ライフサイエンスコーパス: CPU

1 results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for an

2 been reported, but it used more than 150,000 CPU hours and weeks of wall-clock time.

3 ase Control Consortium, RAPID ran in about 1 CPU-hour per dataset, and identified many significant in

4 ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into a

5 with ~300 amino acids, using a maximum of 10 CPU cores in parallel in our cluster system.

6 assign ancestry from 32 populations using 10 CPU).

7 round half an hour on a small server with 10 CPUs to access genotypes of approximately 60 million var

8 from 900 individuals takes about 5 h with 10 CPUs.

9 entire fish genomes (470 and 217 Mb) in 120 CPU hours using 15 processors on a single machine.

10 for a typical sample vary between 0.7 and 14 CPU hours per sample.

11 genome, whereas Elmeri required less than 15 CPU hours and improved the quality of the Rmaps by more

12 sks achieving a reduction up to 30% for a 15 CPUs machine.

13 e projected computation time of ADP was 1628 CPU hours.

14 STX) range mostly from 4 to 10 (resp. With 2 CPU threads and 2 GPUs, H-BLAST can be faster than 16-th

15 AP and OADP required 0.82 and 21 CPU hours, respectively, while the projected computation

16 lPhiPKa and can run a single job on up to 24 CPUs.

17 igh computational demands (currently ~30-250 CPU hours per sample) remain a significant challenge to

18 ,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking.

19 Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE seg

20 ring of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unas

21 ncing sample in 6.5 h using a system with 36 CPU cores.

22 lly reduce wallclock time from 27 days on 40 CPUs to a single day using 4104 tasks, each task utilizi

23 ty to create computing clusters with 16-480+ CPUs.

24 ical alignments take only a median time of 5 CPU seconds in a single R12000 processor.

25 ermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime

26 g arrays to the entire genome in less than 6 CPU hours.

27 ted fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated te

28 More specifically, cOMet required 9.9 CPU days to error correct Rmap data generated from the h

29 A CPU located in the emergency department can be a safe, e

30 7x, and 2.1x speedup over KSW2, Edlib, and a CPU implementation of GenASM, respectively.

31 modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparativ

32 -fold speedup compared to a single core of a CPU can be achieved for a network of one million conduct

33 The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-

34 safety, efficacy, and cost of admission to a CPU as compared with those of regular hospital admission

35 ts with no ischemic ECG changes triaged to a CPU were randomized to CA (n = 123) or ETT (n = 125).

36 es order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.

37 thly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-eff

38 vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvi

39 ral fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customiza

40 add-on required relatively little additional CPU time.

41 fering state of the art ratios at affordable CPU costs.

42 his paper, we design and implement a new-age CPU-GPU HPC framework, called GiCOPS, for efficient and

43 Analogous CPU elements may be found in other receptors and signali

44 flat image sensor array, memory device, and CPU) in conjunction with complicated optics should captu

45 RABiTPy supports GPU and CPU processing as well as cloud computing.

46 y, we developed a freely accessible, GPU and CPU-powered dashboard that combines interactive visual a

47 ures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster run

48 computing instances with varying memory and CPU on multiple cloud platforms.

49 d does not demand a lot of system memory and CPU resources.

50 entials between GPU assisted performance and CPU executions as the computational load increases for h

51 TK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and

52 es in hardware architecture between GPUs and CPUs complicate the porting of existing code.

53 ptimized for parallel processing on GPUs and CPUs.

54 actor IX-deficient plasma with specific anti-CPU antibodies prevents the increased resistance to fibr

55 lecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate.

56 hardware implementations on state-of-the-art CPU/ReRAM.

57 icity in the detection of splices as well as CPU and memory efficiency.

58 including Python, Matlab and Java as well as CPU versus GPU implementations.

59 extensions are limited only by the available CPU.

60 oactive data analysis by utilizing available CPU power from the server to automate the analysis proce

61 aster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us

62 (1)), a modest 4-bit CPU (central processing unit) with 2,300 transistors fab

63 ixel array and partly in a separate on-board CPU/accelerator.

64 ssue types, but existing approaches are both CPU and memory-intensive, limiting their application to

65 rithm is asymptotically optimal O(N) in both CPU time and required memory, and application to the ace

66 called MorphOT, which allows the use of both CPU or GPU resources.

