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Extraskeletal Ewing's sarcoma/primitive neuroectodermal tumor of the mediastinum: Significant response to chemoradiotherapy
Bailong Liu
Lihua Dong
Tao Han
Lei Zhang
View Affiliations
Affiliations: Department of Radiation Oncology, The First Hospital, Jilin University, Changchun, Jilin 130021, P.R. China, Department of Radiology, The First Hospital, Jilin University, Changchun, Jilin 130021, P.R. China
Published online on: December 10, 2014 https://doi.org/10.3892/ol.2014.2788
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
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Primary mediastinal extraskeletal Ewing's sarcoma (EES) is quite rare. To the best of our knowledge, only five cases have been reported. Given the paucity of data, there is consequently no optimal treatment strategy available. The current study presents the case of a 51‑year‑old female with unresectable EES of the posterior mediastinum. Chemoradiotherapy achieved near complete remission without severe side‑effects. The literature associated with EES is also reviewed. The present case highlights the possibility of the diagnosis of EES for a mediastinal mass. Chemoradiotherapy may be a good option for unresectable cases. In the future, large‑scale collaborative clinical trials should be initiated to provide an improved understanding of the characteristics of EES and the best treatment strategy.
Extraskeletal Ewing's sarcoma (EES)/primitive neuroectodermal tumor (PNET) is a rare entity, accounting for 15% of all Ewing's sarcomas (1). The most common clinical manifestation of this disease is a rapidly growing, painful lump (2). Pathological, cytogenetic, immunohistochemical and molecular genetic analysis contribute to an accurate diagnosis (3). Multidisciplinary treatment modalities comprising extended resection, aggressive chemotherapy and local irradiation are recommended (4). At present, EES/PNET is viewed as a potentially curable disease (5).
Primary mediastinal EES is extremely rare. The present study describes the case of a middle-aged female with mediastinal EES who received sequential chemotherapy and radiotherapy and achieved a marked response. Written informed consent was obtained from the patient. Additionally, the associated studies on EES are also reviewed. Further studies are required to establish the standard treatment strategy for EES. Written informed consent was obtained from the patient.
On December 25, 2012, a 51-year-old female presented with intermittent chest pain that had been apparent for one year. The physical examination was unremarkable. The patient's performance status was 1 according to an Eastern Cooperative Oncology Group (ECOG) evaluation (6).
Laboratory investigations revealed a normal complete blood cell count, coagulation routine and serum biochemical profile. The thoracic computed tomography (CT) scan revealed a huge mass in the posterior mediastinum, with invasion of the esophagus, descending aorta and right pulmonary artery (Fig. 1A). On December 31, 2012, a biopsy of the mediastinal mass was performed by thoracoscopy, during which an irregular posterior mediastinal mass measuring ~8×8 cm was located under the arch of the azygos vein. The final pathology showed a malignancy of small round cells, which was consistent with an Ewing's sarcoma/PNET (Fig. 2A). The immunohistochemical results demonstrated a Ki-67 of 20% (Fig. 2B), positivity for cluster of differentiation (CD)99 (Fig. 2C) and synaptophysin (Fig. 2D), partial positivity for CD56, neuron-specific enolase and S-100, and negativity for CD1a, Wilms tumor 1 protein, octamer-binding transcription factor 3/4, vimentin, epithelial membrane antigen, cytokeratin (CK)5/6, desmin, thyroid transcription factor-1, inhibin A, CD34, terminal deoxynucleotidyl transferase, CK and leukocyte common antigen. The following abdominal CT, brain magnetic resonance imaging and bone scan excluded the possibility of metastasis.
Thoracic computed tomgraphy (CT) scan showing the lesion prior to treatment and during varying stages of treatment. (A) CT scan from December 25, 2012, showing a large mass in the posterior mediastinum. (B) Following one cycle of chemotherapy, the tumor was greatly reduced in size. (C) CT scan showing the smaller tumor following three cycles of chemotherapy. (D) Pre-radiotherapy CT scan showing the residual tumor.
Pathological and immunohistochemical results. (A) The lesion was composed of primitive small round cells (H&E staining), with (B) a Ki-67 of 20% and positivity for (C) cluster of differentiation 99 and (D) synaptophysin.
The patient was administered four cycles of dacarbazine and pirarubicin chemotherapy. One cycle consisted of 300 mg dacarbazine on days 1–5 and 40 mg pirarubicin on days 1–2, for 21 days (patient's body surface area, ~1.6 m2). Following one cycle of chemotherapy, the tumor rapidly decreased in size (Fig. 1B) and the patient's discomfort disappeared. The thoracic CT revealed that the tumor became even smaller after three cycles compared with one cycle (Fig. 1C). Minimal residual tumor remained subsequent to four cycles of chemotherapy (Fig. 1D). Radiotherapy was then administered to the tumor bed at a total dose of 54 Gy/30 fractions over 42 days (between May 10, 2013, and June 21, 2013) and the recorded side-effects, such as esophagitis and leukopenia, were mild.
EES is a rare entity with high-grade malignancy commonly involving the soft tissues of the trunk and extremities. The thoracic sites of EES include the chest wall, trachea, spinal epidural space, paraspinal area and mediastinum (7–11). Primary mediastinal EES/PNET is extremely rare (1,12); to the best of our knowledge, only five such cases have previously been reported (1,11–14). The present case will therefore aid in expanding our understanding of this distinct neoplasm.
EES often exhibits translocation of (11;22)(q24;q12) (3). CD99-positive expression plays a crucial role in the diagnosis of EES (15). Tural et al reported indicators of poor overall survival, namely a primary tumor of >8 cm, a high level of lactic dehydrogenase, metastasis at the time of the first hospital visit, a poor response to chemotherapy, radiotherapy as the single method to improve local control and positive margins (16).
