Challenges and opportunities in synthesizing historical geospatial data using statistical models

We classified land cover types from 1940s historical aerial imagery using Object Based Image Analysis (OBIA) and compared these maps with data on recent cover. Few studies have used these kinds of maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. We studied the change in woody cover at four sites in California's North Coast using historical (1948) and recent (2009) high spatial resolution imagery. We classified the imagery using eCognition Developer and aggregated the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. We used Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. We explored the relative influences on current percent woody cover of topographic variables (grouped using principal component analysis) reflecting water retention capacity, exposure, and within-site context, as well as historical percent woody cover and geographical coordinates. We estimated these models for pixel sizes of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. We found that historical woody cover had a consistent positive effect on current woody cover, and that the spatial autoregressive term in the model was significant even after controlling for historical cover. Specific topographic variables emerged as important for different sites at different scales, but no overall pattern emerged across sites or scales for any of the topographic variables we tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.     

Malaria in India: Challenges and opportunities

India contributes about 70% of malaria in the South East Asian Region of WHO. Although annually India reports about two million cases and 1000 deaths attributable to malaria, there is an increasing trend in the proportion of Plasmodium falciparum as the agent. There exists heterogeneity and variability in the risk of malaria transmission between and within the states of the country as many ecotypes/paradigms of malaria have been recognized. The pattern of clinical presentation of severe malaria has also changed and while multi-organ failure is more frequently observed in falciparum malaria, there are reports of vivax malaria presenting with severe manifestations. The high burden populations are ethnic tribes living in the forested pockets of the states like Orissa, Jharkhand, Madhya Pradesh, Chhattisgarh and the North Eastern states which contribute bulk of morbidity and mortality due to malaria in the country. Drug resistance, insecticide resistance, lack of knowledge of actual disease burden along with new paradigms of malaria pose a challenge for malaria control in the country. Considering the existing gaps in reported and estimated morbidity and mortality, need for estimation of true burden of malaria has been stressed. Administrative, financial, technical and operational challenges faced by the national programme have been elucidated. Approaches and priorities that may be helpful in tackling serious issues confronting malaria programme have been outlined.     

Rigid nanoparticle-based delivery of anti-cancer siRNA: Challenges and opportunities

Gene therapy is a promising strategy to treat various genetic and acquired diseases. Small interfering RNA (siRNA) is a revolutionary tool for gene therapy and the analysis of gene function. However, the development of a safe, efficient, and targetable non-viral siRNA delivery system remains a major challenge in gene therapy. An ideal delivery system should be able to encapsulate and protect the siRNA cargo from serum proteins, exhibit target tissue and cell specificity, penetrate the cell membrane, and release its cargo in the desired intracellular compartment. Nanomedicine has the potential to deal with these challenges faced by siRNA delivery. The unique characteristics of rigid nanoparticles mostly inorganic nanoparticles and allotropes of carbon nanomaterials, including high surface area, facile surface modification, controllable size, and excellent magnetic/optical/electrical properties, make them promising candidates for targeted siRNA delivery. In this review, recent progresses on rigid nanoparticle-based siRNA delivery systems will be summarized.     

Pre-clinical and clinical aspects of peptide-based vaccine against human solid tumors

Peptides deriving from tumor-associated antigens and recognized by patient T cells have been firstly defined in the early 90's, and then used as vaccine in animal models and in cancer patients. Early trials showed a variable, often even high frequency of patients developing peptide-specific T-cell mediated immune response usually accompanied by a lower frequency of clinical response. Modified, long peptides could be synthesized with a higher in vitro binding to the corresponding HLA allele that only seldom translated into a clear improvement in the tumor response. However, we show here that more recent studies of multipeptide-based vaccines resulted in a higher and more robust T cell response causing also a more effective clinical response particularly in melanoma and prostate cancer patients. In this article, we also used some of the recent patents describing different inventions related to pre-clinical and clinical aspects of peptide based vaccines against human solid tumors.     

Dendritic cell therapy of primary brain tumors.