67 all supported hardware types (including both CPUs and GPUs) and perform well on all of them.

68 Rats with hippocampus, medial caudoputamen (CPU), lateral CPU, or control lesions were trained on de

69 alignments ten times faster than comparable CPU-only alignment software.

70 bootstrap resampling and only costs computer CPU time.

71 Furthermore, we built a dual-core CPU combining two orthogonal core processors in a single

72 In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5x speed-up over the

73 was commissioned in 2010, yet its eight-core CPUs with only 24GB RAM work well in 2017 for these dual

74 timized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration atte

75 ol for a heterogeneous computer that couples CPUs and GPUs, to accelerate BLASTX and BLASTP-basic too

76 sequence identity was accomplished in 2 days CPU time, and the removal of fragments and close similar

77 the impact of using hardware with different CPU and GPU features on the power consumption and latenc

78 Cu-DOU-CPU demonstrates the highest reported mechanical perform

79 Meanwhile, the Cu-DOU-CPU spontaneously self-heals at room temperature with an

80 complex-based polyurethane elastomer (Cu-DOU-CPU) with synergetic triple dynamic bonds is developed.

81 stretchable circuit constructed from Cu-DOU-CPU.

82 he feedback, DRCA learns to create a dynamic CPU resource schedule while taking several network state

83 heduling in BBU, this paper achieves dynamic CPU resource scheduling in BBUs by proposing Deep Reinfo

84 ing a million-fold speedup over an efficient CPU implementation.

85 mation is derived which allows for efficient CPU processing times.

86 using 4104 tasks, each task utilizing eight CPUs and taking less than 7 minutes to complete.

87 , comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields u

88 s and epistasis networks, and for estimating CPU time and disk space requirements.

89 s in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimi

90 Contrary to the existing fixed CPU scheduling in BBU, this paper achieves dynamic CPU r

91 or parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermedi

92 dividual small tasks tempers competition for CPU time in the shared HPC environment, and jobs submitt

93 s 3D models of proteins without the need for CPU intensive structural alignments by utilizing the Q m

94 e Smith-Waterman algorithm are available for CPUs.

95 water, seawater, and leaching solutions from CPUs.

96 biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interp

97 of over 100 beads in real time on a generic CPU.

98 a laptop with Intel Core i5-2500K @ 3.2 Ghz CPU and 8GB of RAM) our predictions can inform and reduc

99 s takes only a couple of hours (on a 1.2 GHz CPU, 1 GB RAM machine) to run on a dataset 28 Mb of barl

100 s with five threads on Intel Xeon E5 2.6 GHz CPU.

101 ns are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects

102 of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching,

103 ed implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes

104 htly improved results and very much improved CPU/memory performance on large datasets.

105 h, however, requires very little increase in CPU time.

106 RAL-MP code scales very well with increasing CPU cores, and its GPU version, implemented in OpenCL, c

107 oximately equal to the number of independent CPUs operating on the data.

108 mmissioned and approved by the AGA Institute CPU Committee and the AGA Governing Board to provide tim

109 ning 100k contigs took about 4 h on 10 Intel CPU Cores (2.4 GHz), with a memory peak at 27 GB (see Su

110 % the energy of a traditional high end Intel CPU.

111 ormula: see text] speed improvement over its CPU-only predecessor, HiCOPS, and over 10[Formula: see t

112 x (>12x on average), respectively, with its CPU implementation, and by up to 413x and 689 x (>400x o

113 9x (>12x on average), respectively, with its CPU implementation, and by up to 413x and 689x (>400x on

114 RMSD calculations using a laptop CPU are 60x faster than qcprot and 3x faster than curren

115 pocampus, medial caudoputamen (CPU), lateral CPU, or control lesions were trained on declarative and

116 Rats with lateral CPU lesions were not impaired on either version of the t

117 Allele elimination requires less CPU time and memory, but does not always eliminate all i

118 eins in the size range of 10-25 kDa the less CPU intensive restrained Rosetta refinement protocols pr

119 microenvironmental effects takes very little CPU time, the computational speed of the SCP formulation

120 Benchmarking indicates that SeqLib has lower CPU and memory requirements than leading C ++ sequence a

121 f atoms, providing nearly order-of-magnitude CPU time speedups.

