| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.math3.stat.inference; | |
| 18 | ||
| 19 | import java.util.ArrayList; | |
| 20 | import java.util.Collection; | |
| 21 | ||
| 22 | import org.apache.commons.math3.distribution.FDistribution; | |
| 23 | import org.apache.commons.math3.exception.ConvergenceException; | |
| 24 | import org.apache.commons.math3.exception.DimensionMismatchException; | |
| 25 | import org.apache.commons.math3.exception.MaxCountExceededException; | |
| 26 | import org.apache.commons.math3.exception.NullArgumentException; | |
| 27 | import org.apache.commons.math3.exception.OutOfRangeException; | |
| 28 | import org.apache.commons.math3.exception.util.LocalizedFormats; | |
| 29 | import org.apache.commons.math3.stat.descriptive.SummaryStatistics; | |
| 30 | import org.apache.commons.math3.util.MathUtils; | |
| 31 | ||
| 32 | /** | |
| 33 | * Implements one-way ANOVA (analysis of variance) statistics. | |
| 34 | * | |
| 35 | *
 Tests for differences between two or more categories of univariate data | |
| 36 | * (for example, the body mass index of accountants, lawyers, doctors and | |
| 37 | * computer programmers). When two categories are given, this is equivalent to | |
| 38 | * the {@link org.apache.commons.math3.stat.inference.TTest}. | |
| 39 | *

 | |
| 40 | * Uses the {@link org.apache.commons.math3.distribution.FDistribution | |
| 41 | * commons-math F Distribution implementation} to estimate exact p-values.
 | |
| 42 | *
This implementation is based on a description at | |
| 43 | * http://faculty.vassar.edu/lowry/ch13pt1.html
 | |
| 44 | *
 | |
| 45 | * Abbreviations: bg = between groups, | |
| 46 | * wg = within groups, | |
| 47 | * ss = sum squared deviations | |
| 48 | *
 | |
| 49 | * | |
| 50 | * @since 1.2 | |
| 51 | * @version $Id$ | |
| 52 | */ | |
| 53 | public class OneWayAnova { | |
| 54 | ||
| 55 | /** | |
| 56 | * Default constructor. | |
| 57 | */ | |
| 58 | public OneWayAnova() { | |
| 59 | } | |
| 60 | ||
| 61 | /** | |
| 62 | * Computes the ANOVA F-value for a collection of double[] | |
| 63 | * arrays. | |
| 64 | * | |
| 65 | *
Preconditions:
 | |
| 66 | *
The categoryData Collection must contain | |
| 67 | * double[] arrays.
 | |
| 68 | *
 There must be at least two double[] arrays in the | |
| 69 | * categoryData collection and each of these arrays must | |
| 70 | * contain at least two values.

 | |
| 71 | * This implementation computes the F statistic using the definitional | |
| 72 | * formula
 | |
| 73 | * F = msbg/mswg
 | |
| 74 | * where
 | |
| 75 | * msbg = between group mean square | |
| 76 | * mswg = within group mean square
 | |
| 77 | * are as defined  | |
| 78 | * here
 | |
| 79 | * | |
| 80 | * @param categoryData Collection of double[] | |
| 81 | * arrays each containing data for one category | |
| 82 | * @return Fvalue | |
| 83 | * @throws NullArgumentException if categoryData is null | |
| 84 | * @throws DimensionMismatchException if the length of the categoryData | |
| 85 | * array is less than 2 or a contained double[] array does not have | |
| 86 | * at least two values | |
| 87 | */ | |
| 88 | public double anovaFValue(final Collection<double[]> categoryData) | |
| 89 | throws NullArgumentException, DimensionMismatchException { | |
| 90 | ||
| 91 | AnovaStats a = anovaStats(categoryData); | |
| 92 |
1
1. anovaFValue : replaced return of double value with -(x + 1) for org/apache/commons/math3/stat/inference/OneWayAnova::anovaFValue → KILLED |
return a.F; |
| 93 | ||
| 94 | } | |
| 95 | ||
| 96 | /** | |
| 97 | * Computes the ANOVA P-value for a collection of double[] | |
| 98 | * arrays. | |
| 99 | * | |
| 100 | *
Preconditions:
 | |
| 101 | *
The categoryData Collection must contain | |
| 102 | * double[] arrays.
 | |
| 103 | *
 There must be at least two double[] arrays in the | |
| 104 | * categoryData collection and each of these arrays must | |
| 105 | * contain at least two values.