For a long period of time, EES had been regarded as exhibiting no significant differences to osseous Ewing's sarcoma. A study by Applebaum et al (17) was the first to reveal that EES exhibited a different therapeutic response and clinical characteristics. The mean age of onset is older in EES, with a biphasic distribution to its peak age of onset: >35 and <5 years. Furthermore, the preponderance of males is less marked in EES compared with Ewing's sarcoma of the bone. Axial locations are more likely than the pelvic cavity. Additionally, more patients with EES receive radiotherapy.
EES is highly aggressive. Local relapse and distant metastases are frequent. Multidisciplinary measures are important for improvements in survival. A retrospective study of 24 cases of EES showed a 61% five-year overall survival rate following multimodality therapies (18). In a study by Lee et al (7), two cases of EES of the chest achieved at least 30 and 22 months progression free survival, respectively, following comprehensive treatment. The two cases utilized extended resections and a post-operative alternate chemotherapy regimen of vincristine, Adriamycin and cyclophosphamide, and ifosfamide and etoposide (IE). One patient also received 54 Gy/30 fractions of radiotherapy to the tumor bed (7). Extended resection combined with multidrug chemotherapy often results in a clinical benefit (11). However, from a review of the literature, it can be observed that no consensus has yet been reached with regard to a standard chemotherapy regimen. Tural et al recommended an intense strategy of administering vincristine, Adriamycin, cyclophosphamide and actinomycin D alternately with IE (16). Tao et al held the view that the use of platinum-based chemotherapy should be considered (4). In the present case, dacarbazine and pirarubicin achieved a good response and the side-effects were mild.
In terms of local control, no random trials have yet been initiated to demonstrate whether surgery combined with radiotherapy is superior to surgery alone. However, complete resection plus radiotherapy is known to have an advantage over radiotherapy. For unresectable cases or tumors that cannot be removed completely, radiotherapy with a radical dose of 50–60 Gy should be performed (1).
In conclusion, the present study describes a relatively rare case of primary mediastinal EES/PNET that could not be resected. Sequential chemotherapy and radiotherapy achieved a favorable response with mild side effects. We propose that dacarbazine and pirarubicin chemotherapy should be considered for unresectable EES/PNET cases for its beneficial effect, and chemoradiotherapy is a good treatment option for such cases. Successful published protocols using collaborative clinical trials will aid in the development of a standard treatment strategy.
Reali A, Mortellaro G, Allis S, et al: A case of primary mediastinal Ewing's sarcoma/primitive neuroectodermal tumor presenting with initial compression of superior vena cava. Ann Thorac Med. 8:121–123. 2013. View Article : Google Scholar : PubMed/NCBI
Rud NP, Reiman HM, Pritchard DJ, Frassica FJ and Smithson WA: Extraosseous Ewing's sarcoma. A study of 42 cases. Cancer. 64:1548–1553. 1989. View Article : Google Scholar : PubMed/NCBI
Delattre O, Zucman J, Melot T, et al: The Ewing family of tumors - a subgroup of small-round-cell tumors defined by specific chimeric transcripts. N Eng J Med. 331:294–299. 1994. View Article : Google Scholar
Tao HT, Hu Y, Wang JL, et al: Extraskeletal Ewing sarcomas in late adolescence and adults: a study of 37 patients. Asian Pac J Cancer Prev. 14:2967–2971. 2013. View Article : Google Scholar : PubMed/NCBI
Halliday J, Soon SY, Monaghan H, Walker WS and Zamvar V: Extraskeletal Ewing's sarcoma presenting as a mediastinal mass. Ann Thorac Surg. 90:1016–1017. 2010. View Article : Google Scholar : PubMed/NCBI
Oken MM, Creech RH, Tormey DC, et al: Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 5:649–655. 1982. View Article : Google Scholar : PubMed/NCBI
Lee WS, Kim YH, Chee HK, et al: Multimodal treatment of primary extraskeletal Ewing's sarcoma of the chest wall: report of 2 cases. Cancer Res Treat. 41:108–112. 2009. View Article : Google Scholar : PubMed/NCBI
Elmi M, Ko MA, Gupta A, et al: Primary tracheal Ewing's sarcoma. Ann Thorac Surg. 90:1349–1352. 2010. View Article : Google Scholar : PubMed/NCBI
Ozturk E, Mutlu H, Sonmez G, et al: Spinal epidural extraskeletal Ewing sarcoma. J Neuroradiol. 34:63–67. 2007. View Article : Google Scholar : PubMed/NCBI
Perouli E, Chrysikopoulos H, Vlachos A, et al: Imaging findings in paraspinal extra osseous Ewing sarcoma. JBR-BTR. 89:310–312. 2006.
Manduch M, Dexter DF, Ellis PM, et al: Extraskeletal Ewing's sarcoma/primitive neuroectodermal tumor of the posterior mediastinum with t(11;22)(q24;q12). Tumori. 94:888–891. 2008.
Halefoglu AM: Extraskeletal Ewing's sarcoma presenting as a posterior mediastinal mass. Arch Bronconeumol. 49:82–84. 2013. View Article : Google Scholar
El-Essawy MT: Extraskeletal Ewing's sarcoma. Saudi Med J. 30:840–843. 2009.PubMed/NCBI
Kuzucu A, Erkal HS, Soysal O and Serin M: Extraskeletal Ewing's sarcoma presenting with multifocal intrathoracic mass lesions associated with mediastinal shift. Ann Thorac Surg. 81:1487–1488. 2006. View Article : Google Scholar : PubMed/NCBI
Guiter GE, Gamboni MM and Zakowski MF: The cytology of extraskeletal Ewing sarcoma. Cancer. 87:141–148. 1999. View Article : Google Scholar : PubMed/NCBI
Tural D, Molinas Mandel N, Dervisoglu S, et al: Extraskeletal Ewing's sarcoma family of tumors in adults: prognostic factors and clinical outcome. Jpn J Clin Oncol. 42:420–426. 2012. View Article : Google Scholar : PubMed/NCBI
Applebaum MA, Worch J, Matthay KK, et al: Clinical features and outcomes in patients with extraskeletal Ewing sarcoma. Cancer. 117:3027–3032. 2011. View Article : Google Scholar : PubMed/NCBI
Ahmad R, Mayol BR, Davis M and Rougraff BT: Extraskeletal Ewing's sarcoma. Cancer. 85:725–731. 1999. View Article : Google Scholar : PubMed/NCBI
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text:
package org.dataalgorithms.machinelearning.kmeans;

