Establishment and characterization of two human breast carcinoma cell lines by spontaneous immortalization: Discordance between Estrogen, Progesterone and HER2/neu receptors of breast carcinoma tissues with derived cell lines

Background: Breast cancer is one of the most common cancers among women throughout the world. Therefore, established cell lines are widely used as in vitro experimental models in cancer research.Methods: Two continuous human breast cell lines, designated MBC1 and MBC2, were successfully established and characterized from invasive ductal breast carcinoma tissues of Malaysian patients. MBC1 and MBC2 have been characterized in terms of morphology analysis, population doubling time, clonogenic formation, wound healing assay, invasion assay, cell cycle, DNA profiling, fluorescence immunocytochemistry, Western blotting and karyotyping.Results: MBC1 and MBC2 exhibited adherent monolayer epithelial morphology at a passage number of 150. Receptor status of MBC1 and MBC2 show (ER+, PR+, HER2+) and (ER+, PR-, HER2+), respectively. These results are in discordance with histopathological studies of the tumoral tissues, which were triple negative and (ER-, PR-, HER2+) for MBC1 and MBC2, respectively. Both cell lines were capable of growing in soft agar culture, which suggests their metastatic potential. The MBC1 and MBC2 metaphase spreads showed an abnormal karyotype, including hyperdiploidy and complex rearrangements with modes of 52-58 chromosomes per cell.Conclusions: Loss or gain in secondary properties, deregulation and specific genetic changes possibly conferred receptor changes during the culturing of tumoral cells. Thus, we hypothesize that, among heterogenous tumoral cells, only a small minority of ER+/PR+/HER2+ and ER+/PR-/HER2+ cells with lower energy metabolism might survive and adjust easily to in vitro conditions. These cell lines will pave the way for new perspectives in genetic and biological investigations, drug resistance and chemotherapy studies, and would serve as prototype models in Malaysian breast carcinogenesis investigations. © 2012 Kamalidehghan et al.; licensee BioMed Central Ltd

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

The STONE curve: A ROC-derived model performance assessment tool

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier (model) data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve, plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.Comment: 19 pages, including 4 figures. Currently in second-round review with "Earth and Space Science": https://agupubs.onlinelibrary.wiley.com/journal/2333508

Management of COVID-19 myopericarditis with reversal of cardiac dysfunction after blunting of cytokine storm: a case report.

Background:Coronavirus disease 2019 (COVID-19) is a syndrome that has been associated with multiple cardiac complications including myopericarditis. The pathophysiology and treatment for myopericarditis in the setting of COVID-19 infection is still under investigation. Case summary:We present a case of a 60-year-old male admitted for dyspnoea due to COVID-19. He developed new ST-segment elevation, elevated cardiac enzymes, severe left ventricular dysfunction, and high inflammatory markers in the setting of haemodynamic and respiratory collapse from the viral illness. He was diagnosed with COVID-19-induced myopericarditis. He showed rapid clinical improvement with a rapid wean off pressure support, resolution of electrocardiogram (ECG) findings, and recovery of left ventricular systolic function following treatment with intravenous immunoglobulin (IVIG) and methylprednisolone. Discussion:COVID-19\u27s complex and devastating complications continue to create new challenges for clinicians. Cardiac complications, specifically, have been shown to be a signal for worse prognosis in these patients. IVIG and steroids can inhibit the inflammatory cascade and decrease myocardial injury, with implications in treatment of severe myopericarditis

The STONE Curve: A ROC‐Derived Model Performance Assessment Tool

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.Plain Language SummaryScientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event‐definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and preclassified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous‐valued data set.Key PointsA new event‐detection‐based metric for model performance appraisal is given with sliding thresholds in both observational and model valuesThe new metric is like the relative operating characteristic curve but uses continuous observational values, not just categorical statusThe new metric is used on real‐time model predictions of common geomagnetic activity parameters, demonstrating its features and strengthsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/2/ess2610.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/1/ess2610_am.pd

Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.Comment: 25 pages, 7 figures, accepted for publication in Frontiers in Astronomy and Space Sciences for the Research Topic of Machine Learning in Heliophysics at https://www.frontiersin.org/articles/10.3389/fspas.2020.0003

The growth, survival rate and reproductive characteristics of Artemia urmiana fed by Dunaliella tertiolecta, Tetraselmis suecica, Nannochloropsis oculata, Chaetoceros sp., Chlorella sp. and Spirolina sp. as feeding microalgae

This study was performed to compare the efficiency of six microalgae namely Dunaliella tertiolecta, Tetraselmis suecica, Nannochloropsis oculata, Chaetoceros sp., Chlorella sp. and Spirolina sp. on the growth, survival rate and reproduction efficacy in Artemia urmiana in laboratory conditions. Artemia cysts were harvested from Urmia Lake and hatched according to the standard method. Live microalgae were cultured using the f/2 culture medium. Artemia survival was determined in treatments on days 8, 11, 14, 17 and 20. A highly significant difference (p<0.01) were found among three microalgae in terms of length growth, survival rates and reproduction characteristics in A. urmiana. In spite of higher length growth of A.urmiana fed on N. oculata than A. urmiana fed by T. suecica but survival and reproduction in the latter was better than the first treatment. In general, D. tertiolecta was more efficient than other microalgae examined in the present study on A. urmiana concerning not only to growth and survival but also to reproduction mode. So, it is preferred to feed A. urmiana

MESSENGER Observations of Flow Braking and Flux Pileup of Dipolarizations in Mercury’s Magnetotail: Evidence for Current Wedge Formation

Similar to Earth, Mercury’s magnetotail experiences frequent dipolarization of the magnetic field. These rapid (~2 s) increases in the northward component of the tail field (ΔBz ~ 30 nT) at Mercury are associated with fast sunward flows (~200 km/s) that enhance local magnetic field convection. Differences between the two magnetospheres, namely Mercury’s smaller spatiotemporal scales and lack of an ionosphere, influence the dynamics of dipolarizations in these magnetotails. At Earth, the braking of fast dipolarization flows near the inner magnetosphere accumulates magnetic flux and develops the substorm current wedge. At Mercury, flow braking and flux pileup remain open topics. In this work, we develop an automated algorithm to identify dipolarizations, which allows for statistical examination of flow braking and flux pileup in Mercury’s magnetotail. We find that near the inner edge of the plasma sheet, steep magnetic pressure gradients cause substantial braking of fast dipolarization flows. The dipolarization frequency and sunward flow speed decrease significantly within a region ~500 km thick located at ~900 km altitude above Mercury’s local midnight surface. Due to the close proximity of the braking region to the planet, we estimate that ~10–20% of dipolarizations may reach the nightside surface of the planet. The remaining dipolarizations exhibit prolonged statistical flux pileup within the braking region similar to large‐scale dipolarization of Earth’s inner magnetosphere. The existence of flow braking and flux pileup at Mercury indicates that a current wedge may form, although the limitations imposed by Mercury’s magnetosphere require the braking of multiple, continuous dipolarizations for current wedge formation.Key PointsDipolarizations in Mercury’s magnetotail encounter strong magnetic pressure gradients near the planet that brake their fast sunward flowOnly a small fraction of dipolarizations reach the nightside surface; most brake and contribute to magnetic flux pileupPileup results from the interaction of multiple dipolarizations and is consistent with Earth‐like substorm current wedge formationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162780/2/jgra55966.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162780/1/jgra55966_am.pd