 | |
| 106 | * This implementation uses the | |
| 107 | * {@link org.apache.commons.math3.distribution.FDistribution | |
| 108 | * commons-math F Distribution implementation} to estimate the exact | |
| 109 | * p-value, using the formula
 | |
| 110 | * p = 1 - cumulativeProbability(F)
 | |
| 111 | * where F is the F value and cumulativeProbability | |
| 112 | * is the commons-math implementation of the F distribution.
 | |
| 113 | * | |
| 114 | * @param categoryData Collection of double[] | |
| 115 | * arrays each containing data for one category | |
| 116 | * @return Pvalue | |
| 117 | * @throws NullArgumentException if categoryData is null | |
| 118 | * @throws DimensionMismatchException if the length of the categoryData | |
| 119 | * array is less than 2 or a contained double[] array does not have | |
| 120 | * at least two values | |
| 121 | * @throws ConvergenceException if the p-value can not be computed due to a convergence error | |
| 122 | * @throws MaxCountExceededException if the maximum number of iterations is exceeded | |
| 123 | */ | |
| 124 | public double anovaPValue(final Collection<double[]> categoryData) | |
| 125 | throws NullArgumentException, DimensionMismatchException, | |
| 126 | ConvergenceException, MaxCountExceededException { | |
| 127 | ||
| 128 | AnovaStats a = anovaStats(categoryData); | |
| 129 | // No try-catch or advertised exception because args are valid | |
| 130 | FDistribution fdist = new FDistribution(a.dfbg, a.dfwg); | |
| 131 |
2
1. anovaPValue : Replaced double subtraction with addition → KILLED 2. anovaPValue : replaced return of double value with -(x + 1) for org/apache/commons/math3/stat/inference/OneWayAnova::anovaPValue → KILLED |
return 1.0 - fdist.cumulativeProbability(a.F); |
| 132 | ||
| 133 | } | |
| 134 | ||
| 135 | /** | |
| 136 | * Computes the ANOVA P-value for a collection of {@link SummaryStatistics}. | |
| 137 | * | |
| 138 | *
Preconditions:
 | |
| 139 | *
The categoryData Collection must contain | |
| 140 | * {@link SummaryStatistics}.
 | |
| 141 | *
 There must be at least two {@link SummaryStatistics} in the | |
| 142 | * categoryData collection and each of these statistics must | |
| 143 | * contain at least two values.

 | |
| 144 | * This implementation uses the | |
| 145 | * {@link org.apache.commons.math3.distribution.FDistribution | |
| 146 | * commons-math F Distribution implementation} to estimate the exact | |
| 147 | * p-value, using the formula
 | |
| 148 | * p = 1 - cumulativeProbability(F)
 | |
| 149 | * where F is the F value and cumulativeProbability | |
| 150 | * is the commons-math implementation of the F distribution.
 | |
| 151 | * | |
| 152 | * @param categoryData Collection of {@link SummaryStatistics} | |
| 153 | * each containing data for one category | |
| 154 | * @param allowOneElementData if true, allow computation for one catagory | |
| 155 | * only or for one data element per category | |
| 156 | * @return Pvalue | |
| 157 | * @throws NullArgumentException if categoryData is null | |
| 158 | * @throws DimensionMismatchException if the length of the categoryData | |
| 159 | * array is less than 2 or a contained {@link SummaryStatistics} does not have | |
| 160 | * at least two values | |
| 161 | * @throws ConvergenceException if the p-value can not be computed due to a convergence error | |
| 162 | * @throws MaxCountExceededException if the maximum number of iterations is exceeded | |
| 163 | * @since 3.2 | |
| 164 | */ | |
| 165 | public double anovaPValue(final Collection<SummaryStatistics> categoryData, | |
| 166 | final boolean allowOneElementData) | |
| 167 | throws NullArgumentException, DimensionMismatchException, | |
| 168 | ConvergenceException, MaxCountExceededException { | |
| 169 | ||
| 170 | final AnovaStats a = anovaStats(categoryData, allowOneElementData); | |
| 171 | final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg); | |
| 172 |
2
1. anovaPValue : Replaced double subtraction with addition → KILLED 2. anovaPValue : replaced return of double value with -(x + 1) for org/apache/commons/math3/stat/inference/OneWayAnova::anovaPValue → KILLED |
return 1.0 - fdist.cumulativeProbability(a.F); |
| 173 | ||
| 174 | } | |
| 175 | ||
| 176 | /** | |
| 177 | * This method calls the method that actually does the calculations (except | |
| 178 | * P-value). | |
| 179 | * | |
| 180 | * @param categoryData | |
| 181 | * Collection of double[] arrays each | |
| 182 | * containing data for one category | |
| 183 | * @return computed AnovaStats | |
| 184 | * @throws NullArgumentException | |
| 185 | * if categoryData is null | |
| 186 | * @throws DimensionMismatchException | |
| 187 | * if the length of the categoryData array is less | |
| 188 | * than 2 or a contained double[] array does not | |
| 189 | * contain at least two values | |
| 190 | */ | |
| 191 | private AnovaStats anovaStats(final Collection<double[]> categoryData) | |
| 192 | throws NullArgumentException, DimensionMismatchException { | |