import org.apache.log4j.Logger;
//
import org.apache.commons.lang.StringUtils;
//
import scala.Tuple2;
//
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;

/**
 * A standalone Spark Java program to perform featurization of wikistats
 * data defined below.
 *
 * The Scala version of featurization program is given here:
 *   http://ampcamp.berkeley.edu/exercises-strata-conf-2013/featurization.html
 *
 *
 * Data to download: http://dumps.wikimedia.org/other/pagecounts-raw/
 *
 *  Each record in our dataset (input files) consists of a string with the format
 *  "<date_time> <project_code> <page_title> <num_hits> <page_size>".
 *  Note that the format of the "<date-time>" field is YYYYMMDD-HHmmSS
 *  (where 'MM' denotes month, and 'mm' denotes minute). Note the downloaded
 *  data will not have the <date_time> field, so we need to append it from
 *  the filenames.  Please see my notes below.
 *
 *
 * The first few lines of the file are copied here:
 *
 *   20090507-040000 aa ?page=http://www.stockphotosharing.com/Themes/Images/users_raw/id.txt 3 39267
 *   20090507-040000 aa Main_Page 7 51309
 *   20090507-040000 aa Special:Boardvote 1 11631
 *   20090507-040000 aa Special:Imagelist 1 931
 *   20090505-000000 aa.b ?71G4Bo1cAdWyg 1 14463
 *   20090505-000000 aa.b Special:Statistics 1 840
 *   20090505-000000 aa.b Special:Whatlinkshere/MediaWiki:Returnto 1 1019
 *
 * The goal of this class is to convert each record of 5 items:
 * "<date_time> <project_code> <page_title> <num_hits> <page_size>"
 * into 24 features (described below).
 *
 * Note that the data in http://dumps.wikimedia.org/other/pagecounts-raw/ does not
 * have the <date-time> field.  But, you may add/insert the <date-time> field from
 * the filename: for example:  for the downloaded file:
 *    http://dumps.wikimedia.org/other/pagecounts-raw/2015/2015-11/pagecounts-20151101-000000.gz
 * we have (after un-zipping):
 *
 *  # tail -3 pagecounts-20151101-000000
 *  zu.d uwoyela 1 4386
 *  zu.d wildebeest 1 5965
 *  zu.mw zu 28 180647
 *
 * For this data (pagecounts-20151101-000000), you need to generate (append <date-time>
 * from the filename for each record):
 *  20151101-000000 zu.d uwoyela 1 4386
 *  20151101-000000 zu.d wildebeest 1 5965
 *  20151101-000000 zu.mw zu 28 180647
 *
 *
 * @author Mahmoud Parsian (mahmoud.parsian@yahoo.com)
 *
 *
 */
public class Featurization {

    private static final Logger THE_LOGGER = Logger.getLogger(Featurization.class);

    public static void main(String[] args) throws Exception {

        // In this section, we will walk you through the steps
        // to preprocess and featurize the Wikipedia dataset.

        //
        // input:
        //
        final String wikistatsPath = args[0]; // /data/wikistats
        // input record: "<date_time> <project_code> <page_title> <num_hits> <page_size>"
        THE_LOGGER.info("input path="+wikistatsPath);

        //
        // output:
        //
        final String outputPath = args[1]; // for featurized data: /data/wikistats_featurized
        // output record: "(K, V)
        // where
        //   K: <project_code> + " " + <page_title>"
        //   V: <feature_1><,><feature_2><,>...<,><feature_24> (one feature value per hour)
        THE_LOGGER.info("output path="+outputPath);

        // create a JavaSparkContext, used to create RDDs
        JavaSparkContext context = new JavaSparkContext();

        //
        // read input data and create the first RDD
        //
        JavaRDD<String> wikistatsRDD = context.textFile(wikistatsPath);

        // Next, for every line of data, we collect a tuple with elements described next.
        //
        //          The first element is what we will call the "full document title",
        //          a concatenation of the project code and page title.
        //
        //          The second element is a key-value pair whose key is the hour from
        //          the <date-time> field and whose value is the number of views that
        //          occurred in this hour.
        //
        // There are a few new points to note about the code below. First, data.map takes each
        // line of data in the RDD data and applies the function passed to it. The first step
        // splits the line of data into the five data fields we discussed in the Spark exercises
        // above. The second step extracts the hour information from the <date-time> string and
        // we then form the output tuple.
        //
        // scala:
        //    val featureMap = data.map(line => {
        //      val Array(dateTime, projectCode, pageTitle, numViews, numBytes) = line.trim.split("\\s+")
        //      val hour = dateTime.substring(9, 11).toInt
        //      (projectCode+" "+pageTitle, hour -> numViews.toInt)
        //    })
        //
        // featureMap = Tuple2<projectCode+" "+pageTitle, Tuple2<hour, NumViews>>
        JavaPairRDD<String, Tuple2<Integer,Integer>> featureMap = wikistatsRDD.mapToPair(
            new PairFunction<String, String, Tuple2<Integer,Integer>>() {
                @Override
                public Tuple2<String,Tuple2<Integer,Integer>> call(String rec) {
                    // rec =     dateTime, projectCode, pageTitle, numViews, numBytes
                    // tokens[]      0          1           2          3        4
                    String[] tokens = StringUtils.split(rec, " ");
                    String dateTime = tokens[0].trim();
                    // <date-time> field is YYYYMMDD-HHmmSS
                    String projectCode = tokens[1].trim();
                    String pageTitle = tokens[2].trim();
                    int numViews = Integer.parseInt(tokens[3].trim());
                    //String numBytes = tokens[4];
                    //
                    String K = projectCode + " " + pageTitle;
                    int hour = Integer.parseInt(dateTime.substring(9,11)); // returns hour = HH
                    Tuple2<Integer,Integer> V = new Tuple2(hour,numViews);
                    return new Tuple2(K, V);
                }
        });

        // Now we want to find the average hourly views for each article (average for the same
        // hour across different days).  In the code below, we first take our tuples in the RDD
        // featureMap and, treating the first elements (i.e., article name) as keys and the second
        // elements (i.e., hoursViewed) as values, group all the values for a single key (i.e., a
        // single article) together using groupByKey. We put the article name in a variable called
        // article and the multiple tuples of hours and pageviews associated with the current
        // article in a variable called hoursViews. The for loop then collects the number of days
        // for which we have a particular hour of data in counts[hour] and the total pageviews at
        // hour across all these days in sums[hour]. Finally, we use the syntax sums zip counts to
        // make an array of tuples with parallel elements from the sums and counts arrays and use
        // this to calculate the average pageviews at particular hours across days in the data set.
        // scala:
        //      val featureGroup = featureMap.groupByKey.map(grouped => {
        //      val (article, hoursViews) = grouped
        //      val sums = Array.fill[Int](24)(0)
        //      val counts = Array.fill[Int](24)(0)
        //      for((hour, numViews) <- hoursViews) {
        //          counts(hour) += 1
        //          sums(hour) += numViews
        //      }
        //      val avgs: Array[Double] =
        //      for((sum, count) <- sums zip counts) yield
        //          if(count > 0) sum/count.toDouble else 0.0
        //          article -> avgs
        //      })
        JavaPairRDD<String, Iterable<Tuple2<Integer,Integer>>> featureMapGrouped = featureMap.groupByKey();
        JavaPairRDD<String, double[]> featureGroup = featureMapGrouped.mapValues(
            new Function<Iterable<Tuple2<Integer,Integer>>, double[]>() {
            @Override
            public double[] call(Iterable<Tuple2<Integer,Integer>> rs) {
                //
                double[] sums = new double[24];
                double[] counts = new double[24];
                for (Tuple2<Integer,Integer> pair : rs) {
                    int hour = pair._1;
                    int numViews = pair._2;
                    counts[hour] += 1.0;
                    sums[hour] += numViews;
                }
                //
                double[] avgs = new double[24];
                for (int i=0; i < 24; i++) {
                    if (counts[i] == 0) {
                        avgs[i] = 0.0;
                    }
                    else {
                        avgs[i] = sums[i] / counts[i];
                    }
                }
                return avgs;
            }
        });

        // Now suppose we're only interested in those articles that were viewed
        // at least once in each hour during the data collection time.
        // To do this, we filter to find those articles with an average number of
        // views (the second tuple element in an article tuple) greater than zero
        // in every hour.
        //
        // scala:
        //  val featureGroupFiltered = featureGroup.filter(t => t._2.forall(_ > 0))
        //
        JavaPairRDD<String, double[]> featureGroupFiltered = featureGroup.filter(
            new Function<Tuple2<String, double[]>, Boolean>() {
                @Override
                public Boolean call(Tuple2<String, double[]> s) {
                    int nonZero = 0;
                    for (double d : s._2) {
                        if (d > 0.0) {
                            nonZero++;
                        }
                    }
                    //
                    if (nonZero == 24) {
                        return true; // keep these records
                    }
                    else {
                        return false;
                    }
                }
        });

        // So far article popularity is still implicitly in our feature vector
        // (the sum of the average views per hour is the average views per day
        // if the number of days of data is constant across hours). Since we are
        // interested only in which times are more popular viewing times for each
        // article, we next divide out by this sum.
        //
        // Scala:
        //      val featurizedRDD = featureGroupFiltered.map(t => {
        //          val avgsTotal = t._2.sum
        //          t._1 -> t._2.map(_ /avgsTotal)
        //      })
        JavaPairRDD<String, double[]> featurizedRDD = featureGroupFiltered.mapValues(
            new Function<double[], double[]>() {
            @Override
            public double[] call(double[] data) {
                //
                double sum = 0.0;
                for (int i=0; i < 24; i++) {
                    sum += data[i];
                }
                //
                double[] avg = new double[24];
                for (int i=0; i < 24; i++) {
                    avg[i] = data[i]/sum;
                }
                return avg;
            }
        });

        // Count the number of records in the preprocessed data. Recall that we
        // potentially threw away some data when we filtered out records with
        // zero views in a given hour.
        long count = featurizedRDD.count();
        THE_LOGGER.info("count="+count);

        // Finally, we can save the RDD to a file for later use.
        // To save our features to a file, we first create a string of
        // comma-separated values for each data point and then save it
        // in HDFS as file named wikistats_featurized.
        //
        //  Scala:
        //      featurizedRDD.cache.map(t => t._1 + "#" + t._2.mkString(","))
        //
        JavaRDD<String> finalFeaturizedRDD = buildFeaturizedOutput(featurizedRDD);

        //
        // save the final output for future use
        //
        finalFeaturizedRDD.saveAsTextFile(outputPath);

        // done
        context.close();
    }

    /**
     *
     * Build the featureized output, to be used by future applications such as K-Means
     *
     * @param featurizedRDD an RDD, where key is a String of the form [projectCode + " " + pageTitle]
     * and value is a list of 24 features (one per hour)
     *
     * @return JavaRDD<String>, which may be used later on for analytics such as K-Means.
     * Each item of the output RDD will have:
     *     <key><#><feature_1><,><feature_2><,>...<,><feature_24>
     *     where <key> is a String of the form [projectCode + " " + pageTitle]
     *
     */
    static JavaRDD<String> buildFeaturizedOutput(final JavaPairRDD<String, double[]> featurizedRDD) {
        JavaRDD<String> finalFeaturizedRDD = featurizedRDD.map(
                new Function<
                             Tuple2<String, double[]>,      // input
                             String                         // output: final output format
                            >() {
                @Override
                public String call(Tuple2<String, double[]> kv) {
                    StringBuilder builder = new StringBuilder();
                    //
                    builder.append(kv._1); // key
                    builder.append("#");   // separator of key from the values/features
                    //
                    double[] data = kv._2;
                    for (int i=0; i < 23; i++) {
                       builder.append(data[i]); // feature
                       builder.append(",");
                    }
                    builder.append(data[23]); // the last feature (24'th)
                    //
                    return builder.toString();
                }
        });
        return finalFeaturizedRDD;
    }

