dataesr/scientific-paragraphs-categorization huggingface dataset:
percentage (~38%) of genes matching to non-supervised orthologous groups were classified involving in metabolism (categories C, E, F, G, H, I, P, Q) with ~8% of genes related to the carbohydrate transport and metabolism. As shown in Fig. 3 , although the majority of predicted ORFs were related to the membrane transport, this analysis confirmed that many genes matching to KEGG database (~12%) originated from pathways involved in the carbohydrate metabolism.
Inventory of the detected Carbohydrate-Active Enzymes families and putative plant-polysaccharides-targeting Glycoside Hydrolases. In order to identify putative genes and enzymes involved in breakdown, biosynthesis or modification of carbohydrates, the total predicted ORFs in the three investigated biomass samples were compared to the entries of the Carbohydrate-Active Enzymes (CAZymes) database. A total of 1792, 1279 and 2113 putative CAZymes were identified in the samples T3ADSB (from A. donax after 135 days of natural biodegradation in underwood), T3ESB (from E. camaldulensis after 135 days of natural biodegradation in underwood) and T3PSB (from P. nigra after 135 days of natural biodegradation in underwood) respectively, corresponding to 1. 2%, 0. 6% and 3. 4% of the total ORFs (Table  3 ).
In fact, this endophytic fungus is able to secrete xylanases and cellulases also in salt stress conditions  55  as well as produce a considerable amount of ligninolytic enzymes such as laccase  56. However, Sporothrix, Fusarium, Nectria and Trichoderma dominated the eukaryotic biodiversity related to GHs in A. donax and E. camaldulensis biomasses. These Ascomycota are known for their ability to produce cellulolytic enzymes  57, 58  and comprise many species involved in the degradation of recalcitrant substances such as cellulose, hemicellulose, pectin, and lignin  59. Jurado et al. 60  reported that fungi belonged to Ascomycota group were ubiquitous throughout the whole lignocellulose-based composting process. The functional clustering of the predicted ORFs to eggNOG and KEGG databases showed high similarity among the three analyzed samples.
The prevalence of poorly characterized genes obtained by matching to eggNOG categories suggested the three detected biomasses as potential sources of not yet known genes. Moreover, the analysis of functional classification distribution among these three metagenomes, based on both the eggNOG and KEGG database, suggests that a large number of predicted genes were putatively associated with formation, breakdown and interconversion of polysaccharides. In particular, the relative abundance of genes linked to carbohydrates metabolism pathway was higher than or similar to that detected in metagenomes from samples with well-known lignocellulose-degrading ability, such as invasive snail crop microbiome  61  and lower termite Coptotermes gestroi gut  62. This result confirmed the high potentiality of the three analyzed metagenomes to express genes involved in lignocellulosic biomasses biotransformation.
However, most of CAZymes detected in the three samples were related to putative plant-polysaccharidestargeting GHs. Based on the results obtained by Li et al. 63  analyzing 46 finished metagenomic studies collected in Genomes OnLine Database (GOLD) by comparison against the CAZy sequences for homologues of glycosyl hydrolases using an e-value < 10 -40 as a cut-off threshold, the percentages of detected GHs in our study were higher than those present in metagenomic samples from soil, sludge and marine or lake environments. Furthermore, the diversity of GH family enzymes detected in the three samples was greater than that observed in insect or mammalian fecal and gut samples with high lignocellulose-degrading potentiality  64  , in line with the detected high phylogenetic diversity. The putative genes encoding proteins involved in the degradation of plant polysaccharides were detected in the three samples.
Moreover, accepted that the obtained data are sensitive to the bioinformatics workflow used in the different studies, a comparison between the GHs detected in our samples and in metagenomes well known as reservoirs of genes involved in lignocellulose-degradation was attempted (Table  5 ), based on the classification provided by Allgaier et al. 65. The detection and assignment of glycoside hydrolases in our metagenomes and bovine rumen metagenome  66  were performed by BLAST-based procedures against the CAZy database, whilst the searches for glycoside hydrolases in metagenomes from six years old elephant feces  64  , yak  67  and cow rumen  66  , snail crop  61  , macropod gut  68  and termite hindgut  19  were performed by using HMMER hmmsearch with Pfam. The putative ORFs encoding enzymes related to the oligosaccharides degradation represented the majority of the total plant-polysaccharides-targeting GHs and their abundance (~26% for T3ADSB, ~22% for T3ESB and ~24% for T3PSB) was comparable to that detected in samples from cow rumen  20  and termite hindgut  19.
The libraries were then subjected to 151 paired-end sequencing on Illumina HiSeq2000 platform by using TruSeq SBS Kit v3-HS (Illumina, San Diego, CA, USA) following standard pipelines. The generated raw data were trimmed: leading or trailing low quality (below quality 3) or 3 N bases were cut off and reads contaminated by adapter (15 bases overlapped by reads and adapter) or with low quality (20) bases (40% as default, parameter setting at 36 bp) were removed. The data were filtered by using readfq. v5 (unpublished software, BGI). The obtained Clean Data were used to perform the metagenome sequences. Before assembly, k-mer analysis (K-mer length 15) was done to evaluate the sequencing depth for each sample. SOAPdenovo (Version 1. 06)  73  was used to assemble filtered data in contigs and scaffolds and assembly results were optimized by in-house scripts (key parameters: -r 2; -l 35; -M 4; -p 1) using the SOAP-aligner tool.
Metagenome analyses. To evaluate the microbial composition, the assembled contigs were matched against the bacteria, fungi and archaea sequences extracted from NCBI NR database (release-20130408) by BLASTx with 1 × 10 -8 and ≥ 90% identity cut-off. Each contig was subsequently taxonomically assigned by MEGAN version 4. 70. 4  74  , based on lowest common ancestor (LCA). The taxonomic abundance was determined by read count of each taxon, after mapping to the assembled contigs using SOAPaligner version 2. 21  75   Table  5. Comparison of plant polysaccharides hydrolyzing enzymes in our samples T3ADSB, T3ESB and T3PSB and in samples with the highest lignocellulose-degrading potentiality. GHs are grouped according to the major functional roles as classified in Allgaier et al. 62. For each GH family, the total number and the percentage on total GHs detected in the respective sample were shown.
a Searches for glycoside hydrolases were performed by using HMMER hmmsearch with Pfam_Is HMMs (full-length models) to identify complete matches to the family, which were named in accordance with the CAZy nomenclature scheme. b  The detection and assignment of glycoside hydrolases were performed by BLAST-based procedures against the CAZy database. default parameters. Assembled contigs are used to predict genes by using MetaGeneMark Software 76 (version 2. 10, default parameters) based on assembly results. Functional annotations of predicted amino acid sequences were performed by BGI Tech Solutions Co. , Ltd. (Hongkong, China) by using BLASTP (version 2. 2. 23). In particular, the metabolism pathway assignment of the predicted protein was performed using the Enzyme Commission (EC) number in the Kyoto Encyclopedia of Genes and Genomes (KEGG)-version 59-databases  77  and the annotation of each contig with functional categories was carried out by matching against Evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG)-version 3.
0  78. Both comparison were performed by using BLAST  79  with e-value threshold of 1e-5 and a 40% minimum percentage of identity to assign the subject sequence to a specific function family. Moreover, in order to explore in depth the ability of the microbial biodiversity detected in the samples to degrade lignocellulose, the putative encoded protein sequences were first compared to the full length sequences of the CAZy database using BLAST  75  and query sequences that produced a e-value > 10 -6 were discarded. Query sequences that produced an e-value < 10 -6 and aligned over their entire length with a protein in the database with > 50% identity were automatically assigned to the same family as the subject sequence. The remaining query sequences were subjected to manual curation which involved BLAST searches against a library built with partial sequences corresponding to individual GH, PL, CE and CBM modules and examination of the conservation of specific family patterns and features such as catalytic residues (where known).
Acknowledgements This work was supported by grant from the  Ministero dell'Università e della Ricerca Scientifica Industrial Research Project  " Development of green technologies for production of BIOchemicals and their use in preparation and industrial application of POLImeric materials from agricultural biomasses cultivated in a sustainable way in Campania region-BioPoliS "  PON03PE_00107_1/1 , funded in the frame of Operative National Programme Research and Competitiveness 2007-2013 D. D. Prot. n. 713/Ric. 29. 10. 2010.
