text:
And when he had landed at Caesarea and had gone up and saluted the church, he went down to Antioch.
And when he had landed at Caesarea, he went up and saluted the church, and went down to Antioch.
When he had landed at Caesarea, he went up and greeted the church [at Jerusalem], and then went down to Antioch.
When he landed at Caesarea, he went up and saluted the church [at Jerusalem], and then went down to Antioch.
And when he had landed at Caesarea, and gone up, and saluted the church, he went down to Antioch.
On landing at Caesarea, he went up to Jerusalem and greeted the church, then went down to Antioch.
He arrived in Caesarea, went up to Jerusalem and greeted the church, and then went down to Antioch.
After landing at Caesarea, he went up to Yerushalayim and greeted the Messianic community. Then he came down to Antioch,
Paul sailed to Caesarea, where he greeted the church. Then he went on to Antioch.
And landing at Caesarea, and having gone up and saluted the assembly, he went down to Antioch.
DLNT
And having come down to Caesarea, having gone up and greeted the church, he went down to Antioch.
And going down to Caesarea, he went up to Jerusalem, and saluted the church, and so came down to Antioch.
When Paul arrived at Caesarea, he went to Jerusalem and visited the church there. After that he went to Antioch.
When he landed at Caesarea, he went up and greeted the church. Then he went down to Antioch.
When he had landed at Caesarea, he went up and greeted the church, and then went down to Antioch.
When Paul landed at Caesarea [C a port city to the west of Jerusalem on the Mediterranean], he went [L up] and gave greetings to the ·church in Jerusalem [L the church; C the Greek does not mention Jerusalem, but going "up" implies the church in Jerusalem; less likely, it could be the church in Caesarea]. After that, Paul went [L down] to Antioch [C in Syria; the church that sent Paul out].
¶ And when he came down to Caesarea, he went up to Jerusalem: and when he had saluted the Church, he went down unto Antioch.
and arrived in the city of Caesarea. He went ⌞to Jerusalem⌟, greeted the church, and went back to the city of Antioch.
When he arrived at Caesarea, he went to Jerusalem and greeted the church, and then went to Antioch.
On landing at Caesarea, he went up and greeted the church and went down to Antioch.
Paul landed at Caesarea. Then he went and gave greetings to the church in Jerusalem. After that, Paul went to Antioch.
When he arrived in Caesarea, he went up to Jerusalem, greeted the church there, and then returned to Antioch.
Paul stayed for some time after this incident and then took leave of the brothers and sailed for Syria, taking Priscilla and Aquila with him. At Cenchrea he had his hair cut short, for he had taken a solemn vow. They all arrived at Ephesus and there Paul left Aquila and Priscilla, but he himself went into the synagogue and debated with the Jews. When they asked him to stay longer he refused, bidding them farewell with the words, "If it is God's will I will come back to you again". Then he set sail from Ephesus and went down to Caesarea. Here he disembarked and after paying his respects to the Church in Jerusalem, he went down to Antioch. He spent some time there before he left and proceeded to visit systematically throughout Galatia and Phyrgia, putting new heart into all the disciples as he went.
And when he had landed at Caesarea and gone up to Jerusalem and after greeting the congregation, {Gr. ekklesia – called out ones} he went down to Antioch.
And when he had landed at Cæsarea, and gone up, and saluted the church, he went down to Antioch.
And when he arrived at Caesarea, he went up and greeted the church, and went down to Antioch.
The next stop was at the port of Caesarea from where he visited the church at Jerusalem and then sailed on to Antioch.
They landed in Ephesus, where Priscilla and Aquila got off and stayed. Paul left the ship briefly to go to the meeting place and preach to the Jews. They wanted him to stay longer, but he said he couldn't. But after saying good-bye, he promised, "I'll be back, God willing." From Ephesus he sailed to Caesarea. He greeted the church there, and then went on to Antioch, completing the journey.
When he had landed at Caesarea, he went up and greeted the church and then went down to Antioch.
MOUNCE
and when he touched land at Caesarea, he went up and greeted the church and then went down to Antioch.
and arrived in the city of Caesarea. He went to Jerusalem, greeted the church, and went back to the city of Antioch.
Upon landing at Caesarea, he went up and greeted the church and then went down to Antioch.
When he had landed in Caesarea, he went up to Jerusalem and greeted the church, and went down to Antioch.
When he had landed at Caesarea, he went up and greeted the church, and went down to Antioch.
When he landed at Caesarea, he went up and greeted the Church, and then he went down to Antioch.
When Paul landed at Caesarea, he went and gave greetings to the church in Jerusalem. After that, Paul went to Antioch.
and when he arrived at Caesarea, he went up and greeted the church at Jerusalem and then went down to Antioch.
When he landed at Caesarea, he went up to Jerusalem. There he greeted the church. He then went down to Antioch.
When he landed at Caesarea, he went up to Jerusalem and greeted the church and then went down to Antioch.
And when he had landed at Caesarea, and gone up and greeted the church, he went down to Antioch.
He stopped in the city of Caesarea to greet the people in the church. Then he went down to the city of Antioch.
The next stop was at the port of Caesarea. From there he went up and visited the church at Jerusalem and then went back to Antioch.
and landed at Ceasarea, and ascended to Jerusalem, and briefly visited the congregation. And he went down to Antioch (in Syria).
When he had landed at Caesarea, he went up to Jerusalem and greeted the church, and then went down to Antioch.
and went to Caesarea. Then he went up to Jerusalem, greeted the church, and went back to Antioch.
And having come down to Caesarea and having gone up and greeted the [Yerushalayim] Kehillah of Moshiach, then Rav Sha'ul went down to Antioch.
When he arrived there he traveled on to Jerusalem to visit the church and pray for them, then he left for Antioch.
And when he came down to Caesarea, he went up to Jerusalem. And after he had greeted the church, he went down to Antioch.
When he had landed at Caesare′a, he went up and greeted the church, and then went down to Antioch.
After landing at Caesarea, he went up and greeted Messiah's community; then he went down to Antioch.
Paul: If God wills, I'll return at some point. He caught a ship bound south and east for Caesarea by the sea. There he went up for a brief visit with the believers in the church at Jerusalem; then he headed north to Antioch.
When he had landed at Caesarea, he went up and greeted the assembly, and went down to Antioch.
When he reached Caesarea, he went to greet the church people. Then he went on to Antioch.
And he came down to Caesarea, and he went up [and ascended up], and greeted the church, and came down to Antioch.
and having come down to Cesarea, having gone up, and having saluted the assembly, he went down to Antioch.
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text:
package dk.bayes.factorgraph.ep.calibrate.fb
import org.junit._
import org.junit.Assert._
import dk.bayes.factorgraph.ep.util.TennisFactorGraph._
import org.junit.Assert._
import dk.bayes.factorgraph.factor.GaussianFactor
import dk.bayes.math.linear._
import breeze.linalg.DenseMatrix
import breeze.linalg.DenseVector
import dk.bayes.factorgraph.factor.BivariateGaussianFactor
import dk.bayes.factorgraph.ep.GenericEP
import dk.bayes.factorgraph.ep.calibrate.fb.ForwardBackwardEPCalibrate

/**
 * This is a test for a skill update with TrueSkill rating system in a two-person game, like Tennis.
 *
 * Bayesian inference is performed with Expectation Propagation algorithm.
 *
 * @author Daniel Korzekwa
 */
class TrueSkillOnlineTennisEPTest {

  private def progress(currIter: Int) = println("EP iteration: " + currIter)