BACKGROUND: Although current treatment modalities for malignant gliomas, such as surgery, radiation and chemotherapy, have been improved markedly in the past two decades, the prognosis of these neoplasms remains poor, the two year survival rate being approximately 5%. Therefore, alternative treatment options, such as gene therapy and immunotherapy are rapidly gaining momentum. One of the most promising immunotherapeutic approaches for the treatment of cancer is the vaccination of cancer patients with dendritic cells (DC) pulsed with tumor antigens. Immunotherapy with DC seems to be able to overcome, at least partially, the immunosuppressive state associated with primary malignant gliomas. DC therapy proved to be safe in both animal models and clinical trials. No serious side effects and no evidence of autoimmune toxicity occurred. Most studies used DC pulsed with an array of tumor-associated antigens rather than single peptides, to allow for presentation of unknown tumor-specific antigens to DC. Routes of administration either were subcutaneous, intradermal or intraperitoneal, with multiple injections of DC to enhance antitumor immunity. DC therapy as an adjuvant treatment for patients with malignant glioma seems to be biologically safe. Further clinical studies are warranted.     

Zebrafish heart failure models: opportunities and challenges

Heart failure is a complex pathophysiological syndrome of pumping functional failure that results from injury, infection or toxin-induced damage on the myocardium, as well as genetic influence. Gene mutations associated with cardiomyopathies can lead to various pathologies of heart failure. In recent years, zebrafish, Danio rerio, has emerged as an excellent model to study human cardiovascular diseases such as congenital heart defects, cardiomyopathy, and preclinical development of drugs targeting these diseases. In this review, we will first summarize zebrafish genetic models of heart failure arose from cardiomyopathy, which is caused by mutations in sarcomere, calcium or mitochondrial-associated genes. Moreover, we outline zebrafish heart failure models triggered by chemical compounds. Elucidation of these models will improve the understanding of the mechanism of pathogenesis and provide potential targets for novel therapies.

     

Challenges and opportunities in proteomics data analysis

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.     

Challenges and opportunities of defining clinical leptin resistance

The widespread use of the inadequately defined term "leptin resistance" led the National Institutes of Health to convene a workshop aimed at developing a quantitative definition of this term that would facilitate mechanistic research into leptin's actions in human health and disease. Although leptin-responsive conditions are recognized, the field is limited by a lack of robust, easily quantifiable behavioral or metabolic biomarkers of the hormone's action. Further advances require biomarkers that can be used to identify patients who may benefit from leptin therapy and that are useful for understanding the determinants of clinical leptin responsiveness.     

Mouse models and type 2 diabetes: translational opportunities

Type 2 diabetes prevalence is increasing worldwide. Treatments are available, but glycaemic control is not always effective in many patients. Better models are needed to create new and improved therapies and to expand our understanding of how type 2 diabetes begins and progresses. Translational research involves the transformation of knowledge from basic scientific discoveries to impacting on public health. This can allow identification of novel molecular mechanisms underlying the disease which can lead to preventative measures, biomarkers for diagnosis, or future therapies. Generation of genetically modified mice has allowed us to investigate the function of genes and develop reproducible models in which the phenotype of the animal can be tested. Mouse models have already given us insight into glucose metabolism and insulin secretion, identified novel pathways, and have been used to confirm genome-wide association studies. In this review we discuss the use of the mouse to clarify human genome-wide association study loci, understand genes and pathways involved in type 2 diabetes, and uncover novel targets for drug discovery.     

Challenges,achievements and opportunities in allelopathy research

Abstract

Allelopathy is defined as the suppression of any aspect of growth and/or development of one plant by another through the release of chemical compounds. Although allelopathic interference has been demonstrated many times using in vitro experiments, few studies have clearly demonstrated allelopathy in natural settings. This difficulty reflects the complexity in examining and demonstrating allelopathic interactions under field conditions. In this paper we address a number of issues related to the complexity of allelopathic interference in higher plants: These are: (i) is a demonstrated pattern or zone of inhibition important in documenting allelopathy? (ii) is it ecologically relevant to explain the allelopathic potential of a species based on a single bioactive chemical? (iii) what is the significance of the various modes of allelochemical release from the plant into the environment? (iv) do soil characteristics clearly influence allelopathic activity? (v) is it necessary to exclude other plant interference mechanisms?, and (vi) how can new achievements in allelopathy research aid in solving problems related to relevant ecological issues encountered in research conducted upon natural systems and agroecosystems? A greater knowledge of plant interactions in ecologically relevant environments, as well as the study of biochemical pathways, will enhance our understanding of the role of allelopathy in agricultural and natural settings. In addition, novel findings related to the relevant enzymes and genes involved in production of putative allelochemicals, allelochemical persistence in the rhizosphere, the molecular target sites of allelochemicals in sensitive plant species and the influence of allelochemicals upon other organisms will likely lead to enhanced utilization of natural products for pest management or as pharmaceuticals and nutraceuticals. This review will address these recent findings, as well as the major challenges which continue to influence the outcomes of allelopathy research.     

