ライフサイエンスコーパス: kernel

1 by a single parameter from the gravity-like kernel.

2 he endocarp, or shell, surrounding the fruit kernel.

3 c interface (e.g., helical stripes), and the kernel.

4 Penrose tiling protects the M(54) decahedral kernel.

5 that introduces a customizable memory decay kernel.

6 ensional feature data, are used to build the kernel.

7 s-only code onto the isotropic dual-mobility kernel.

8 in a subpopulation forming the rhythmogenic kernel.

9 based on the recently described di-mismatch kernel.

10 cess regression with a radial basis function kernel.

11 rnel, to gradually spreading over the entire kernel.

12 weighted over time, known as the integration kernel.

13 ole kernel but it was not found in deskinned kernels.

14 the distributions of parameters and fitness kernels.

15 han the WT plants in the ear leaves and seed kernels.

16 ther with two-dimensional (2D) convolutional kernels.

17 en the gkm kernel is combined with nonlinear kernels.

18 ed by kernels from the middle and then basal kernels.

19 o select for maize lines that produce larger kernels.

20 uction of some phenolic compounds of apricot kernels.

21 fitting algorithm, and gravity-like mobility kernels.

22 ten pearling fines and corresponding pearled kernels.

23 he relationships between the input tasks and kernels.

24 lgorithm for calculating gapped k-mer string kernels.

25 -reactive proteins were purified from almond kernels.

26 (0.781), support vector machine with linear kernel (0.780), and [Formula: see text] penalized logist

27 Defective Kernel 1 (DEK1) is genetically at the nexus of the 3D mo

28 chemical parameters analyzed make A. boehmii kernel a source of good quality oil.

29 optimize feature maps towards the resulting kernels' ability to separate points, i.e., their "resolu

30 approach based on analog forecasting, called kernel analog forecasting (KAF), which avoids assumption

31 Wiener-kernel analyses show that even mild NIHL causes grossly

32 remain largely inaccessible to the examined kernel and deep learning methods.

33 U)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vect

34 chine (SVM) with radial basis function (RBF) kernel and leave-one-out method to classify time-series

35 S)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least s

36 e.g., support vector regressors with linear kernel and radial kernel in terms of accurate prediction

37 e application of transfer learning, multiple kernels and attention layer in the design of the deep ne

38 Fusarium-damaged kernels and deoxynivalenol analyses supported the findin

39 ylamide, beta-glucan) and viscosities of oat kernels and flakes.

40 Besides, we visualize the convolutional kernels and successfully identify the key features of T3

41 measured by DPPH, 1500 times higher than its kernel) and pistachio (13 mol/kg, 630 times higher) make

42 scription of movement is available (a travel kernel), and a natural history consistent with pandemic

43 to Au(22)Cd(1) with a 13-atom cuboctahedral kernel, and correspondingly, the optical properties are

44 from SDCT is high and independent from dose, kernel, and denoising settings; however, shows a depende

45 ematically profiled the performance of deep, kernel, and linear models as a function of sample size o

46 different dose levels, section thicknesses, kernels, and reconstruction algorithm settings.

47 able from the literature, all using the same kernel approach but with different parameterizations and

48 ith machine learning and motif-based profile-kernel approaches with word-based (ProtVec) solutions to

49 ry shows that only noise and the integration kernel are related to the change in pupil response.

50 In this article, we propose the Composite Kernel Association Test (CKAT), a flexible and robust ke

51 The burden test and sequence kernel association test (SKAT) are two widely used varia

52 HH1 or ADH1 variants, and we used a Sequence Kernel Association Test (SKAT) to identify rare variant-

53 d tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V.

54 was tested with both kernel-based (sequence kernel association test [SKAT]) and burden methods.

55 To validate the bioinformatics and sequence kernel association test analyses, we functionally charac

56 ene-level associations by an adapted SNP-set Kernel Association Test approach.

57 ered by outcome, microbiome regression-based kernel association test to evaluate whether between-samp

58 scores as weights in the group-wise sequence kernel association test to identify candidates.