  /**
   * Tests for variable marginals.
   */

  @Test(expected = classOf[NoSuchElementException]) def variable_marginal_var_id_not_found:Unit = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(1, 44), epCalibrate.calibrate(10, progress))

    val ep = GenericEP(tennisFactorGraph)
    val outcomeMarginal = ep.marginal(123)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Test def variable_marginal_no_result_set:Unit = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(1, 44), epCalibrate.calibrate(10, progress))

    val ep = GenericEP(tennisFactorGraph)

    val outcomeMarginal = ep.marginal(outcomeVarId)
    assertEquals(0.24463, outcomeMarginal.getValue((outcomeVarId, 0)), 0.00001)
    assertEquals(0.75537, outcomeMarginal.getValue((outcomeVarId, 1)), 0.00001)

    val skill1Marginal = ep.marginal(skill1VarId).asInstanceOf[GaussianFactor]
    assertEquals(27.1742, skill1Marginal.m, 0.0001)
    assertEquals(37.5013, skill1Marginal.v, 0.0001)

    val skill2Marginal = ep.marginal(skill2VarId).asInstanceOf[GaussianFactor]
    assertEquals(33.846, skill2Marginal.m, 0.0001)
    assertEquals(20.861, skill2Marginal.v, 0.0001)

    assertEquals(EPSummary(1, 44), epCalibrate.calibrate(10, progress))

    val outcomeMarginal2 = ep.marginal(outcomeVarId)
    assertEquals(0.24463, outcomeMarginal2.getValue((outcomeVarId, 0)), 0.00001)
    assertEquals(0.75537, outcomeMarginal2.getValue((outcomeVarId, 1)), 0.00001)

    val skill1MarginalLater = ep.marginal(skill1VarId).asInstanceOf[GaussianFactor]
    assertEquals(27.1742, skill1MarginalLater.m, 0.0001)
    assertEquals(37.5013, skill1MarginalLater.v, 0.0001)

    val skill2MarginalLater = ep.marginal(skill2VarId).asInstanceOf[GaussianFactor]
    assertEquals(33.846, skill2MarginalLater.m, 0.0001)
    assertEquals(20.861, skill2MarginalLater.v, 0.0001)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Test def variable_marginal_player1_wins:Unit = {
    val tennisFactorGraph = createTennisFactorGraph()

    val ep = GenericEP(tennisFactorGraph)
    ep.setEvidence(outcomeVarId, true)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(2, 88), epCalibrate.calibrate(70, progress))

    val outcomeMarginal = ep.marginal(outcomeVarId)
    assertEquals(1, outcomeMarginal.getValue((outcomeVarId, 0)), 0.0001)
    assertEquals(0, outcomeMarginal.getValue((outcomeVarId, 1)), 0.0001)

    val skill1Marginal = ep.marginal(skill1VarId).asInstanceOf[GaussianFactor]
    assertEquals(27.1744, skill1Marginal.m, 0.0001)
    assertEquals(37.4973, skill1Marginal.v, 0.0001)

    val skill2Marginal = ep.marginal(skill2VarId).asInstanceOf[GaussianFactor]
    assertEquals(33.8473, skill2Marginal.m, 0.0001)
    assertEquals(20.8559, skill2Marginal.v, 0.0001)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Test def variable_marginal_player1_looses:Unit = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()
    val ep = GenericEP(tennisFactorGraph)
    ep.setEvidence(outcomeVarId, false)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(2, 88), epCalibrate.calibrate(100, progress))

    val outcomeMarginal = ep.marginal(outcomeVarId)
    assertEquals(0, outcomeMarginal.getValue((outcomeVarId, 0)), 0.0001)
    assertEquals(1, outcomeMarginal.getValue((outcomeVarId, 1)), 0.0001)

    val skill1Marginal = ep.marginal(skill1VarId).asInstanceOf[GaussianFactor]
    assertEquals(25.558, skill1Marginal.m, 0.0001)
    assertEquals(30.5446, skill1Marginal.v, 0.0001)

    val skill2Marginal = ep.marginal(skill2VarId).asInstanceOf[GaussianFactor]
    assertEquals(34.745, skill2Marginal.m, 0.0001)
    assertEquals(18.7083, skill2Marginal.v, 0.0001)
  }

  /**
   * Tests for factor marginals.
   */

  @Test(expected = classOf[NoSuchElementException]) def factor_marginal_var_id_not_found:Unit = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()
    val ep = GenericEP(tennisFactorGraph)

    val perfMarginal = ep.marginal(skill1VarId, 345)
  }

  @Test(expected = classOf[NoSuchElementException]) def factor_marginal_var_id_not_found2:Unit = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()
    val ep = GenericEP(tennisFactorGraph)

    val perfMarginal = ep.marginal(skill1VarId, perf1VarId, 45)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Ignore @Test def factor_marginal_no_result_set = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()
    val ep = GenericEP(tennisFactorGraph)

    val perfMarginal = ep.marginal(skill1VarId, perf1VarId).asInstanceOf[BivariateGaussianFactor]
    assertEquals(Vector(1, 3), perfMarginal.getVariableIds())
    assertEquals(DenseVector(Double.NaN, Double.NaN).toString, perfMarginal.mean.toString)
    assertEquals(new DenseMatrix(2, 2, Array(Double.NaN, Double.NaN, Double.NaN, Double.NaN)).toString, perfMarginal.variance.toString)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(1, 44), epCalibrate.calibrate(10, progress))

    val perfMarginal2 = ep.marginal(skill1VarId, perf1VarId).asInstanceOf[BivariateGaussianFactor]
    assertEquals(Vector(1, 3), perfMarginal2.getVariableIds())
    assertEquals(DenseVector(27.174, 27.174).toString, perfMarginal2.mean.toString)
    assertEquals(new DenseMatrix(2, 2, Array(37.501, 37.501, 37.501, 54.862)).toString, perfMarginal2.variance.toString)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Test def factor_marginal_player1_wins = {
    val tennisFactorGraph = createTennisFactorGraph()
    val ep = GenericEP(tennisFactorGraph)
    ep.setEvidence(outcomeVarId, true)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(2, 88), epCalibrate.calibrate(70, progress))

    val perfFactorMarginal = ep.marginal(skill1VarId, perf1VarId).asInstanceOf[BivariateGaussianFactor]
    assertEquals(Vector(1, 3), perfFactorMarginal.getVariableIds())
    assertTrue(isIdentical(DenseVector(27.174, 32.142), perfFactorMarginal.mean, 0.001))
    assertTrue(isIdentical(new DenseMatrix(2, 2, Array(37.497, 28.173, 28.173, 34.212)), perfFactorMarginal.variance, 0.001))

    val player1PerfMarginal = ep.marginal(perf1VarId).asInstanceOf[GaussianFactor]
    assertEquals(32.1415, player1PerfMarginal.m, 0.0001)
    assertEquals(34.2117, player1PerfMarginal.v, 0.0001)
  }

  /**http://atom.research.microsoft.com/trueskill/rankcalculator.aspx*/
  @Test def factor_marginal_player1_looses = {

    val tennisFactorGraph = createTennisFactorGraphAfterPlayer1Won()
    val ep = GenericEP(tennisFactorGraph)
    ep.setEvidence(outcomeVarId, false)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(2, 88), epCalibrate.calibrate(100, progress))

    val perfFactorMarginal = ep.marginal(skill1VarId, perf1VarId).asInstanceOf[BivariateGaussianFactor]
    assertEquals(Vector(1, 3), perfFactorMarginal.getVariableIds)
    assertTrue(isIdentical(DenseVector(25.558, 24.810), perfFactorMarginal.mean, 0.001))
    assertTrue(isIdentical(new DenseMatrix(2, 2, Array(30.545, 27.324, 27.324, 39.974)), perfFactorMarginal.variance, 0.001))

    val player1PerfMarginal = ep.marginal(perf1VarId).asInstanceOf[GaussianFactor]
    assertEquals(24.8097, player1PerfMarginal.m, 0.0001)
    assertEquals(39.9735, player1PerfMarginal.v, 0.0001)
  }

  /**
   * Testing inference including GenericFactor
   */
  @Test def genericfactor_test_factor_marginal_player1_wins:Unit = {
    val tennisFactorGraph = createTennisFactorGraphWithGenFactor()
    val ep = GenericEP(tennisFactorGraph)
    ep.setEvidence(outcomeVarId, true)

    val epCalibrate = ForwardBackwardEPCalibrate(tennisFactorGraph)
    assertEquals(EPSummary(2, 88), epCalibrate.calibrate(70, progress))

    val perfFactorMarginal = ep.marginal(skill1VarId, perf1VarId).asInstanceOf[BivariateGaussianFactor]
    assertEquals(Vector(1, 3), perfFactorMarginal.getVariableIds())
    assertTrue(isIdentical(DenseVector(27.174, 32.142), perfFactorMarginal.mean, 0.001))
    assertTrue(isIdentical(new DenseMatrix(2, 2, Array(37.497, 28.173, 28.173, 34.212)), perfFactorMarginal.variance, 0.001))

    val player1PerfMarginal = ep.marginal(perf1VarId).asInstanceOf[GaussianFactor]
    assertEquals(32.1415, player1PerfMarginal.m, 0.0001)
    assertEquals(34.2117, player1PerfMarginal.v, 0.0001)
  }
}

redpajama_set_name: RedPajamaGithub


Chronic Obstructive Pulmonary Disease or COPD: The most common chronic lung disease.
COPD affects an estimated 17 million Americans, and its incidence is rising. It now ranks fifth among the major causes of death in the United States. The disorder affects men more frequently than women. COPD dosesn't always produce symptoms and causes only minimal disability in many patients, it tends to worsen with time.
COPD includes: emphysema, chronic bronchitis, asthma, or any combination of them. Frequently, more than one of these underlying conditions coexist. Usually bronchitis and emphysema occur together.
Cause: May be brought on by cigarette smoking, recurrent or chronic respiratory infection, and or allergies.
See Emphysema, Chronic bronchitis , and Asthma for further info.
The typical patient is asymptomatic until middle age, when his ability to exercise or do strenuous work gradually declines, and he begins to develop a productive cough.
See Emphysema, Chronic bronchitis, and Asthma for further info.
Patient teaching: Because most COPD patients receive outpatient treatment, they need comprehensive teaching to help them comply with therapy and understand the nature of this chronic, progressive disease.
-To help mobilize secretions; cough and get rid of mucous (if secretions are thick, try 15 glasses of fluid a day, home humidifier, particularly in the winter.) - Postural drainage and chest physiotherapy helpful.
-It is important to have a balanced diet.


