Genetic heterogeneity of HER2 amplification and telomere shortening in papillary thyroid carcinoma

Extensive research is dedicated to understanding if sporadic and familial papillary thyroid carcinoma are distinct biological entities. We have previously demonstrated that familial papillary thyroid cancer (fPTC) cells exhibit short relative telomere length (RTL) in both blood and tissues and that these features may be associated with chromosome instability. Here, we investigated the frequency of HER2 (Human Epidermal Growth Factor Receptor 2) amplification, and other recently reported genetic alterations in sporadic PTC (sPTC) and fPTC, and assessed correlations with RTL and BRAF mutational status. We analyzed HER2 gene amplification and the integrity of ALK, ETV6, RET, and BRAF genes by fluorescence in situ hybridization in isolated nuclei and paraffin-embedded formalin-fixed sections of 13 fPTC and 18 sPTC patients. We analyzed BRAFV600E mutation and RTL by qRT-PCR. Significant HER2 amplification (p = 0.0076), which was restricted to scattered groups of cells, was found in fPTC samples. HER2 amplification in fPTCs was invariably associated with BRAFV600E mutation. RTL was shorter in fPTCs than sPTCs (p < 0.001). No rearrangements of other tested genes were observed. These findings suggest that the association of HER2 amplification with BRAFV600E mutation and telomere shortening may represent a marker of tumor aggressiveness, and, in refractory thyroid cancer, may warrant exploration as a site for targeted therapy

A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Monge, David. Universidad Nacional de Cuyo; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Arsenic, iron and manganese removal from ground water in a pilot plant located at a rural school

En el Centro de Ingeniería Sanitaria (CIS) se desarrolló el Proceso BioCIS-UNR® para remoción de hierro (Fe) y manganeso (Mn) y mediante este trabajo, se pretende ampliar su campo de aplicación para remoción de arsénico (As) en presencia de Fe y Mn. Se operó una planta piloto en una escuela en Zuripozo (Provincia de Santiago del Estero, Argentina), en una perforación cuya agua tiene: Fe total 0.35 mg.L-1 , Fe 0.04 mg.L-1 , Mn total 0.16 mg.L-1 , As total 42 g.L-1 y bacterias del Fe. Con las concentraciones naturales las eficiencias de remoción fueron 35% Fe total, 90% Mn total y 10% As (con valores máximos de remoción observados de 40%). Para mejorar el proceso, se agregó sulfato ferroso al agua cruda; las eficiencias fueron 92% Fe Total, 95% Fe2+, 93% Mn Total y 66% As Total. La baja eficiencia de remoción de As inicial podría deberse a la baja cantidad de precipitados biológicos de Fe formados, que da lugar a escasos sitios de adsorción disponibles para el As. Este proceso, de fácil operación, bajos costos de instalación y operación, es una tecnología aplicable a abastecimientos de agua potable con escasos recursos y alejados de centros urbanos.The Process BioCIS-UNR® developed by the Centro de Ingeniería Sanitaria (CIS) for iron (Fe) and manganese (Mn) removal was further developed in this work to remove arsenic (As) in presence of Fe and Mn. We operated a pilot plant located in a school at Zuripozo (Province of Santiago del Estero, Argentina. The plant was connected to a well with water containing total Fe 0.35 mg.L-1, Fe+2 0.04 mg.L-1 , total Mn 0.16 mg.L-1 , total As 42 g.L-1 and Fe bacteria. This element concentration was compared with element concentration in natural water. The efficiency of element removal was 35% of total Fe, 90% of total Mn and 10% of As (maximum 40%). Ferrous sulfate was added to raw water to improve process efficiency. The efficiencies obtained were 92% for total Fe, 95% for Fe2+, 93% for total Mn and 66% for total As. Initial low efficiency of As removal may be due to a low formation of Fe biological precipitates, causing a shortage of available sites for As adsorption. This easily operated process with low installation and operation costs is an affordable technical solution for drinking water supply for rural areas with scarce resource located far from urban centers.Comité de Medio Ambient

