ライフサイエンスコーパス: kernel

1 l) and sinogram-affirmed IR (SAFIRE) (I31f-5 kernel).

2 ng upon physical distribution of pigments in kernel.

3 o visualize the evolution of the early flame kernel.

4 ced a non-linear time alignment into the SVM kernel.

5 ntirely localised in the outer layers of the kernel.

6 mSVM, kmer-SVM, mismatch kernel and wildcard kernel.

7 based on the recently described di-mismatch kernel.

8 d a highly porous structure of expanded rice kernel.

9 ligand with a face-centered cubic (FCC) Au22 kernel.

10 frontal frontoparietal task control network kernel.

11 e and reduces the likelihood of smashing the kernel.

12 lutamatergic interneurons of the respiratory kernel.

13 aternal compartments of the maize (Zea mays) kernel.

14 rnels and even visualisation within the same kernel.

15 ierarchy into a function-function similarity kernel.

16 acq.) is the shell thickness surrounding the kernel.

17 for its potential to grade whole white maize kernels.

18 phytochemical content than the steam-treated kernels.

19 ed a similar colour profile to steam-treated kernels.

20 uction of some phenolic compounds of apricot kernels.

21 s as well as beverages prepared from crushed kernels.

22 een quantified in liqueurs compared to fruit kernels.

23 pedigrees, permutation tests, and covariance kernels.

24 s of choice probabilities and psychophysical kernels.

25 rangements and by comparing their dose-point kernels.

26 nd Fe were distributed in other parts of the kernels.

27 o select for maize lines that produce larger kernels.

28 thods that is based on the concept of string kernels.

29 cally use a mixture of univariate transition kernels.

30 Kernel abortion resulting from osmotic stress is not fro

31 and object-wise, were investigated to group kernels according to hardness.

32 e expansion of the nonlinear Wiener-Volterra kernels, accounting for the long-term information given

33 e used general system-identification (Wiener kernel) analyses of chinchilla auditory nerve fiber resp

34 Network kernel analysis holds promise as a sensitive method for

35 Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.739, and F

36 , and fatty acid composition of some apricot kernel and oils was investigated.

37 nding mode and translational symmetry in the kernel and on the surface of the Au92 nanocrystal can be

38 data-driven approach to determine the memory kernel and random noise in generalized Langevin equation

39 and secondary metabolites in jackfruit seed kernel and seed coating membrane was studied.

40 city of aqueous extracts from jackfruit seed kernel and seed coating membranes to scavenge nitric oxi

41 llows one to map out the respective roles of kernel and surface in determining the electronic and opt

42 hm with a L1-regularized logistic regression kernel and were then fitted with logistic regression mod

43 rnels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel.

44 crostructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves.

45 igned a class to every pixel from individual kernels and did not give acceptable results because of h

46 fication of the protein distribution between kernels and even visualisation within the same kernel.

47 te sample workup was adapted to lupin seeds, kernels and flakes, respectively.

48 The uncooked kernels and flours surface composition of the two select

49 ation of starch, proteins and lipids in rice kernels and flours.

50 tarch), proteins and lipids in uncooked rice kernels and flours.

51 to calculate the surface composition of rice kernels and flours.

52 Starch is the major component of oat kernels and may account up to 60% of the dry weight.

53 ecific PhytoPs that were not detected in raw kernels and thus could be biomarkers of the degree of ox

54 was expanded to popcorn kernels, white maize kernels and yellow maize grits.

55 ed with filtered back projection (FBP) (B31f kernel) and sinogram-affirmed IR (SAFIRE) (I31f-5 kernel

56 o the elasticity of lambda to the projection kernel, and (ii) the elasticity of lambda to underlying

57 y in their value, the shape of the dispersal kernel, and the dimensionality of the landscape all cont

58 ursting; one part is the primary half-center kernel, and the other part, which consists of a bilatera

59 score) by Bayesian network and multi-variate kernel approach to make predictions.

60 ttern in metabolomics data, two new kinds of kernels are designed.

61 s in characterizing and parametrizing string kernels as alignment-based methods for comparing protein

62 approach, we further visualize convolutional kernels as sequence logos and successfully identify simi

63 We propose a novel CNV kernel association test (CKAT) in this paper.

64 The kernel association test (KAT) is popular in biological s

65 We propose the microbiome regression-based kernel association test (MiRKAT), which directly regress

66 The sequence kernel association test (SKAT) is probably the most popu

67 The sequence kernel association test (SKAT) tests for associations be

68 The sequence kernel association test was used to interrogate variant

69 sured using logistic regression and sequence kernel association test-optimal gene tests, respectively

70 analyses were undertaken using the sequence kernel association test.