About this journey The average travel time between Marseille and Frankfurt is 7h59 minutes. The quickest route is 7h59 minutes. The first train leaving Marseille is at 07:59, the last at . There is an average of 1 trains a day between Marseille and Frankfurt, leaving approximately every -1J 12h minutes.
"Absolutely tops for comfort, convenience and value for money. We got a great fare by booking in advance. We thoroughly enjoyed the high-speed parts of the journey - it was great to be kept informed by the cabin monitor of the high speeds we were achieving. Our 'picnic' bags were fresh and tasty, and we enjoyed the drinks options. We arrived rested and relaxed. "


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Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions
Luca Mazzei1, Stefano Ciurli1, Barbara Zambelli1
1Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna
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Isothermal titration calorimetry measures heat flow released or absorbed in chemical reactions. This method can be used to quantify enzyme-catalysis. In this paper, the protocol for instrumental setup, experiment running, and data analysis is generally described, and applied to the characterization of enzymatic urea hydrolysis by jack bean urease.
Mazzei, L., Ciurli, S., Zambelli, B. Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions. J. Vis. Exp. (86), e51487, doi:10.3791/51487 (2014).
Isothermal titration calorimetry (ITC) is a well-described technique that measures the heat released or absorbed during a chemical reaction, using it as an intrinsic probe to characterize virtually every chemical process. Nowadays, this technique is extensively applied to determine thermodynamic parameters of biomolecular binding equilibria. In addition, ITC has been demonstrated to be able of directly measuring kinetics and thermodynamic parameters (kcat, KM, ΔH) of enzymatic reactions, even though this application is still underexploited. As heat changes spontaneously occur during enzymatic catalysis, ITC does not require any modification or labeling of the system under analysis and can be performed in solution. Moreover, the method needs little amount of material. These properties make ITC an invaluable, powerful and unique tool to study enzyme kinetics in several applications, such as, for example, drug discovery.
In this work an experimental ITC-based method to quantify kinetics and thermodynamics of enzymatic reactions is thoroughly described. This method is applied to determine kcat and KM of the enzymatic hydrolysis of urea by Canavalia ensiformis (jack bean) urease. Calculation of intrinsic molar enthalpy (ΔHint) of the reaction is performed. The values thus obtained are consistent with previous data reported in literature, demonstrating the reliability of the methodology.
Quantitative determination of biochemical reactions provides insights into the biological processes at the basis of life. Calorimetry offers a label-free methodology to quantitatively characterize virtually every chemical reaction in solution. This technique measures the heat released or absorbed over time, and is therefore a universal detection system and a very convenient methodology to quantify the amount of reacting molecules (i.e. binding thermodynamics), as well as to measure the reaction rate (i.e. kinetics). In particular, isothermal titration calorimetry (ITC) has been adopted as method of choice to characterize the thermodynamics of biomolecular equilibria, involving protein-ligand, protein-protein, protein-metal ions and protein-DNA interactions1-6. In addition, the ability of ITC to provide kinetic information makes it a very powerful system to measure enzyme catalysis, although the potential of this application is still underestimated7-9.
The Michaelis-Menten equation10 is a quantitative description of enzymatic reactions, as it provides a relationship between the reaction rate and the substrate concentration, depending on two kinetic parameters: the Michaelis constant (KM) and the catalytic rate constant (kcat). The kcat/KM ratio is referred as the catalytic efficiency of an enzyme. In practice, determination of KM and kcat for a specific reaction provides a complete description of the catalysis.
In a typical enzymatic reaction (Figure 1), a substrate (S) interacts with the enzyme (E) forming the enzyme-substrate (ES) complex, which is subsequently activated into the transition state (ES*). The latter is converted into the enzyme-product (EP) complex that eventually dissociates. These steps are described by the following reaction.
where k1 is the rate constant for the formation of the ES complex, k-1 is the rate constant for the dissociation of the ES complex, while kcat is the catalytic rate constant or turnover number.
According to the Michaelis-Menten equation10, the rate of the reaction can be calculated as:
in which KM = (k-1 + kcat)/k1 and kcat = vmax/[E], with vmax being the maximal velocity reached when all enzyme is bound to the substrate.
The isothermal titration calorimeter is the instrument used in this study to characterize the enzymatic hydrolysis of urea. This instrument is made of an adiabatic shield containing two coined-shaped cells (Figure 1). These are connected to the outside with narrow access tubes. The sample cell (ca. 1.4 ml) is loaded with the enzyme solution, while the reference cell is generally filled with water or with the solvent used for the analysis. A rotating syringe with a long needle and a stir paddle attached, usually containing ca. 0.3 ml of substrate solution, is mounted on the sample cell. A thermoelectric device measures the difference of temperature between the sample and the reference cell and, using a "cell feedback network", it maintains this difference at zero by adding or subtracting heat. During the experiment, the substrate is injected into the enzyme solution at a constant chosen temperature. When the enzymatic reaction takes place, the amount of heat released or absorbed is proportional to the number of substrate molecules that are converted into product molecules. In addition, the rate of heat flow is directly related to the rate of the reaction. The measured data, appearing as a deviation of the heat trace from initial baseline (Figure 1), represent the thermal power (μcal/sec) supplied by the instrument to the sample cell, which is proportional to the heat flow occurring in the sample cell over time.
Figure 1. Schematic representation of isothermal titration calorimeter to study enzymatic reactions. An enzymatic reaction occurring upon titration of the substrate (in the injection syringe) into the enzyme solution (in the sample cell) results in a change of the thermal power released by the calorimeter, needed to keep the difference of temperature between the sample cell and the reference cell constant. Click here to view larger image.
Overall, the heat change (Q) is proportional to the molar enthalpy of the reaction (ΔH) and the number of moles of product generated (n), which in turn is given by the total volume times the concentration:
The product formation over time (dP/dt), which corresponds to the reaction rate, can thus be related to the amount of heat generated over the same time (dQ/dt) through the relation:
According to this equation, in order to obtain a Michaelis-Menten plot it is necessary to measure i) the total molar enthalpy ΔH, and ii) the heat flow dQ/dt at different substrate concentrations. Usually, this is performed in two different experiments: in the first experiment (Method 1, M1), the substrate is injected into the enzyme solution and the heat for complete substrate conversion is measured; in the second experiment (Method 2, M2), multiple injections of substrate are performed and the rate of heat production is measured at different substrate concentrations. These two sets of data are sufficient to derive the kinetic parameters KM and kcat.
In the present article, a general protocol to determine the kinetic parameters for enzymatic reactions performed using ITC is described. We applied the method to urea hydrolysis by Canavalia ensiformis urease, as a reference system. The good agreement between the results obtained using this method and the data reported in literature demonstrates the reliability of this approach.
1. Preparing Samples
Prepare 2 ml of enzyme solution and 0.5 ml of substrate solution for each experimental run. Dilute concentrated stock solutions of enzyme and substrate in buffer solutions having identical composition to minimize the heat of dilution and mixing during the substrate addition.
Choose the buffer conditions that are adequate to prevent pH change during experiment. For example, 20 mM HEPES pH 7 is adequate for measurements at neutral pH.
NOTE: If proton exchange is involved, protonation enthalpies of the used buffers must be considered, because they affect the measured ΔH of the reaction. Possible specific effects of the buffer or additives molecules on the system under analysis must be taken into account. If organic solvents (e.g. DMSO) are included in the enzyme solution, add them at exactly the same concentration into the substrate solution.
For the M1 experiment, start with enzyme concentration in the nM range (e.g. 1 nM) and with a substrate concentration at least three orders of magnitude higher than the enzyme concentration and above the KM.
For the M2 experiment, start with enzyme concentration in the pM-nM range (e.g. 15 pM). Substrate concentration in the syringe is in the mM range (e.g. 400 mM).
NOTE: In the single injection M1 experiment, concentrations should be adequate to achieve the total substrate conversion into the product over the experimental time. Therefore, the concentration of the enzyme depends on the enzyme rate: if enzymes with low kcat are under study, higher enzyme concentrations (up to 10 µM) must be used. On the other hand, enzyme concentrations used in the M2 experiment must assure that the injected substrate is only marginally (less than 5%) consumed and that the enzyme reaction proceeds at the steady state. For this reason, the higher the enzyme efficiency, the lower the required enzyme concentration. At the end of the experiment, substrate concentration in the sample cell should be higher than KM.
Carefully check enzyme and substrate concentrations with an appropriate analytical procedure (e.g. absorbance at 280 nm, colorimetric, BCA assays11. This is required to obtain accurate and reliable calculation of the thermodynamic and kinetic parameters.
Measure the pH of the solutions and verify that the pH mismatch of both the enzyme and the substrate solutions is minimal under the experimental conditions (±0.05 pH units).
2. Performing the Experiment
NOTE: The same procedure must be applied both for the M1 and the M2 experiment, which are performed one after the other.
Verify that the sample cell and the injection syringe are cleaned according to the manufacturer's instructions. Fill the loading syringe provided with the instrument with distilled water, gently insert the needle in the sample cell, fill the cell and remove the liquid using the same syringe. Using this method, wash the sample cell twice with distilled water and twice with the buffer.
Load the sample cell with 2 ml of enzyme solution using the loading syringe, carefully avoiding formation of air bubbles. Slowly inject the solution into the cell until it spills out the top of the cell stem. Produce a spurt of ca. 0.25 ml of solution into the cell. Repeat twice. This step removes air bubbles trapped in the sample cell.
Place the needle of the loading syringe on the ledge between the cell stem and the cell port and remove any excess solution.
Start the VP-Viewer program and, from the computer interface, equilibrate the ITC instrument to a temperature 3 °C below the desired experimental temperature. This is required to avoid long equilibration periods before the experiment, due to the passive instrumental cooling device.
Fill the reference cell with distilled water with the same procedure above. When buffers with high ionic strength or high osmolality are in the sample solution, the same buffer should be used as a reference solution.
Link a plastic syringe to the fill port of the injection syringe, using a thin silicon tube. Fill the injection syringe placing the needle tip into water and drawing up, until water comes out of the top filling port, indicating that the injection syringe is full. Then move the syringe tip from water and draw up air, to empty the injection syringe. With this procedure, wash the injection syringe with buffer, and draw air through the system. Subsequently, place the injection needle in a narrow tube containing 0.5 ml of substrate solution, carefully draw up and completely fill the injection syringe, leaving small amount of solution at the bottom of the tube.
From the computer interface, press "Close fill port". Remove the silicon loading tube. Press "Purge and refill" button to allow the injection syringe to dislodge any air bubbles and to expel them back into the bulk solution. Repeat twice.
Move the injection syringe, wipe on the sides to remove any drops and place the needle of the injection syringe into the sample cell.
On the computer, set the appropriate running parameters. The experimental enzyme and substrate concentrations can be indicated.
In the M1 experiment, set at least two additions (5-30 µl) of substrate to verify the reproducibility. Set the spacing time between each injection (e.g. 1,000 sec) large enough to ensure that the heat signal returns to the baseline before the next addition.
In the M2 experiment, set multiple (e.g. 15 x 5-10 µl) injections. Set the interval between the injections (e.g. 180 sec) allowing the system to stabilize the thermal power to the new baseline after each injection.
NOTE: The spacing time between injections in the M2 experiment should be short enough to avoid the conversion of a significant amount of substrate, allowing the measurements to be performed under steady state conditions.
In the M2 experiment, use small volumes (e.g. 2 μl) for the first injection, whose corresponding value is discarded during the subsequent data analysis. Indeed, it often presents artifacts due to the initial substrate diffusion through the syringe tip, and for the presence of air bubbles trapped in the syringe needle.
Set the reference power, the approximate level in which the baseline will be placed before the reaction starts, at a value of 20. Then define the experimental enzyme and substrate concentrations and chose a name for the experiment.
Define the experimental temperature, typically at 25 °C. ITC allows working temperatures between 2 °C and 80 °C. The experiment is ready for running. Press "Start" button to start the experiment.
Once the experiment has finished, clean the sample cell and the syringe according to the manufacturer's instructions.
Repeat the experiment at least one or two more times to check the reproducibility of the data.
From the analysis program, click on the "Read data" button, and navigate to the folder where the .itc file of the performed M2 experiment is located. Click on the scroll down arrow of the "Files of type" and select "Enzyme Assay (it?)". Subsequently, click and open the .itc file of the M2 experiment.
Obtain the ΔH of the reaction from integrating the curve of the M1 experiment according to Equation 5.
Click on the "Read data" button, and select the file .itc of the performed M1 experiment. Using Origin, divide the trace in different parts containing one peak each and save each peak as an .opj file. Open the file corresponding to the first peak, resulting from baseline deviation in the thermogram of the single injection M1 experiment, integrate it and divide the obtained area value, expressed in µcal, by the final substrate concentration in the sample cell, expressed in µM, and by the cell volume expressed in liters, determining, according to Equation 5, the ΔH of the reaction.