Arsenic, iron and manganese removal from ground water in a pilot plant located at a rural school

En el Centro de Ingeniería Sanitaria (CIS) se desarrolló el Proceso BioCIS-UNR® para remoción de hierro (Fe) y manganeso (Mn) y mediante este trabajo, se pretende ampliar su campo de aplicación para remoción de arsénico (As) en presencia de Fe y Mn. Se operó una planta piloto en una escuela en Zuripozo (Provincia de Santiago del Estero, Argentina), en una perforación cuya agua tiene: Fe total 0.35 mg.L-1 , Fe 0.04 mg.L-1 , Mn total 0.16 mg.L-1 , As total 42 g.L-1 y bacterias del Fe. Con las concentraciones naturales las eficiencias de remoción fueron 35% Fe total, 90% Mn total y 10% As (con valores máximos de remoción observados de 40%). Para mejorar el proceso, se agregó sulfato ferroso al agua cruda; las eficiencias fueron 92% Fe Total, 95% Fe2+, 93% Mn Total y 66% As Total. La baja eficiencia de remoción de As inicial podría deberse a la baja cantidad de precipitados biológicos de Fe formados, que da lugar a escasos sitios de adsorción disponibles para el As. Este proceso, de fácil operación, bajos costos de instalación y operación, es una tecnología aplicable a abastecimientos de agua potable con escasos recursos y alejados de centros urbanos.The Process BioCIS-UNR® developed by the Centro de Ingeniería Sanitaria (CIS) for iron (Fe) and manganese (Mn) removal was further developed in this work to remove arsenic (As) in presence of Fe and Mn. We operated a pilot plant located in a school at Zuripozo (Province of Santiago del Estero, Argentina. The plant was connected to a well with water containing total Fe 0.35 mg.L-1, Fe+2 0.04 mg.L-1 , total Mn 0.16 mg.L-1 , total As 42 g.L-1 and Fe bacteria. This element concentration was compared with element concentration in natural water. The efficiency of element removal was 35% of total Fe, 90% of total Mn and 10% of As (maximum 40%). Ferrous sulfate was added to raw water to improve process efficiency. The efficiencies obtained were 92% for total Fe, 95% for Fe2+, 93% for total Mn and 66% for total As. Initial low efficiency of As removal may be due to a low formation of Fe biological precipitates, causing a shortage of available sites for As adsorption. This easily operated process with low installation and operation costs is an affordable technical solution for drinking water supply for rural areas with scarce resource located far from urban centers.Comité de Medio Ambient

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%).Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentin

Tratamiento biológico del agua subterránea para la remoción de Arsénico, un caso particular

La remoción del arsénico del agua que consumen los pobladores en las zonas rurales es una pr eocupación de investigadores dedicados a desarrollar métodos de bajo costo para tal fin. Algunos desarrollando métodos que aseguren un caudal constante agua segura, como el Centro de Ingeniería Sanitaria de la UNR que en conjunto con investigadores del Departamento Académico de Geología y Geotecnia de la UNSE, realizaron una prueba piloto para la eliminación del arsénico conjuntamente con el hierro y manganeso utilizando la técnica de la doble filtración biológica patentada por ellos como Proceso BioCIS-UNR®, en una Escuela en el interior de la Provincia de Santiago del Estero, con resultados alentadores. Actualmente esta Planta se traslado a la localidad de Negra Muerta con el objeto de tratar el agua de la perforación que abastece a pobladores del lugar. El agua, con una concentración inicial de 1324 μg/L de arsénico y 30 μg/L de hierro, fue tratada y los primeros resultados de filtrado mostraron una remoción de arsénico del orden del 93 %.The arsenic removal of rural people’s water is a concern for researchers dedicated to developing low-co st methods for this purpose. Some have developed methods to ensure a constant flow rate of safe water, such as the “Centro de Ingeniería Sanitaria de la UNR” (Center for Sanitary Engineering at UNR) working alongside with researchers of the “Académico de Geología y Geotecnia de la UNSE” (Academic Department of Geology and Geotechnics of UNSE), conducted a pilot test in order to remove arsenic in conjunction with iron and manganese, using the technique of dual biological filtration process patented by them as BioCIS-UNR®, in a inland school of the Province of Santiago del Estero, with encouraging results. Currently this plant was moved to the locality of “Negra Muerta” in order to treat water drilling that supplies local people. The water, with an initial concentration of 1324 μ g/L of arsenic and 30 μ g/L of iron, was treated and the first filtering results showed a 93% of arsenic removal.Universidad Nacional de La Plat