59 To perform a kernel association test, a CNV locus needs to be defined

60 We utilized sequence kernel association testing and polygenic risk score (PRS

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

62 mon variants; gene-based burden and sequence kernel association tests examined low-frequency variant-

63 We used sequence kernel association tests to assess the disease associati

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

65 ies of Amazonian fats/oils (murumuru, tucuma kernel, bacuri, pracaxi, patawa, and Brazil nut).

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

67 nonsynonymous variants was tested with both kernel-based (sequence kernel association test [SKAT]) a

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

69 to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained o

70 Specifically, our kernel-based method, MUNK, integrates sequence and netwo

71 To detect rare CNV associations, kernel-based methods have been shown to be a powerful to

72 roportional hazards regression adjusted with Kernel-based propensity score matching.

73 implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensiona

74 We demonstrate such kernel-based quantum machine learning using specialized

75 Then a kernel-based semi-supervised transductive algorithm is a

76 In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profi

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

78 microwave treatment on hydrated durum wheat kernels blocks gluten protein conformation through SS bo

79 redominant compound in almond skin and whole kernel but it was not found in deskinned kernels.

80 nt pulp, leek leaves, parsley waste, pumpkin kernel cake, and mushroom waste), which ones have valuab

81 ified kernel formulation that decomposes the kernel calculation into a set of independent counting op

82 We show how the node kernel can generate a large number of discriminative fea

83 ve that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours

84 ion, which we termed as Generalized Additive Kernel Canonical Correlation Analysis (GAKCCA).

85 Both sparse partial least squares and kernel canonical correlation analysis captured two hidde

86 es (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our n

87 es, such as sparse partial least squares and kernel canonical correlation analysis, for identifying h

88 CCA has been extended to a kernel CCA for nonlinear relationships and generalized C

89 e workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF.

90 Measurements and Main Results: Multiple-kernel clustering revealed four clusters of individuals

91 C curve, while achieving average speedups in kernel computation of ~100x and speedups of ~800x for la

92 existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on

93 As kernels continued to mature, pigment coverage across the

94 actor correction component, which is a large-kernel convolution neural network that focuses on calibr

95 del, SVM extensions found in the literature, kernel Cox regression and Cox model.

96 arable to Cox proportional hazards model and kernel Cox regression.

97 The fiber in corn kernels, currently unutilized in the corn to ethanol pro

98 kernels, since the presence of the evaluated kernel defects (broken, fermented, rotten, moldy, germin

99 dan generated a chemically induced defective kernel (dek) mutant collection with the potential to unc

100 thalamus were significant using multi-level kernel density analysis (p(corrected) < 0.05) but nonsig

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

102 We use a kernel density estimate to combine 29 U-Pb ages into a s

103 d localized signals by utilizing clusterwise kernel density estimates (KDEs) of peak distributions.

104 We used kernel density estimates to compare the distributions of

105 cations among participants (quantified using kernel density estimation).

106 er of qubits than a direct generalization of kernels described in the literature.

107 of amyloplast membranes is increased during kernel desiccation.

108 es, PGD1 and PGD2, that are not required for kernel development.

109 d, insect-damaged, and shrunken and immature kernels) did not provide significant changes on starch p

110 framework, based on the electron dose point kernel (DPK) method, by combining Geant4 MC simulations

111 nd arbitrary side information in the form of kernels (e.g. from protein-protein interaction networks)

112 084813 filed on 17th December 2015) on wheat kernel endosperm morphology and gluten protein structure

113 pendent time series data using nonparametric kernel estimated long-run variance.

114 The other method, a quantum kernel estimator, estimates the kernel function on the q

115 In this hybrid approach, kernel evaluations are outsourced to projective measurem

116 y starch isolated from shrunken and immature kernels exhibited reduced extractability.

117 Starch obtained from germinated kernels exhibited the greatest solubility.

118 The deployed kernel exhibits exponentially better scaling in the requ

119 t has been confirmed in this study that seed kernel exhibits more phenolic compounds with bioactive p

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

121 sole environmental variable during Zea mays kernel-fill, from 12 days after pollination to maturity.

122 d using nonlinear support vector machine and kernel Fisher discriminant analysis.