| 193 | ||
| 194 |
1
1. anovaStats : removed call to org/apache/commons/math3/util/MathUtils::checkNotNull → SURVIVED |
MathUtils.checkNotNull(categoryData); |
| 195 | ||
| 196 | final Collection<SummaryStatistics> categoryDataSummaryStatistics = | |
| 197 | new ArrayList<SummaryStatistics>(categoryData.size()); | |
| 198 | ||
| 199 | // convert arrays to SummaryStatistics | |
| 200 | for (final double[] data : categoryData) { | |
| 201 | final SummaryStatistics dataSummaryStatistics = new SummaryStatistics(); | |
| 202 | categoryDataSummaryStatistics.add(dataSummaryStatistics); | |
| 203 |
3
1. anovaStats : changed conditional boundary → KILLED 2. anovaStats : Changed increment from 1 to -1 → KILLED 3. anovaStats : negated conditional → KILLED |
for (final double val : data) { |
| 204 |
1
1. anovaStats : removed call to org/apache/commons/math3/stat/descriptive/SummaryStatistics::addValue → KILLED |
dataSummaryStatistics.addValue(val); |
| 205 | } | |
| 206 | } | |
| 207 | ||
| 208 |
1
1. anovaStats : mutated return of Object value for org/apache/commons/math3/stat/inference/OneWayAnova::anovaStats to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return anovaStats(categoryDataSummaryStatistics, false); |
| 209 | ||
| 210 | } | |
| 211 | ||
| 212 | /** | |
| 213 | * Performs an ANOVA test, evaluating the null hypothesis that there | |
| 214 | * is no difference among the means of the data categories. | |
| 215 | * | |
| 216 | *
Preconditions:
 | |
| 217 | *
The categoryData Collection must contain | |
| 218 | * double[] arrays.
 | |
| 219 | *
 There must be at least two double[] arrays in the | |
| 220 | * categoryData collection and each of these arrays must | |
| 221 | * contain at least two values.
 | |
| 222 | *
alpha must be strictly greater than 0 and less than or equal to 0.5. | |
| 223 | *

 | |
| 224 | * This implementation uses the | |
| 225 | * {@link org.apache.commons.math3.distribution.FDistribution | |
| 226 | * commons-math F Distribution implementation} to estimate the exact | |
| 227 | * p-value, using the formula
 | |
| 228 | * p = 1 - cumulativeProbability(F)
 | |
| 229 | * where F is the F value and cumulativeProbability | |
| 230 | * is the commons-math implementation of the F distribution.
 | |
| 231 | *
True is returned iff the estimated p-value is less than alpha.
 | |
| 232 | * | |
| 233 | * @param categoryData Collection of double[] | |
| 234 | * arrays each containing data for one category | |
| 235 | * @param alpha significance level of the test | |
| 236 | * @return true if the null hypothesis can be rejected with | |
| 237 | * confidence 1 - alpha | |
| 238 | * @throws NullArgumentException if categoryData is null | |
| 239 | * @throws DimensionMismatchException if the length of the categoryData | |
| 240 | * array is less than 2 or a contained double[] array does not have | |
| 241 | * at least two values | |
| 242 | * @throws OutOfRangeException if alpha is not in the range (0, 0.5] | |
| 243 | * @throws ConvergenceException if the p-value can not be computed due to a convergence error | |
| 244 | * @throws MaxCountExceededException if the maximum number of iterations is exceeded | |
| 245 | */ | |
| 246 | public boolean anovaTest(final Collection<double[]> categoryData, | |
| 247 | final double alpha) | |
| 248 | throws NullArgumentException, DimensionMismatchException, | |
| 249 | OutOfRangeException, ConvergenceException, MaxCountExceededException { | |
| 250 | ||
| 251 |
4
1. anovaTest : changed conditional boundary → SURVIVED 2. anovaTest : changed conditional boundary → SURVIVED 3. anovaTest : negated conditional → KILLED 4. anovaTest : negated conditional → KILLED |
if ((alpha <= 0) || (alpha > 0.5)) { |
| 252 | throw new OutOfRangeException( | |
| 253 | LocalizedFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL, | |
| 254 | alpha, 0, 0.5); | |
| 255 | } | |
| 256 |
3
1. anovaTest : changed conditional boundary → SURVIVED 2. anovaTest : negated conditional → KILLED 3. anovaTest : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return anovaPValue(categoryData) < alpha; |
| 257 | ||
| 258 | } | |
| 259 | ||
| 260 | /** | |
| 261 | * This method actually does the calculations (except P-value). | |
| 262 | * | |
| 263 | * @param categoryData Collection of double[] | |
| 264 | * arrays each containing data for one category | |
| 265 | * @param allowOneElementData if true, allow computation for one catagory | |
| 266 | * only or for one data element per category | |
| 267 | * @return computed AnovaStats | |
| 268 | * @throws NullArgumentException if categoryData is null | |
| 269 | * @throws DimensionMismatchException if allowOneElementData is false and the number of | |
| 270 | * categories is less than 2 or a contained SummaryStatistics does not contain | |
| 271 | * at least two values | |
| 272 | */ | |
| 273 | private AnovaStats anovaStats(final Collection<SummaryStatistics> categoryData, | |
| 274 | final boolean allowOneElementData) | |