Repeat the same procedure for the second peak of the M1 experiment and obtain an average value for the two ΔH measurements.
Determine dQ/dt from the M2 experiment measuring the difference between the original baseline and the new baseline following each injection. Convert the experimental data to reaction rates according to Equation 4, using the enthalpy value obtained in the M1 experiment and fit the data to the Michaelis-Menten equation.
From the analysis program, click on the "Read data" button, and select the file .itc of the performed M2 experiment. Click on the scroll down arrow of the "Files of type" and select "Enzyme Assay (it?)".
The Enzyme Assay dialog box opens, allowing selecting one of the four models. Select "Method 2 - Substrate only" from the window.
In the ΔH window, indicate the value of ΔH obtained in step 4.2.
Click the "Concentration" button to check the concentration values to be used in the fitting procedure. Click the "Average Time (P)" button. The dialog box opens giving the opportunity to change or accept the default value. This value represents the time before each injection in which the instrument averages the power signal to determine the power level at each substrate concentration. Click OK to confirm the default value.
Click the "Zero Y Axis" button. The cursor turns to a cross hair allowing to double click a point, to place at y=0. Double click just before the first injection point.
Click the Calculate Rate button. Rate is plotted versus the substrate concentration, derived by dividing the substrate added during the titration for the sample cell volume (taking into account dilution effects). This operation gives a typical Michaelis-Menten plot that can be fitted to obtain KM and kcat.
If some points should be deleted, select the "Truncate Data" button. Move the data markers to exclude the bad data points and double click on one of the markers or press enter.
Use the "Fit to model" function to fit the curve and to obtain the kinetic constants.
Representative Results
Urease (EC 3.5.1.5; urea amidohydrolase) is a multisubunit nickel-containing enzyme found in archea, bacteria, unicellular eukaryotes and plants. This protein acts in the last step of organic nitrogen mineralization, catalyzing the hydrolysis of urea to ammonia and carbamate, which spontaneously decomposes to give a second molecule of ammonia and bicarbonate (Equation 6)12.
This reaction causes an overall increase of the pH of the environment, which is responsible of negative effects for human health and agriculture13. In plants, the primary role of urease is to recycle nutrient nitrogen from arginase-derived urea14. Plant ureases are generally homo-hexamers a6 with each subunit containing an active site with two Ni2+ ions15. Among them, Canavalia ensiformis (jack bean) urease (JBU) has been the first protein to be crystallized in 192616. Since then, several studies have extensively characterized the structural and enzymatic activity of ureases from several sources13,17. Therefore, the ureolytic activity of JBU was chosen as a representative reaction to demonstrate the applicability of ITC in quantitatively determining enzymatic catalysis. A value of ΔH = -10.5 kcal/mol was measured in a M1 experiment (Figure 2A) by integrating the thermal power resulting by injecting urea substrate into the urease solution and allowing the reaction to proceed to completion. The thermal power registered in a M2 experiment (Figure 2B) was converted to the reaction rate using Equation 4 (Figure 2C). Fit of the obtained data to the Michaelis-Menten equation provided the kinetic parameters for JBU in 20 mM HEPES pH 7 as kcat = 30,200 sec-1 and KM = 3.45 mM, in agreement with previously reported data18,19.
Figure 2. Urea hydrolysis by urease determined by ITC. JBU (type C3) was purchased as lyophilized enzyme (nominal activity 1,538 U/mg) and diluted with 20 mM HEPES pH 7 to the final concentration. The concentration of the enzyme in the reaction mixture was calculated by comparing the determined activity of the purchased product to the activity of the pure enzyme, reported to be 6,200 U/mg of pure enzyme20, and a molar mass of 545 kDa, corresponding to the homo-hexameric protein. (A) Thermal power observed in the M1 experiment, performed at 25 °C with 2 x 5 µl injections of 20 mM urea (in the injection syringe) into 1 nM JBU (in the sample cell). A spacing of 750 sec between the injections was applied to allow the baseline returning to the initial value. The enthalpy of the reaction is calculated from the integration of each peak and averaging the two values, according to Equation 5. The value is reported in the text. (B) Thermal power observed in the M2 experiment, obtained by titrating 400 mM urea (20 x 5 µl injections) into 15 pM JBU. A spacing of 180 sec was applied between injections, in order to allow the system to reach and maintain the steady state level. dQ/dt at different substrate concentrations is obtained as displacement of the baseline after each addition. The heat of dilution is shown as a blank experiment (gray), performed by titrating substrate into the buffer alone. (C) The baseline displacement in Figure 2B was converted to the reaction rate using Equation 4. Fit of the obtained data (filled squares) was performed using Equation 2, and is represented as a solid line. The value of KM and kcat obtained from the fit are reported in the text.
The total heat change measured in the M1 experiment represents the sum of all events occurring during the reaction under analysis, and depends on the molar enthalpy of all the processes involved, including, if applicable, proton release or uptake from the buffer. In a generic process in which the reacting system acquires protons in a buffered solution, the buffer releases protons, producing additional heat within the reaction cell. Therefore, the measured ΔH is apparent (ΔHapp), and includes the intrinsic ΔH of the reaction (ΔHint), as well as the ionization enthalpy of the buffer (ΔHion), and ii) the number of exchanging protons (n), according Equation 7:
Using this equation, in M1 experiments, we can determine n and ΔHint by performing the same experiment in buffers with different ionization enthalpies, and plotting the measured ΔHapp as a function of ΔHion specific for the buffer. From the resulting linear regression, n is derived from the slope: a negative slope reflects protons acquired by the buffer, while a positive slope represents protons released from the buffer; ΔHint is derived from the intercept on y-axis.
As observed in Equation 6, the overall urea hydrolysis reaction results in the acquisition of a proton H+ from the buffer. Accordingly, as described above, the heat of reaction includes the contribution of buffer deprotonation. In order to calculate the intrinsic ΔH of the hydrolysis reaction, three M1 experiments were performed in different buffers (HEPES, TrisHCl, phosphate), characterized by three different ΔHion. ΔHint and the number of proton released from the buffer, calculated from the intercept and the slope of the linear fit of the experimental data (Figure 3) according to Equation 7, were -14.7 kcal/mol and 0.98 respectively, in full agreement with data previously reported for the same reaction catalyzed by Helicobacter pylori urease7. To our knowledge, no thermodynamic data have been reported for JBU, so far.
Figure 3. Determination of ΔHint of urease reaction. Values of ΔHapp in different buffers (filled squares) were determined by performing the M1 experiments in 20 mM HEPES (ΔHion = 4.88 kcal/mol), 20 mM TrisHCl (ΔHion = 11.34 kcal/mol), and 20 mM phosphate (ΔHion = 0.86 kcal/mol)21. Linear regression analysis (solid line) of the data allowed determining ΔHint of the reaction, as well as the number of protons exchanged during urea turnover.
Significance of ITC to study enzymatic activity with respect to existing methods
In addition to its classical applications to study binding equilibria, isothermal titration calorimetry provides a reliable and fast method to characterize enzymatic reactions in solution using the heat of reaction as a probe, without requiring system modification or labeling. The kinetic parameters kcat and KM are usually obtained through a set of time course experiments, in which the product formation (or substrate consumption) is monitored in continuous or discontinuous assays. Typically, the substrate or product concentration is measured by light absorbance or fluorescence at characteristic wavelengths. However, most substrates and/or products are not spectroscopically active, and therefore a chromophore or fluorophore must be included to follow the reaction. Alternatively, coupled assays, with the product of the catalysis being a substrate for another reaction with measurable product, could be employed. These methods, however, introduce additional variables that reduce the accuracy of the determined values of KM and kcat. In addition, the characterization of enzyme inhibition by small ligands using traditional methods could be complicated by the absorbance or fluorescence of the small ligand itself, which may alter the reliability of the spectroscopic assay. Alternative methods involve stopped flow techniques, in which the amount of product is quantified using mass spectrometry or chromatography. These techniques are very precise and reliable, but are time consuming and expensive for routine analysis. The probe followed in ITC experiments, that is the heat released or absorbed, is an intrinsic property of almost all chemical reactions. Therefore ITC does not require any modification or labeling of the system under analysis. Moreover, this technique is straightforward and fast, requires small amount of material and can be performed in solution22,23.
Critical steps within the protocol and possible modifications
The reaction analysis is generally performed in two different experiments, which provide the total molar enthalpy of the reaction and the heat change over time at different substrate concentrations. In order to obtain good quality data, the experiment must be planned in detail at the beginning of the investigation. The protein and substrate concentrations must be carefully determined, as their correct value is essential for calculating reliable parameters in data analysis. The buffer composition and the pH of both the enzyme and the substrate solutions must be identical, in order to minimize heat of mixing during substrate addition. This is generally ensured by performing a dialysis or a size exclusion chromatography step on the enzyme solution before running the experiment, and using the buffer eluting from the column to dissolve the substrate. A control experiment must be performed with identical solutions of substrate injected into the reaction buffer in the absence of the enzyme, in order to subtract the heat of dilution or to verify that it is negligible.
Data analysis is generally performed with the Origin program for ITC provided by the manufacturer. An alternative program of choice for data analysis can be employed, if needed.
Troubleshooting and limitations of the technique
A good signal-to-noise ratio is obtained only if the reaction proceeds at a rate that allows the system to produce enough heat in the ITC cell to overcome the instrumental detection limit. This is usually achieved for systems with kcat higher than 1 min-1. In this case, the Origin software for ITC automatically calculates the Michaelis-Menten plot from raw data, and using nonlinear least-squares regression analysis, it calculates kcat and KM values. This analysis assumes no significant product inhibition. The latter is generally visible when subsequent injections in the M1 experiment produce peaks with different shapes and, in particular, with the power trace of the second peak taking longer to return to the initial baseline. If product inhibition occurs, another software of choice, implemented with purpose-specific equations, must be used.
Given the intrinsic difference of every enzymatic system, and the different heat produced by different reactions, which cannot be quantified a priori, determining the optimal running conditions (i.e. enzyme and substrate concentrations, number and volume of injections, spacing time, etc.) for a specific system sometimes require few exploratory titrations.
During the analysis, care must be applied to choose the experimental conditions according to the system under analysis. For example, phosphate inhibits urea hydrolysis by urease, and therefore, performing the experiment in phosphate buffer decreases the total rate of the enzymatic reaction24,25.
Once the kinetics of enzymatic reaction has been determined, the reaction can be repeated in the presence of different types and concentrations of enzyme inhibitors in the sample cell, in order to measure, using the general inhibition Equation 8 26, competitive (Kic) and uncompetitive (Kuic) inhibition constants, thus allowing us to distinguish, among different types of inhibition.
In conclusion, this method can be used as a fast and economic way to test a large number of enzyme inhibitors for pharmaceutical and industrial applications, such as in drug screening.
The authors have nothing to disclose.
The Specialty Fertilizer Products Company (SFP) is acknowledged for providing the funds necessary for this study.
HEPES Sigma H3375 dissolving in water and adjusting pH with NaOH
TRIZMA-Base Sigma T1503 dissolving in water and adjusting pH with HCl
Sodium dihydrogen phosphate Riedel-de-Haen 4270 dissolving in water
Sodium phosphate dibasic Riedel-de-Haen 30427 dissolving in water
Urea Sigma U4128 dissolving in water at 40 °C
Canavalia ensiformis urease (type C-3) Sigma U0251 dissolving in 20 mM HEPES pH 7 and stored at -80 °C
VP-ITC on Origin 7.0 MicroCal (GE Healthcare) SYS13901 instrument
VPViewer2000 1.30.00 on Origin 7.0 MicroCal (GE Healthcare) data acquisition software supplied with the instrument
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Hausinger, R. P., Karplus, P. A. Handbook of Metalloproteins. Huber, R., Poulos, T., Wieghardt, K. John Wiley & Sons. 867-879 (2001).
Goldberg, R., Kishore, N., Lennen, R. Thermodynamic quantities for the ionization reactions of buffers. J. Phys. Chem. Ref. Data. 31, 231-370 (2002).
Baumann, M. J., et al. Advantages of isothermal titration calorimetry for xylanase kinetics in comparison to chemical-reducing-end assays. Anal. Biochem. 410, 19-26 (2011).
Noske, R., Cornelius, F., Clarke, R. J. Investigation of the enzymatic activity of the Na+,K+-ATPase via isothermal titration microcalorimetry. Biochim. Biophys. Acta. 1797, 1540-1545 (2010).
Harmon, K. M., Niemann, C. The competitive inhibition of the urease-catalyzed hydrolysis of urea by phosphate. J. Biol. Chem. 177, 601-605 (1949).
Benini, S., Rypniewski, W. R., Wilson, K. S., Ciurli, S., Mangani, S. Structure-based rationalization of urease inhibition by phosphate: novel insights into the enzyme mechanism. J. Biol. Inorg. Chem. 6, 778-790 (2001).
Segel, I. H. Enzyme kinetics: behavior and analysis of rapid equilibrium and steady-state enzyme systems. Wiley, NY. (1993).
Optimization and Utilization of Agrobacterium-mediated Transient Protein Production in Nicotiana…
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Reaction Kinetics and Combustion Dynamics of I4O9 and Aluminum Mixtures…
Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2…
Quantifying Abdominal Pigmentation in Drosophila melanogaster
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Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle.
Useful to replicate and rearrange a MultiIndex for combination with another Index with n_repeat items.
Controls the reordering of the labels. If the result is going to be an inner level in a MultiIndex, n_shuffle will need to be greater than one. The size of each label must divisible by n_shuffle.


