Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time

The climate impact of bioenergy is commonly quantified in terms of CO₂ equivalents, using a fixed 100‐year global warming potential as an equivalency metric. This method has been criticized for the inability to appropriately address emissions timing and the focus on a single impact metric, which may lead to inaccurate or incomplete quantification of the climate impact of bioenergy production. In this study, we introduce Dynamic Relative Climate Impact (DRCI) curves, a novel approach to visualize and quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category (e.g., emissions, radiative forcing, and temperature change), equivalency metric, and analytical time horizon. The DRCI curves constructed for fourteen bioenergy systems illustrate how value judgments affect the merit order of bioenergy systems, because they alter the importance of one‐time (associated with land use change emissions) versus sustained (associated with carbon debt or foregone sequestration) emission fluxes and short‐ versus long‐lived climate forcers. Best practices for bioenergy production (irrespective of value judgments) include high feedstock yields, high conversion efficiencies, and the application of carbon capture and storage. Furthermore, this study provides examples of production contexts in which the risk of land use change emissions, carbon debt, or foregone sequestration can be mitigated. For example, the risk of indirect land use change emissions can be mitigated by accompanying bioenergy production with increasing agricultural yields. Moreover, production contexts in which the counterfactual scenario yields immediate or additional climate impacts can provide significant climate benefits. This paper is accompanied by an Excel‐based calculation tool to reproduce the calculation steps outlined in this paper and construct DRCI curves for bioenergy systems of choice.     

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.     

Efficacy of Lapatinib in Therapy-Resistant HER2-Positive Circulating Tumor Cells in Metastatic Breast Cancer

Background

To evaluate the efficacy of lapatinib, a dual EGFR and HER2 tyrosine kinase inhibitor, in therapy-resistant HER2-positive CTCs in metastatic breast cancer (MBC).

Patients and Methods

Patients with MBC and HER2-positive CTCs despite disease stabilization or response to prior therapy, received lapatinib 1500 mg daily in monthly cycles, till disease progression or CTC increase. CTC monitoring was performed by immunofluorescent microscopy using cytospins of peripheral blood mononuclear cells (PBMCs) double stained for HER2 or EGFR and cytokeratin.

Results

A total of 120 cycles were administered in 22 patients; median age was 62.5 years, 15 (68.2%) patients were post-menopausal and 20 (90.1%) had HER2-negative primary tumors. At the end of the second course, HER2-positive CTC counts decreased in 76.2% of patients; the median number of HER2-positive CTCs/patient also declined significantly (p = 0.013), however the decrease was significant only among patients presenting disease stabilization (p = 0.018) but not among those with disease progression during lapatinib treatment. No objective responses were observed. All CTC-positive patients harbored EGFR-positive CTCs on progression compared to 62.5% at baseline (p = 0.054). The ratio of EGFR-positive CTCs/total CTCs detected in all patients increased from 17.1% at baseline to 37.6% on progression, whereas the mean percentage of HER2-negative CTCs/patient increased from 2.4% to 30.6% (p = 0.03).

Conclusions

The above results indicate that lapatinib is effective in decreasing HER2-positive CTCs in patients with MBC irrespectively of the HER2 status of the primary tumor and imply the feasibility of monitoring the molecular changes on CTCs during treatment with targeted agents.

Trial Registration

Clinical trial.gov NCT00694252     

Functional Loss of Semaphorin 3C and/or Semaphorin 3D and Their Epistatic Interaction with Ret Are Critical to Hirschsprung Disease Liability

Innervation of the gut is segmentally lost in Hirschsprung disease (HSCR), a consequence of cell-autonomous and non-autonomous defects in enteric neuronal cell differentiation, proliferation, migration, or survival. Rare, high-penetrance coding variants and common, low-penetrance non-coding variants in 13 genes are known to underlie HSCR risk, with the most frequent variants in the ret proto-oncogene (RET). We used a genome-wide association (220 trios) and replication (429 trios) study to reveal a second non-coding variant distal to RET and a non-coding allele on chromosome 7 within the class 3 Semaphorin gene cluster. Analysis in Ret wild-type and Ret-null mice demonstrates specific expression of Sema3a, Sema3c, and Sema3d in the enteric nervous system (ENS). In zebrafish embryos, sema3 knockdowns show reduction of migratory ENS precursors with complete ablation under conjoint ret loss of function. Seven candidate receptors of Sema3 proteins are also expressed within the mouse ENS and their expression is also lost in the ENS of Ret-null embryos. Sequencing of SEMA3A, SEMA3C, and SEMA3D in 254 HSCR-affected subjects followed by in silico protein structure modeling and functional analyses identified five disease-associated alleles with loss-of-function defects in semaphorin dimerization and binding to their cognate neuropilin and plexin receptors. Thus, semaphorin 3C/3D signaling is an evolutionarily conserved regulator of ENS development whose dys-regulation is a cause of enteric aganglionosis.     