| 275 | throws NullArgumentException, DimensionMismatchException { | |
| 276 | ||
| 277 |
1
1. anovaStats : removed call to org/apache/commons/math3/util/MathUtils::checkNotNull → SURVIVED |
MathUtils.checkNotNull(categoryData); |
| 278 | ||
| 279 |
1
1. anovaStats : negated conditional → KILLED |
if (!allowOneElementData) { |
| 280 | // check if we have enough categories | |
| 281 |
2
1. anovaStats : changed conditional boundary → KILLED 2. anovaStats : negated conditional → KILLED |
if (categoryData.size() < 2) { |
| 282 | throw new DimensionMismatchException(LocalizedFormats.TWO_OR_MORE_CATEGORIES_REQUIRED, | |
| 283 | categoryData.size(), 2); | |
| 284 | } | |
| 285 | ||
| 286 | // check if each category has enough data | |
| 287 | for (final SummaryStatistics array : categoryData) { | |
| 288 |
2
1. anovaStats : changed conditional boundary → SURVIVED 2. anovaStats : negated conditional → KILLED |
if (array.getN() <= 1) { |
| 289 | throw new DimensionMismatchException(LocalizedFormats.TWO_OR_MORE_VALUES_IN_CATEGORY_REQUIRED, | |
| 290 | (int) array.getN(), 2); | |
| 291 | } | |
| 292 | } | |
| 293 | } | |
| 294 | ||
| 295 | int dfwg = 0; | |
| 296 | double sswg = 0; | |
| 297 | double totsum = 0; | |
| 298 | double totsumsq = 0; | |
| 299 | int totnum = 0; | |
| 300 | ||
| 301 | for (final SummaryStatistics data : categoryData) { | |
| 302 | ||
| 303 | final double sum = data.getSum(); | |
| 304 | final double sumsq = data.getSumsq(); | |
| 305 | final int num = (int) data.getN(); | |
| 306 |
1
1. anovaStats : Replaced integer addition with subtraction → KILLED |
totnum += num; |
| 307 |
1
1. anovaStats : Replaced double addition with subtraction → SURVIVED |
totsum += sum; |
| 308 |
1
1. anovaStats : Replaced double addition with subtraction → KILLED |
totsumsq += sumsq; |
| 309 | ||
| 310 |
2
1. anovaStats : Replaced integer subtraction with addition → KILLED 2. anovaStats : Replaced integer addition with subtraction → KILLED |
dfwg += num - 1; |
| 311 |
3
1. anovaStats : Replaced double multiplication with division → KILLED 2. anovaStats : Replaced double division with multiplication → KILLED 3. anovaStats : Replaced double subtraction with addition → KILLED |
final double ss = sumsq - ((sum * sum) / num); |
| 312 |
1
1. anovaStats : Replaced double addition with subtraction → KILLED |
sswg += ss; |
| 313 | } | |
| 314 | ||
| 315 |
3
1. anovaStats : Replaced double multiplication with division → KILLED 2. anovaStats : Replaced double division with multiplication → KILLED 3. anovaStats : Replaced double subtraction with addition → KILLED |
final double sst = totsumsq - ((totsum * totsum) / totnum); |
| 316 |
1
1. anovaStats : Replaced double subtraction with addition → KILLED |
final double ssbg = sst - sswg; |
| 317 |
1
1. anovaStats : Replaced integer subtraction with addition → KILLED |
final int dfbg = categoryData.size() - 1; |
| 318 |
1
1. anovaStats : Replaced double division with multiplication → KILLED |
final double msbg = ssbg / dfbg; |
| 319 |
1
1. anovaStats : Replaced double division with multiplication → KILLED |
final double mswg = sswg / dfwg; |
| 320 |
1
1. anovaStats : Replaced double division with multiplication → KILLED |
final double F = msbg / mswg; |
| 321 | ||
| 322 |
1
1. anovaStats : mutated return of Object value for org/apache/commons/math3/stat/inference/OneWayAnova::anovaStats to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return new AnovaStats(dfbg, dfwg, F); |
| 323 | ||
| 324 | } | |
| 325 | ||
| 326 | /** | |
| 327 | Convenience class to pass dfbg,dfwg,F values around within OneWayAnova. | |
| 328 | No get/set methods provided. | |
| 329 | */ | |
| 330 | private static class AnovaStats { | |
| 331 | ||
| 332 | /** Degrees of freedom in numerator (between groups). */ | |
| 333 | private final int dfbg; | |
| 334 | ||
| 335 | /** Degrees of freedom in denominator (within groups). */ | |
| 336 | private final int dfwg; | |
| 337 | ||
| 338 | /** Statistic. */ | |
| 339 | private final double F; | |
| 340 | ||
| 341 | /** | |
| 342 | * Constructor | |
| 343 | * @param dfbg degrees of freedom in numerator (between groups) | |
| 344 | * @param dfwg degrees of freedom in denominator (within groups) | |
| 345 | * @param F statistic | |
| 346 | */ | |
| 347 | private AnovaStats(int dfbg, int dfwg, double F) { | |
| 348 | this.dfbg = dfbg; | |
| 349 | this.dfwg = dfwg; | |
| 350 | this.F = F; | |
| 351 | } | |
| 352 | } | |
| 353 | ||
| 354 | } | |
Mutations | ||
| 92 |
1.1 |
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| 131 |
1.1 2.2 |
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| 172 |
1.1 2.2 |
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| 194 |
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| 203 |
1.1 2.2 3.3 |
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| 204 |
1.1 |
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| 208 |
1.1 |
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| 251 |
1.1 2.2 3.3 4.4 |
|
| 256 |
1.1 2.2 3.3 |
