colonizacion esta determinado por la estructura del helecho y no por el hongo. Tanto el gametofito como el pie del embrion tienen crecimiento definido y colonizacion tipo Paris, mientras que las raices del esporofito presentan un crecimiento indeterminado y una colonizacion tipo Arum. Las estructuras que se encontraron bajo cultivo coinciden con las que se encontraron en condiciones naturales.