Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data.

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics.

Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

Modal interference discrepancy and its application to a modified fiber Mach-Zehnder Vernier interferometer.

In this paper, modal interference discrepancy in an all-fiber MZI is theoretically analyzed and experimentally verified. Theoretical analysis demonstrates that ambient refractive index (RI) response of core-cladding modal interference in an all-fiber MZI is blue-shift, while that of cladding-cladding modal interference is red-shift. Temperature response trends of the two kinds of modal interference are uniformly red-shift. The discrepancy is used to fabricate an improved Vernier sensor which is cascaded by two unit MZIs. One MZI is slightly core-offset fused to obtain core-cladding modal interference, and the other is obviously offset fused to get cladding-cladding modal interference. Ambient RI sensitivity of the cascaded sensor is improved with temperature cross-talk restrained. Ambient RI responses of the two unit MZIs are measured to be opposite, which are -54.009 nm/RIU (within RI range of 1.3362∼1.3811) for the slight and 142.581 nm/RIU for the obvious offset unit MZI. While, temperature response trends of them are consistent, which are 0.042 nm/°C for the slight and 0.025 nm/°C for the obvious offset unit MZI, respectively. For the cascaded Vernier sensor ambient RI sensitivity reaches -1788.160 nm/RIU, which is 33.1 and 12.5 folds improved over the two unit MZIs, respectively. Temperature sensitivity of the cascaded sensor is as low as 0.167 nm/°C and only causes a slight RI error of 9.339 × 10-5 RIU/°C. Due to the simple structure, ease of fabrication, and low temperature cross-talk, the modal interference discrepancy-based Vernier sensor is believed to have potential application prospects in biochemical sensing fields.

Relationship between the stroke mechanism of symptomatic middle cerebral artery atherosclerotic diseases and culprit plaques based on high-resolution vessel wall imaging.

Purpose: For patients with symptomatic middle cerebral artery (MCA) atherosclerotic stenosis, identifying the potential stroke mechanisms may contribute to secondary prevention. The purpose of the study is to explore the relationship between stroke mechanisms and the characteristics of culprit plaques in patients with atherosclerotic ischemic stroke in the M1 segment of the middle cerebral artery (MCA) based on high-resolution vessel wall imaging (HR-VWI). Methods: We recruited 61 patients with acute ischemic stroke due to MCA atherosclerotic stenosis from Shenzhen Bao'an District People's Hospital. According to prespecified criteria based on infarct topography and magnetic resonance angiography, possible stroke mechanisms were divided into parent artery atherosclerosis occluding penetrating artery (P), artery-to-artery embolism (A), hypoperfusion (H), and mixed mechanisms (M). The correlation between the characteristics of MCA M1 culprit plaque and different stroke mechanisms was analyzed using HR-VWI. The indicators included plaque surface irregularity, T1 hyperintensity, location, plaque burden (PB), remodeling index (RI), enhancement rate, and stenosis rate. Results: Parental artery atherosclerosis occluding penetrating artery was the most common mechanism (37.7%). The proposed criteria showed substantial to excellent interrater reproducibility (κ, 0.728; 0.593-0.863). Compared with the P group, the surface irregularity, T1 hyperintensity, and obvious enhancement of the culprit plaque in the A group were more common (p < 0.0125). Compared with the other stroke mechanisms, positive remodeling of culprit plaques was more common (p < 0.0125), the RI was greater (p < 0.05), and the PB was the smallest (p < 0.05) in the P group. The enhancement ratio (ER) was smaller in the P group (p < 0.05). Compared with the A group, T1 hyperintensity of the culprit plaque was more common in the H group (p < 0.0125), and the stenosis rate was greater (p < 0.05). After adjustment for clinical demographic factors in the binary logistic regression analysis, the enhancement level (odds ratio [OR] 0.213, 95% CI (0.05-0.91), p = 0.037) and PB of culprit plaque (OR 0, 95% CI (0-0.477), p = 0.034) were negatively associated with P groups. Conclusion: The culprit plaque characteristics of patients with symptomatic MCA atherosclerotic in different stroke mechanisms may be evaluated using HR-VWI. The plaque characteristics of different stroke mechanisms may have clinical value for the selection of treatment strategies and prevention of stroke recurrence. Clinical trial registration: Identifier: ChiCTR1900028533.

