Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Synthesis And Antitubercular Activity Of 1,2,4-Trisubstitued Piperazines

Parallel solid phase synthesis offers a unique opportunity for the synthesis and screening of large numbers of compounds and significantly enhances the prospect of finding new leads. We report the synthesis and antitubercular activity of chiral 1,2,4-trisubstituted piperazines derived from resin bound acylated dipeptides against Mycobacterium tuberculosis strain H37Rv

Synthesis of Trifunctional Thiazolyl Amino Acids And Their Use for the Solid-Phase Synthesis of Small Molecule Compounds and Cyclic Peptidomimetics

Chiral thiazolyl amino acid building blocks for the solid-phase synthesis of small molecules, peptides, and cyclic peptides have been designed and synthesized starting from Fmoc protected asparagine and glutamine. In efforts to demonstrate the usefulness and validity of such building blocks, a small library of 16 new thiazole containing small molecules has been prepared and characterized. Additionally, we report the use of the newly prepared trifunctional thiazolyl glutamine for the on-resin, head-to-tail synthesis of cyclic peptides

Identification of Bis-Cyclic Guanidines as Antiplasmodial Compounds from Positional Scanning Mixture-Based Libraries

The screening of more than 30 million compounds derived from 81 small molecule libraries built on 81 distinct scaffolds identified pyrrolidine bis-cyclic guanidine library (TPI-1955) to be one of the most active and selective antiplasmodial libraries. The screening of the positional scanning library TPI-1955 arranged on four sets of sublibraries (26 + 26 + 26 + 40), totaling 120 samples for testing provided information about the most important groups of each variable position in the TPI-1955 library containing 738,192 unique compounds. The parallel synthesis of the individual compounds derived from the deconvolution of the positional scanning library led to the identification of active selective antiplasmodial pyrrolidine bis-cyclic guanidines

Fused Polycyclic Compounds via Cycloaddition of 4‑(1′-Cyclohexenyl)-5-iodo-1,2,3-triazoles with 4‑Phenyl-1,2,4-triazoline-3,5-dione: The Importance of a Sacrificial Iodide Leaving Group

4-(1′-Cyclohexenyl)-5-iodo-1,2,3-triazole and 4-phenyl-1,2,4-triazoline-3,5-dione undergo a formal Diels–Alder reaction, which following an S_N2′ solvolysis process to displace the iodo group affords a fused polycyclic compound

Novel Cyclic Lipopeptides Fusaricidin Analogs for Treating Wound Infections

Both acute and chronic cutaneous wounds are often difficult to treat due to the high-risk for bacterial contamination. Once hospitalized, open wounds are at a high-risk for developing hospital-associated infections caused by multi drug-resistant bacteria such as Staphylococcus aureus and Pseudomonas aeruginosa. Treating these infections is challenging, not only because of antibiotic resistance, but also due to the production of biofilms. New treatment strategies are needed that will help in both stimulating the wound healing process, as well as preventing and eliminating bacterial wound infections. Fusaricidins are naturally occurring cyclic lipopeptides with antimicrobial properties that have shown to be effective against a variety of fungi and Gram-positive bacteria, with low toxicity. Continuing with our efforts toward the identification of novel cyclic lipopeptides Fusaricidin analogs, herein we report the synthesis and evaluation of the antimicrobial activity for two novel cyclic lipopeptides (CLP), CLP 2605-4 and CLP 2612-8.1 against methicillin resistant S. aureus and P. aeruginosa, respectively, in in vivo porcine full thickness wound model. Both CLPs were able to reduce bacterial counts by approximately 3 log CFU/g by the last assessment day. Peptide 2612-8.1 slightly enhanced the wound healing, however, wounds treated with peptide 2605-4, have shown higher levels of inflammation and impaired wound healing process. This study highlights the importance of identifying new antimicrobials that can combat bacterial infection while not impeding tissue repair