Investment Planning to Minimize Climate Risk in Agricultural Production

The primary aim of this study is to prioritize investment required for scaling up climate-smart agriculture (CSA) technologies across different districts of Telangana state, which is in the semi-arid region of India. First, we analysed the trade-offs between expected agricultural income and its deviation across districts under drought and normal weather scenarios. The conventional MOTAD model was extended with various climate-smart technologies to assess their role in minimizing the trade-offs under various weather scenarios. A district-level panel dataset on cost of cultivation and crop production for 11 major crops under six different climate-smart technologies and farmers’ traditional practices (FTPs) for five years (2010-11 to 2014-15) has been used. The dataset comprised a collation of official statistics on cost of cultivation, focus group interviews with farmers over the years, and data from experimental plots of Regional Agricultural Research Stations. The analysis reveals that the adoption of CSA technologies is likely to reduce production risk by 16% compared to FTPs while achieving optimum levels of crop income. Under a scenario of higher probability of drought, production risk is likely to increase by 12% in the state under FTPs; the adoption of CSA technologies could reduce the risk by 25%. The study suggests increasing investments in farm ponds and un-puddled machine transplanting in rice to minimize the risk-return trade-offs under a higher drought frequency scenario. Finally, the study generates evidence for policymakers to make informed investment decisions on CSA in order to enhance farming systems resilience across districts in the semi-arid state of Telangana, India

Measurements, Models, Systems and Design

531 s.

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion

Tumor-initiating cell frequency is relevant for glioblastoma aggressiveness

Glioblastoma (GBM) is maintained by a small subpopulation of tumor-initiating cells (TICs). The arduous assessment of TIC frequencies challenges the prognostic role of TICs in predicting the clinical outcome in GBM patients. We estimated the TIC frequency in human GBM injecting intracerebrally in mice dissociated cells without any passage in culture.All GBMs contained rare TICsand were tumorigenic in vivo but only 54% of them grew in vitro as neurospheres. We demonstrated that neurosphere formation in vitro did not foretell tumorigenic ability in vivo and frequencies calculated in vitro overestimated the TIC content.Our findings assert the pathological significance of GBM TICs. TIC number correlated positively with tumor incidence and inversely with survival of tumor-bearing mice. Stratification of GBM patients according to TIC content revealed that patients with low TIC frequency experienced a trend towards a longer progression free survival. The expression of either putative stem-cell markers or markers associated with different GBM molecular subtypes did not associate with either TIC content or neurosphere formation underlying the limitations of TIC identification based on the expression of some putative stem cell-markers

Assessing the rural food environment for advancing sustainable healthy diets: Insights from India

World agricultural production has seen significant growth in the past four decades, yet malnutrition remains a persistent problem, particularly in the global south and more so in the rural areas. Need for a holistic approach to food systems is becoming crucial in designing policies that support the transition to sustainable and healthy diets. The present study is aimed to understand the rural food environment in the Telangana state in southern India by analyzing the combination of external and personal factors affecting food choices, attitudes, and consumption behavior. We developed a scoring-based methodology to assess the external and personal domains and dimensions to understand the food environment. The results showed that rural households favored carbohydrate-rich food groups obtained mostly from their own production or subsidized sources. On the other hand, protein and micronutrient-rich food groups were neglected due to affordability and preference for taste, cultural factors, and the limitations of external food environment. The findings of this study provide a deeper understanding of the food environment in low and middle-income countries (LMICs) conext. By highlighting the interplay between agriculture, food environments, and nutrition outcomes, this study contributes to the ongoing effort to address the global malnutrition crisis and support the development of healthier and more sustainable food systems. These findings can be useful to guide policy actions towards achieving food security and nutrition in the rural regions where food environments are under rapid transitions in the LMICs