71 e, the cohort allelic sums test and sequence kernel association test.

72 ancreatic cancer risk, based on the sequence-kernel association test.

73 sis of rare variants (MAF<1%) using sequence kernel association testing reveals a novel association w

74 han single-nucleotide polymorphism (SNP)-set kernel association tests and burden tests.

75 However, pressure-treated kernels at 700MPa showed a similar colour profile to ste

76 e-6-phosphate was elevated in leaf, cob, and kernels at silking as a consequence of salt treatment bu

77 se (Suc) and hexose sugars in leaf, cob, and kernels at silking, pollination, and 3 d after pollinati

78 his system for measuring maize ear, cob, and kernel attributes is being used by multiple research gro

79 e potential of eliminating severely sprouted kernels based on density differences in NaCl solutions w

80 predefined threshold and classifying entire kernels based on the number of correctly predicted pixel

81 ds an existing class of k-mer based sequence kernels, based on the recently described di-mismatch ker

82 In general, kernel-based algorithms create more complex models and r

83 We propose a joint kernel-based feature selection and classification framew

84 gy has been developed and verified to assess kernel-based gluten contamination (i.e., wheat, barley a

85 relevant textures, we use several different kernel-based machine learning techniques to classify pro

86 We propose a non-parametric, kernel-based method, M(3)D, to detect higher order chang

87 We propose a new QSRR model based on a Kernel-based partial least-squares method for predicting

88 tical analysis, we proposed to use Laplacian Kernel-based similarity for GSU to boost power and enhan

89 -related traits is assessed by comparing the kernel-based similarity with the similarity in the trait

90 odes and relationships denoted by edges, and kernel-based that can generate a classifier in feature s

91 n content in whole wheat kernels on a single kernel basis, and to apply HSI to predict this distribut

92 Shape of the rice kernels became bolder on processing.

93 We propose a bivariate null kernel (BNK) hypothesis testing method, which characteri

94 d to achieve higher lipid recovery from plum kernels, but also resulted in oils with better quality a

95 igosaccharides (MOS) were isolated from palm kernel cake by aqueous extraction using high temperature

96 oxidant capacity revealed by digested puffed kernels can be ascribed to both the new formed Maillard

97 s that ecological and evolutionary dispersal kernels can be equivalent, and that the apparent disagre

98 ntified in a recent GWAS of maize (Zea mays) kernel carotenoid variation.

99 phenol contents of control group of apricot kernels change between 54.41mgGAE/100g (Sogancioglu) and

100 n score histograms performed better for hard kernel classification (sensitivity and specificity of 0.

101 short, tillering, and segregating for yellow kernel color.

102 n modules associated with single or multiple kernel compartments including modules for the endosperm

103 stribution of 0.25-g gluten test results for kernel contaminated oats, twelve 50g samples of pure oat

104 ed among neighboring genomic positions using kernel correlation.

105 ctric constant and loss factor of the almond kernels decreased sharply with increasing frequency over

106 to tag a gene that gives rise to a defective kernel (dek) phenotype.

107 Kernel density analysis determined that the large invert

108 ivity analysis using different lag times and kernel density bandwidths were tested to establish the c

109 nd best estimation for changes in emissions (Kernel density distribution estimator).

110 Kernel density estimate using the Crimestat program was

111 Kernel density estimate using the Crimestat program was

112 Additionally, kernel density estimation analysis across the genome fou

113 method is combined with a judicious Gaussian kernel density estimation in the remaining low-dimension

114 at relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to

115 The analysis included kernel density estimation, K-function analysis, and a t

116 the cohort of tissues by employing suitable kernel density estimators.

117 Kernel density maps indicated 3 areas of intense concent

118 lthy food environment was measured using the kernel density of healthful food venues.

119 a likelihood-based classification, using the kernel-density smoothed data, were developed to discrimi

120 lso enables dynamic loading and switching of kernels depending on the query model size, in order to a

121 interneurons of the respiratory rhythmogenic kernel, derived from Dbx1-expressing precursors, may pro

122 r response reveals an instantaneous friction kernel despite the complexity of the bacterial bath.