123 The microwave heating of wheat kernels, flour, and gluten, has attracted attention late

124 nsity of data points on MDiMs using Gaussian kernels followed by a curve fitting with an adapted vers

125 er Complex (preBotC) neurons, which form the kernel for inspiratory rhythm generation, directly modul

126 sequences are related to a powerful subtree kernel for unlabeled graph classification.

127 ose using approximate simulations to build a kernel for use in kernelized machine learning methods, s

128 We find the corresponding collision kernel [Formula: see text], where l, A, and [Formula: se

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

130 Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculatio

131 However, seed kernel from 'Osteen' variety showed higher amounts of he

132 enolic composition by HPLC-DAD-Q-ToF-MS seed kernel from different cultivars ('Keitt', 'Kent'and 'Ost

133 h, the highest concentration was followed by kernels from the middle and then basal kernels.

134 Kernels from the tip-end had highest zeaxanthin and TC i

135 maturity stages, and the above variables in kernels from the tip-end, middle and base of the cobs we

136 d, a quantum kernel estimator, estimates the kernel function on the quantum computer and optimizes a

137 -expression network dynamics, and the use of kernel function to measure the similarity between subjec

138 hen the feature space becomes large, and the kernel functions become computationally expensive to est

139 put-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tac

140 t also their interactions via using multiple kernel functions.

141 to 28 DAP) and continued to increase as the kernels further matured (>28 DAP).

142 Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive mo

143 N stored in stems than in leaves to support kernel growth.

144 e (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the

145 d semi-parametric kernel models (Reproducing Kernel Hilbert space).

146 detection experiment-we show how this decay kernel improves the model's predictive performance for s

147 vide a formal expression for the integration kernel in divisive normalization, and show that divisive

148 ecahedron with the truncated homogold Au(49) kernel in similar-sized gold nanoparticles provides for

149 tor regressors with linear kernel and radial kernel in terms of accurate predictions.

150 ed inputs, replication of multiple identical kernels in memristor arrays was demonstrated for process

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

152 g N50 of 15.78 megabases) of the maize small-kernel inbred line, which is derived from a tropical lan

153 Mango seed kernel is a by-product which is usually discarded.

154 The Ag(53) kernel is an Ag(13) Ino decahedron encaged by an Ag(40)

155 can produce misleading results when the gkm kernel is combined with nonlinear kernels.

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

157 eates vitreous endosperm in the mature maize kernel is poorly understood.

158 hat the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, su

159 The M(7) kernel is then enclosed by a second shell of homogold (A

160 o demonstrate the performance of the package kernel isotopic niches in r (rKIN) under various scenari

161 Methods: Multiple-kernel k-means clustering was applied to 100 clinical, p

162 Multiple kernel learning (MKL) is well suited for the integration

163 this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step

164 Multiple kernel learning coupled with modern optimization techniq

165 We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alte

166 etworks and then employs a Bayesian multiple kernel learning method to predict exposure response.

167 ombining clustering structures as a multiple kernel learning problem, in which different datasets eac

168 with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear st

169 port vector machine and single-task multiple kernel learning) in terms of predictive power.

170 non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for t

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

172 ntly performs these two steps using multiple kernel learning.

173 umber of spikelets per spike, kernel weight, kernel length, and test weight.

174 Using statistical methods and Kernel Logistic Regression (KLR) models, this data was u

175 s are located in the inner 7-atom decahedral kernel (M(7), M = Au/Cu).

176 Constructed under a kernel machine framework, POINT performs local associati

177 ncipal component analysis (PCA) and Bayesian kernel machine regression (BKMR) to identify the main pa

178 Multivariable linear regression and Bayesian kernel machine regression (BKMR) were used to estimate a

179 Covariate-adjusted Bayesian kernel machine regression was used to investigate metal

180 Using Bayesian kernel machine regression, we found that higher exposure

181 sociation Test (CKAT), a flexible and robust kernel machine-based approach to jointly test the geneti

182 However, the complexity of the kernel makes it difficult to understand intercompartment

183 on of anthocyanin components in complex corn-kernel matrices.

184 rom purple Peruvian maize, and the effect of kernel maturity on anthocyanin accumulation.