Environmental Bacteria Involved in Manganese(II) Oxidation and Removal From Groundwater

The presence of iron (Fe) and manganese (Mn) in groundwater is an important concern in populations that use it as source of drinking water. The ingestion of high concentrations of these metals may affect human health. In addition, these metals cause aesthetic and organoleptic problems that affect water quality and also induce corrosion in distribution networks, generating operational and system maintenance problems. Biological sand filter systems are widely used to remove Fe and Mn from groundwater since they are a cost-effective technology and minimize the use of chemical oxidants. In this work, the bacterial communities of two biological water treatment plants from Argentina, exposed to long term presence of Mn(II) and with a high Mn(II) removal efficiency, were characterized using 16S rRNA gene Illumina sequencing. Several selective media were used to culture Mn-oxidizing bacteria (MOB) and a large number of known MOB and several isolates that have never been reported before as MOB were cultivated. These bacteria were characterized to select those with the highest Mn(II) oxidation and biofilm formation capacities and also those that can oxidize Mn(II) at different environmental growth conditions. In addition, studies were performed to determine if the selected MOB were able to oxidize Mn(II) present in groundwater while immobilized on sand. This work allowed the isolation of several bacterial strains adequate to develop an inoculum applicable to improve Mn(II) removal efficiency of sand filter water treatment plants

Under-ice phytoplankton blooms inhibited by spring convective mixing in refreezing leads

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 123 (2018): 90–109, doi:10.1002/2016JC012575.Spring phytoplankton growth in polar marine ecosystems is limited by light availability beneath ice-covered waters, particularly early in the season prior to snowmelt and melt pond formation. Leads of open water increase light transmission to the ice-covered ocean and are sites of air-sea exchange. We explore the role of leads in controlling phytoplankton bloom dynamics within the sea ice zone of the Arctic Ocean. Data are presented from spring measurements in the Chukchi Sea during the Study of Under-ice Blooms In the Chukchi Ecosystem (SUBICE) program in May and June 2014. We observed that fully consolidated sea ice supported modest under-ice blooms, while waters beneath sea ice with leads had significantly lower phytoplankton biomass, despite high nutrient availability. Through an analysis of hydrographic and biological properties, we attribute this counterintuitive finding to springtime convective mixing in refreezing leads of open water. Our results demonstrate that waters beneath loosely consolidated sea ice (84–95% ice concentration) had weak stratification and were frequently mixed below the critical depth (the depth at which depth-integrated production balances depth-integrated respiration). These findings are supported by theoretical model calculations of under-ice light, primary production, and critical depth at varied lead fractions. The model demonstrates that under-ice blooms can form even beneath snow-covered sea ice in the absence of mixing but not in more deeply mixed waters beneath sea ice with refreezing leads. Future estimates of primary production should account for these phytoplankton dynamics in ice-covered waters.National Science Foundation (NSF) Grant Numbers: PLR-1304563 , PLR-1303617; KEL; NSF Graduate Research Fellowship Program Grant Number: DGE-06459622018-07-0

Inter-observer Variability of Expert-derived Morphologic Risk Predictors in Aortic Dissection

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models

Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) in Uncomplicated Type B Aortic Dissection: Study Design and Rationale

PURPOSE To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD). MATERIALS AND METHODS The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique. RESULTS The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling. CONCLUSION This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue