scholarly journals Pyrrovobasine, Hybrid Alkylated Pyrraline Monoterpene Indole Alkaloid Pseudodimer Discovered Using a Combination of Mass Spectral and NMR-Based Machine Learning Annotations

2021 ◽  
Author(s):  
Hugues Fouotsa ◽  
Pierre Mkounga ◽  
Alain Meli Lannang ◽  
Jérôme Vanheuverzwijn ◽  
Zhiyu Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indole Alkaloid ◽  
Stem Bark ◽  
Minor Constituent ◽  
Mass Spectral ◽  
A Minor

A new vobasine-tryptamine-based monoterpene indole alkaloid pseudodimer was isolated from the stem bark of Voacanga africana. As a minor constituent occurring in a thoroughly investigated plant, this molecule was targeted...

A new dimeric monoterpene indole alkaloid from the stem bark of Pleiocarpa mutica Benth. (Apocynaceae)

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
EO N'nang ◽  
L Evanno ◽  
K Leblanc ◽  
P Grellier ◽  
B Kumulungui ◽  
...  
Keyword(s):  
Indole Alkaloid ◽  
Stem Bark

scholarly journals The choices we make and the impacts they have: Machine learning and species delimitation in North American box turtles (Terrapene spp.)

2020 ◽  
Author(s):  
Bradley T. Martin ◽  
Tyler K. Chafin ◽  
Marlis R. Douglas ◽  
John S. Placyk ◽  
Roger D. Birkhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
North American ◽  
Species Delimitation ◽  
Species Concept ◽  
Incomplete Lineage Sorting ◽  
Signal To Noise Ratio ◽  
Lineage Sorting ◽  
Biogeographic History ◽  
Box Turtles ◽  
A Minor

AbstractModel-based approaches that attempt to delimit species are hampered by computational limitations as well as the unfortunate tendency by users to disregard algorithmic assumptions. Alternatives are clearly needed, and machine-learning (M-L) is attractive in this regard as it functions without the need to explicitly define a species concept. Unfortunately, its performance will vary according to which (of several) bioinformatic parameters are invoked. Herein, we gauge the effectiveness of M-L-based species-delimitation algorithms by parsing 64 variably-filtered versions of a ddRAD-derived SNP dataset involving North American box turtles (Terrapene spp.). Our filtering strategies included: (A) minor allele frequencies (MAF) of 5%, 3%, 1%, and 0% (=none), and (B) maximum missing data per-individual/per-population at 25%, 50%, 75%, and 100% (=none). We found that species-delimitation via unsupervised M-L impacted the signal-to-noise ratio in our data, as well as the discordance among resolved clades. The latter may also reflect biogeographic history, gene flow, incomplete lineage sorting, or combinations thereof (as corroborated from previously observed patterns of differential introgression). Our results substantiate M-L as a viable species-delimitation method, but also demonstrate how commonly observed patterns of phylogenetic discord can seriously impact M-L-classification.

Heterogeneous Formation of Oriented Silicon Oxynitride on α-Si3N4 Seed Crystals : Habits And Radiation Stability

1999 ◽  
Vol 5 (S2) ◽  
pp. 778-779
Author(s):  
R.W Carpenter ◽  
W Braue ◽  
M.J. Kim
Keyword(s):  
Silicon Nitride ◽  
Heterogeneous Nucleation ◽  
Radiation Stability ◽  
Silicon Oxynitride ◽  
Minor Constituent ◽  
Sintering Aids ◽  
Sintering Aid ◽  
Nucleation Sites ◽  
Heterogeneous Formation ◽  
A Minor

Lath-like silicon oxynitride crystals have often been observed in the microstructure of silicon nitride based ceramics after processing. They are usually located in glassy regions which are siliceous solidified sintering aid liquid, and usually contain a small (∼100nm) a-Si3N4 crystal. These nitride crystals are considered to be seeds, incompletely dissolved in the melt, that are heterogeneous nucleation sites for the oxynitride crystals. We present here the first observations of morphological and crystallographic habits between the seed nanocrystals and the host oxynitride laths.Fig. 1 shows a typical oxynitride lath containing a nitride seed crystal. The lath is surrounded by glass and ß-Si3N4 particles, and a small cristobalite particle (a minor constituent). This microstructure is from an Si02-Si3N4 ceramic processed with Al2O3 sintering aid. The same oxynitride lath/seed structures were observed when other sintering aids (eg. Y2O3, MgO, ZrO2) were used, so they are independent of sintering aid.

scholarly journals Betaine Yields from Marine Algal Species Utilized in the Preparation of Seaweed Extracts Used in Agriculture