185 or compared to the influence of genotype and kernel maturity.

186 evolutionarily conserved network motifs, or kernels, may function throughout development to stabiliz

187 feedstock or even as edible oil and the seed kernel meal as animal feed ingredient.

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

189 te pathway is a heat-sensitive step in maize kernel metabolism that contributes to yield loss during

190 rlying dynamics through the use of nonlinear kernel methods for machine learning and dimension reduct

191 Kernel methods for machine learning are ubiquitous in pa

192 e techniques, including k-nearest neighbors, kernel methods, and multiscale analysis.

193 e artificial CNV loci as required in current kernel methods.

194 is end, we proposed three distance dispersal kernels model: 1) exponential-short-range dispersal, 2)

195 t and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space).

196 The proposed multi-kernel models that combined physiological and genomic da

197 , we identified two genes of maize defective kernel mutants with newly developed analysis pipelines t

198 erine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 (KNR6) determines pistillate flor

199 ghttime-temperature conditions by increasing kernel number.

200 horylation by KNR6 may affect ear length and kernel number.

201 ffect of honey bees on fruit set, yield, and kernel nutritional quality at tree level.

202 hages treated with extracts from the coat or kernel of glanded and glandless cottonseed.

203 stical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accu

204 The kernel of the package features an efficient algorithm fo

205 Nevertheless, the internal layers of the kernel of this cereal are characterized on average by hi

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

207 microwave treatment of hydrated durum wheat kernels of two different cultivars (cv Aureo and Sfinge)

208 study are decisive in adoption of A. boehmii kernel oil as an alternative source of edible oil.

209 composition and toxicity test of A. boehmii kernel oil from two sites in eastern Burundi.

210 icity test reveals no toxicity of A. boehmii kernel oil.

211 based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Reg

212 CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying b

213 different slice thicknesses, reconstruction kernels or timings after contrast material administratio

214 Implementing these kernels, our setup delivers viable decision boundaries f

215 ection of correlation changes sign for other kernel parameter values.

216 We present a multi-kernel penalized linear mixed model with adaptive lasso

217 oth species, and in particular for the sweet kernel phenotype, a key agronomic and domestication char

218 GD2 transgenes complement the pgd3-defective kernel phenotype, indicating the fusion proteins are tar

219 rm lysine content, but it causes an inferior kernel phenotype.

220 ns, creating air spaces that cause an opaque kernel phenotype.

221 mpared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased pr

222 unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yie

223 Influence of kernel position on zeaxanthin, lutein, total carotenoid

224 The current trial indicates that, although kernel position within a cob can significantly affect th

225 arameters were not significantly affected by kernel position.

226 Carbon discrimination (Delta(13)C) in kernels presented a negative relationship with WUEp, at

227 equally over time (that is, the 'integration kernel'), previous trial effects and an overall bias.

228 MKL also provides an automated scheme for kernel prioritization and parameter tuning.

229 red-grain wheats were used for extrusion and kernel puffing.

230 arium-damaged rachis rate, Fusarium-diseased kernel rate and DON content in harvested kernels, while

231 erent mixture models, using Bayesian machine kernel regression (BKMR).

232 ify loci underlying the variation in nut and kernel related traits using the new Axiom J. regia 700K

233 5 SNPs significantly associated with nut and kernel-related traits.

234 HIP includes a new kernel renormalization reducing boundary effects associa

235 ss N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants.

236 mg/kg and 7030 mg/kg in walnut and pistachio kernels respectively, with a corresponding strong radica

237 and total phenolics, which were assessed in kernels, revealed differences between the two cultivatio

238 The Cu-SR Penrose tiling retains the M(54) kernel's high symmetry (D(5h)).

239 While radiation dose, kernel setting, and denoising level did not influence VN

240 raphy dose indices (CTDIvol 10, 15, 20 mGy), kernel settings (soft, standard, sharp), and denoising l

241 While flour from moldy kernels showed 7.5 ppb of aflatoxin A1, 25.0 ppb of afla

242 S), the disease Gaussian interaction profile kernel similarity (DISGS), and the lncRNA-disease intera

243 FS), the lncRNA Gaussian interaction profile kernel similarity (LNCGS), the disease Gaussian interact

244 d circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-dis

245 demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex sy

246 luable alternative to defective yellow maize kernels, since the presence of the evaluated kernel defe

247 number of spikelets per spike and decreased kernel size.