123 mmature wheats especially at early stages of kernel development are rich sources of functional nutrie

124 ening by maximal chemical similarity, binary kernel discrimination, and support vector machine method

125 red to higher density kernels, lower density kernels displayed higher alpha-amylase, endoxylanase, an

126 products (bran fractions, BF, and debranned kernels, DK) of durum wheat was investigated by evaluati

127 n contamination (i.e., wheat, barley and rye kernels) during gluten-free (GF) oat production.

128 084813 filed on 17th December 2015) on wheat kernel endosperm morphology and gluten protein structure

129 We used Biased Random Bridge kernel estimates linked to residence time as a surrogate

130 The kernel exhibited a low phenolic compound content and ant

131 This kernel extends an existing class of k-mer based sequence

132 solated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated

133 kernel performs better than the di-mismatch kernel for intermediate k values.

134 lignment free method based on a mathematical kernel for scoring the similarity of protein sequences.

135 ons indicate charge transfer from surface to kernel for the HOMO-LUMO transition.

136 c parentage analysis to quantify a dispersal kernel for the reef fish Elacatinus lori, demonstrating

137 trahedral [Cu4](2+) core, which is a unique "kernel" for a Cu-only superatom.

138 smatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape

139 dual-pulse scheme can be used to control the kernel formation process in ways that extend the lean ig

140 hemical potential, by computing the softness kernel, [Formula: see text] The softness kernel is nears

141 actical implementations, we parameterize the kernel function in the Laplace domain by a rational func

142 ing the radial basis function and polynomial kernel function, we found that the predictability of hyb

143 We further propose kernel functions, specifically designed for our non-nega

144 vides additional advanced gapped k-mer based kernel functions.

145 total elasticity of lambda to the projection kernel generally increased with length.

146 The pressure-treated kernels had a higher phytochemical content than the stea

147 a statistically robust, empirical dispersal kernel has been lacking for any marine species.

148 The thermal processing of almond kernels implies the use of techniques that produce chemi

149 and among the most widely used computational kernels in science.

150 This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel an

151 ielectric constant and loss factor of almond kernels increased with increasing temperature and moistu

152 ugh the internet, an optimized computational kernel installed on a remote computer cluster.

153 s were identified and quantified in hazelnut kernel, instead nine free and six bound phenolic compoun

154 Second, we expand the concept of string kernels into a novel framework for deriving information

155 Zn were mainly localized at the insertion of kernels into cobs, but Ca and Fe were distributed in oth

156 ., Eg approximately 0.45 eV), indicating the kernel is decisive for Eg while the surface is less crit

157 ess kernel, [Formula: see text] The softness kernel is nearsighted, indicating that under constant-ch

158 The nonlocal kernel is simplified by limiting the nonlocal atomic int

159 erically simulated low pass RC, and Gaussian kernels, is compared.

160 Such a kernel leads to smart stretching with nonuniform spectra

161 With multi-kernel learning, complementary features from multiple ti

162 KL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% w

163 85% SDI) had no detrimental impact on almond kernel lipid content, but severe and extreme deficiencie

164 Compared to higher density kernels, lower density kernels displayed higher alpha-am

165 Moreover, we showed that the kernel machine learning method consistently outperformed

166 nostic assessment and presented a multi-omic kernel machine learning method to systematically quantif

167 loyed a novel statistical approach, Bayesian kernel machine regression (BKMR), to study the joint eff

168 microbiome profiles via the semi-parametric kernel machine regression framework.

169 ness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage

170 We describe an improved algorithm for kernel matrix calculation that speeds run time by about

171 le kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) dif

172 kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4

173 rug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix

174 ug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matri

175 t profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothi

176 The performance of the single kernel measurement was poorer than that typically obtain

177 cancer metabolomics study indicates that our kernel method has a better performance than some existin

178 We use the kernel method, which converts the spatial distribution o

179 ant weights strongly influenced the power of kernel methods and burden tests.

180 cle, we present a predictive system based on kernel methods, a type of machine learning algorithm gro

181 this problem, personalized PageRank and heat kernel methods, operate in the space of "landing probabi

182 ts indicated that a single general dispersal kernel model was sufficient to predict the majority of v

183 We showed that a common constant dispersal kernel model, simulated from the discovery date, explain

184 Correlations among network kernels more accurately classified Parkinson's disease f

185 algorithm determines the average space each kernel occupies along the cob axis using a sliding-windo

186 n investigation on the proteome of the sweet kernel of apricot, based on equalisation with combinator

187 Our results highlight ways in which a kernel of conserved genes may be alternatively wired to

188 The kernel of DeepEM is built upon a convolutional neural ne

189 se essential interactions is called herein a kernel of the circadian system.