A review of respirable fine particulate matter (PM2.5)-induced brain damage.

Respirable fine particulate matter (PM2.5) has been one of the most widely publicized indicators of pollution in recent years. Epidemiological studies have established a strong association between PM2.5, lung disease, and cardiovascular disease. Recent studies have shown that PM2.5 is also strongly associated with brain damage, mainly cerebrovascular damage (stroke) and neurological damage to the brain (changes in cognitive function, dementia, psychiatric disorders, etc.). PM2.5 can pass through the lung-gas-blood barrier and the "gut-microbial-brain" axis to cause systemic oxidative stress and inflammation, or directly enter brain tissue via the olfactory nerve, eventually damaging the cerebral blood vessels and brain nerves. It is worth mentioning that there is a time window for PM2.5-induced brain damage to repair itself. However, the exact pathophysiological mechanisms of brain injury and brain repair are not yet fully understood. This article collects and discusses the mechanisms of PM2.5-induced brain injury and self-repair after injury, which may provide new ideas for the prevention and treatment of cerebrovascular and cerebral neurological diseases.

Safety and Efficacy of Tirofiban During Intravenous Thrombolysis Bridging to Mechanical Thrombectomy for Acute Ischemic Stroke Patients: A Meta-Analysis.

Introduction: The safety and efficacy of tirofiban in intravenous thrombolysis (IVT) bridging to mechanical thrombectomy in patients with acute ischemic stroke (AIS) is unknown. The purpose of this meta-analysis was to evaluate the safety and efficacy of tirofiban in IVT bridging to mechanical thrombectomy in acute ischemic stroke. Methods: We systematically searched PubMed, EMBASE, Web of Science, and The Cochrane Library, CNKI, and Wan Fang databases for randomized controlled trials and observational studies (case-control studies and cohort studies) comparing the tirofiban and non-tirofiban groups in AIS intravenous thrombolysis bridging to mechanical thrombectomy (Published by November 20, 2021). Our primary safety endpoints were symptomatic cerebral hemorrhage (sICH), intracranial hemorrhage (ICH), postoperative re-occlusion, and 3-month mortality; the efficacy endpoints were 3-month favorable functional outcome (MRS ≤ 2) and successful recanalization rate (modified thrombolytic therapy in cerebral infarction (mTICI) 2b or 3). Results: A total of 7 studies with 1,176 patients were included in this meta-analysis. A comprehensive analysis of the included literature showed that the difference between the tirofiban and non-tirofiban groups in terms of successful recanalization (OR = 1.19, 95% Cl [0.69, 2.03], p = 0.53, I 2 = 22%) and favorable functional outcome at 3 months (OR = 1.13, 95% Cl [0.81, 1.60], p = 0.47, I 2 = 17%) in patients with IVT bridging mechanical thrombectomy of AIS was not statistically significant. Also, the differences in the incidence of sICH (OR = 0.97, 95% Cl [0.58, 1.62], p = 0.89) and ICH (OR = 0.83, 95% Cl [0.55, 1.24], p = 0.36) between the two groups were not statistically significant. However, the use of tirofiban during IVT bridging mechanical thrombectomy reduced the rate of postoperative re-occlusion (OR = 0.36, 95% Cl [0.14, 0.91], p = 0.03) and mortality within 3 months (OR = 0.54, 95% Cl [0.33, 0.87], p = 0.01) in patients. Conclusion: The use of tirofiban during IVT bridging mechanical thrombectomy for AIS does not increase the risk of sICH and ICH in patients and reduces the risk of postoperative re-occlusion and mortality in patients within 3 months. However, this result needs to be further confirmed by additional large-sample, multicenter, prospective randomized controlled trials. Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022297441.

Are Changes in Sleep Quality/Quantity or Baseline Sleep Parameters Related to Changes in Clinical Outcomes in Patients With Nonspecific Chronic Low Back Pain?: A Systematic Review.

