Endoplasmic Reticulum Stress Induced Proliferation Remains Intact in Aging Mouse beta-Cells

Aging is associated with loss of proliferation of the insulin-secreting beta-cell, a possible contributing factor to the increased prevalence of type 2 diabetes in the elderly. Our group previously discovered that moderate endoplasmic reticulum (ER) stress occurring during glucose exposure increases the adaptive beta-cell proliferation response. Specifically, the ATF6alpha arm of the tripartite Unfolded Protein Response (UPR) promotes beta-cell replication in glucose excess conditions. We hypothesized that beta-cells from older mice have reduced proliferation due to aberrant UPR signaling or an impaired proliferative response to ER stress or ATF6alpha activation. To investigate, young and old mouse islet cells were exposed to high glucose with low-dose thapsigargin or activation of overexpressed ATF6alpha, and beta-cell proliferation was quantified by BrdU incorporation. UPR pathway activation was compared by qPCR of target genes and semi-quantitative Xbp1 splicing assay. Intriguingly, although old beta-cells had reduced proliferation in high glucose compared to young beta-cells, UPR activation and induction of proliferation in response to low-dose thapsigargin or ATF6alpha activation in high glucose were largely similar between young and old. These results suggest that loss of UPR-led adaptive proliferation does not explain the reduced cell cycle entry in old beta-cells, and raise the exciting possibility that future therapies that engage adaptive UPR could increase beta-cell number through proliferation even in older individuals

Insulin clearance and the incidence of type 2 diabetes in Hispanics and African Americans: the IRAS Family Study.

ObjectiveWe aimed to identify factors that are independently associated with the metabolic clearance rate of insulin (MCRI) and to examine the association of MCRI with incident type 2 diabetes in nondiabetic Hispanics and African Americans.Research design and methodsWe investigated 1,116 participants in the Insulin Resistance Atherosclerosis Study (IRAS) Family Study with baseline examinations from 2000 to 2002 and follow-up examinations from 2005 to 2006. Insulin sensitivity (S(I)), acute insulin response (AIR), and MCRI were determined at baseline from frequently sampled intravenous glucose tolerance tests. MCRI was calculated as the ratio of the insulin dose over the incremental area under the curve of insulin. Incident diabetes was defined as fasting glucose ≥126 mg/dL or antidiabetic medication use by self-report.ResultsWe observed that S(I) and HDL cholesterol were independent positive correlates of MCRI, whereas fasting insulin, fasting glucose, subcutaneous adipose tissue, visceral adipose tissue, and AIR were independent negative correlates (all P < 0.05) at baseline. After 5 years of follow-up, 71 (6.4%) participants developed type 2 diabetes. Lower MCRI was associated with a higher risk of incident diabetes after adjusting for demographics, lifestyle factors, HDL cholesterol, indexes of obesity and adiposity, and insulin secretion (odds ratio 2.01 [95% CI 1.30-3.10], P = 0.0064, per one-SD decrease in loge-transformed MCRI).ConclusionsOur data showed that lower MCRI predicts the incidence of type 2 diabetes

Yield, biochemical properties and cooking quality traits of sweet potatoes (Ipomoea batatas) as affected by Nitrogen and Potassium Fertilizer rates

This study evaluated the effects of mineral fertilizer rates on biochemical properties, cooking quality traits and root yield of sweetpotatoes. The experimental design was 4 x 4 factorial in randomized complete block with three replications. The treatment factors were four varieties of sweetpotato (Ligri, Bohye, Dadanyuie and Apomuden) and four fertilizer amendments (T1: 30-30-30 kg /ha NPK, T2: 30-30-60 kg NPK+50 kg Muriate of Potash, T3: 30-30-90 kg/ha NPK+ 100 kg Muriate of Potash and T4: Control (No fertilizer). Results showed that the fertilizer rates did not influence root yield but variety had significant difference (P<0.05). Apomuden recorded the highest average root yield of 14.5 t/ha which was significantly higher than Ligri 5.1 t/ha. Ligri recorded the highest dry matter and sugar contents of 34.63% and 67.98% respectively while Apomuden recorded the lowest dry matter content and starch content of 23.75% and 50.00% respectively. However, it recorded appreciable amount of beta-carotene and sugar contents of 32.38 mg/100g and 28.04% respectively. There were significant variety × location interactions effect (P < 0.05) on average root yield and biomass yield. The significant varietal response observed in this study implies that choice of variety is an important factor to consider in sweetpotato production

Sustainability of Mahogany Production in Plantations: Does Resource Availability Influence Susceptibility of Young Mahogany Plantation Stands to Hypsipyla robusta Infestation?