2014) a Yak rumen (Dai et al. , 2012) a Snail crop (Cardoso et al. , 2012) a Cow rumen (Hess et al. , 2011) a Bovine rumen(Brulc et al. , 2009) b Macropod gut (Pope et al. , 2010) a Termite hindgut (Warnecke et al. , 2007) a with Metagenomic samples Six years old elephant feces CAZy Pfam (Ilmberger et family al. , Endo-Celluases Main known activity domain T3ADSB b T3ESB b T3PSB b GH5 cellulase PF00150 40 3. 8% 15 2. 0% 23 2. 0% 517 4. 7% 1302 3. 5% 36 1. 4% 1451 5. 2% 7 0. 7% 10 1. 8% 56 8. 0% GH6 endoglucanase PF01341 11 1. 0% 5 0. 7% 12 1. 1% 0 0. 0% 0 0. 0% 4 0. 2% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% GH7 endoglucanase PF00840 11 1. 0% 1 0. 1% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 1 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% GH9 endoglucanase PF00759 2 0. 2% 3 0. 4% 5 0. 4% 119 1. 1% 767 2. 0% 15 0. 6% 795 2. 9% 6 0.
6% 0 0. 0% 9 1. 3% GH44 endoglucanase NA 0 0. 0% 0 0. 0% 0 0. 0% 7 0. 1% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 6 0. 9% GH45 endoglucanase PF02015 1 0. 1% 0 0. 0% 0 0. 0% 7 0. 1% 13 0. 0% 0 0. 0% 115 0. 4% 0 0. 0% 0 0. 0% 4 0. 6% GH48 endo-processive cellulase PF02011 1 0. 1% 1 0. 1% 3 0. 3% 0 0. 0% 32 0. 1% 2 0. 1% 3 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% total 66 6. 2% 25 3. 3% 43 3. 8% 650 5. 9% 2114 5. 6% 57 2. 2% 2365 8. 5% 13 1. 4% 10 1. 8% 75 10. 7% Endo-hemicellulases GH8 endo-xylanases PF02011 1 0. 1% 1 0. 1% 7 0. 6% 85 0. 8% 174 0. 5% 46 1. 8% 329 1. 2% 4 0. 4% 1 0. 2% 5 0. 7% GH10 endo-1,4-β xylanase PF00331 15 1. 4% 5 0. 7% 8 0. 7% 258 2. 3% 2664 7. 1% 25 1. 0% 1025 3. 7% 7 0. 7% 11 2. 0% 46 6. 5% GH11 xylanase PF00457 4 0. 4% 2 0. 3% 4 0. 4% 20 0. 2% 244 0. 6% 1 0. 0% 165 0. 6% 1 0. 1% 0 0.
0% 14 2. 0% GH12 endoglucanase & xyloglucan hydrolases PF01670 3 0. 3% 1 0. 1% 3 0. 3% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% GH26 β -mannanase & xylanase PF02156 5 0. 5% 6 0. 8% 10 0. 9% 103 0. 9% 537 1. 4% 11 0. 4% 369 1. 3% 5 0. 5% 5 0. 9% 15 2. 1% GH28 galacturonases PF00295 11 1. 0% 8 1. 1% 20 1. 8% 242 2. 2% 244 0. 6% 69 2. 7% 472 1. 7% 5 0. 5% 2 0. 4% 6 0. 9% GH53 endo-1,4-β -galactanase PF07745 4 0. 4% 1 0. 1% 5 0. 4% 88 0. 8% 1066 2. 8% 9 0. 3% 0 0. 0% 17 1. 8% 9 1. 6% 12 1. 7% total 43 4. 1% 24 3. 2% 57 5. 0% 796 7. 2% 4929 13. 1% 161 6. 2% 2360 8. 5% 39 4. 1% 28 5. 0% 98 13. 9% Debranching enzymes GH51 α -L-arabinofuranosidase NA 14 1. 3% 7 0. 9% 13 1. 1% 239 2. 2% 0 0. 0% 22 0. 8% 0 0. 0% 64 6. 7% 12 2. 2% 18 2. 6% GH54 α -L-arabinofuranosidase PF09206 0 0. 0% 3 0.