OBJECTIVES: Sleep disturbance is prevalent among patients with chronic low back pain (CLBP). This systematic review aimed to summarize the evidence regarding the: (1) temporal relations between changes in sleep quality/quantity and the corresponding changes in pain and/or disability; and (2) role of baseline sleep quality/quantity in predicting future pain and/or disability in patients with CLBP. METHODS: Four databases were searched from their inception to February 2021. Two reviewers independently screened the abstract and full text, extracted data, assessed the methodological quality of the included studies, and evaluated the quality of evidence of the findings using the Grading of Recommendations Assessment Development and Evaluation (GRADE). RESULTS: Of 1995 identified references, 6 articles involving 1641 participants with CLBP were included. Moderate-quality evidence substantiated that improvements in self-reported sleep quality and total sleep time were significantly correlated with the corresponding LBP reduction. Low-quality evidence showed that self-reported improvements in sleep quality were related to the corresponding improvements in CLBP-related disability. There was conflicting evidence regarding the relation between baseline sleep quality/quantity and future pain/disability in patients with CLBP. DISCUSSION: This is the first systematic review to accentuate that improved self-reported sleep quality/quantity may be associated with improved pain/disability, although it remains unclear whether baseline sleep quality/quantity is a prognostic factor for CLBP. These findings highlight the importance of understanding the mechanisms underlying the relation between sleep and CLBP, which may inform the necessity of assessing or treating sleep disturbance in people with CLBP.

Cross-cultural adaptation and measurement properties of the VISA-A questionnaire for Chinese patients with Achilles Tendinopathy.

OBJECTIVE: To culturally adapt the VISA-A into a simplified Chinese version (VISA-A-CHN) and test its measurement properties. DESIGN: Methodological study; SETTING: Hospital and university laboratory. PARTICIPANTS: 240 subjects were divided into the healthy (n = 80), at-risk (n = 80), and tendinopathy groups (n = 80). MAIN OUTCOMES MEASURES: The internal consistency, test-retest reliability, construct validity, and the floor and ceiling effect of the VISA-A-CHN. RESULTS: The VISA-A-CHN showed adequate internal consistency (Cronbach's α = 0.73, 95% CI 0.63 to 0.81), excellent test-retest reliability (ICC3A,1 = 0.97, 95%CI = 0.95 to 0.98), standard error of measurement of 2.2 points, minimum detectable change of 6.0 points, with no floor and ceiling effects. Two factors (pain/symptoms and physical function/activity) were extracted in exploratory factor analysis. There were moderate associations of VISA-A-CHN score with scores of Lower Extremity Functional Scale and SF-36 physical components (rs = 0.53-0.74, P < 0.01) but low associations with SF-36 mental components (rs = 0.12-0.22, P > 0.05). VISA-A-CHN mean score of Achilles tendinopathy group was significantly lower than those of healthy and at-risk groups (P < 0.01). CONCLUSIONS: The VISA-A-CHN is equivalent to the original version in terms of language and measurement properties. It can be used as the outcome measure for Chinese patients with Achilles tendinopathy.

Quantitative and Qualitative Analysis of Atherosclerotic Stenosis in the Middle Cerebral Artery Using High-Resolution Magnetic Resonance Imaging.

PURPOSE: We analyzed and compared the imaging characteristics of the vessel wall of the middle cerebral artery (MCA) in symptomatic and asymptomatic patients using a 3.0-T high-resolution magnetic resonance imaging (HR-MRI) protocol, including a 3-dimensional T1-sampling perfection with application-optimized contrasts using different flip angle evolutions sequence. METHODS: Fifty-three patients with atherosclerotic stenosis of the MCA underwent 3.0-T HR-MRI examinations. The characteristics of atherosclerotic plaques in 53 patients (28 symptomatic, 25 asymptomatic) were analyzed, including plaque distribution and signal intensity. Plaque burden (PB), stenosis degree, and the remodeling index were measured and compared between symptomatic and asymptomatic patients. RESULTS: The PB of the symptomatic group was significantly higher than that of the asymptomatic group (P = .006), and moderate-severe stenosis was more common (P = .01). The remodeling index of the symptomatic group was also lower (P = .015) and negative remodeling (NR) was more common (P = .043). Binary logistic regression analysis showed that stenosis degree was a risk factor in symptomatic patients (odds ratio = 135, P = .023). CONCLUSION: There is a trend that some characteristics of plaques and vessels, including the moderate-severe stenosis, larger PB, and NR, were observed more frequently among patients with symptomatic atherosclerotic stenosis of the MCA than among asymptomatic patients.

