James Devillers

Bio: James Devillers is an academic researcher. The author has contributed to research in topics: Quantitative structure–activity relationship & Glycol ethers. The author has an hindex of 15, co-authored 37 publications receiving 650 citations.

SAR and QSAR modeling of endocrine disruptors

Abstract: A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Residues of plant protection products in grey partridge eggs in French cereal ecosystems

Abstract: The contamination of the eggs of farmland birds by currently used plant protection products (PPPs) is poorly documented despite a potential to adversely impact their breeding performance. In this context, 139 eggs of 52 grey partridge Perdix perdix clutches, collected on 12 intensively cultivated farmlands in France in 2010-2011, were analysed. Given the great diversity of PPPs applied on agricultural fields, we used exploratory GC/MS-MS and LC/MS-MS screenings measuring ca. 500 compounds. The limit of quantification was 0.01 mg/kg, a statutory reference. A total of 15 different compounds were detected in 24 clutches. Nine of them have been used by farmers to protect crops against fungi (difenoconazole, tebuconazole, cyproconazole, fenpropidin and prochloraz), insects (lambda-cyhalothrin and thiamethoxam/clothianidin) and weeds (bromoxynil and diflufenican). Some old PPPs were also detected (fipronil(+sulfone), HCH(α,β,δ isomers), diphenylamine, heptachlor(+epoxyde), DDT(Σisomers)), as well as PCBs(153, 180). Concentrations ranged between <0.01 and 0.05 mg/kg but reached 0.067 (thiamethoxam/clothianidin), 0.11 (heptachlor + epoxyde) and 0.34 (fenpropidin) mg/kg in some cases. These results testify an actual exposure of females and/or their eggs to PPPs in operational conditions, as well as to organochlorine pollutants or their residues, banned in France since several years if not several decades, that persistently contaminate the environment.Routes of exposure, probability to detect a contamination in the eggs, and effects on egg/embryo characteristics are discussed with regard to the scientific literature.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Handbook of Chemistry and Physics

Abstract: There is a special reason for reviewing this book at this time: it is the 50th edition of a compendium that is known and used frequently in most chemical and physical laboratories in many parts of the world. Surely, a publication that has been published for 56 years, withstanding the vagaries of science in this century, must have had something to offer. There is another reason: while the book is a standard fixture in most chemical and physical laboratories, including those in medical centers, it is not as frequently seen in the laboratories of physician's offices (those either in solo or group practice). I believe that the Handbook can be useful in those laboratories. One of the reasons, among others, is that the various basic items of information it offers may be helpful in new tests, either physical or chemical, which are continuously being published. The basic information may relate

Polycyclic Aromatic Hydrocarbons

Abstract: Polycyclic Hydrocarbons Vol. 1. Pp. xxvi + 487. 126S. (With a chapter on carcinogenesis by Regina Schoental.) Vol. 2. Pp. lvii + 487. 140s. By E. Clar. (London and New York: Academic Press; Berlin: Springer-Verlag, 1964.)

Cyanobacterial peptides – Nature's own combinatorial biosynthesis

Abstract: Cyanobacterial secondary metabolites have attracted increasing scientific interest due to bioactivity of many compounds in various test systems. Among the known structures, oligopeptides are often found with many congeners sharing conserved substructures, while being highly variable in others. A major part of known oligopeptides are of non-ribosomal origin and can be grouped into classes with conserved structural properties. Thus, the overall structural diversity of cyanobacterial oligopeptides only seemingly suggests an equally high diversity of biosynthetic pathways and respective genes. For each class of peptides, some of which have been found in all major branches of the cyanobacterial evolutionary tree, homologous synthetases and genes can be inferred. This implies that non-ribosomal peptide synthetase genes are a very ancient part of the cyanobacterial genome and presumably have evolved by recombination and duplication events to reach the present structural diversity of cyanobacterial oligopeptides. In addition, peptide synthetases would appear to be an essential part of the cyanobacterial evolution and physiology. The present review presents an overview of the biosynthesis of cyanobacterial peptides and corresponding gene clusters, the structural diversity of structural types and structural variations within peptide classes, and implications for the evolution and plasticity of biosynthetic genes and the potential function of cyanobacterial peptides.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects—2010

Abstract: Sarah AdlerDavid BasketterStuart CretonOlavi PelkonenJan van BenthemValerie Zuang • Klaus Ejner AndersenAlexandre Angers-LoustauAynur AptulaAnna Bal-PriceEmilio Benfenati • Ulrike BernauerJos BessemsFrederic Y. BoisAlan BoobisEsther BrandonSusanne Bremer • Thomas BroschardSilvia CasatiSandra CoeckeRaffaella CorviMark CroninGeorge Daston • Wolfgang DekantSusan FelterElise GrignardUrsula Gundert-RemyTuula HeinonenIan Kimber • Jos KleinjansHannu KomulainenReinhard KreilingJoachim KreysaSofia Batista LeiteGeorge Loizou • Gavin MaxwellPaolo MazzatortaSharon MunnStefan PfuhlerPascal PhrakonkhamAldert Piersma • Albrecht PothPilar PrietoGuillermo RepettoVera RogiersGreet SchoetersMichael Schwarz • Rositsa SerafimovaHanna TahtiEmanuela TestaiJoost van DelftHenk van LoverenMathieu Vinken • Andrew WorthJose ´-Manuel Zaldivar