Glutamate release by primary brain tumors induces epileptic activity

Epileptic seizures are a common and poorly understood comorbidity for individuals with primary brain tumors. To investigate peritumoral seizure etiology, we implanted human-derived glioma cells into severe combined immunodeficient mice. Within 14-18 d, glioma-bearing mice developed spontaneous and recurring abnormal electroencephalogram events consistent with progressive epileptic activity. Acute brain slices from these mice showed marked glutamate release from the tumor mediated by the system x(c)(-) cystine-glutamate transporter (encoded by Slc7a11). Biophysical and optical recordings showed glutamatergic epileptiform hyperexcitability that spread into adjacent brain tissue. We inhibited glutamate release from the tumor and the ensuing hyperexcitability by sulfasalazine (SAS), a US Food and Drug Administration-approved drug that blocks system x(c)(-). We found that acute administration of SAS at concentrations equivalent to those used to treat Crohn's disease in humans reduced epileptic event frequency in tumor-bearing mice compared with untreated controls. SAS should be considered as an adjuvant treatment to ameliorate peritumoral seizures associated with glioma in humans.     

A role for glutamate in growth and invasion of primary brain tumors

The vast majority of primary brain tumors derive from glial cells and are collectively called gliomas. While, they share some genetic mutations with other cancers, they do present with a unique biology and have developed adaptations to meet specific biological needs. Notably, glioma growth is physically restricted by the skull, and, unless normal brain cells are destroyed, tumors cannot expand. To overcome this challenge, glioma cells release glutamate which causes excitotoxic death to surrounding neurons, thereby vacating room for tumor expansion. The released glutamate also explains peritumoral seizures which are a common symptom early in the disease. Glutamate release occurs via system X^c, a cystine–glutamate exchanger that releases glutamate in exchange for cystine being imported for the synthesis of the cellular antioxidant GSH. It protects tumor cells from endogenously produced reactive oxygen and nitrogen species but also endows tumors with an enhanced resistance to radiation- and chemotherapy. Pre-clinical data demonstrates that pharmacological inhibition of system X^c causes GSH depletion which slows tumor growth and curtails tumor invasion in vivo . An Food and Drug Administration approved drug candidate is currently being introduced into clinical trials for the treatment of malignant glioma.     

Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors

Background

Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results

We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions

The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

Reviewers

This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck).     

Challenges and opportunities in the purification of recombinant tagged proteins

The purification of recombinant proteins by affinity chromatography is one of the most efficient strategies due to the high recovery yields and purity achieved. However, this is dependent on the availability of specific affinity adsorbents for each particular target protein. The diversity of proteins to be purified augments the complexity and number of specific affinity adsorbents needed, and therefore generic platforms for the purification of recombinant proteins are appealing strategies. This justifies why genetically encoded affinity tags became so popular for recombinant protein purification, as these systems only require specific ligands for the capture of the fusion protein through a pre-defined affinity tag tail. There is a wide range of available affinity pairs “tag-ligand” combining biological or structural affinity ligands with the respective binding tags. This review gives a general overview of the well-established “tag-ligand” systems available for fusion protein purification and also explores current unconventional strategies under development.     