}

redpajama_set_name: RedPajamaGithub


text:
Linux company Lineo lays off 13 percent
The company, which is working to get its version of Linux established in devices such as set-tops and handhelds, lays off about 40 employees, citing sluggish revenue growth.
Stephen Shankland
Jan. 16, 2002 2:58 p.m. PT
Lineo, a company working to get its version of Linux established in devices such as set-top boxes and handheld computers, laid off about 40 employees Friday, citing slower-than-expected revenue growth.
The cut, about 13 percent of Lineo's staff, affected all divisions of the company, said spokesman Lyle Ball. Ball, a Lineo co-founder, is exchanging his job at the company for an equivalent position at a newly established public relations company that will contract with Lineo. Also leaving the communications staff is Blake Stowell, who has taken a job at rival Microsoft coordinating Windows XP public relations, Ball said.
"When we (made) projections for last quarter, we projected it would do better than it did," Ball said.
The quarter ended June 30, though, was Lineo's best ever, measured by revenue. Ball declined to offers specifics but said the company's work in "embedded" Linux software for a variety of special-purpose computing devices exceeded that of any rival--including the overall Linux leader, Red Hat. Red Hat reported $4.6 million in embedded Linux revenue in its most recent quarter.
The slowing economy has been tough on Linux companies. Lineo rival MontaVista Software cut 20 percent of its staff recently. LynuxWorks, another rival, withdrew its IPO plans in June, five months after Lineo made the same move.
Lineo's recent deals include an agreement to have its software used in Sharp Electronics' Zaurus handheld computer. Sharp has committed to shipments of 1 million units by March 2002, Ball said.
Discuss: Linux company Lineo lays off 13 percent

redpajama_set_name: RedPajamaCommonCrawl


text:
China defeated the Corona virus with the use of dried apricots and raisins
April 12, 2020 by Muhammad Mohsin
China almost got rid of the Corona virus, and now Wuhan, the most affected city, has also been reopened after 76 days, where normal life has been restored.
Chinese Ministry of Health spokesmen have made it clear that they overcome the epidemic by simply shutting down.
According to details, China began importing dried apricots from Turkey after the outbreak of Corona, which has increased Turkey's exports by 125% to $ 45 million from $ 2 million.
Significantly, the export of dry asparagus was underway in August to September and November, however, when the cases of Corona began to appear in December, exports increased by 125%.
From August 2019 to March 30, 2020, the export of 76 thousand tonnes of apricot yielded $ 197 million, according to Burel Jelip, head of the union, which exports dried apricots, and other fruits.
He said that the use of dried fruits such as raisins dry apricots and figs boost the immune system, which led to a large order from China and we sent goods.
After China, 105 countries around the world have started ordering export of asparagus, US $ 26 million was exported to Germany, 18 to Germany and $ 17 million to France.
The PM imran khan says the villagers are making the segment a part of the Billion Tree Project
New York Google deleted an app called Network VPN from its Play Store