4% 0 0. 0% 13 0. 1% 111 0. 3% 0 0. 0% 0 0. 0% 1 0. 1% 0 0. 0% 0 0. 0% GH62 α -L-arabinofuranosidase PF03664 3 0. 3% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 2 0. 1% 1 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% GH67 α -glucuronidase PF07477 PF07488 0 0. 0% 3 0. 4% 8 0. 7% 0 0. 0% 1090 2. 9% 5 0. 2% 120 0. 4% 0 0. 0% 5 0. 9% 10 1. 4% GH78 α -L-rhamnosidase PF05592 0 0. 0% 19 2. 5% 7 0. 6% 413 3. 7% 426 1. 1% 73 2. 8% 1260 4. 5% 34 3. 6% 25 4. 5% 0 0. 0% total 17 1. 6% 32 4. 3% 28 2. 5% 665 6. 0% 1627 4. 3% 102 3. 9% 1381 5. 0% 99 10. 3% 42 7. 5% 28 4. 0% Oligosaccharide-degrading enzymes GH1 β -glucosidase & other β -linked dimers PF00232 26 2. 5% 23 3. 1% 46 4. 0% 103 0. 9% 331 0. 9% 294 11. 4% 253 0. 9% 10 1. 0% 61 11. 0% 22 3. 1% GH2 β -galactosidases & other β -linked dimers PF02836 PF02837 PF00703 59 5. 6% 22 2.
9% 37 3. 3% 917 8. 3% 942 2. 5% 66 2. 5% 1436 5. 2% 186 19. 4% 24 4. 3% 23 3. 3% GH3 mainly β glucosidases PF00933 69 6. 5% 39 5. 2% 70 6. 2% 804 7. 3% 5448 14. 5% 219 8. 5% 2844 10. 2% 176 18. 4% 72 12. 9% 69 9. 8% GH29 α -L-fucosidase PF01120 15 1. 4% 11 1. 5% 9 0. 8% 376 3. 4% 899 2. 4% 70 2. 7% 939 3. 4% 74 7. 7% 2 0. 4% 0 0. 0% GH35 β -galactosidase PF01301 12 1. 1% 7 0. 9% 9 0. 8% 123 1. 1% 468 1. 2% 32 1. 2% 158 0. 6% 12 1. 3% 3 0. 5% 3 0. 4% GH38 α -mannosidase PF01074 PF07748 15 1. 4% 12 1. 6% 22 1. 9% 81 0. 7% 90 0. 2% 18 0. 7% 272 1. 0% 17 1. 8% 3 0. 5% 11 1. 6% GH39 β -xylosidase PF01229 7 0. 7% 6 0. 8% 4 0. 4% 89 0. 8% 159 0. 4% 6 0. 2% 315 1. 1% 2 0. 2% 1 0. 2% 3 0. 4% PF02449 GH42 β -galactosidase PF08533 16 1. 5% 14 1. 9% 24 2. 1% 37 0. 3% 207 0. 6% 54 2. 1% 374 1. 3% 11 1.
1% 8 1. 4% 24 3. 4% PF08532 GH43 arabinases & xylosidases PF04616 56 5. 3% 28 3. 7% 52 4. 6% 894 8. 1% 2313 6. 2% 185 7. 1% 0 0. 0% 61 6. 4% 10 1. 8% 16 2. 3% GH52 β -xylosidase PF03512 0 0. 0% 0 0. 0% 2 0. 2% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 0 0. 0% 3 0. 4% total 275 26. 0% 162 21. 6% 275 24. 2% 3424 31. 0% 10857 28. 9% 944 36. 4% 6591 23. 7% 549 57. 4% 184 33. 0% 174 24. 7% total plant plysaccharides targeting GHs 401 37. 9% 243 32. 4% 403 35. 5% 5535 50. 1% 19527 52. 0% 1264 48. 8% 12697 45. 7% 700 73. 1% 264 47. 4% 375 53. 3% total GHs 1059 750 1136 11038 37563 2590 27755 957 557 704.