A whole month free of usage.
Of course with there's some condition that comes with this deal. You'll get the first month of broadband internet usage for free upon signing with them aka subscribe to their services. Not only you can get save up on a month of fee, you also will get a free wireless router. As I know, wireless router is not exactly cheap.
So those residing in uk…tunggu apa lagi…go take a look at this awesome deal. Wish we had other broadband service in Malaysia so we do not need to depend solely on Streamyx. Damn…I canot even download my Desperate Housewives now..
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Background: This case study is designed for upper-level undergraduate students in a basic operations management course. Prerequisites would include statistics and computer science. Objective: The purpose of the case is to give the students an opportunity to apply statistical quality control techniques. The case presents an interesting problem in a real setting and students will be forced to consider realistic issues in tradeoffs between cost and quality. The setting is a job shop, where jobs arrive randomly and have different requirements, as opposed to a mass-production type of system. Methodology: The analysis lends itself nicely to working on spreadsheets and the data may be changed easily to provide for reuse of the case. Based on probability calculations of the number of defects found in a group of current jobs, students calculate the costs of various sampling techniques. Results: Students are expected to try different sampling alternatives in order to minimize costs and also to consider nonquantitative ramifications of different alternatives and the realities of the situation. Conclusion: From this case study students should develop a good appreciation of statistical quality control techniques, as well as other aspects of a total quality management program.
© 2010 Michael R. Summers. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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Q: QueryDSL: How to apply type mappings via the querydsl-maven-plugin I've run into a problem concerning the mapping of DB column types to JAVA types in the generated metadata classes generated from the querydsl-maven-plugin.
We have a Oracle DB column with the following definition
priority number(1,0) not null default(5)