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Mining & Construction
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video galleryphoto gallery
The Humanihut concept was initially designed in 2013 as a solution to the growing humanitarian crisis.
Humanihut's Story
As a former soldier, Co-Founder and Managing Director of Humanihut, Neale Sutton, had first-hand experience in the hardships of conflict. That experience was his biggest inspiration behind designing an innovative, robust temporary emergency accommodation solution, which was rapidly deployable, had design flexibility allowing a structure to be used for multiple applications, and did not compromise on durability and performance.
2013 - Establishment of HumanihutHumanihut Pty Ltd was established.
2015 - Humanihut Pty Ltd LaunchedHumanihut Pty Ltd was launched by the Premier of the Government of South Australia, the Honourable Mr Jay Weatherill. The news release can be downloaded here. Video footage and photos of the launch can be found here.
2016 - Humanihut Featured in Disrupt 100Humanihut was included in Disrupt 100 – a bi-annual publication celebrating the businesses with the most potential to influence, change or create new global markets. Download Humanihut's Media Release and the Disrupt 100 Press Release.
2018 - Humanihut Awarded the Good Design AwardThe Humanihut Field Infrastructure System was awarded Best in Class for Engineering Design and was also a Nominee for the Award of the Year for Engineering Design in May 2018. Good Design Australia is an international design promotional organisation who is committed to promoting the importance of design to business, industry, government and the general public, and the critical role it plays in creating a better, safer and more prosperous world.
2019 - Business Council Design for Export Awards: WinnerThe Humanihut Field Infrastructure System was judged the Overall Winner in the Design Strategy category at the Business SA Export Awards in October 2019. These awards are a joint initiative between Good Design Australia and Business SA, and celebrate the success of South Australia's design-led exporters. For more information, click here.
Humanihut was judged Best in Class for Engineering Design by Good Design Australia.
Humanihut was included in Disrupt 100 – a bi-annual publication celebrating the businesses with the most potential to influence, change or create new global markets.
The Humanihut Field Infrastructure System was judged the Overall Winner in the Design Strategy category at the Business SA Export Awards in October 2019.
A Word From Our Managing Director
The Humanihut concept was initially designed in 2013 as a solution to the growing humanitarian crisis which had its origins in the Syrian War, and to aid the citizens that the conflict was displacing into Jordan and Lebanon. Since then, the Humanihut Field Infrastructure System (HFIS) has gone through a series of developments that have matured into the complete Systems that you can explore across this website. Now focussed into four separate international markets, the HFIS' reputation has continued to grow, and is now in many locations throughout the world, driving improvements in field infrastructure and accommodation.
The HFIS provides excellent outcomes for those who are accommodated within in it, and Camp Managers who are looking for a financially efficient, technically innovative, and physically robust solution to their field infrastructure needs.
I recommend this solution to you if you require comfortable, economical, robust, rigid, re-deployable infrastructure.
Neale Sutton
Understanding and anticipating the individual needs and requirements of our clients; and providing them with solutions that exceed their expectations; logistically, financially, operationally, and environmentally.
Creating a culture & atmosphere built on trust, respect, accountability, appreciation & belonging.
Communicating both internally and externally in an effective, timely, respectful and efficient manner.
We are driven in the pursuit of delivering the highest quality products through continuous improvement.
Growing the Humanihut brand to be recognised as the world leader in rapidly deployable, innovative, adaptive solutions, for multiple uses across many sectors including humanitarian, medical infrastructure, emergency management, defence, and the mining and construction industries.
Humanihut recognises that its operations have a broader economic, environmental and social impact. Humanihut incorporates sustainable development and manufacturing processes in its business model. We aim to positively impact social, economic and environmental factors through the following commitments.
We aim to use materials and manufacturing processes that have the least detrimental impact on the environment.
Ethical Labour
We ensure that our partners follow ethical labour practices
Competent Staff
Our staff have a broad base of design and construction experience, and volunteer and military service.
Legally Conducted
All Humanihut business is conducted transparently and legally.
Commitments to Design, Manufacturing & Operational Excellence
The Humanihut System uses innovative technology which is unrivalled by other alternatives.
We deliver a personalised service experience that involves building relationships with our customers.
Humanihut is committed to world-class design and manufacturing processes.
As a business, Humanihut cares about the environment, and we passionately believe in protecting the environment and using renewable services, energy and materials where possible.
We want to hear from our customers; the more we understand about your requirements, the better we are able to adapt a solution to suit your needs.
The Humanihut Field Infrastructure System (HFIS) has minimal environmental impact; with all essential services being above ground. When the HFIS of any size is disestablished and re-deployed, there is only minor, temporary environmental impact that is easily repaired.
Humanihut products are manufactured using energy-efficient materials to reduce cooling and heating costs when established, and are sourced to ensure minimal environmental impact.
The HFIS has a lifespan of 20 years, much longer than alternative accommodation solutions, and is designed to be re-deployed as often as required throughout its lifespan.
The high-performance insulation used in the Humanihut System maximises energy savings.
Our commitment to reducing/re-using/recycling, and our adherence to international environmental and quality standards, all contribute to a smaller global footprint.
Humanihut Head Office
Adelaide, SA - Australia
PO Box 215, Edwardstown SA 5039
Humanihut North America
Monroe, CT – United States of America
Humanihut Middle East
Riyadh - Kingdom of Saudi Arabia
Humanihut United Kingdom
Cornwall - United Kingdom
Humanihut Europe
Ennery - France
sales@humanihut.com
© Copyright Humanihut 2020