190 The kernel of the nanocluster is a Marks decahedron of Au79,

191 decahedron of Au79, which is the same as the kernel of the previously reported Au102(pMBA)44 (pMBA =

192 or the first time, we show how to design the kernel of the transform and specifically, the nonlinear

193 The kernel of this algorithm is the subdomain level disconti

194 k and behave are widely shared, suggesting a kernel of truth.

195 llport stated that stereotypes might have a "kernel of truth." Recent research in social neuroscience

196 We studied the PhytoP profile in kernels of almond cultivars under different conditions,

197 Starch extracted from broken kernels of Basmati and Irri rice varieties of Pakistani

198 od for analysis of nutrient elements in seed kernels of cucurbits.

199 Kernels of forty-one Indian peanut cultivars were analyz

200 By discarding lower density kernels of mildly and severely sprouted wheat batches (1

201 tral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and s

202 ats are easily contaminated with gluten-rich kernels of wheat, rye and barley.

203 e also show functionally that the conserved "kernel" of the bilaterian heart mesoderm GRN is operatio

204 l-scavenging activity were determined in the kernel oil of 20 local almond selections originating fro

205 rop years in order to evaluate variations in kernel oil yield and composition, and oil oxidative para

206 erest for almond breeding focused to improve kernel oil yield and composition.

207 , carotenoids, and volatile compounds in the kernel oils from six walnut (Juglans regia L.) varieties

208 and stearic acids) in almond (Prunus dulcis) kernel oils with temperature (150 or 180 degrees C) and

209 stribution of protein content in whole wheat kernels on a single kernel basis, and to apply HSI to pr

210 n be recommended for classification of maize kernels on production scale.

211 Starting from recently proposed kernels on the discrete space of protein sequences, we i

212 lled the safe operating space, the viability kernel or the sunny region.

213 d antioxidant activities of purple waxy corn kernels (p<0.05).

214 Furthermore, kernel partial least squares is used to predict adaptive

215 erforms better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mis

216 larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for i

217 rly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger

218 partitioning in different coproducts to corn kernel phenotype.

219 ilt by separating hard and soft wheat, or on kernels placed on similar orientation did not greatly im

220 mmetrical, hexagonal-close-packed (HCP) Au18 kernel protected by six dimeric Au2 (SR)3 staple motifs.

221 -octahedral (or hexagonal close packing) Au9 kernel protected by staple-like motifs including one tet

222 The calibration performance for single kernel protein content was R(2) of 0.82 and 0.79, and RM

223 er, little information is known about single kernel protein variation within batches.

224 mal requirement, without significant loss of kernel quality.

225 The gallic acid contents of control apricot kernels ranged between 7.23mg/100g (Kabaasi) and 11.23mg

226 termined ranges, the use of pH 4.5, moisture/kernel ratio of 8:1 (w/w), and 300stroke/min shaking spe

227 orphic deformable registration in space with kernel regression in age.

228 A second counts individual kernels removed from ears, including those in clusters.

229 Proteome profiles of the maize kernels revealed alterations in the levels of enzymes of

230 representations of atomic neighborhoods and Kernel Ridge Regression, we show that an accurate and tr

231 taloglu) whereas the gallic acid contents of kernels roasted in 540W changed between 15.35mg/100g (So

232 aize (Zea mays) using the well-characterized kernel row number trait, which was selected to enable co

233 A third measures each kernel's major and minor axis after a Bayesian analysis

234 lls of the hazelnuts, while six raw hazelnut kernel samples (12%) and five roasted hazelnut kernel sa

235 rnel samples (12%) and five roasted hazelnut kernel samples (8.3%) contained AFs ranging from 0.09 to

236 AE/100g (Hasanbey), total phenol contents of kernel samples roasted in 720W were determined between 2

237 mean spectra gave better results for medium kernels (sensitivity and specificity of 0.95 and 0.93).

238 laser ignition at lean conditions, the flame kernel separates through third lobe detachment, correspo

239 t-free method based on the concept of string kernels, SeqKernel.

240 to reduce spikelet growth, silk growth, and kernel set.