Diagnostic value of blood parameters for community-acquired pneumonia.

BACKGROUND: Community-acquired pneumonia (CAP) has a high rate of morbidity and mortality. Blood parameters, including neutrophil, platelet, lymphocyte, monocyte, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR), have been proposed as indicators of systemic inflammation and infection. However, few studies have focused on the diagnostic value of blood parameters for CAP. OBJECTIVE: The study aims to determine the diagnostic value of blood parameters for CAP and to investigate their relationship with disease severity. METHODS: CAP patients who fulfilled the inclusion criteria were enrolled in this study. Healthy age- and gender-matched subjects were also enrolled as a control group. Blood parameters, blood biochemistry, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), days in hospital, body temperature, pneumonia severity index (PSI), and CURB-65 were recorded. The area under the curve (AUC) values was determined using the receiver-operating characteristic (ROC) curve. The correlation between the variables was tested with Pearson correlation analysis. RESULTS: The study included 80 CAP patients and 49 healthy subjects. White blood cell (WBC), neutrophil, monocyte, MLR, PLR, and NLR levels in the CAP group were higher than that of control group, while lymphocyte and hemoglobin (HGB) levels were lower (P < 0.05). The ROC curve result showed that NLR and MLR yielded higher AUC values than other variables. Monocyte was positively correlated with ESR and negatively with body temperature, aspartate aminotransferase (AST), and creatinine (CREA). NLR was positively correlated with CRP, PCT, days in hospital, alanine aminotransferase (ALT), AST, and PSI. MLR was positively correlated with CRP, PCT, and body temperature. An increase in ALT or AST of >2 times of normal was defined as liver injury, and CAP patients were divided into the liver normal group and liver injury group. Sixty-nine patients belonged to the liver normal group, and 11 patients belonged to the liver injury group. Blood parameters, ESR, CRP, PCT, PSI, and CURB-65 were compared between the two groups. The results demonstrated that the monocyte level in the liver injury group was lower than that of the liver normal group (P < 0.05). The ROC curve result showed that the AUC value of monocyte for liver injury was 0.838 (95% confidence interval: 0.733-0.943), which was higher than other variables. CONCLUSIONS: NLR and MLR were elevated in CAP patients, resulting in a higher diagnostic value for CAP. NLR showed a significant correlation to PSI, indicating the disease severity of CAP. Monocyte had a higher diagnostic value for liver injury in CAP patients.

Parallel Metabolomic Profiling of Cerebrospinal Fluid and Serum for Identifying Biomarkers of Injury Severity after Acute Human Spinal Cord Injury.

Suffering an acute spinal cord injury (SCI) can result in catastrophic physical and emotional loss. Efforts to translate novel therapies in acute clinical trials are impeded by the SCI community's singular dependence upon functional outcome measures. Therefore, a compelling rationale exists to establish neurochemical biomarkers for the objective classification of injury severity. In this study, CSF and serum samples were obtained at 3 time points (~24, 48, and 72 hours post-injury) from 30 acute SCI patients (10 AIS A, 12 AIS B, and 8 AIS C). A differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) with a universal metabolome standard (UMS) was applied to the metabolomic profiling of these samples. This method provided enhanced detection of the amine- and phenol-containing submetabolome. Metabolic pathway analysis revealed dysregulations in arginine-proline metabolism following SCI. Six CSF metabolites were identified as potential biomarkers of baseline injury severity, and good classification performance (AUC > 0.869) was achieved by using combinations of these metabolites in pair-wise comparisons of AIS A, B and C patients. Using the UMS strategy, the current data set can be expanded to a larger cohort for biomarker validation, as well as discovering biomarkers for predicting neurologic outcome.

Whole genome SNP genotype piecemeal imputation.