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Cassper Nyovest celebrates 2 million followers on Instagram
SA Hip Hop royal, Cassper Nyovest has again proven his assertion that he is loved by his fans as he clinches the golden mark of 2 million followers on social media platform, Instagram.
The "Tito Mboweni" hitmaker remains one of the finest rappers on the continent of Africa, as well as one of the most successful having been recently featured on the Forbes Magazine 2018 edition of its Africa Under 30.
The list celebrates Africa's most promising young change-makers in the areas of business, technology and creatives.
However, in celebrating his latest feat, Mufasa, thanked Team Nyovest on the same channel for the love and support they continue to show him.
"2 MILLION STRONG on Instagram. THANK YOU for following my Journey. We just getting started!!! Shout out to everyone who likes the pictures instead of just peeping, hahaha. love y'all!!! #TeamNyovest Don't forget to DOUBLE TAP" Cassper posted.
Source: Cassper Nyovest/Instagram
The Family Tree Records Boss is also about to celebrate the same milestone on Twitter as he currently sits at 1.88 million followers.
Congratulations are in order for Cassper Nyovest on his latest win. You can as well share your thoughts and wishes in the comments section below.
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What makes The Weeknd and Dua Lipa's marketing campaigns perfect?
Positioning is an essential component of any marketing campaign. Searing the image of a brand into the consciousness of potential consumers is a must if said marketing is meant to succeed.
The basic approach of positioning is not to create something new and different, but to manipulate what's already up there in the mind, to retie the connections that already exist.
Al Reis, Positioning: The Battle for Your Mind
Late 2019, Canadian crooner The Weeknd dropped 2 singles of his upcoming album After Hours – "Heartless" and "Blinding Lights". The former instantly topped charts worldwide (though its fall off was steep), while the latter eventually parked itself atop both global iTunes and Spotify rankings as well, and for multiple weeks in a row.
"Heartless" was propelled solely through the hype of a new Weekend era, thus its descent from the top was to be expected; while "Blinding Lights" (easily the superior song) took its time to hook listeners and slow burn its way to the top. Both are top quality songs that could hold their own among Weeknd's best, but they are also the opening chapters into what is shaping up to be the After Hours narrative.
Re-branding himself as a drug-laced mess that walked straight out of a 80s Scarface parody, The Weeknd doubled down on the persona he's set up for the album. From each of his music videos to his live performances, the consistency in his image preps listeners for a record that will most likely be dark, psychedelic and drenched in a drug-induced haze.
His marketing campaign becomes an extension to the After Hours plot line. The music will still be doing the heavy lifting, but the material he's put out so far are busy world building, readying listeners for the whole album experience.
Across the other side of the pond, UK superstar Dua Lipa's Future Nostalgia rollout is the antithesis to After Hours'. Instead of banking solely on controlled media like music videos and TV performances, she employs a maximalist approach that ranges from festival performances to sketches (as seen above).
Her 3 singles so far – "Don't Start Now", "Future Nostalgia" and "Physical" all have multiple music videos to their name respectively, which is an obvious aim at Billboard's new rules regarding video streams. Still, exposure wasn't the only thing her team focused on. The dance-heavy tunes also paved the way for Dua to silence her detractors when it came to her once "middling stage presence".
Less like a re-branding and more of an absolute tonal shift, the Future Nostalgia era can already be defined by eye-popping outfit aesthetics, heavy 80s/90s nostalgia baiting and killer live performances.
Her debut performance for "Don't Start Now" at EMAs was a declaration by a new-and-improved artist, commanding attention towards the transfixing product that is her new era.
The unshakable confidence exuded by Dua Lipa set the tone for everything that followed, as each subsequent single felt more formidable than the last. It began setting the expectation that Future Nostalgia will be a career-defining era for one of the biggest pop stars in the world.
How not to market a song ^
It's impossible to improve a bad product with good marketing, but effective marketing can turn a great product into one that's seemingly untouchable relative to the competition.
The Weeknd and Dua Lipa are very talented artists that have dropped one incredible single after the other. Even with minimal marketing, either artists would have easily topped charts worldwide. But as After Hours nears its Friday release date and Future Nostalgia's 3 weeks from now, both are expected to make tsunami-esque splashes in sales.
Choosing to commit to a single narrative/aesthetic will pay off handsomely, given that expectations for the record are firmly set in place and consumers become confident that the artists will very likely to deliver on the final product.
By keeping things consistent, not only are the expectations set high, they will also be realistic. Hope for their album to be great and blow audiences away will be high, while at the same time whatever product that is delivered will be kept within the boundaries of their own expectations.
Through this, the artists themselves have allowed for fans to expect nothing but brilliance from them.
Jensen Ooi 17/03/2020 01/05/2020 Dua Lipa, Justin Bieber, The Weeknd
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Nintendo announces sky-high March Switch sales and full-year forecast
Image source: Serpell-Rand/REX/Shutterstock
By Zach Epstein @zacharye
There was some concern that sales of Nintendo's new video game console might be stunted by the sour taste left in gamers' mouths from the Wii U. The post-launch frenzy seemed to dispel those concerns, and we can now confirm that sales of the new Nintendo Switch console are soaring. Along with the company's earnings report for the fourth quarter of fiscal 2016, Nintendo announced that Nintendo Switch sales in the month of March totaled an impressive 2.74 million units.
Despite continued supply shortages — paying a premium for the Switch on Amazon is still the only way to buy one online right now — Switch sales have indeed been impressive so far. What's more, Nintendo has issued an even more impressive forecast for Switch sales in fiscal 2017.
On top of sky-high Nintendo Switch console sales in the month of march, Nintendo confirmed in its fiscal Q4 2016 earnings report that sales of Switch games reached 5.46 million copies. Incredibly, that includes 3.84 million copies of the Switch's flagship launch title, The Legend of Zelda: Breath of the Wild. That figure includes Wii U titles, but believe it or not, the Switch version of the game did in fact out-sell the Switch console itself — Nintendo says 2.76 million copies of the Switch version were shipped.
For fiscal 2017, Nintendo expects the strong demand for its new Nintendo Switch console to persist, and it expects to increase production so that supply will eventually catch up with demand. Nintendo says that it expects to sell an additional 10 million Switch consoles in fiscal 2017. If that figure is accurate, the company will have sold nearly 13 million Switch units by this time next year.
Tags: Nintendo, Nintendo Switch
Zach Epstein has worked in and around ICT for more than 15 years, first in marketing and business development with two private telcos, then as a writer and editor covering business news, consumer electronics and telecommunications. Zach's work has been quoted by countless top news publications in the US and around the world. He was also recently named one of the world's top-10 "power mobile influencers" by Forbes, as well as one of Inc. Magazine's top-30 Internet of Things experts.
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