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| 277 |
1.1 |
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| 279 |
1.1 |
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| 281 |
1.1 2.2 |
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| 288 |
1.1 2.2 |
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| 306 |
1.1 |
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| 307 |
1.1 |
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| 308 |
1.1 |
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| 310 |
1.1 2.2 |
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| 311 |
1.1 2.2 3.3 |
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| 312 |
1.1 |
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| 315 |
1.1 2.2 3.3 |
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| 316 |
1.1 |
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| 317 |
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| 318 |
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| 319 |
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| 320 |
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| 322 |
1.1 |


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When it comes to my hobbies, I won't call myself a photographer, but saying I take good photos
would be a safe bet.


{% extends "layout_task-list.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block home_link %} ../../task-list {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Thank you for your feedback You can now close this window. {% include 'prototypes/prototype-nov-2018/includes/contact.html' %} {% endblock %}
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Not(expr) element d’exclusion en jquery
which one is physical sport and which is not ?
Basketball
Gaming
Football
Chess
HandBall
Tennis
Formula 1


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10 promising developments that can help Alzheimer’s patients
For Alzheimer’s Awareness Month, ISRAEL21c looks at new advances from Israel that are making headlines.
by Abigail Klein LeichmanNovember is Alzheimer’s Awareness Month. It’s a fitting time to look at the latest Israeli advances in preventing, diagnosing and treating the progressive and incurable brain disorder.
Alzheimer’s disease (AD) is the most common cause of the 9.9 million new cases of dementia diagnosed each year worldwide. The disease primarily strikes the elderly population, affecting 30 percent of those over age of 85.
AD impacts memory, thinking and language skills, and even the ability to carry out simple tasks.
The disease occurs when a protein called amyloid beta aggregates in brain tissues. These protein clumps kill nerve cells, leading to damage in the brain-function mechanisms.
Here are 10 examples of promising Israeli approaches reported within the past two years alone.
PREVENTION
Various genetic, lifestyle and environmental factors can put a person at risk for AD. Among them are diabetes, high blood pressure, obesity, smoking, depression, cognitive inactivity or low education, and physical inactivity.
Preventing the onset of AD is the focus of these approaches:
- A potential anti-Alzheimer’s vaccine is being developed in Prof. Eitan Okun’s Paul E. Feder Alzheimer’s Research Lab at Bar-Ilan University.
Most vaccines work by mounting an immune response toward a weakened pathogen to boost the immune system’s ability to fight the real pathogen.
Okun’s approach primes the body to attack amyloid beta protein clumps in the brain, the signature sign of AD.
Following experiments on mice, Okun is preparing for human trials on people at known risk of developing the disease in their 50s or younger: those genetically inclined toward Alzheimer’s and people with Down syndrome.
“These critical trials will determine whether the vaccine actually works in humans,” said Okun. “Depending on the success rate and side effects from [human] testing, we will be able to know how much more time is needed to make the vaccine available on a global scale.”
Okun also is investigating new ways to diagnose AD earlier and more accurately using advanced MRI (magnetic resonance imaging) technologies to detect initial signs of amyloid protein aggregation in the brain.
- Combining two FDA-approved drugs that protect the blood-brain barrier (BBB) could potentially prevent neurological disorders including stroke and Alzheimer’s disease.
Introduced by BGN Technologies of Ben-Gurion University of the Negev, the novel therapy hinges on the fact that a malfunctioning BBB allows neurotoxic blood products to enter the brain and cause damage leading to neurological diseases.
The lab of Prof. Alon Friedman discovered that treating the BBB at early stages can protect the brain and prevent disease development.
Their proposed treatment would combine Memantine and Losartan, which have been shown in preclinical studies to protect the integrity of the BBB when administered together. Partners are being sought to continue development.
