3,334 research outputs found
Geographical classification of malaria parasites through applying machine learning to whole genome sequence data
No full textMalaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities
(29) G. Stanley Hall to Sigmund Freud, January 31, 1917
No full textThe twenty-ninth piece of correspondence between G. Stanley Hall and Sigmund Freud. Hall laments that one result of the war (World War I) is that he has stopped receiving Freud\u27s Jahrbuch. He mentions reports that it has ceased publication and inquires if there is any option for the volumes to be sent to him personally. In his book Hall the King-Maker: The Expedition to America (1909) by Saul Rosenzweig (1992), the author questions whether this letter ever made it to Freud. The letter is not located in the Freud Archives in London, and Hall never appears to have received a response.
Clark University\u27s 1909 conference was a celebration of the institution\u27s twentieth anniversary. The conference is most notable for the participation of Sigmund Freud who, along with Carl Jung, would take their first and only trip to America to attend. The five lectures Freud gave, collectively titled “The Origin and Development of Psychoanalysis” and subsequently known in print as “Five Lectures on Psychoanalysis”, mark the formal introduction of his theories to the United States
A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.
No full textBACKGROUND: Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. RESULTS: We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). CONCLUSION: Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications
Rational ellipticity of G-manifolds from their quotients
No full textWe prove that if a compact, simply connected Riemannian G-manifold M has orbit space M/G isometric to some other quotient N/H with N having zero topological entropy, then M is rationally elliptic. This result, which generalizes most conditions on rational ellipticity, is a particular case of a more general result involving manifold submetries. © The Author(s), 2025
Using deep learning to identify recent positive selection in malaria parasite sequence data
No full textAbstract Background Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods A deep learning-based approach (called “DeepSweep”) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . Results Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). Conclusion The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making
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3346: Samuel G. Freedman, author, 2013
No full textPhotograph of author Samuel G. Freedman, at NT Daily Slash meeting in the Mayborn School of Journalism at UNT
Bioinformatic analysis of Mycobacterium tuberculosis whole genome data
No full textTuberculosis (TB) caused by bacteria of the Mycobacterium- tuberculosis complex
(MTBC) is the second major cause of death from an infectious disease worldwide.
Recent advances in DNA sequencing are leading to the ability to generate whole
genome information of clinical isolates of MTBC. The objectives of this work include
developing bioinformatic tools for processing and making accessible MTBC genomic
data, as well as the identification of informative genetic markers, both strainOspecific
and associated with drug resistance (DR), to barcode MTBC isolates in research and
clinical settings.
SpolPred software was developed to accurately predict the spoligotype from raw
sequence reads, and used to bridge the gap between classical genotyping and highO
throughput sequencing. A genome variation discovery pipeline was implemented to
derive genomic polymorphisms from MTBC raw sequence data. This pipeline was
applied to >1,500 publicly available isolates and the characterised genomic variation
hosted in PolyTB, a webObased tool where genetic variants can be investigated using a
genome browser, a world map showing their global allele distribution, and an
additional phylogenetic view. An extensive repertoire of strainOspecific mutations was
identified, of which a subset was proposed to accurately discriminate known MTBC
circulating strains. A curated list of DR associated mutations was compiled from the
literature and their diagnostic accuracy for predicting phenotypic resistance assessed.