122 , but may require considerable time and many CPUs.

123 Medial CPU lesions impaired rats' ability to learn the procedur

124 est a double dissociation between the medial CPU and hippocampus in processing egocentric-procedural

125 Median CPU time for ortholog prediction per gene by OrthoReD ex

126 We analyse the memory, CPU, I/O usage and file sizes used by Gap5.

127 t networks of several thousand genes in mere CPU seconds on a desktop workstation.

128 stributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid).

129 on Multiple Data (SIMD) operations on modern CPUs for speed.

130 ies, while a low-memory footprint and modest CPU requirements allow it to operate on a personal compu

131 memory per computational thread and 15x more CPU time than Beagle.

132 sometimes better, than popular and much more CPU-intensive methods for discrimination, including lass

133 upled with computers that provide 40 or more CPU threads and multiple GPU (general-purpose graphics p

134 ull MD simulations require 200 times as much CPU time as the implicit water LD simulations.

135 The system can run on multi-CPU architectures including SMP and PVM.

136 The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation

137 and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based

138 the compute capabilities of common multicore CPU clusters.

139 ltiview image fusion optimized for multicore CPU architectures, reducing image data size 30-500-fold;

140 On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP a

141 ony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude

142 L-MP can take advantage of not just multiple CPU cores but also one or several graphics processing un

143 can be effectively parallelized on multiple CPU cores.

144 mposite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation e

145 Unix-based desktops or servers with multiple CPUs.

146 mpute node with two multi-core Intel Nehalem CPUs, from approximately 17 h to approximately 11 min.

147 ence-free tools, Nubeam-dedup uses 50-70% of CPU time and 10-15% of RAM.

148 pression rates, or require a great amount of CPU time for decompression and loading every time the da

149 or which they require prohibitive amounts of CPU-time and memory.

150 ntation of eFAST amounted to several days of CPU time for the complex ABM.

151 onal challenge for search, requiring days of CPU time to annotate an organism's proteome.

152 ulin, presumably by increasing the degree of CPU activation produced by the low levels of thrombin ge

153 e on-pathway intermediate, and the demand of CPU power is moderate.

154 ese additions enhance the rate and extent of CPU activation: in the case of factor IX, presumably by

155 ng with Langevin dynamics required 2-10 h of CPU time on average with a single AMD Athlon MP 2800+ pr

156 average, this requires approximately 29 h of CPU time per sequence.

157 two weeks, a day and a half, and an hour of CPU time, respectively.

158 overall memory usage but also the number of CPU operations per alignment.

159 We implement co-parallelization of CPU-GPU processing, which leads to a significant reducti

160 If we solved the problem of CPU-time required to apply AGAPE on millions of proteins

161 ightning-fast, consuming only few seconds of CPU time to generate fragment library for a protein of t

162 f the Markov Chain is short both in terms of CPU times and number of proposals.

163 of variations (CV), and the shortest time of CPU usage.

164 ed suffix arrays, EMSAR minimizes the use of CPU time and memory while achieving accuracy comparable

165 nds necessitate the efficient utilization of CPU resources for increased RAN performance.

166 ets with 30 taxa or more after many weeks of CPU runtime.

167 hat rivals the speed achieved by hundreds of CPUs.

168 firm that REST greatly reduces the number of CPUs required by regular replica exchange and increases

169 ng 3D model quality; however, they are often CPU intensive as they carry out multiple structural alig

170 Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements.

171 ompared to single-core CPM code execution on CPU.

172 17 M (for 03 classes), yielding 19.82 FPS on CPU and 408.25 FPS on GPU in our setup.

173 This leads to increasing strain on CPU resources and decreasing density of first-hand annot

174 run tens to hundreds of times faster than on CPU.

175 al cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON's ef

176 makes SneakySnake efficient to implement on CPUs, GPUs and FPGAs.

177 makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs.

178 list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

179 yper takes 1.5 h to genotype a genome on one CPU.

180 Retention of ECs seeded onto CPU precoated with SMCs was significantly improved by a

181 ear 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the

182 ATI 5870 GPU, on average, than the original CPU single-threaded implementation on an AMD Phenom II 8

183 es ABEA performance compared to the original CPU-based implementation in Nanopolish as well as the st

184 speedup on a single MIC against the original CPU-based implementation.