We therefore examined striatal transcript alterations using both targeted (QRT-PCR) and untargeted (RNA Sequencing, RNASeq) methods. As expected, 10-month-old Htt Q111/+ mice have reduced steady state levels of key striatal synaptic and signaling genes, including Drd1a and Pde10a (Fig  5 ). In each case, transcript levels were reduced in the striatum of Htt Q111/+ mice relative to Htt +/+ mice using both RNASeq (Drd1a: F ). Peripheral treatment with Htt ASO did not impact steady state expression levels of any of these transcripts, suggesting that striatal transcriptional dysregulation was not rescued by this treatment.
Transcriptional profiling Total RNA was extracted from the hemistriatum as described previously  [16]  for both RNA sequencing (RNAseq) and quantitative reverse-transcription polymerase chain reaction (QRT-PCR). RNAseq was conducted at EA | Q 2 Solutions with cDNA libraries prepared using the TruSeq Stranded mRNA sample preparation kit (Illumina # RS-122-2103) for 2x50bp PE sequencing. Quality control, as well as gene and isoform quantification, were performed with an EA | Q 2 Solutions developed analysis pipeline, mRNA v7. Bowtie version 0. 12. 9 was used to align reads to the mouse transcriptome MGSCv37, followed by quantification with RSEM version 1. 1. 18. RNAseq data are publicly available via the Gene Expression Omnibus (GEO) accession number GSE97101. For QRT-PCR, messenger RNA was reverse transcribed using the Superscript III First Strand Synthesis System (Life Technologies) according to the manufacturer's protocol.
Statistical analyses Data were analyzed using R version 3. 3. 1  [47]. Data presented in Figs 1, 2, 3B, 4, S3 and S5 were tested for normality (Shapiro-Wilk test) and homoscedasticity (Levene's test). All data met or approximated parametric assumptions within groups and were fit with linear models analyzed by ANOVA. Data are presented as boxplots created with ggplot2  [48] -horizontal lines indicate 25th, 50th and 75th percentile, while the vertical whiskers indicate the range of data. Data falling outside 1. 5 times the interquartile range are graphed as isolated points, but were not excluded from statistical analysis. RNASeq data were analyzed using the limma  [49]  package of Bioconductor  [50]  after normalizing with the voom function. Weekly body weights were considered spurious and excluded if the LNP was greater than 130 for two or more consecutive weeks with LNP defined as LNP ¼ j100 x lnW T À lnW ðTÀ 1Þ lnT À lnðT À 1Þ j where W T is the weight at time T and W (T-1) is the weight at time T-1.
It does this by applying an adaptive Least Mean Squares (LMS) filter (cf. Widrow and Stearns 1985)  to the data. The LMS filter consists of a number of filter coefficients. The filter coefficients can be thought of as a transfer function to transform the noise data (N ) to the same space as the data channel (S + N). After applying the transfer function, the noise can be subtracted from the (S + N) data to produce the signal-only data (S). The LMS scheme minimizes, in the least squares sense, the output S. The noise coefficients are continuously modified to produce the minimum output. For each of the three LFR channels, a filter table of length 33 is initially configured, with all values of the filter coefficients set to zero (i. e. , nothing is known about the transfer function). The length can be increased, up to a maximum of 65, but at a CPU usage cost.
Using 33 coefficients for all 3 channels results in a CPU usage which allows the processing of spectra once every second. Going to 65 coefficients would require that the cadence be decreased to once every 2 seconds. The default output is the filtered data S, Fourier-analyzed and binned. Two diagnostic modes are implemented. First, a High Speed diagnostic mode is implemented, where the raw waveforms of the S + N , N , and S data sets are output via the HRS interface. Second, a Low Speed diagnostic mode is implemented, in which the Fourier-analyzed and binned spectra of the S + N , N , and S data sets are downlinked via the LRS interface. Inflight performance of this system to date has been inconclusive, primarily because minimal noise on the PDDU voltage has been observed. The algorithm is best suited for continuous signals, while the majority of the noise seen by the instrument has been impulsive.