248 A kernel-smoothed likelihood method is developed to estima

249 rategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that co

250 est benefit the classification scheme in the kernel space.

251 he parts and finally, we employ a novel node kernel suitable for graphs with typed edges.

252 le insights into the respective roles of the kernel, surface staples, and the type of ligands on cata

253 tructure comprised of an all-metallic Ag(53) kernel surrounded by an Ag(25) discontinuous metal-organ

254 Results obtained using linear and non-linear kernel SVM models yielded sensitivities of over 87 and 9

255 an order of magnitude lower runtime than the kernel SVM.

256 pheral processing were assessed using Wiener-kernel (system identification) analyses of single AN fib

257 ected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic

258 In maize kernels, the endosperm and the embryo are the main sites

259 ylated and succinylated anthocyanins in corn kernels; these compounds were found to be artefact pigme

260 creased with higher roasting temperatures in kernels, thin and thick flakes.

261 ny genes are identified in both the leaf and kernel tissues, pointing to the biological significance

262 tion from Au(22) with a 10-atom bioctahedral kernel to Au(22)Cd(1) with a 13-atom cuboctahedral kerne

263 ge of the large size of the maize (Zea mays) kernel to characterize genome-wide expression profiles o

264 iased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypoth

265 centration, reducing the geometric collision kernel to the clearance rate, which we model accurately

266 sults of the present study reveal A. boehmii kernels to be an oil-rich source, yielding up to 29% of

267 (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from

268 the first successful demonstration of graph kernels to protein interfaces for effective discriminati

269 and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects.

270 d from a small spot at the stigma end of the kernel, to gradually spreading over the entire kernel.

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

272 absorption of folic acid into the brown rice kernel up to 5.195 x 10(4) mug/100 g, a 1,982-fold incre

273 t the presence of undigested fibres and corn kernels using a deep learning approach.

274 ipse area (SEA), and add novel metrics using kernel utilization density (KUD) estimators to measure i

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

276 tudies provide insight for breeding vitreous kernel varieties and high vitamin A content in maize.

277 x (SBW), sorghum DDGS wax (SDW), and sorghum kernel wax (SKW), as an oleogelator.

278 Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between

279 To select the appropriate dispersal kernel we used the human case data and adopted a model s

280 mize the application range of the deployable kernels, we optimize feature maps towards the resulting

281 tion in grain-filling duration by 3.33 days, kernel weight by 1.25% per degrees C, grain number by 2.

282 with QTL for number of spikelets per spike, kernel weight, kernel length, and test weight.

283 d via the disease incidence severity and 100-kernel weight, was strongly correlated (r > 0.78, p < 0.

284 effect quantitative trait locus controlling kernel weight-a key trait selected during maize improvem

285 5% increase in the thousand kernel weight.

286 Similar findings were observed in triangular kernel-weighted models (OR, 1.38 [95% CI, 1.34 to 1.44];

287 crostructures of the ultrasound-treated oats kernel were investigated using Environmental Scanning El

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

289 ties of the isolated starches from defective kernels were determined, which does not impair its use i

290 Peanut kernels were treated by Alcalase, papain, Neutrase and b

291 th individual differences in the integration kernel, while trial-by-trial fluctuations in pupil respo

292 sed kernel rate and DON content in harvested kernels, while silencing WFhb1-1 in Qfhb1-carrier wheat

293 LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs).

294 However, a small minority of kernels with a mutant endosperm carry a nonconcordant no

295 Mutations that arise at male meiosis produce kernels with concordant mutant phenotypes in both endosp

296 y properties of starches isolated from maize kernels with different types of defects.

297 ion to parallel convolutions using different kernels with shared inputs, replication of multiple iden

298 Gapped k-mer kernels with support vector machines (gkm-SVMs) have ach

299 e the key to increasing oil content in maize kernels without affecting yield.

300 ees, which translated into a 20% increase in kernel yield.