Mechanics of the brain: perspectives,challenges, and opportunities

The human brain is the continuous subject of extensive investigation aimed at understanding its behavior and function. Despite a clear evidence that mechanical factors play an important role in regulating brain activity, current research efforts focus mainly on the biochemical or electrophysiological activity of the brain. Here, we show that classical mechanical concepts including deformations, stretch, strain, strain rate, pressure, and stress play a crucial role in modulating both brain form and brain function. This opinion piece synthesizes expertise in applied mathematics, solid and fluid mechanics, biomechanics, experimentation, material sciences, neuropathology, and neurosurgery to address today’s open questions at the forefront of neuromechanics. We critically review the current literature and discuss challenges related to neurodevelopment, cerebral edema, lissencephaly, polymicrogyria, hydrocephaly, craniectomy, spinal cord injury, tumor growth, traumatic brain injury, and shaken baby syndrome. The multi-disciplinary analysis of these various phenomena and pathologies presents new opportunities and suggests that mechanical modeling is a central tool to bridge the scales by synthesizing information from the molecular via the cellular and tissue all the way to the organ level.     

Clinical relevance of tumor cells with stem-like properties in pediatric brain tumors

Background

Primitive brain tumors are the leading cause of cancer-related death in children. Tumor cells with stem-like properties (TSCs), thought to account for tumorigenesis and therapeutic resistance, have been isolated from high-grade gliomas in adults. Whether TSCs are a common component of pediatric brain tumors and are of clinical relevance remains to be determined.

Methodology/Principal Findings

Tumor cells with self-renewal properties were isolated with cell biology techniques from a majority of 55 pediatric brain tumors samples, regardless of their histopathologies and grades of malignancy (57% of embryonal tumors, 57% of low-grade gliomas and neuro-glial tumors, 70% of ependymomas, 91% of high-grade gliomas). Most high-grade glioma-derived oncospheres (10/12) sustained long-term self-renewal akin to neural stem cells (>7 self-renewals), whereas cells with limited renewing abilities akin to neural progenitors dominated in all other tumors. Regardless of tumor entities, the young age group was associated with self-renewal properties akin to neural stem cells (P = 0.05, chi-square test). Survival analysis of the cohort showed an association between isolation of cells with long-term self-renewal abilities and a higher patient mortality rate (P = 0.013, log-rank test). Sampling of low- and high-grade glioma cultures showed that self-renewing cells forming oncospheres shared a molecular profile comprising embryonic and neural stem cell markers. Further characterization performed on subsets of high-grade gliomas and one low-grade glioma culture showed combination of this profile with mesenchymal markers, the radio-chemoresistance of the cells and the formation of aggressive tumors after intracerebral grafting.

Conclusions/Significance

In brain tumors affecting adult patients, TSCs have been isolated only from high-grade gliomas. In contrast, our data show that tumor cells with stem cell-like or progenitor-like properties can be isolated from a wide range of histological sub-types and grades of pediatric brain tumors. They suggest that cellular mechanisms fueling tumor development differ between adult and pediatric brain tumors.     

microRNA-34a is tumor suppressive in brain tumors and glioma stem cells

We recently found that microRNA-34a (miR-34a) is downregulated in human glioma tumors as compared to normal brain, and that miR-34a levels in mutant-p53 gliomas were lower than in wildtype-p53 tumors. We showed that miR-34a expression in glioma and medulloblastoma cells inhibits cell proliferation, G1/S cell cycle progression, cell survival, cell migration and cell invasion, but that miR-34a expression in human astrocytes does not affect cell survival and cell cycle. We uncovered the oncogenes c-Met, Notch-1 and Notch-2 as direct targets of miR-34a that are inhibited by miR-34a transfection. We found that c-Met levels in human glioma specimens inversely correlate with miR-34a levels. We showed that c-Met and Notch partially mediate the inhibitory effects of miR-34a on cell proliferation and cell death. We also found that mir-34a expression inhibits in vivo glioma xenograft growth. We concluded that miR-34a is a potential tumor suppressor in brain tumors that acts by targeting multiple oncogenes. In this extra view, we briefly review and discuss the implications of these findings and present new data on the effects of miR-34a in glioma stem cells. The new data show that miR-34a expression inhibits various malignancy endpoints in glioma stem cells. Importantly, they also show for the first time that miR-34a expression induces glioma stem cell differentiation. Altogether, the data suggest that miR-34a is a tumor suppressor and a potential potent therapeutic agent that acts by targeting multiple oncogenic pathways in brain tumors and by inducing the differentiation of cancer stem cells.