Insecticide resistance in Bemisia tabaci from Cyprus

Abstract A comprehensive study on the Bemisia tabaci (biotype B) resistance to neonicotinoid insecticides imidacloprid, acetamiprid and thiamethoxam, and pyrethroid bifenthrin was conducted in Cyprus. The resistance level to eight field‐collected B. tabaci populations was investigated. The activities of enzymes involved in metabolic detoxification and the frequencies of pyrethroid and organophosphates target site resistance mutations were determined. Moderate to high levels of resistance were detected for imidacloprid (resistance factor [RF] 77–392) and thiamethoxam (RF 50–164) while low resistance levels were observed for acetamiprid (RF 7–12). Uniform responses by the Cypriot whiteflies could be observed against all neonicotinoid insecticides. No cross‐resistance between the neonicotinoids was detected as well as no association with the activity of the P450 microsomal oxidases. Only imidacloprid resistance correlated with carboxylesterase activity. Low to extremely high resistance was observed for insecticide bifenthrin (RF 49–1 243) which was associated with the frequency of the resistant allele in the sodium channel gene but not with the activity of the detoxification enzymes. Finally, the F331W mutation in the acetylcholinesterase enzyme ace1 gene was fixed in all B. tabaci populations from Cyprus.     

Afforestation for climate change mitigation: Potentials,risks and trade‐offs

Afforestation is considered a cost‐effective and readily available climate change mitigation option. In recent studies afforestation is presented as a major solution to limit climate change. However, estimates of afforestation potential vary widely. Moreover, the risks in global mitigation policy and the negative trade‐offs with food security are often not considered. Here we present a new approach to assess the economic potential of afforestation with the IMAGE 3.0 integrated assessment model framework. In addition, we discuss the role of afforestation in mitigation pathways and the effects of afforestation on the food system under increasingly ambitious climate targets. We show that afforestation has a mitigation potential of 4.9 GtCO₂/year at 200 US$/tCO₂ in 2050 leading to large‐scale application in an SSP2 scenario aiming for 2°C (410 GtCO₂ cumulative up to 2100). Afforestation reduces the overall costs of mitigation policy. However, it may lead to lower mitigation ambition and lock‐in situations in other sectors. Moreover, it bears risks to implementation and permanence as the negative emissions are increasingly located in regions with high investment risks and weak governance, for example in Sub‐Saharan Africa. Afforestation also requires large amounts of land (up to 1,100 Mha) leading to large reductions in agricultural land. The increased competition for land could lead to higher food prices and an increased population at risk of hunger. Our results confirm that afforestation has substantial potential for mitigation. At the same time, we highlight that major risks and trade‐offs are involved. Pathways aiming to limit climate change to 2°C or even 1.5°C need to minimize these risks and trade‐offs in order to achieve mitigation sustainably.     

Prevalence of BRCA1 Mutations in Familial and Sporadic Greek Ovarian Cancer Cases

Germline mutations in the BRCA1 and BRCA2 genes contribute to approximately 18% of hereditary ovarian cancers conferring an estimated lifetime risk from 15% to 50%. A variable incidence of mutations has been reported for these genes in ovarian cancer cases from different populations. In Greece, six mutations in BRCA1 account for 63% of all mutations detected in both BRCA1 and BRCA2 genes. This study aimed to determine the prevalence of BRCA1 mutations in a Greek cohort of 106 familial ovarian cancer patients that had strong family history or metachronous breast cancer and 592 sporadic ovarian cancer cases. All 698 patients were screened for the six recurrent Greek mutations (including founder mutations c.5266dupC, p.G1738R and the three large deletions of exon 20, exons 23–24 and exon 24). In familial cases, the BRCA1 gene was consequently screened for exons 5, 11, 12, 20, 21, 22, 23, 24. A deleterious BRCA1 mutation was found in 43/106 (40.6%) of familial cancer cases and in 27/592 (4.6%) of sporadic cases. The variant of unknown clinical significance p.V1833M was identified in 9/698 patients (1.3%). The majority of BRCA1 carriers (71.2%) presented a high-grade serous phenotype. Identifying a mutation in the BRCA1 gene among breast and/or ovarian cancer families is important, as it enables carriers to take preventive measures. All ovarian cancer patients with a serous phenotype should be considered for genetic testing. Further studies are warranted to determine the prevalence of mutations in the rest of the BRCA1 gene, in the BRCA2 gene, and other novel predisposing genes for breast and ovarian cancer.