- Researchers at the Technion–Israel Institute of Technology and Bar-Ilan University have developed a novel delivery system for a protein known to inhibit the development of Alzheimer’s disease.
They reported on this advance in a recent cover story of the journal Small.
Nanoscale silicon chips invented in Prof. Ester Segal’s lab allow for the direct insertion of neural growth factor protein into the brain and its gradual release into the target tissue, bypassing the BBB (see above). Afterward delivering all the therapeutic protein loaded onto them, the chips safely dissolve.
“In a series of experiments, we showed in mice that the two ways of delivering the platform into the brain led to the desired result,” said Technion doctoral student Michal Rosenberg.
“Our technology has also been tested in a cellular model of Alzheimer’s disease and indeed, the protein release has led to rescuing the nerve cells.”
DIAGNOSIS
PET scans and spinal taps are now the gold standard for diagnosing AD. They’re both expensive and carry risks.
Cheaper, noninvasive tests being developed in Israel also could be critical in providing a much earlier diagnosis, when treatment would be most effective.
- RetiSpec was built on the premise that noninvasively and inexpensively scanning a person’s retina can diagnose AD at an earlier stage.
That’s because the same beta-amyloid proteins that clump in the brain of AD patients appear in the retina of the eyes up to 15 years before the onset of AD symptoms.
RetiSpec developed the retinal scanner at the Ontario Brain Institute in Canada. Clinical studies are ongoing in Israel and Canada.
In October, RetiSpec received the Alzheimer’s Drug Discovery Foundation’s Diagnostics Accelerator Award to fund continued development of its hyperspectral imaging technology.
- A new MRI technique that records molecular changes in brain tissue could aid early diagnosis of Alzheimer’s and Parkinson’s, researchers from the Hebrew University of Jerusalem recently reported in Nature Communications.
“This could allow doctors to compare brain scans taken over time from the same patient, and to differentiate between healthy and diseased brain tissue, without resorting to invasive or dangerous procedures such as brain tissue biopsies,” explained lead researcher Dr. Aviv Mezer.
- An artificially intelligent chatbot named Clara could help detect early signs of AD, says Dr. Shahar Arzy, director of the computational neuropsychiatry lab at Hadassah Hebrew University Medical Center in Jerusalem.
Clara is based on a relatively recent understanding that AD affects the brain’s “orientation system” before it affects memory.
“The overlap between how the self is oriented to the world and the brain mechanisms that are disturbed by Alzheimer’s disease is astonishing,” Arzy told ISRAEL21c.
Clara asks patients questions about themselves and their relationships to people, places and events. It then compares that information to a baseline and generates a computer-based test tailored for the individual that can diagnose very early Alzheimer’s.
According to a study Arzy’s team published in the Proceedings of the National Academy of Sciences and in the American Psychological Association’s journal Neuropsychology, Clara is 95 percent accurate.
Clara is now in the midst of a five-year test at Harvard to compare data generated by the system with data from AD markers taken via amyloid PET scan, quantitative and functional MRI and other neuropsychological tests.
- Dr. Efraim Jaul, director of geriatric skilled nursing at Herzog Hospital in Jerusalem, noticed that patients with dementia — especially those with AD — seemed more prone to developing pressure ulcers (bedsores).
Jaul and Oded Meiron (a cognitive neuroscientist who heads the Electrophysiology and Neuro-cognition Lab in Herzog’s Clinical Research Center for Brain Sciences) published an article in the Journal of Alzheimer’s Disease outlining their discovery of the link between the two conditions.
The reason is that the abnormal changes in the brain that lead to dementia are happening in other parts of the body, including the skin. Skin tissue and brain tissue derive from the same embryonic stem cells.
Jaul and Meiron are working with an American company to develop a test to identify a biomarker for abnormal cell density in the skin of dementia patients. They hope that this skin test could pinpoint an individual’s type and stage of dementia. The biomarkers show the most promise in identifying AD, they say.
TREATMENT
A variety of approved medications for AD – including Exelon, developed in Israel — cannot cure or stop the progression of the disease. They only relieve or delay AD symptoms, such as memory loss and confusion.
A few Israeli pharmaceuticals under development aim to improve Alzheimer treatment options.
- Hyperbaric oxygen therapy may ease symptoms commonly experienced by people with AD, according to a Tel Aviv University study.
Breathing in pure oxygen in a pressurized room or chamber stimulates the release of growth factors and stem cells, which promote healing.
“This revolutionary treatment for Alzheimer’s disease uses a hyperbaric oxygen chamber, which has been shown in the past to be extremely effective in treating wounds that were slow to heal,” said lead researcher Prof. Uri Ashery.
Ashery’s group tested the therapy on a mouse model of Alzheimer’s disease. The treatment was found to reduce behavioral deficiencies compared to control mice.
Remarkably, the treatment also reduced plaque pathology and neuroinflammation in the test mice by about 40 percent.