2010 ◽  
Vol 5 (4) ◽  
pp. 1934578X1000500 ◽  
Author(s):  
Gerald Blunden ◽  
Peter F. Morse ◽  
Imre Mathe ◽  
Judit Hohmann ◽  
Alan T. Critchley ◽  
...  
Keyword(s):  
Marine Algae ◽  
Algal Species ◽  
First Record ◽  
Commercial Production ◽  
The Other ◽  
Minor Constituent ◽  
Laminaria Digitata ◽  
Seaweed Extracts ◽  
Marine Algal ◽  
A Minor

Ascophyllum nodosum, and to a lesser extent, Laminaria digitata, L. hyperborea and Fucus serratus, are marine algal species utilized in the commercial production of seaweed extracts used in agriculture. Betaines have been shown to be important constituents of these extracts, but there appears to have been no study made on whether there are variations in the betaine contents of these species based on either the place or date of collection. Samples of each of the four species were collected from widely separated areas at different times of the year. Also, in the case of A. nodosum, approximately monthly collections were made from one location. The betaines detected in the various collections of the same species showed little variation, although in the case of A. nodosum, glycinebetaine was found as a minor constituent in some samples, but was not detected in others. Trigonelline was found in all the tested samples of the two Laminaria species; this is, to our knowledge, the first record of this betaine in marine algae. With the exception of trigonelline in the Laminaria species, the betaine yields from the various samples of L. digitata, L. hyperborea and F. serratus showed little variation, regardless of either the place or date of collection. The trigonelline contents of the Laminaria species collected at one location (Finavarra, Ireland), in particular of L. hyperborea, was substantially greater than those from the other places of collection. In the case of A. nodosum, the betaine yields from samples collected at one site (Dale, Pembrokeshire, UK) were significantly higher than those from the other places of collection, which were very similar to each other. There was no clear indication of seasonal variation in betaine yields from A. nodosum.

scholarly journals Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5116
Author(s):  
Antonio-Javier Garcia-Sanchez ◽  
Enrique Garcia Angosto ◽  
Jose Luis Llor ◽  
Alfredo Serna Berna ◽  
David Ramos
Keyword(s):  
Machine Learning ◽  
Dose Level ◽  
Machine Learning Techniques ◽  
Axial Tomography ◽  
Analytical Technique ◽  
Dose Prediction ◽  
Regression Curves ◽  
Learning Techniques ◽  
Cell Mutation ◽  
A Minor

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.

scholarly journals A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery

2019 ◽  
Vol 8 (5) ◽  
pp. 668 ◽  
Author(s):  
Yang Cao ◽  
Xin Fang ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Machine Learning ◽  
Bariatric Surgery ◽  
Test Data ◽  
Imbalanced Data ◽  
Preoperative Assessment ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Global Public Health ◽  
Bariatric Surgical Procedure ◽  
A Minor

Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.

scholarly journals MOMILACTONE-C, A MINOR CONSTITUENT OF GROWTH INHIBITORS IN RICE HUSK

1976 ◽  
Vol 5 (11) ◽  
pp. 1157-1158 ◽  
Author(s):  
Mitsuaki Tsunakawa ◽  
Akihiro Ohba ◽  
Nobuki Sasaki ◽  
Chizuko Kabuto ◽  
Tadahiro Kato ◽  
...  
Keyword(s):  
Rice Husk ◽  
Growth Inhibitors ◽  
Minor Constituent ◽  
A Minor

Synthesis of ginsenoside Rg3, a minor constituent of Ginseng Radix

1997 ◽  
Vol 304 (2) ◽  
pp. 179-182 ◽  
Author(s):  
Victor Ph. Anufriev ◽  
Galina V. Malinovskaya ◽  
Vladimir A. Denisenko ◽  
Nina I. Uvarova ◽  
Georgi B. Elyakov ◽  
...  
Keyword(s):  
Minor Constituent ◽  
Ginsenoside Rg3 ◽  
A Minor

scholarly journals Quinolizidine Alkaloid Profiles of Two Taxa of Teline maderensis

2003 ◽  
Vol 58 (11-12) ◽  
pp. 776-778 ◽  
Author(s):  
Alice Martins ◽  
Michael Wink ◽  
Andreas Tei ◽  
Amélia P. Rauter
Keyword(s):  
The Other ◽  
Minor Constituent ◽  
Alkaloid Composition ◽  
Quinolizidine Alkaloid ◽  
Aerial Parts ◽  
Capillary Glc ◽  
A Minor ◽  
Alkaloid Profiles

Abstract The alkaloid composition of the aerial parts of two taxa of Teline maderensis was studied by capillary GLC and GLC-MS. N-Methylcytisine was the major alkaloid found in both plants. Contents of cytisine and lupanine were higher in T. maderensis var. paivae while anagyrine content was more pronounced in T. maderensis var. maderensis. The alkaloids dehydrocytisine, N-acetylcytisine and epibaptifoline appeared only in T. maderensis var. maderensis and N-formylcytisine was identified as a minor constituent in T. maderensis var. paivae, and detected only in trace amounts in the other variety of the plant.

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