The FeH Wing-Ford Band in Spectra of M Stars

We study the FeH Wing-Ford band at 9850 - 10200 Angstrons by means of the fit of synthetic spectra to the observations of M stars, employing recent model atmospheres. On the basis of the spectrum synthesis, we analyze the dependence of the band upon atmospheric parameters. FeH lines are a very sensitive surface gravity indicator, being stronger in dwarfs. They are also sensitive to metallicity (Allard & Hauschildt 1995). The blending with CN lines, which are stronger in giants, does not affect the response of the Wing-Ford band to surface gravity at low resolution (or high velocity dispersions) because CN lines, which are spread all along the spectrum, are smeared out at convolutions of FWHM \simgreat 3 Angstrons. We conclude that the Wing-Ford band is a suitable dwarf/giant indicator for the study of composite stellar populations.Comment: 23 pages + 11 figures in postscript format + 3 ps figures (Nos. 2, 6 and 7) available under request to [email protected]. Accepted for publication in The Astrophysical Journa

High Circulating Methylated DNA Is a Negative Predictive and Prognostic Marker in Metastatic Colorectal Cancer Patients Treated With Regorafenib

Background: Regorafenib improves progression free survival (PFS) in a subset of metastatic colorectal cancer (mCRC) patients, although no biomarkers of efficacy are available. Circulating methylated DNA (cmDNA) assessed by a five-gene panel was previously associated with outcome in chemotherapy treated mCRC patients. We hypothesized that cmDNA could be used to identify cases most likely to benefit from regorafenib (i.e., patients with PFS longer than 4 months). Methods: Plasma samples from mCRC patients were collected prior to (baseline samples N = 60) and/or during regorafenib treatment (N = 62) for the assessment of cmDNA and total amount of cell free DNA (cfDNA). Results: In almost all patients, treatment with regorafenib increased the total cfDNA, but decreased cmDNA warranting the normalization of cmDNA to the total amount of circulating DNA (i.e., cmDNA/ml). We report that cmDNA/ml dynamics reflects clinical response with an increase in cmDNA/ml associated with higher risk of progression (HR for progression = 1.78 [95%CI: 1.01-3.13], p = 0.028). Taken individually, high baseline cmDNA/ml (above median) was associated with worst prognosis (HR for death = 3.471 [95%CI: 1.83-6.57], p < 0.0001) and also predicted shorter PFS (<16 weeks with PPV 86%). In addition, high cmDNA/ml values during regorafenib treatment predicted with higher accuracy shorter PFS (<16 weeks with a PPV of 96%), therefore associated with increased risk of progression (HR for progression = 2.985; [95%CI: 1.63-5.46; p < 0.0001). Conclusions: Our data highlight the predictive and prognostic value of cmDNA/ml in mCRC patients treated with regorafenib

Identification of Novel Pro-Migratory, Cancer-Associated Genes Using Quantitative, Microscopy-Based Screening

Background: Cell migration is a highly complex process, regulated by multiple genes, signaling pathways and external stimuli. To discover genes or pharmacological agents that can modulate the migratory activity of cells, screening strategies that enable the monitoring of diverse migratory parameters in a large number of samples are necessary. Methodology: In the present study, we describe the development of a quantitative, high-throughput cell migration assay, based on a modified phagokinetic tracks (PKT) procedure, and apply it for identifying novel pro-migratory genes in a cancer-related gene library. In brief, cells are seeded on fibronectin-coated 96-well plates, covered with a monolayer of carboxylated latex beads. Motile cells clear the beads, located along their migratory paths, forming tracks that are visualized using an automated, transmitted-light screening microscope. The tracks are then segmented and characterized by multi-parametric, morphometric analysis, resolving a variety of morphological and kinetic features. Conclusions: In this screen we identified 4 novel genes derived from breast carcinoma related cDNA library, whose over-expression induces major alteration in the migration of the stationary MCF7 cells. This approach can serve for high throughput screening for novel ways to modulate cellular migration in pathological states such as tumor metastasis and invasion