In addition, potentially novel genes involved in DR were discovered by applying
genomeOwide association approaches to a global population of more than 2,500 MTBC
strains. Whole genome sequencing (WGS) promises to be transformative for the practice of
clinical microbiology, and the rapidly falling cost and turnaround time mean that this
will become a viable technology in clinical settings. In this new paradigm, the
presented work will facilitate the transition to and applications of WGS in clinical
settings as an important tool for TB control
The neglected 95%, a challenge to psychology\u27s philosophy of science
No full textResponds to the comments of LoSchiavo F. M. and Shatz M. A. (see record 2009-13007-013); Webster G. D., Nichols A. L., and Schember T. O. (see record 2009-13007-014); Stroebe W. and Nijstad B. (see record 2009-13007-015); and Haeffel et al. (see record 2009-13007-016) on the author\u27s original article (see record 200814338-003) regarding the assertion that American psychology focuses too narrowly on Americans while neglecting the other 95% of the world’s population. The author indicates that the four comments were well chosen in that they represent quite different reactions to his article. In this rejoinder the author addresses the issues raised in each of the comments, first the two supporting comments and then the two opposing comments. Following this, he addresses the more general problem that cuts across the comments: American psychology’s dominant philosophy of science. (PsycINFO Database Record (c) 2016 APA, all rights reserved
Analysis of MRSA Staphylococcal Chromosome cassette mecA status from next generation sequence data
No full textNGS sequencing libraries prepared on an Illumina NGS platform for 10 isolates of Staphylococcus aureus were analysed. After extensive pre-processing to address library quality issues, for each isolate the status of the Staphylococcal Chromosome Cassette, and its mecA gene specifying resistance to meticillin, was determined. All mecA-positive isolates encoded canonical mecA. None encoded the new variant mecA identified in strain LGA251.Biotechnology and Biological (BBSRC)Applied Bioinformatic
Calvadosia Clark 1863
No full textGenus Calvadosia Clark, 1863 Remarks. Calvadosia was originally proposed by Clark (1863) to accommodate a species described by Lamouroux (1815), “ Lucernaire campanulée ” (or Lucernaria campanulata), from Calvados, France, therefore proposing the name Calvadosia campanulata (Lamouroux, 1815). Its main difference from other Lucernaria is the “four pilasters […] not muscular, as are the pilasters in the pedicel of Lucernaria quadricornis ” (Clark 1863: 556), i.e., absence of interradial longitudinal muscles associated with the septa of the peduncle. However, Clark’s (1863) proposal was overlooked for many years. Later, Uchida (1929) proposed a new genus, Lucernariopsis, for the same “ Lucernaria campanulata ”, including species with one-chambered peduncle without muscles, overlooking the availability of the older name Calvadosia Clark, 1863. Apparently, Gwilliam (1956: 10) was the only author to notice this nomenclatural issue, concluding that according to the “law of priority, the proper generic name of Lucernariopsis Uchida, 1929 is Calvadosia Clark, 1863 ”, but he never published his PhD Dissertation on the taxonomy of the Stauromedusae. More recently, Lucernariopsis Uchida, 1929 was officially recognized as a synonym of Calvadosia Clark, 1863 (Miranda et al. 2016b). In addition, based on molecular and morphological evidence, the former genera Kishinouyea Mayer, 1910 and Sasakiella Okubo, 1917 were also incorporated into Calvadosia (Miranda et al. 2016b). Therefore, Calvadosia is currently one of the most diverse genera in Staurozoa, with 11 species: Calvadosia campanulata (Lamouroux, 1815), Calvadosia nagatensis (Oka, 1897), Calvadosia vanhoeffeni (Browne, 1910), Calvadosia cruciformis (Okubo, 1917), Calvadosia hawaiiensis (Edmondson, 1930), Calvadosia tsingtaoensis (Ling, 1937), Calvadosia capensis (Carlgren, 1938), Calvadosia cruxmelitensis (Corbin, 1978), Calvadosia corbini (Larson, 1980), Calvadosia tasmaniensis (Zagal, Hirano, Mills, Edgar & Barrett, 2011), and Calvadosia lewisi sp. nov. described in this study.Published as part of Miranda, Lucília S., Branch, George M., Collins, Allen G., Hirano, Yayoi M., Marques, Antonio C. & Griffiths, Charles L., 2017, Stalked jellyfishes (Cnidaria: Staurozoa) of South Africa, with the description of Calvadosia lewisi sp. nov., pp. 369-389 in Zootaxa 4227 (3) on pages 371-372, DOI: 10.11646/zootaxa.4227.3.5, http://zenodo.org/record/26834
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