which takes integers from 1 to 9.
Now the metadata generator maps the column type specification to Boolean (which IMHO is anyway a wrong interpretation of the DB column).
Is there a possiblity, to configure the maven plugin such that I can provide a different type mapping for some table/column pairs or for some DB data types?
Although I could set up the metadata generation with a separate JAVA program this is not an option because I want to keep my build as it is.
Version of QueryDSL is 3.2.4.
Any help appreciated.
EDIT: An alternative may be that in the select list some typecasting is done.

A: This is currently not possible, but I created a ticket for it https://github.com/mysema/querydsl/issues/577
I will also look into the default numeric mappings again.

redpajama_set_name: RedPajamaStackExchange


My next article in Splurge is going to be about The Shockers!
I need your "quotes" to make this fun!
Here is my question for "Cindy Says..."
What was your favorite design or saying during the Shocker's run to the Final Four? What was the Slogan you wish you would have seen on a T-shirt:) Wichita Kansas is a great place to call home....and now the Shocker's have told the world!


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Q: Set expire date in UTC time format Need to set expire date of item as present date till midnight as shown below in UTC format.
"2021-05-28T23:59:59Z"
Any way to do this?

A: Use DateTime.UtcNow.Date:
DateTime.UtcNow.Date.AddDays(1).AddSeconds(-1).ToString("o")

The output of above is
2021-05-28T23:59:59.0000000Z
I guess this is what u need.

A: var currentUtcDate = DateTime.UtcNow;
Console.WriteLine($"Current UTC Date: {currentUtcDate:yyyy-MM-ddTHH:mm:ssZ}");

var expiryDate = new DateTime(currentUtcDate.Year, currentUtcDate.Month, currentUtcDate.Day, 23, 59, 59);
Console.WriteLine($"Expiry UTC Date: {expiryDate:yyyy-MM-ddTHH:mm:ssZ}");

Note that "shashank shekhar" use of the date format operator "o" is probably a more useful approach for formatting the date.  However, the solution above follows your requested date/time format.


I had my job interview today for an administrative position at one of our local hospitals. Last night I made the mistake of looking for tips on Google regarding experience of NHS interviews for this particular position…I ended up certain I wasn't prepared enough, and was destined to fail.
This morning I looked back over the original advert, and found it reassuring. I'm not applying to be a rocket scientist, and the job description files suggested I'd be part of a really friendly team. I decided to take propranolol (80mg slow release) as a precaution, and headed out.
I think it went well! The interviews were 20 minutes long so there weren't too many questions, and there wasn't anything that completely stumped me (contrary to the internet predictions). I'm absolutely happy with my performance – whether or not I get the job, I don't have any regrets. Doctors sometimes suggest that I lack confidence or self-esteem, but they have never seen me in these kinds of situations. I know what I'm good at, and if you're thinking of offering me a job, I'll tell you all about it.
Unfortunately the odds are against me in actually getting the job – there were a LOT of people being interviewed for just one position, but the nice thing is I'll be told tomorrow, whichever way the decision goes.
I can't comment on my mental health today. Let's pretend the concept doesn't even exist.
If you live in the UK, the chances are you've heard about the care.data debate. If you haven't, this is about a change in how personal medical information is shared. As I understand it, originally GPs have been responsible for protecting information regarding their patients, in compliance with the Data Protection Act 1998. As such, personal data has been stored on secure servers protected by firewalls and passwords, and access is limited to members of the practice team, and on occasion other health care professionals (e.g. District Nurses) on a strict need-to-know basis. A limited amount of patient data is shared at local and national levels to help design health services, but this data is anonymised before it leaves the practice.
This system is being 'revolutionised' by care.data, a programme administered by the HSCIC (Health & Social Care Information Centre) which is extracting personal information about all patients in England to a national database. Complete records are being created for each patient in this database, containing not only their medical history, but also their date of birth, NHS number, postcode, and gender. The idea is that this database will help the NHS plan and improve services, and access to some of the contents will be sold to third parties. To be clear, care.data is not about sharing medical information between health care professionals as they will not have access to the database. If patients do not want their data to be extracted and sold on in these ways, they have to get in touch with their GP practice to formally opt out.
There are so many objections to this that it's hard to know where to begin. Let's start with patient consent/awareness. This programme has already received fierce criticism for inadequately informing patients of the coming changes, which has spurred the distribution of a leaflet entitled 'Better information means better care'. It's highly questionable that this leaflet can be considered to improve the situation, primarily because it says very little about the right to opt out, and does not contain an opt-out form, instead giving the impression that you have to make an appointment with your GP if you object to your information being shared. It might also be added that sending out leaflets is not an effective way of informing patients about such important changes to how their data is stored, because they tend to be discarded as junk mail, but that's another matter.
By running an opt-out initiative, the HSCIC is assuming that it has the consent of all patients to collect and share personal medical information, but that's arguably an outrageous assumption if it can be suggested that much of the country doesn't fully understand what's going on – and a recent BBC article says exactly that. It is undeniable that at the very least, communication regarding care.data has been confusing, and on that basis the programme should be halted until it can truthfully state that it has gained informed consent from all the patients within its database.
So, what is to be done? Well, nothing if you're happy with your personal information being shared. If on the other hand you're concerned, you need to get in touch with your GP practice and opt out.
This is hitting the news again today because a GP, Dr Gordon Gancz, has bravely decided to automatically opt-out all of his patients, requiring them to get in touch and opt-in if they do want their information to be extracted by care.data. He contacted all of his patients to make them aware of his decision, and gave them clear instructions and ample opportunity to opt-out. As a result he has been threatened by the NHS and fears losing his job. Apparently this is what happens if you stand up for informed consent, and it is ridiculous.
If you live in the UK, I urge you to take charge of your information by reading up about care.data, and then make your own decision about whether or not you want your details shared in this national database. Don't let the HSCIC make the decision for you.