Further research will investigate the underlying mechanisms of the therapy and evaluate its beneficial effects in Alzheimer patients.
- ProteKt Therapeutics recently received $3.6 million to further develop a drug that could enhance long-term memory in cases of mild cognitive impairment, traumatic brain injury and neurological disorders such as AD.
Incubated at FutuRx in Ness Ziona, ProteKt was spun out of PKR kinase inhibitor research by University of Haifa Prof. Kobi Rosenblum. Inhibition of the enzyme PKR is a unique idea for improving memory consolidation.
- Research from Tel Aviv University suggests it may be possible to reverse the protein clumping involved in neurodegenerative diseases such Alzheimer’s and Parkinson’s diseases.
Protein aggregation tends to increase with age and can lead to neurodegeneration because proteins “can adopt an erroneous configuration, where they’re misfolded,” explains Prof. Martin Kupiec.
The paper he and his colleagues published in Molecular Cell describes how removing glucose from a particular aggregated protein made the blob dissolve.
If the results can be replicated in more complex proteins, scientists will have a new research avenue toward treatments that could reverse the neurodegenerative effect of protein aggregates, Kupiec says.
More about Health
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By Brian Blum -
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Using artificial intelligence to find new drugs faster
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Movies can be early detection system for autism
By Daniel Ben Tal -
The village that’s been vegan for 50 years
By Abigail Klein Leichman -
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By ISRAEL21c Staff -
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name:
honorific-prefix:
given-name:
additional-name:
family-name:
honorific-suffix:
nickname:
organization-title:
username:
new-password:
current-password:
organization:
street-address:
address-line1:
address-line2:
address-line3:
address-level4:
address-level3:
address-level2:
address-level1:
country:
country-name:
postal-code:
cc-name:
cc-given-name:
cc-additional-name:
cc-family-name:
cc-number:
cc-exp:
cc-exp-month:
cc-exp-year:
cc-csc:
cc-type:
transaction-currency:
transaction-amount:
language:
bday:
bday-day:
bday-month:
bday-year:
sex:
url:
photo:
tel:
tel-country-code:
tel-national:
tel-area-code:
tel-local:
tel-local-prefix:
tel-local-suffix:
tel-extension:
email:
impp:


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QueenArmies::QueenBranch Class Reference
Inherits Gecode::Branching.Detailed Description
Custom branching for Peacable queens.Custom branching that tries to place white queens so that they maximise the amount of un-attacked squares that become attacked.
Definition at line 137 of file queen-armies.cc.
Public Member Functions | |
| virtual bool | status (const Space *home) const |
| Check status of branching, return true if alternatives left. | |
| virtual BranchingDesc * | description (const Space *) const |
| Return branching description. | |
| virtual ExecStatus | commit (Space *home, const BranchingDesc *d, unsigned int a) |
| Commit for branching description d and alternative a. | |
| virtual Actor * | copy (Space *home, bool share) |
| Create copy. | |
Static Public Member Functions | |
| static void | post (QueenArmies *home) |
Member Function Documentation
|
|
Check status of branching, return true if alternatives left. This method is called when Space::status is called, it determines whether to continue branching with this branching or move on to the (possibly) next branching. Implements Gecode::Branching. Definition at line 145 of file queen-armies.cc. |
|
|
Return branching description. Note that this method can rely on the fact that it is called immediately after a previous call to status. Hence, it is safe to remember computation from status in order to speed up description. Implements Gecode::Branching. Definition at line 164 of file queen-armies.cc. |
|
|
Commit for branching description d and alternative a. The current branching in the space home performs a commit from the information provided by the branching description d and the alternative a. Implements Gecode::Branching. Definition at line 168 of file queen-armies.cc. |
|
|
Create copy.
Implements Gecode::Actor. Definition at line 176 of file queen-armies.cc. |
|
|
Definition at line 180 of file queen-armies.cc. |
The documentation for this class was generated from the following file:
- examples/queen-armies.cc (Revision: 3783)


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I began to become very, very depressed, to the point of seeing a therapist. The therapist helped a little, but I was still drinking and drugging to keep from thinking about how shitty my life felt. That went on for about two months, my therapist refused to prescribe any sort of anti-depressants for me, even though I knew what I was feeling was irrational and wrong, and there had to be some way to fix it.
I asked my regular doctor about being depressed. This is the same doctor I've been going to for about two years, so she knows me fairly well. We talked at length, I filled out an evaluation card (didn't realize I was quite as depressed as I was until I saw my answers to all those questions). She prescribed Effexor, starting with a 37.5mg dosage.
The first couple weeks were like being stoned all the time. Massive body high, spaciness, inability to form and complete complex thoughts. After the third week, with my dosage upped to 75mg I started to feel better. Less depressed, less negative, less angry.  I felt really good about my life sometimes, too. It had been a long time since I felt that way, like about 8 years or so.