BACKGROUND: Despite ongoing reductions in the cost of sequencing technologies, whole genome SNP genotype imputation is often used as an alternative for obtaining abundant SNP genotypes for genome wide association studies. Several existing genotype imputation methods can be efficient for this purpose, while achieving various levels of imputation accuracy. Recent empirical results have shown that the two-step imputation may improve accuracy by imputing the low density genotyped study animals to a medium density array first and then to the target density. We are interested in building a series of staircase arrays that lead the low density array to the high density array or even the whole genome, such that genotype imputation along these staircases can achieve the highest accuracy. RESULTS: For genotype imputation from a lower density to a higher density, we first show how to select untyped SNPs to construct a medium density array. Subsequently, we determine for each selected SNP those untyped SNPs to be imputed in the add-one two-step imputation, and lastly how the clusters of imputed genotype are pieced together as the final imputation result. We design extensive empirical experiments using several hundred sequenced and genotyped animals to demonstrate that our novel two-step piecemeal imputation always achieves an improvement compared to the one-step imputation by the state-of-the-art methods Beagle and FImpute. Using the two-step piecemeal imputation, we present some preliminary success on whole genome SNP genotype imputation for genotyped animals via a series of staircase arrays. CONCLUSIONS: From a low SNP density to the whole genome, intermediate pseudo-arrays can be computationally constructed by selecting the most informative SNPs for untyped SNP genotype imputation. Such pseudo-array staircases are able to impute more accurately than the classic one-step imputation.

DnsID in MyCompoundID for rapid identification of dansylated amine- and phenol-containing metabolites in LC-MS-based metabolomics.

High-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS.

MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites.

We report an analytical tool to facilitate metabolite identification based on an MS/MS spectral match of an unknown to a library of predicted MS/MS spectra of possible human metabolites. To construct the spectral library, the known endogenous human metabolites in the Human Metabolome Database (HMDB) (8,021 metabolites) and their predicted metabolic products via one metabolic reaction in the Evidence-based Metabolome Library (EML) (375,809 predicted metabolites) were subjected to in silico fragmentation to produce the predicted MS/MS spectra. This spectral library is hosted at the public MCID Web site ( www.MyCompoundID.org ), and a spectral search program, MCID MS/MS, has been developed to allow a user to search one or a batch of experimental MS/MS spectra against the library spectra for possible match(s). Using MS/MS spectra generated from standard metabolites and a human urine sample, we demonstrate that this tool is very useful for putative metabolite identification. It allows a user to narrow down many possible structures initially found by using an accurate mass search of an unknown metabolite to only one or a few candidates, thereby saving time and effort in selecting or synthesizing metabolite standard(s) for eventual positive metabolite identification.

Isomorphism and similarity for 2-generation pedigrees.

We consider the emerging problem of comparing the similarity between (unlabeled) pedigrees. More specifically, we focus on the simplest pedigrees, namely, the 2-generation pedigrees. We show that the isomorphism testing for two 2-generation pedigrees is GI-hard. If the 2-generation pedigrees are monogamous (i.e., each individual at level-1 can mate with exactly one partner) then the isomorphism testing problem can be solved in polynomial time. We then consider the problem by relaxing it into an NP-complete decomposition problem which can be formulated as the Minimum Common Integer Pair Partition (MCIPP) problem, which we show to be FPT by exploiting a property of the optimal solution. While there is still some difficulty to overcome, this lays down a solid foundation for this research.

[Comparative study of biological characters and paeoniflorin content of wild and asexual cultivated Paeonia lactiflora growing at Duolun county, Inner Mongolia].

This study aims to investigate whether the cultivation peony, can take the place of wild herbaceous peony by comparing the biological traits and paeoniflorin content between them. The result showed that the biomass of the stem, leaf, crown, fleshy root and fine root of wild plants were all smaller than that of bud asexual cultivated plants, while there was no significant differences in below-ground and aboveground biomass ratio between these two plants. The stele diameter, the proportion of stele, and the ratio of stele diameter to cortex thickness of wild plants were significantly higher than that of bud asexual cultivated plants, while the cortex thickness and the proportion of cortex were significantly smaller than bud asexual cultivated plants. Although the biological traits of bud asexual cultivated plants have changed significantly, the paeoniflorin content in fleshy roots has no significant difference between wild and bud asexual cultivated plants. Therefore, it is feasible to use the bud asexual cultivation to the conservation and large-scale cultivation of Paeonia laciflora, which is an endangered species.