Well informed friends and family members can answer questions and be alert for signs of illicit drug use by their loved ones. This chart provides basic information to help you learn more about illicit drugs, their symptoms, and potential consequences. The street names for drugs change frequently, so if what you are looking for is not here, it is probably a new term.


Venture's 20' Grand Deluxe shuffleboard table is custom made in the US and just under regulation size, allowing you to enhance your skills without having to worry about space constraints. The Grand Deluxe was first created in the 1930s and has been around ever since, offering timeless American style and setting the standard for shuffleboard manufacturers everywhere.
In order to ensure stability and a beautiful finish, Venture uses a 6 step finishing process, table gutters tongued and grooved directly into the frame, and a cabinet constructed of solid woods and real wood veneers.The 3" thick playing surface is coated in Venture's exclusive hard clear synthetic lacquer, giving the table its distinctive shine. Pick from 3 standard stains: Stelter Mahogany, Chestnut and Dark Walnut, or choose a custom stain for an even more distinguished look. Add an element of classic American design to your entertainment space and order the Grand Deluxe today!


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Much Worse Than I Expected
All I can say is: the president gave a speech he could have given at any point in the last three years. No one in that room could disagree with any of the things he said. I sure don't (with the exception of the hate crimes hooey). And he said it well and movingly. Like we didn't know he could do that.
But the point of electing a president who pledged to actually do things is to hold him to account, and to see if he is willing to take any risk of any kind to actually do something. I had a few prior tests of his seriousness or signs that he gets it, a few ways to judge if this speech had anything new or specific or clear. He failed every test.
To wit:
He says he will end Don't Ask Don't Tell but he has done nothing, and he offered no time-line, no deadline for action and no verifiable record that he has done anything, despite his claims that he has.
He says he is ending the HIV ban, but it is still in force, a year and a half after it was signed by George W. Bush and passed by massive majorities in both houses.
He says he favors equality for gay couples but said nothing tonight to support the initiatives in Maine or in Washngton State or the struggle in Washington DC for marriage equality. That's a test of real sincerity on this matter. He failed it.
He says he wants to end discrimination in employment even as he is firing more gay people solely for being gay than any other employer in the country - as commander-in-chief. And if an employer is firing gay people all the time, is it tolerable to accept as a response that he will stop doing it one day - but gives no time-line at all to hold him to?
Look: I didn't expect these issues to be front and center given his appalling inheritance; I know he has many other things on his plate; I didn't expect the moon; I didn't believe he would do any of this immediately; I understand that the real job is for us to do, not him, and that most of the action is in the states. And I remain a strong supporter of him in foreign policy and in the way he is clearly trying to move this country past the ideological divides of the recent past.
But the sad truth is: he is refusing to take any responsibility for his clear refusal to fulfill clear campaign pledges on the core matter of civil rights and has given no substantive, verifiable pledges or deadlines by which he can be held accountable. What that means, I'm afraid, is that this speech was highfalutin bullshit. There were no meaningful commitments within a time certain, not even a commitment to fulfilling them in his first term; just meaningless, feel-good commitments that we have no way of holding him to. Once the dust settles, ask yourself. What did he promise to achieve in the next year? Or two years? Or four years? The answer is: nothing.
HRC, of course, is putting no pressure on him; Joe Solmonese's disgraceful email actually took all pressure off him by saying he'd be happy to wait till 2017 for HRC to hold Obama accountable. HRC are putting pressure, as they always have, on gay people to go to the back of the line and be grateful a president attends their fundraising event. The only word for this is a racket. And if gay people do not rise up and demand change from this organization and stop funding a group whose goal has always been to sell the Democrats to gay people rather than secure civil rights, then they will continue to suffer the discrimination they live under day after day.


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Every participant receives a food supply package by PowerBar at the Volksschule. The voucher for this is included in your starting package.
En route there are service stations offering bananas, apples, oranges, dried fruit, pretzels, bread and rolls with sausages and cheese, Ausseer Gingerbread, cake, Prinzen Rolle, water, PowerBar IsoActive, Coca-Cola and Red Bull mixed with water. Self supply may only be given at the assistance points (AP) marked in the course plan.
Pieces of clothing may be left at any service station and are to be picked up later at the "Lost & Found".
Attention! No littering beyond the service points. For more information see regulations.
The "PowerBar Nutrition Coach" explains the key elements of the PowerBar sports nutrition strategy specifically for the bike sport.


Roots Peppersauce started out of a quest for real authentic island style hot sauce with a fresh and modern twist. Roots is a very simple hot sauce created with two crucial goals, pack lots of heat and offer great taste.
I wanted a real hot sauce, so each bottle is packed with as many habaneros as possible and I added only enough seasoning to make it interesting. There is minimal processing going on here, you're getting all the health benefits, vitamin c, antitoxins .... but who cares, its about the heat!


