Ellesmeris sphenopteroides,gen. ET SP. NOV., a new zygopterid fern from the Upper Devonian (Frasnian) of Ellesmere,N.W.T., Arctic Canada

A new fern-like fossil plant is described from the lower Upper Devonian of southern Ellesmere Island, Canadian Arctic Archipelago. The plant occurs in an Archaeopteris-dominated flora preserved in the Nordstrand Point Formation (Mid-Late Frasnian) near Bird Fiord. The plant has a pinnate vegetative system with three branch orders and laminate sphenopteroid pinnules. Primary pinnae usually diverge from the main axis in distichous pairs (quadriseriate), but can depart singly (biseriate). Each primary pinna bears a basal catadromic aphlebia. Anatomically, the plant exhibits a mesarch, bipolar protostele that is ribbon- to clepsydropsoid-shaped in the main axis. Primary pinna traces are also initially bipolar and crescent-shaped, but may become four-ribbed before dividing into a pair of bipolar traces. The morphology and anatomy of this plant are nongymnospermous and are most similar to Zygopteridales (particularly Rhacophytaceae and Zygopteridaceae). The Frasnian age of Ellesmeris shows that laminated foliage had evolved in some zygopterid ferns much earlier than previously recognized. The Sphenopteris-like pinnules of Ellesmeris indicate the need for caution when attributing such a convergent foliar design to other plant groups, such as the Devonian gymnosperms.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Cloning and characterization of a SnRK1-encoding gene from <Emphasis Type="Italic">Malus hupehensis</Emphasis> Rehd. and heterologous expression in tomato

Sucrose non-fermenting-1-related protein kinase-1 (SnRK1) plays an important role in metabolic regulation in plant. To understand the molecular mechanism of amino acids and carbohydrate metabolism in Malus hupehensis Rehd. var. pinyiensis Jiang (Pingyi Tiancha, PYTC), a full-length cDNA clone encoding homologue of SnRK1 was isolated from PYTC by Rapid Amplification of cDNA Ends (RACE). The clone, designated as MhSnRK1, contains 2063 nucleotides with an open reading frame of 1548 nucleotides. The deduced 515 amino acids showed high identities with other plant SnRK1 genes. Quantitative real-time PCR analysis revealed this gene was expressed in roots, stems and leaves. Exposing seedlings to nitrate caused and initial decrease in expression of the MhSnRK1 gene in roots, leaves and stems in short term. Ectopic expression of MhSnRK1 in tomato mainly resulted in higher starch content in leaf and red-ripening fruit than wild-type plants. This result supports the hypothesis that overexpression of SnRK1 causes the accumulation of starch in plant cells. All the results suggest that MhSnRK1 may play important roles in carbohydrate and amino acid metabolisms.     

An experimental analysis of cache recovery in Clark's nutcracker

Five hypotheses of cache recovery behaviour in Clark's nutcracker (Nucifraga columbiana) were examined experimentally. Most caches were made in soil within 5 cm of conspicuous large objects. Both seed-caching and non-seed-caching nutcrackers were able to locate caches. Seed-caching nutcrackers relocated caches using large objects as remembered visual cues. Soil microtopography and small (<2 cm diameter) objects may be used as cues to facilitate cache recovery but are not essential. Non-seed-caching nutcrackers located caches by using soil disturbances at cache sites as visual cues and by searching preferentially near objects where caches were concentrated. Success rates of seed-caching nutcrackers ranged from 52 to 78% and those of non-seed-caching nutcrackers ranged from 8 to 12%. Nutcrackers do not use random search or olfactory cues to locate caches.     

Calculation of effective diffusivities for biofilms and tissues.

In this study we describe a scheme for numerically calculating the effective diffusivity of cellular systems such as biofilms and tissues. This work extends previous studies in which we developed the macroscale representations of the transport equations for cellular systems based on the subcellular-scale transport and reaction processes. A finite-difference model is used to predict the effective diffusivity of a cellular system on the basis of the subcellular-scale geometry and transport parameters. The effective diffusivity is predicted for a complex three-dimensional structure that is based on laboratory observations of a biofilm, and these numerical predictions are compared with predictions from a simple analytical solution and with experimental data. Our results indicate that, under many practical circumstances, the simple analytical solution can be used to provide reasonable estimates of the effective diffusivity.