Advances toward engineering functionally mature human pluripotent stem cell-derived β cells

Human stem cell-derived β (SC-β) cells have the potential to revolutionize diabetes treatment through disease modeling, drug screening, and cellular therapy. SC-β cells are likely to represent an early clinical translation of differentiated human pluripotent stem cells (hPSC). In 2014, two groups generated the firs

SIX2 regulates human β cell differentiation from stem cells and functional maturation in vitro

Generation of insulin-secreting β cells in vitro is a promising approach for diabetes cell therapy. Human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) are differentiated to β cells (SC-β cells) and mature to undergo glucose-stimulated insulin secretion, but molecular regulation of this defining β cell phenotype is unknown. Here, we show that maturation of SC-β cells is regulated by the transcription factor SIX2. Knockdown (KD) or knockout (KO) of SIX2 in SC-β cells drastically limits glucose-stimulated insulin secretion in both static and dynamic assays, along with the upstream processes of cytoplasmic calcium flux and mitochondrial respiration. Furthermore, SIX2 regulates the expression of genes associated with these key β cell processes, and its expression is restricted to endocrine cells. Our results demonstrate that expression of SIX2 influences the generation of human SC-β cells in vitro

Land Suitability Evaluation of Abandoned Tin-mining Areas for Agricultural Development in Bangka Island, Indonesia

Kepulauan Bangka Belitung, Indonesia is one of the tin mineral-producer in the world. Agricultural crops could be a wise option for the reclamation since abandoned tin-mining lands have a high potency to be used as agricultural lands. This study was aimed to evaluate of the land/soil characteristics of abandoned tin-mining areas and to establish land suitability of the land area for agriculture used to formulate appropriate land development measures and amelioration strategies for utilization of mined areas for crop production. The land evaluation was conducted by comparing the land characteristics in every type of abandoned tin-mining areas with its crop requirements. The current suitability showed that in general food crops, vegetable crops, fruit crops, and industrial crops were consider as not suitable (N). Spice and medicinal crops [pepper (Piper nigrum L.) and citronella (Andropogoh nardus L. Rendle)] were consider as not suitable (N), while the Jatropha (Jatropha curcas L.) and Kemiri Sunan (Aleurites moluccana L. Willd) crops were considered as marginally suitable (S3) in abandoned tin-mining areas. The forest crops and forage crops were considered as marginally suitable (S3). The water availability, soil texture, and low soil fertility were considered as the limiting factors of all crops to get optimum production. For agricultural development, the soil physical and chemical properties of abandoned tin-mining land must be improved through integrated farming

Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures

Background: pH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to "spillover effects" on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management (e.g., tillage and chemical inputs), plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic (archaeal and bacterial) community structures. Results: We evaluated an agricultural soil pH gradient (from 4 to 6, the typical range for tropical farms), in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical-chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes (e.g., some Actinobacteria), while others were more affected by the indirect pH effects (e.g., some Proteobacteria). HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements (e.g., Al, Fe, and Cu), and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO2, CH4, and N2O greenhouse gas fluxes. Conclusions: Our results indicate that there are two distinct pH-related mechanisms driving prokaryotic community structures, the direct effect and "spillover effects" of pH (indirect effects). Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology.Peer reviewe

Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures

Background: pH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to "spillover effects" on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management (e.g., tillage and chemical inputs), plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic (archaeal and bacterial) community structures. Results: We evaluated an agricultural soil pH gradient (from 4 to 6, the typical range for tropical farms), in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical-chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes (e.g., some Actinobacteria), while others were more affected by the indirect pH effects (e.g., some Proteobacteria). HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements (e.g., Al, Fe, and Cu), and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO2, CH4, and N2O greenhouse gas fluxes. Conclusions: Our results indicate that there are two distinct pH-related mechanisms driving prokaryotic community structures, the direct effect and "spillover effects" of pH (indirect effects). Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology.Peer reviewe

Estimators for local non-Gaussianities

We study the Likelihood function of data given f_NL for the so-called local type of non-Gaussianity. In this case the curvature perturbation is a non-linear function, local in real space, of a Gaussian random field. We compute the Cramer-Rao bound for f_NL and show that for small values of f_NL the 3-point function estimator saturates the bound and is equivalent to calculating the full Likelihood of the data. However, for sufficiently large f_NL, the naive 3-point function estimator has a much larger variance than previously thought. In the limit in which the departure from Gaussianity is detected with high confidence, error bars on f_NL only decrease as 1/ln Npix rather than Npix^-1/2 as the size of the data set increases. We identify the physical origin of this behavior and explain why it only affects the local type of non-Gaussianity, where the contribution of the first multipoles is always relevant. We find a simple improvement to the 3-point function estimator that makes the square root of its variance decrease as Npix^-1/2 even for large f_NL, asymptotically approaching the Cramer-Rao bound. We show that using the modified estimator is practically equivalent to computing the full Likelihood of f_NL given the data. Thus other statistics of the data, such as the 4-point function and Minkowski functionals, contain no additional information on f_NL. In particular, we explicitly show that the recent claims about the relevance of the 4-point function are not correct. By direct inspection of the Likelihood, we show that the data do not contain enough information for any statistic to be able to constrain higher order terms in the relation between the Gaussian field and the curvature perturbation, unless these are orders of magnitude larger than the size suggested by the current limits on f_NL.Comment: 26 pages. v2: added comments about the approximations used, published JCAP versio