text:
- Voice of San Diego - https://www.voiceofsandiego.org -
The Learning Curve: 'Restorative Justice' Can Make Schools More Violent If Not Done Right
Posted By Mario Koran On March 29, 2018 @ 2:43 pm In Corrections,Education,School Discipline,The Learning Curve | Comments Disabled
In 2014, a team of Harvard researchers visited San Diego Unified and produced a report that convinced school district administrators [1] their punitive, zero-tolerance policies weren't working.
According to the researchers, a disproportionate number of suspensions and expulsions had involved students of color and those with disabilities. Students repeatedly suspended from school were more likely to drop out of school or be involved with the criminal justice system.
The year before the report was released, black students in the district represented 10 percent of the student body but made up 25 percent of suspensions and 21 percent of expulsions. They were more than three and half times more likely to be suspended than their white peers. Students with special needs made up only 10 percent of the student body, but 34 percent of suspensions.
Along with school districts across the country, that year San Diego Unified shifted its approach to discipline, moving to what it calls "restorative practices" — a more therapeutic approach than turning to suspensions as a first resort. It was part of a nationwide push under the Obama administration to close the gap between the way students of color and their white peers are disciplined.
Restorative justice is more focused on prevention — addressing students' underlying issues before they act out — than punishment. It includes things like mediation and dialogue. It stresses the need for students to build relationships in school and understand the impact of their decisions.
The problem is that while San Diego Unified changed its discipline policies four years ago, it's only now in its first year of training staff members districtwide. This year, it hired a staff member to oversee the program and inked a three-year, $866,000 contract [2] with National Conflict Resolution Center to train teachers and administrators on how to implement the strategies. But according to the district's timeline, restorative justice practices won't be fully implemented until 2020.
In September, school board members diverged from their typically glowing evaluation of Superintendent Cindy Marten when they dinged her for being slow to implement restorative justice practices across the district.
And now some educators are voicing concern about the unexpected consequences of the softened approach to discipline. They say the change has actually increased violence in schools.
Lindsay Burningham, president of the San Diego Educators Association, said she's heard concern from teachers about how the program is being implemented.
"Members have raised concerns about the implementation of what should be a great opportunity to keep kids in school, end the disproportionate number of suspensions for students of color and put a dent in the school-to-prison pipeline," she said. "I think, in general, our members believe in restorative justice but they don't have the resources or training to make it successful."
Two educators who have worked at Lincoln High told VOSD that teachers and administrators are under pressure to keep suspension rates low [3]. But without adequate training or support, administrators improvised by sending students home informally instead of suspending them.
Behavior reports from Lincoln High show that's what happened in 2014, when two students who'd been challenging each other for two consecutive days got into a fight. A student bear-hugged the other and slammed him to the ground. The document says administrators sent them home informally that day to "cool down."
A district spokesperson said the practice, known as "blue slipping," doesn't violate any rules, and can be an effective way to diffuse a tense situation. But informally sending students home appears nowhere on the district's Uniform Discipline Plan [4], a framework for how to handle discipline.
And blue slipping students in lieu of documented suspensions puts into question the accuracy of the school's — and potentially the district's — suspension rates. Kicking kids out of school for the day is a suspension, whether it's labeled that way or not.
The Washington Post reported on a similar trend [5] in D.C. public schools in July, when it found at least seven of the district's 18 high schools had kicked kids out of school for the day without labeling them as suspensions.
The Lincoln educators I spoke with say the practice creates unclear and inconsistent consequences for students, which contributes to a cycle of negative behavior.
Similar tensions are playing out in other cities. In Buffalo, N.Y., a survey of 1,217 teachers [6] — representing a third of the district's teachers — found that less than 10 percent of teachers believe disruptive student behavior is being handled quickly and properly in their schools.
In Oklahoma City, teachers said they felt more like babysitters than educators, and one reported [7] that teachers were told misbehavior would not result in suspension unless there was blood.
Anthony Ceja, a senior manager for the San Diego County Office of Education who helps train schools on how to implement restorative practices, said they absolutely can lead to more chaotic classrooms when they're not done properly.
To do restorative practices effectively, he said, administrators and teachers need to have clear communication. He often hears from frustrated teachers who say they send kids out of the room, only to see them reappear later. Because the teacher doesn't know what was said in the office, he or she may assume the student faced no consequences.
"When you have a disconnect between teachers and the main office, that's a formula for disaster," he said.
Ceja said one of the biggest mistakes school districts have made is conflating lower suspension rates with restorative justice.
"The mistake that a number of school districts have made is tying restorative practices to reduced suspensions. If you do restorative practices well, the suspension rates will probably go down, but it's not meant to be an alternative to discipline."
Too often overlooked, Ceja said, is the relationship-building component of restorative justice, which helps students and staff see each other as people.
"This is what some schools really aren't willing to do: Invest the time in the relationship building aspect of restorative justice. When students are able to humanize staff and other students, they're much less likely to act out or hurt their classmates," he said.
The Link Between Safety and School Discipline
Last week, San Diego County District Attorney Summer Stephan joined Superintendent Cindy Marten for a press conference to assure the public that students who call in threats to schools will be identified and held accountable.
In the weeks that followed the shooting in Parkland, Fla., Marten said the district received 49 threats to schools, which has resulted in more than 125 hours of overtime for officers, lost staff time and missed instructional time. In addition to student walkouts and protests calling for increased gun control, one outcome of the shootings in Parkland has been a nationwide conversation about the link between safety and discipline in schools.
Florida Sen. Marco Rubio is among those who called on U.S. Education Secretary Betsy DeVos to revise [8] Obama-era discipline policies, arguing the "federal guidance may have contributed to systemic failures to report Nikolas Cruz's dangerous behaviors to local law enforcement."
Any effort to roll back the federal guidelines, however, would likely have little impact on California's statewide discipline policies or its newly established accountability measures, EdSource reports [9].
When a former Minneapolis schools superintendent launched her own review of discipline referrals for kindergarten boys, she had a troubling revelation, reports the New York Times [10].
"The descriptions of white children by teachers included 'gifted but can't use his words' and 'high strung,' with their actions excused because they 'had a hard day,' the Times reports. "Black children … were 'destructive' and 'violent,' and 'cannot be managed.'"
That finding raises uncomfortable questions about teachers' biases — something that research indicates is seen as early as preschool [11].
It's difficult to explain away those differences without confronting racism. That point is at the center of a groundbreaking new study [12] by researchers at Stanford, Harvard and the Census Bureau, which found "black boys raised in America, even in the wealthiest families and living in some of the most well-to-do neighborhoods, still earn less in adulthood than white boys with similar backgrounds."
Among other revelations, the study found that most white boys raised in wealthy families will stay rich or upper middle class as adults, but black boys raised in similarly rich households will not.
The tentacles of racism and discrimination reach across generations. Only 60 years ago, the Supreme Court ruled that separate schools for white and black students were fundamentally unequal. Linda Brown, who was at the center of Brown v. Board of Education, died this week at 76 [13].
• A San Francisco public school student failed every class in high school and not a single adult stepped up to help him. The story lays bare [14] the ugly results of social promotion. (San Francisco Chronicle)
Charter Schools Group Endorses Villaraigosa
With just 70 days to go before the June primary election, the battle lines are drawn. Charter school advocates are throwing their support behind Antonio Villaraigosa for governor, while teachers unions are going for Gavin Newsom.
The California Charter Schools Association Advocates, CCSA's political arm, emphatically endorsed Villaraigosa at its annual charter school conference in San Diego this week.
CCSA president Jed Wallace said that if elected governor, Newsom would "'inflict major harm on our schools and claim to be our friend,'" EdSource reports [15]. Newsom's camp dismissed the criticisms as "name-calling and distortion."
Correction: An earlier version of this post said the California Charter Schools Association endorsed Antonio Villaraigosa for governor. The endorsement came from The California Charter Schools Association Advocates, the group's political arm.
Article printed from Voice of San Diego: https://www.voiceofsandiego.org
URL to article: https://www.voiceofsandiego.org/topics/education/the-learning-curve-restorative-justice-can-make-schools-more-violent-if-not-done-right/
[1] a report that convinced school district administrators: https://www.voiceofsandiego.org/topics/education/a-new-school-of-thought-on-discipline/
[2] inked a three-year, $866,000 contract: https://www.boarddocs.com/ca/sandi/Board.nsf/files/AP5PQR65C09F/$file/PS-18-0249-24%20National%20Conflict%20Resolution%20Center%20Agreement%20fbo%20Leadership%20and%20Learning.pdf
[3] under pressure to keep suspension rates low: https://www.voiceofsandiego.org/topics/education/discipline-policies-complicate-response-to-violent-episodes-at-lincoln-high/
[4] Uniform Discipline Plan: https://www.sandiegounified.org/sites/default/files_link/district/files/policy/discipline/UniformDisciplinePlan.pdf
[5] The Washington Post reported on a similar trend: https://www.washingtonpost.com/local/education/some-dc-high-schools-reported-only-a-small-fraction-of-suspensions/2017/07/17/045c387e-5762-11e7-ba90-f5875b7d1876_story.html?utm_term=.d0052cfa1590
[6] a survey of 1,217 teachers: http://buffalonews.com/2018/01/29/buffalo-teacher-survey-cites-disruptive-kids-lack-of-discipline/
[7] reported: http://newsok.com/article/5457335
[8] called on U.S. Education Secretary Betsy DeVos to revise: https://www.rubio.senate.gov/public/_cache/files/19f88f3d-0f3c-4753-babe-43890c1bf7fa/6FE29ECD9BE495D655650802F6B5160F.18.03.05.-letter-to-sec-devos-and-ag-sessions-2014-ed-directive-final.pdf
[9] EdSource reports: https://edsource.org/2018/how-trump-repeal-of-obama-era-school-discipline-guidelines-could-affect-california/594893
[10] reports the New York Times: https://www.nytimes.com/2018/03/18/us/politics/school-discipline-disparities-white-black-students.html
[11] research indicates is seen as early as preschool: http://www.latimes.com/science/sciencenow/la-sci-sn-black-boys-preschool-20160928-snap-story.html
[12] a groundbreaking new study: https://www.nytimes.com/interactive/2018/03/19/upshot/race-class-white-and-black-men.html
[13] died this week at 76: https://www.npr.org/sections/thetwo-way/2018/03/26/597154953/linda-brown-who-was-at-center-of-brown-v-board-of-education-dies
[14] The story lays bare: https://www.sfchronicle.com/bayarea/article/A-child-left-behind-SF-student-failed-every-12789302.php?utm_campaign=twitter-mobile&utm_source=CMS%20Sharing%20Button&utm_medium=social
[15] EdSource reports: https://edsource.org/2018/while-criticizing-newsom-california-charter-schools-association-endorses-villaraigosa-for-governor/595340
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