Effect of goniothalamin on the development of Ehrlich solid tumor in mice

In this work the antiproliferative activity of goniothalamin (1), both in racemic and in its enantiomeric pure forms, in a solid tumor experimental model using laboratory animals is described. The antiedematogenic activity displayed by racemic 1 in the carrageenan edema model in mice together with the reduction of Ehrlich solid tumor model suggest a relationship between anticancer and antiinflammatory activities with the antiinflammatory activity favoring the antiproliferative activity itself.     

Enalapril and losartan restored blood pressure and vascular reactivity in intrauterine undernourished rats

Epidemiological studies suggest that intrauterine undernutrition plays an important role in the development of arterial hypertension and endothelial dysfunction in adulthood. We have evaluated the effect of the Renin Angiotensin System inhibition on the blood pressure and the mesenteric arteriolar reactivity of the intrauterine undernourished rats. Wistar rats were fed either normal or 50% of the normal intake diets, during the whole gestational period. In this study only the male offspring was used. At 16 weeks of age, the rats were used for the study of blood pressure, microvascular reactivity studied in vivo-in situ to Angiotensin II (Ang II), Bradykinin (Bk) and Acetylcholine (Ach) before and after either losartan (10 mg/kg/15 days) or enalapril (15 mg/kg/21 days) treatment. We also evaluated the mesenteric and plasmatic Angiotensin Converting Enzyme (ACE), renal function, lipid plasmatic content, and insulin and glucose metabolism. Intrauterine undernutrition induced hypertension and increased response of mesenteric arterioles to Ang II and decreased vasodilation to Bk and Ach. The treatments with losartan or enalapril normalized the blood pressure levels and significantly improved the arteriolar responses to Bk, Ach and reduced the response to Ang II. No differences have been detected to ACE activity, renal function, lipid content and insulin and glucose metabolism. This study shows for the first time that Renin Angiotensin System inhibitors can normalize the cardiovascular alterations induced by intrauterine undernutrition.     

Synthesis, characterization, X-ray molecular structure and catalase-like activity of a non-heme iron complex: Dichloro[N-propanoate-N,N-bis-(2-pyridylmethyl)amine]iron(III)

In this work we present the synthesis and characterization of the complex dichloro[N-propanoate-N,N-bis-(2-pyridylmethyl)amine]iron(III) [Fe^III(PBMPA)Cl₂]. The ligand LiPBMPA was synthesized through the Michael reaction of BMPA with methylacrylate, followed by alkaline hydrolysis. The complex [Fe^III(PBMPA)Cl₂] has been synthesized by the reaction of the ligand with FeCl₃ · H₂O and was mainly characterized by cyclic voltammetry, conductivimetry, and electronic, infrared and Mössbauer spectroscopies, and by X-ray structural analysis, which showed an iron center coordinated by one carboxylate oxygen in a monodentate way, one tertiary amine, two pyridine groups and two chloride ions. It has been proposed that in water the chloride ligands are shifted by the solvent molecules and the species [Fe^III(PBMPA)(H₂O)₂]Cl₂ is predominant. The catalase-like activity of the complex was tested in water, and it proved to be active in the hydrogen peroxide dismutation. Kinetics studies were conducted following the initial rates method. The reaction is first order in relation to both the complex and the hydrogen peroxide. Based on the presence of a lag phase that depends on the initial complex concentration, we propose that the active species that shows in situ catalase-like activity, is a binuclear complex.     

Genetic diversity of peanut (Arachis hypogaea L.) and its wild relatives based on the analysis of hypervariable regions of the genome

Background  

The genus Arachis is native to a region that includes Central Brazil and neighboring countries. Little is known about the genetic variability of the Brazilian cultivated peanut (Arachis hypogaea, genome AABB) germplasm collection at the DNA level. The understanding of the genetic diversity of cultivated and wild species of peanut (Arachis spp.) is essential to develop strategies of collection, conservation and use of the germplasm in variety development. The identity of the ancestor progenitor species of cultivated peanut has also been of great interest. Several species have been suggested as putative AA and BB genome donors to allotetraploid A. hypogaea. Microsatellite or SSR (Simple Sequence Repeat) markers are co-dominant, multiallelic, and highly polymorphic genetic markers, appropriate for genetic diversity studies. Microsatellite markers may also, to some extent, support phylogenetic inferences. Here we report the use of a set of microsatellite markers, including newly developed ones, for phylogenetic inferences and the analysis of genetic variation of accessions of A. hypogea and its wild relatives.     

A new genus and species of Coenosiini from Costa Rica (Diptera,Muscidae, Coenosiinae)

Palpilongus gen. n. is herein described for one species – Palpilongus bifurcus sp. n., from Costa Rica, based on male and females. The striking morphological characters of the species – palpus very long, about as long as prementum; upper calypter truncate and very short and setae of male sternite 5 bifurcated, confirm that this new species is also a new genus in the tribe Coenosiini. Male and female terminalia were dissected and illustrated.     

Canadian polar bear population structure using genome‐wide markers

Predicting the consequences of environmental changes, including human‐mediated climate change on species, requires that we quantify range‐wide patterns of genetic diversity and identify the ecological, environmental, and historical factors that have contributed to it. Here, we generate baseline data on polar bear population structure across most Canadian subpopulations (n = 358) using 13,488 genome‐wide single nucleotide polymorphisms (SNPs) identified with double‐digest restriction site‐associated DNA sequencing (ddRAD). Our ddRAD dataset showed three genetic clusters in the sampled Canadian range, congruent with previous studies based on microsatellites across the same regions; however, due to a lack of sampling in Norwegian Bay, we were unable to confirm the existence of a unique cluster in that subpopulation. These data on the genetic structure of polar bears using SNPs provide a detailed baseline against which future shifts in population structure can be assessed, and opportunities to develop new noninvasive tools for monitoring polar bears across their range.     

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.