241 Dimensionless ear and kernel shape traits that may interrelate yield component

242 Micrographs of kernel showed pigments concentrated in pericarp layer of

243 rk and a frontoparietal task control network kernel showed that the right insula, an area implicated

244 mples of pure oats, each spiked with a wheat kernel, showed that 0.25g test results followed log-norm

245 ted in an animated display using a bivariate kernel smoother.

246 Here, we apply kernel smoothing to construct predictive features and bu

247 ls such as peri-stimulus time histograms and kernel smoothing.

248 est benefit the classification scheme in the kernel space.

249 Single kernel spectra were used to develop partial least square

250 h molecular profiling methods of NK603 maize kernels (sprayed or unsprayed with Roundup) and the isog

251 The kernel structure unbiasedly reveals four interlocked neg

252 Strikingly, the kernel structure, as well as the whole clock circuitry,

253 e gold-thiolate interface independent of the kernel structure, which in turn allows one to map out th

254 densed tannins and phenolic acids) of almond kernels substantially lost in the initial phase; afterwa

255 with possible deep extensions and high-order kernel support vector machines to predict major histocom

256 Dose-point kernels taken together with the energy spectra of the ra

257 es studied were medium-hard to hard based on kernel texture.

258 metrically modeling the microbiome through a kernel that incorporates phylogenetic distance.

259 We describe a sequence + shape kernel that leverages DNA sequence and shape information

260 Despite the same kernel, the surface structure of Au103 is quite differen

261 In wild-type kernels, the nkd transcripts were most abundant around 1

262 e sizes, shapes, and numbers of ears and the kernels they bear.

263 an analysis of contour points identifies the kernel tip.

264 Images of isolated wheat kernel tissues and whole wheat kernels following hydroth

265 two CNVs, and next aggregate the single-CNV kernel to a CNVR kernel which summarizes the similarity

266 ch using an adaptive multivariate transition kernel to estimate in parallel a large number of paramet

267 Due to the high kernel to kernel variability in enzyme activity in a bat

268 ing Support Vector Machine (SVM) with linear kernel to select the final model.

269 We find that adding convolutional kernels to a network is important for motif-based tasks.

270 power of descriptors by learning convolution kernels to reduce the within-class variation and increas

271 omplex yeast-fermentation beer from the corn kernel-to-ethanol industry into primarily n-caproic acid

272 fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per

273 (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %)

274 orption spectroscopy analysis, revealing the kernel-to-surface relaxation pathway of electron dynamic

275 imeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a di

276 r searching for a correct higher dimensional kernel transform.

277 1.5% (w/w) of unsaponifiable matters of bene kernel (UKO) and hull (UHO) oils were studied and compar

278 The temperature profiles of RF heated almond kernels under three moisture levels were made using expe

279 eme, we can remove the length effects on the kernel values.

280 Due to the high kernel to kernel variability in enzyme activity in a batch of spro

281 content and major lipid classes in the plum kernels was also studied.

282 , calculated from correlations among network kernels, was correlated with cerebrospinal fluid biomark

283 t is associated with a well-behaved integral kernel, we prove that I is invariant under arbitrary vol

284 Transgenic kernels weighed the same on average as nontransgenic sib

285 A nonlinear relationship with total kernel weight also was observed whereby lines with relat

286 els of flecking had, on average, lower total kernel weight.

287 ase was observed for the hectoliter and 1000 kernel weights whereas a decrease for ash and protein co

288 Guinea, we found that evolutionary dispersal kernels were 17 km (95% confidence interval: 12-24 km) w

289 K millers and wheat breeders, and individual kernels were analysed by HSI and by the Dumas combustion

290 Dose-point kernels were determined up to 30 mum.

291 (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-lin

292 Wheat kernels were subjected to microwave treatment, and the p

293 xt aggregate the single-CNV kernel to a CNVR kernel which summarizes the similarity between two CNVRs

294 atures of CNVs, we first design a single-CNV kernel which summarizes the similarity between two CNVs,

295 The method scope was expanded to popcorn kernels, white maize kernels and yellow maize grits.

296 rect larval dispersal observations suggested kernel widths of 27 km (19-36 km) or 19 km (15-27 km) ac

297 ractically used for pasteurization of almond kernels with acceptable heating uniformity.

298 strongly affected by the applied resolution kernel, with accuracy differing by about 20%-50% between

299 out network similarity is captured via graph kernels, with the networks additionally informed by spec

300 irst, we developed a weighted version of the kernel, WSeqKernel.