PEP search in MyCompoundID: detection and identification of dipeptides and tripeptides using dimethyl labeling and hydrophilic interaction liquid chromatography tandem mass spectrometry.

Small peptides, such as dipeptides and tripeptides, are naturally present in many biological samples (e.g., human biofluids and cell extracts). They have attracted great attention in many research fields because of their important biological functions as well as potential roles as disease biomarkers. Tandem mass spectrometry (MS/MS) can be used to profile these small peptides. However, the type and number of fragment ions generated in MS/MS are often limited for unambiguous identification. Herein we report a novel database-search strategy based on the use of MS/MS spectra of both unlabeled and dimethyl labeled peptides to identify and confirm amino acid sequences of di/tripeptides that are separated using hydrophilic interaction (HILIC) liquid chromatography (LC). To facilitate the di/tripeptide identification, a database consisting of all the predicted MS/MS spectra from 400 dipeptides and 8000 tripeptides was created, and a search tool, PEP Search, was developed and housed at the MyCompoundID website ( www.mycompoundid.org/PEP). To evaluate the identification specificity of this method, we used acid hydrolysis to degrade a standard protein, cytochrome c, to produce many di/tripeptides with known sequences for LC/MS/MS. The resultant MS/MS spectra were searched against the database to generate a list of matches which were compared to the known sequences. We correctly identified the di/tripeptides in the protein hydrolysate. We then applied this method to detect and identify di/tripeptides naturally present in human urine samples with high confidence. We envisage the use of this method as a complementary tool to various LC/MS techniques currently available for small molecule or metabolome profiling with an added benefit of covering all di/tripeptide chemical space.

Whole genome identity-by-descent determination.

High-throughput single nucleotide polymorphism genotyping assays conveniently produce genotype data for genome-wide genetic linkage and association studies. For pedigree datasets, the unphased genotype data is used to infer the haplotypes for individuals, according to Mendelian inheritance rules. Linkage studies can then locate putative chromosomal regions based on the haplotype allele sharing among the pedigree members and their disease status. Most existing haplotyping programs require rather strict pedigree structures and return a single inferred solution for downstream analysis. In this research, we relax the pedigree structure to contain ungenotyped founders and present a cubic time whole genome haplotyping algorithm to minimize the number of zero-recombination haplotype blocks. With or without explicitly enumerating all the haplotyping solutions, the algorithm determines all distinct haplotype allele identity-by-descent (IBD) sharings among the pedigree members, in linear time in the total number of haplotyping solutions. Our algorithm is implemented as a computer program iBDD. Extensive simulation experiments using 2 sets of 16 pedigree structures from previous studies showed that, in general, there are trillions of haplotyping solutions, but only up to a few thousand distinct haplotype allele IBD sharings. iBDD is able to return all these sharings for downstream genome-wide linkage and association studies.

MyCompoundID: using an evidence-based metabolome library for metabolite identification.

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool ( http://www.mycompoundid.org ) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8,021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.

Protein-chemical interaction prediction via kernelized sparse learning SVM.

Given the difficulty of experimental determination of drug-protein interactions, there is a significant motivation to develop effective in silico prediction methods that can provide both new predictions for experimental verification and supporting evidence for experimental results. Most recently, classification methods such as support vector machines (SVMs) have been applied to drug-target prediction. Unfortunately, these methods generally rely on measures of the maximum "local similarity" between two protein sequences, which could mask important drug-protein interaction information since drugs are much smaller molecules than proteins and drug-target binding regions must comprise only small local regions of the proteins. We therefore develop a novel sparse learning method that considers sets of short peptides. Our method integrates feature selection, multi-instance learning, and Gaussian kernelization into an L(1) norm support vector machine classifier. Experimental results show that it not only outperformed the previous methods but also pointed to an optimal subset of potential binding regions. Supplementary materials are available at "www.cs.ualberta.ca/~ys3/drug_target".