185 ning highly parallel programs and outperform CPUs in terms of raw computing power.

186 e training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively.

187 hances processing speed 10- to 100-fold over CPU processing.

188 However, peak CPU temperatures were high.

189 and decodes up to 420 million genotypes per CPU second.

190 d processes 30 million name-strings/hour per CPU thread.

191 ystems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-S

192 tes significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit m

193 t in many fold higher speedups over previous CPU implementations.

194 riventricular nucleus (Pe), caudate putamen (CPU) and the ependymal lining of the ventricles.

195 n was first detected in the caudate-putamen (CPU) at e12.5, and by e15.5, activity had not only incre

196 The optimized sampling also reduces CPU time across algorithms by 1.3-22.4% due to less vari

197 ib structure of some populations and reduces CPU time.

198 eduling in BBUs by proposing Deep Reinforced CPU Allocation (DRCA) framework within RAN intelligent c

199 meters were optimized to reduce the required CPU time to approximately 17 min, while retaining TASSER

200 ver, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations.

201 nt to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular

202 orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an op

203 s a ~40X speedup when compared with BEAGLE's CPU implementation on a dual Xeon 5520 and 3X speedup ve

204 ata processing is done locally by the user's CPU to ensure the security of patient data.

205 sers to process multiple samples on the same CPU.

206 ess files up to 16 GB, with linearly scaling CPU-times.

207 f the CS algorithms studied, including SeSCI(CPU), two-step iterative shrinkage/thresholding (TwIST),

208 node comparison against a corresponding SIMD CPU implementation.

209 terman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods

210 r than comparable tools, even using a single CPU core, and efficiently and robustly scores the potent

211 PS-based implementations running on a single CPU core.

212 undreds of hours of running time on a single CPU even for the fastest known implementations.

213 a 700-fold speedup with respect to a single CPU implementation.

214 Using a single CPU Roary can produce a pan genome consisting of 1000 is

215 I finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would t

216 sequences at a rate of 3.5 Mb/s on a single CPU.

217 source focal mechanism reliably on a single CPU.

218 s using less than 239 MB of RAM and a single CPU.

219 runtime is attained when compared to single CPU implementations.

220 MoTeX-II comes in three flavors: a standard CPU version; an OpenMP-based version; and an MPI-based v

221 a dramatically lower cost than the standard CPU-based implementations.

222 pular Java application that runs on standard CPUs (Central Processing Units).

223 datasets compared to the conventional static CPU allocation, highlighting the efficacy of DRCA framew

224 istakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

225 ome somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficie

226 ivers 6x faster single-protein searches than CPU methods on 2 x 64 cores, speeds previously requiring

227 The CPU implementation of this approach makes the calculatio

228 The CPU was managed by the emergency department staff.

229 p, and underwent internal peer review by the CPU Committee and external peer review through standard

230 Review underwent internal peer review by the CPU Committee and external peer review through the stand

231 Finally, the CPU-GPU methods and optimizations proposed in our work f

232 s between the two groups (odds ratio for the CPU group as compared with the hospital-admission group,

233 me was 65 times and 69 times shorter for the CPU-based and GPU-based CNN pipelines (216.6 seconds +/-

234 However, the CPU-intensive nature of document comparison has limited

235 edial habenula, and medulla and at p1 in the CPU at levels noticeably less than those of the MOR.

236 heart failure), and the 212 patients in the CPU group had 7 events (5 myocardial infarctions, 1 deat

237 In low-risk patients in the CPU, a strategy of CA detects more CAD than ETT, reduces

238 rownian simulation, but at a fraction of the CPU time (10(-4) to 10(-3), depending on the model).

239 usands of conditions in a few minutes of the CPU time on a desktop computer.

240 -Skim uses <4% of the k-mers and <10% of the CPU time required by Sailfish.

241 ized and parallelized across 16 cores on the CPU.

242 0.4x, 6.8x, 12.6x, and 5.9x speedup over the CPU version of Scrooge, KSW2, Edlib, Darwin-GPU, and a G

243 erage throughput speedup of 10.05 x over the CPU-only implementation, an average 1.81 x speedup over

244 For long reads, the CPU version of Scrooge achieves a 20.1x, 1.7x, and 2.1x

245 ethod allowed us to significantly reduce the CPU time required to cluster these large compound librar

246 y, this method has significantly reduced the CPU time for modelling.