I started having some sexual dysfunction -- I could get it up, but couldn't have an orgasm, unless I worked at it for over 45 minutes. Sure, fun sometimes, but keeping up a constant pace for 45 minutes is... well, less fun than you might think. I told my doctor about this, and she upped my dose to 150mg. Didn't notice any difference in how I felt, but it took a bit of the edge off of the sexual dysfunction.
I'm now on the 2nd month of 225mg (about 9 months of taking it), again, without any strong side effects, and the sexual dysfunction kind of comes and goes. Hopefully that will change soon. The lack of anxiety has translated into some very vivid dreams, but nothing disturbing, and there have been a few nights where I have clenched my jaw hard enough while sleeping to make it ache.
Other drug interactions have been varied. Weed gets me incredibly stoned, and in a slightly more spacey way. I feel very disconnected while stoned (I smoke weed maybe once a week), but not unpleasant. In fact, I seem to enjoy music and movies even more now.
Alcohol has been a mixed bag. Sometimes I can drink all night, other times, a couple drinks will put me down.  One evening, while on 150mg, I had three drinks, and spent at least a half hour sitting at the bar, biting my thumbs to keep the room from spinning out of control. That was an uncomfortable experience. I didn't feel drunk, I felt dosed, like when I hit the first peak on E. I wouldn't want to repeat that.
I did Mushrooms with friends while on Effexor, the effects were minimal on me, while my friends were having very strong reactions. Given that I had not tried mushrooms prior to being on Effexor, I'm not sure if my experience was altered because of that.
So, all in all, Effexor has been positive for me. The couple of days (single days only) where I have skipped a dose left me feeling listless and tired. I don't think I'd enjoy the effects of skipping two or more days. I talked to my doctor about how to get off Effexor, and she said that it would be a gradual decrease in dosage, until I wasn't taking it anymore. It's not healthy to quit this one cold turkey.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


intermol.forces.forcefunctions module¶
- intermol.forces.forcefunctions.build_paramlist(program)[source]¶
Create a paramlist specific for a given program.
- intermol.forces.forcefunctions.build_unitvars(program, paramlist, dumself=None)[source]¶
Takes a string program name (one of the supported programs), and a ‘self’ object it looks like the keyword is not being used, but it is used in the line eval(unit). The test name ‘dumself’ needs to match what is in the force data arrays. Currently only used for lammps.
- intermol.forces.forcefunctions.capifyname(forcename)[source]¶
Return name of the class in camelCase.
- intermol.forces.forcefunctions.create_kwd_dict(unitvars, paramlist, force_type_object, values, optvalues=None)[source]¶
- intermol.forces.forcefunctions.create_kwds_from_entries(unitvars, paramlist, entries, force_type, offset=0)[source]¶
Create a keyword dictionary given an array of information from a file format
requires the master set of units, the master set of parameter lists, an object (either a force_class or force_type), the list of information to be converted into a keyword, and an offset.
Parameters: offset (int) – how far over from the first entry we translate
- intermol.forces.forcefunctions.get_parameter_kwds_from_force(force, forceparams, paramlist)[source]¶
- intermol.forces.forcefunctions.get_parameter_list_from_force(force, paramlist)[source]¶
Create a function that returns the paramters of a function type.
First, we need make some additions to the parameter list dictionary, which we do once when the forcedata script is imported. Useful to put the forces here as well. We won’t make this a function for now since it’s needed in this module.
- intermol.forces.forcefunctions.optforceparams(force_type, forcetype_object=None)[source]¶
Return the dictionary of optional paramters of an abstract force type.
If no object is given, we fill with blanks.
- intermol.forces.forcefunctions.optparamkeylookup(force_type)[source]¶
Given a force_type object, determine the key associated with the optional parameters.


Test passes if the orange stripe is on the left-hand side of the blue stripe and both stripes are above the yellow bar.


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wybiral/firehose Real-time news aggregate system. Contribute to wybiral/firehose development by creating an account on GitHub.
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Announcing .NET Core 3.1 Preview 1 | .NET Blog Today, we’re announcing .NET Core 3.1 Preview 1. .NET Core 3.1 will be a small release focused on key improvements in Blazor and Windows desktop, the two big additions in .NET Core 3.0. It will be a long term support (LTS) release with an expected final s...
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%%HTML
<style> code {background-color : pink !important;} </style>
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib qt
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('calibration_wide/GO*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (8,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (8,6), corners, ret)
#write_name = 'corners_found'+str(idx)+'.jpg'
#cv2.imwrite(write_name, img)
cv2.imshow('img', img)
cv2.waitKey(500)
cv2.destroyAllWindows()
objpoints
and imgpoints
needed for camera calibration. Run the cell below to calibrate, calculate distortion coefficients, and test undistortion on an image!¶import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('calibration_wide/test_image.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('calibration_wide/test_undist.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "calibration_wide/wide_dist_pickle.p", "wb" ) )
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)


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