Magnetic Antenna The amplitude response of the search coil sensor and amplifier were determined in a twostep process. Initially, a detailed calibration of the search coil was performed in a Mu-metal shield by applying a known signal to a calibration coil to produce a magnetic field with a known magnitude. The resulting data relate input field strength to the voltage at the magnetic preamplifier output over the frequency range of the sensor. The second step related the voltage input to the receiver and the output telemetry value. Combining these two steps provides an overall end-to-end calibration of the magnetic field wave measurements. The search coil was also attached to the Waves instrument and end-to-end calibrations were performed by driving the calibration coil to produce a known magnetic field strength and verifying the expected telemetry values.
High Frequency Receiver The Juno Waves instrument contains two nearly identical high frequency receivers HFR and HFWBR. In principle, one of the receivers operates as the HFR (High Frequency Receiver) and the other as the HFWBR (High Frequency Waveform Receiver). In practice, the HFR sensitivity is better than the HFWBR, so both survey and burst mode observations use the HFR. HFR products are reduced frequency resolution products and provide a constant background survey. Waveform products contain roughly 1000 times the frequency resolution of survey products and correspondingly have much greater storage and telemetry bandwidth requirements. The HFR utilizes signals from the electric dipole and is capable of covering the range from 100 kHz to 45 MHz, though typical science data from the HFR cover the frequency range from ∼ 137 kHz to ∼ 41 MHz.
The baseband channel of the HFR (100 kHz-3 MHz) is a broadband channel that is sampled at a rate of 7 Msps with 12 bit resolution. This waveform is sent to the DSP for onboard spectrum analysis and is converted to survey data products covering the range of 137 kHz to 2. 98 MHz. The frequency range above ∼ 3 MHz is covered by using a synthesized frequency mixed with the incoming signals in 1-MHz bands and the amplitude in each of the channels is detected sequentially as in a swept frequency receiver. The power in each band is recorded via a log amplifier whose output is sampled with 8-bit resolution. Though the instrument may be commanded to set the center frequency of the 1 MHz bands as high as 44. 75 MHz, standard HFR science operations are conducted with a 41 MHz top edge. For burst waveform data, the baseband (100 kHz-3 MHz) is sampled at 7 Msps with 12-bit resolution.
Performance Waves performance after launch has mostly been as expected from prelaunch testing. Shortly after launch some anomalous operations of the instrument were observed. These all seemed to be associated with an invalid address appearing on a particular bus in the WvFE processor. To date, the root cause of this issue has not been determined. However, Waves flight software has been modified to re-load software to the WvFE processor after the address fault is detected. While this reset and reload of the processor can result in the loss of a few instrument cycles of data acquisition, no long-lived issues have been noted since this flight software change. One issue that was known prior to launch was some variation in the output of the engineering analog-to-digital converter (ADC) which results in some fluctuations in engineering parameter readings and in the HFR survey values for frequencies above 3 MHz.
Survey Data The purpose of Waves survey data is to capture complete electric and magnetic spectra for the entire frequency range on a regular cadence. This means that spectra from each of the receivers will be collected covering the frequency range of 50 Hz to 41 MHz for the electric field and from 50 Hz to 20 kHz for the magnetic field. The cadence is determined by where Juno is located, coupled with telemetry restrictions. In Jupiter orbit, it is anticipated that the highest cadence will be used during the approximately 12 hours around perijove. This mode is called Periapsis (PER) mode and returns a complete electric and magnetic spectrum every second. For several hours before and after the periapsis period, an intermediate cadence may be used, called Intermediate (INT) mode, which returns a complete electric and magnetic spectrum every 10 seconds.
Burst Data Binning Mode Waves burst data are waveform series from one or more of the Waves receivers. The waveform series include a defined number of consecutive samples at the appropriate sampling rate for each receiver that are typically losslessly compressed and telemetered to the ground to provide high temporal resolution measurements, or alternately, high spectral resolutions for particular periods of interest. See Table  1. For the LFR channels (LFR-B, LFR-Lo and LFR-Hi), the series include 6144 consecutive 16-bit samples. For the baseband channel of the wideband function of the HFR or HFWBR, 4096 12-bit samples are collected. For the WBRH (high frequency band) there are 4096 12-bit samples in each of the I and Q data sets. These modes can generate large volumes of data in a short time period, hence, can only be afforded infrequently and for brief periods of time.