247 This method reduces the CPU time required for calculating thermodynamic averages

248 For long sequences, the CPU implementation of SneakySnake accelerates Parasail a

249 For long sequences, the CPU implementation of SneakySnake accelerates Parasail a

250 l admission than among those assigned to the CPU (P<0.01 by the rank-sum test).

251 the cardiology service) or admission to the CPU (where patients were cared for according to a strict

252 ong the 97 patients who were assigned to the CPU and discharged.

253 tructure prediction accuracy compared to the CPU version of the I-TASSER.

254 the device remains operational even when the CPU is overheated.

255 This CPU Expert Review underwent internal peer review by the

256 This CPU Expert Review was commissioned and approved by the A

257 The purpose of this CPU Expert Review is to provide clinicians with guidance

258 ever, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU

259 When compared with naive, single-threaded CPU implementations, the GPU yields a large improvement

260 llumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets.

261 tivated by the observation that, compared to CPUs and GPUs, cutting-edge FPGAs demonstrate-in certain

262 d ~ 5-10-fold reductions in cost relative to CPUs.

263 using various configurations of traditional CPU computing infrastructures, Graphics Processing Units

264 ame computing environments where traditional CPU-based analyses are convenient, the second module may

265 iated in part by plasma carboxypeptidase-U ([CPU] carboxypeptidase-R, procarboxypeptidase-B, thrombin

266 admission to a chest-pain observation unit (CPU) located in the emergency department for such patien

267 in low-risk patients in the chest pain unit (CPU) to reduce repeat emergency department (ED) visits a

268 essors, both in the central processing unit (CPU) and Graphics processing unit processor markets, ena

269 tilizes a simulated central processing unit (CPU) as the heat source.

270 essors are based on central processing unit (CPU) platform, which might be inefficient and expensive

271 niques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hun

272 unction as a simple central processing unit (CPU) that senses multiple input signals, integrates thes

273 run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes o

274 ee times as fast in central processing unit (CPU) time compared with a purely molecular dynamics (MD)

275 fold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasin

276 temperature of the central processing unit (CPU), allowing for highly efficient PCR.

277 ion of the NRM on a central processing unit (CPU).

278 f biocomputers use central processing units (CPUs) assembled from multiple protein-based gene switche

279 create "biological central processing units (CPUs)" with multiple BSC elements, capable of processing

280 on (AGA) Institute Clinical Practice Update (CPU) aims to review the available evidence and provide e

281 Association (AGA) Clinical Practice Update (CPU) Expert Review is to provide best practice advice fo

282 ion (AGA) issued a clinical practice update (CPU) focusing on endoscopic screening and surveillance o

283 Association (AGA) Clinical Practice Update (CPU) is to describe the various techniques for endoscopi

284 Association (AGA) Clinical Practice Update (CPU) is to provide best practice advice statements, prim

285 Association (AGA) Clinical Practice Update (CPU) is to provide best practice advice statements, prim

286 on (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide exp

287 on (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide exp

288 on (AGA) Institute Clinical Practice Update (CPU) is to summarize the available evidence and offer ex

289 on (AGA) Institute Clinical Practice Update (CPU) is to summarize the available evidence and offer ex

290 d of compliant poly(carbonate-urea)urethane (CPU), incorporated with human smooth muscle cells (SMCs)

291 s defined as 'chronic persistent urticaria' (CPU), while the presence of urticaria for 2-4 days a wee

292 echanism for better efficiency among various CPUs and GPUs combinations.

293 ed out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.

294 and run the searches on thousands of virtual CPUs (if desired), deleting resources when it is done.

295 ion power donated from over 20,000 volunteer CPUs, FALCON@home shows a throughput as high as processi

296 n revealed that 14 of 58 patients (24%) with CPU and one of 10 patients with CRU (10%) were aspirin h

297 llers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensi

298 ferent m values, and storage complexity with CPU utilization.

299 SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processin

300 nts with CRU compared with the patients with CPU (P < 0.016, P = 0.024, respectively).

301 In a modern machine (2 Intel Xeon X5650 CPUs, 48 GB memory), when fast turn-around is needed, Se

302 implementations have been developed for x86 CPUs, most are embedded into larger database search tool