Feeding and trapping fish withPiper auritum

The “natives” at Rio Indio, on the central Caribbean slope of Panama, trap the river fishBrycon chagrensis using leaves ofPiper auritum as bait in feeders. The people use this technique because the fish tastes like the leaf after it has fed regularly on the leaf. This technique has potential use in aquaculture. The plant could also be cultivated for marketing as a food-flavoring condiment and fish food.     

Hysteresis,oscillations, and pattern formation in realistic immobilized enzyme systems

Summary Hysteresis, oscillations, and pattern formation in realistic biochemical systems governed by P.D.E.s are considered from both numerical and mathematical points of view. Analysis of multiple steady states in the case of hysteresis, and bifurcation theory in the cases of oscillations and pattern formation, account for the observed numerical results. The possibility to realize these systems experimentally is their main interest, thus bringing further arguments in favor of theories explaining basic biological phenomena by diffusion and reaction.     

Ethanol-induced cytochrome P-450: Catalytic activity after partial purification

Cytochrome P-450 was partially purified from liver microsomes obtained from control, ethanol, phenobarbital, and 3-methylcholanthrene-treated rats. Benzphetamine demethylation, benzpyrene hydroxylation and aniline hydroxylation activities were assayed in a reconstituted system using fixed amounts of reductase and lipids, and increasing amounts of cytochrome P-450 from each source. Cytochrome P-450 from ethanol-fed rats showed substrate specificity differing from cytochrome P-450 obtained from control, phenobarbital and 3-methylcholanthrene-treated rats.     

Primary structure of sorghum malate dehydrogenase (NADP) deduced from cDNA sequence. Homology with malate dehydrogenase (NAD)

Malate dehydrogenase (NADP) (NADP-MDH) is an important enzyme of the photosynthetic CO2 fixation pathway of C4 plants. We have isolated two clones from a sorghum lambda gt11 cDNA library (CM3, 932 bp, and CM7, 1441 bp). Nucleotide sequence analysis of the cDNAs CM3 and CM7 showed the existence of two NADP-MDH mRNA species encoding different enzyme subunits. Microsequencing of the N-terminus of the mature protein indicated that a specific cleavage of 13 amino acids occurred during the purification steps of the enzyme. The full-length cDNA CM7 contains a large open reading frame encoding an NH2-terminal transit peptide of 40 amino acids and a mature protein of 389 amino acids (42.207 kDa). Alignment of the NADP-MDH sequence with those of several malate dehydrogenases revealed some similarities with NAD-MDHs.     

Structural specificity in plant–filamentous pathogen interactions

Plant diseases bear names such as leaf blights, root rots, sheath blights, tuber scabs, and stem cankers, indicating that symptoms occur preferentially on specific parts of host plants. Accordingly, many plant pathogens are specialized to infect and cause disease in specific tissues and organs. Conversely, others are able to infect a range of tissues, albeit often disease symptoms fluctuate in different organs infected by the same pathogen. The structural specificity of a pathogen defines the degree to which it is reliant on a given tissue, organ, or host developmental stage. It is influenced by both the microbe and the host but the processes shaping it are not well established. Here we review the current status on structural specificity of plant–filamentous pathogen interactions and highlight important research questions. Notably, this review addresses how constitutive defence and induced immunity as well as virulence processes vary across plant organs, tissues, and even cells. A better understanding of the mechanisms underlying structural specificity will aid targeted approaches for plant health, for instance by considering the variation in the nature and the amplitude of defence responses across distinct plant organs and tissues when performing selective breeding.     

Transferability,development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials.  The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.