Hypsipyla robusta Moore (Lepidoptera: Pyralidae), like many other moth species, shows selectivity when choosing host plants for its eggs. Four Meliaceae species (Khaya grandifoliola, K. ivorensis, Swietenia macrophyla, and Entandrophragma cylindricum) were established in a moist semideciduous forest in Ghana to study this selectivity at 12 and 21 months after planting. The analysis of variance (ANOVA) at a P-value of 0.05 was used to test the significance of differences in infestation by H. robusta between the species. H. robusta attacks were recorded by month 12 after planting in the field, and only Khaya spp. was attacked, with attacks evident on 15.5% of K grandifoliola and 6.6% K. ivorensis. Saplings in blocks closer to an older H. robusta infested K. grandifoliola stand had more infestation compared to saplings further away. The mean percentage of K. grandifoliola attacked was 38.9%, 38.9%, 13.3%, and 7.4% in 4 different plots located increasingly further away from the older infested plantation. A similar trend was found in K. ivorensis with 28.4%, 7.1%, 0.0%, and 0.0% in the plots located increasingly further away from the infested stand. These results indicate a higher number of shoot borer attacks at the edge of the plantation and in proximity to other infested plantations. After 21 months, the fastest-growing species and the fastest-growing individuals within the species were the most infested. K. grandifoliola recorded the fastest growth and most attacks followed by K. ivorensis and S. macrophylla. E. cylindricum recorded the least growth and no H. robusta infestation. After 21 months, the mean percentages of trees attacked were 59.1%, 23.7%, 5.6%, and 0.0% for K. grandifoliola, K. ivorensis, S. macrophylla, and E. cylindricum, respectively. Within species, the fastest-growing saplings experienced the most attacks. A positive correlation was observed between the plant size and H. robusta attacks (R2 = 0.76). Attacks resulted in the death of the apical shoot and the proliferation of multiple shoots in only the Khaya spp., with K. ivorensis recording a lower number of shoots than K. grandifoliola. These proliferated shoots were also attacked, and a positive correlation was observed between the number of proliferated shoots and H. robusta attacks (R2 = 0.84). These findings will assist plantation developers, forest managers, and investors in mahogany plantations to devise integrated pest management strategies to reduce the impact of Hypsipyla attacks on their plantations

AKA-TPG: A Program for Kinetic and Epidemiological Analysis of Data from Labeled Glucose Investigations Using the Two-Pool Model and Database Technology

Background: The Two-Pool Glucose (TPG) model has an important role to play in diabetes research since it enables analysis of data obtained from the frequently sampled labeled (hot) glucose tolerance test (FSHGT). TPG modeling allows determination of the separate effects of insulin on the disposal of glucose and on the hepatic production of glucose. It therefore provides a basis for the accurate estimation of glucose effectiveness, insulin sensitivity, and the profile of the rate of endogenous glucose production. Until now, there has been no program available dedicated to the TPG model, and a number of technical reasons have deterred researchers from performing TPG analysis. Methods and Results: In this paper, we describe AKA-TPG, a new program that combines automatic kinetic analysis of the TPG model data with database technologies. AKA-TPG enables researchers who have no expertise in modeling to quickly fit the TPG model to individual FSHGT data sets consisting of plasma concentrations of unlabeled glucose, labeled glucose, and insulin. Most importantly, because the entire process is automated, parameters are almost always identified, and parameter estimates are accurate and reproducible. AKA-TPG enables the demographic data of hundreds of individual subjects, their individual unlabeled and labeled glucose and insulin data, and each subject\u27s parameters and indices derived from AKA-TPG to be securely stored in, and retrieved from, a database. We describe how the stratification and population analysis tools in AKA-TPG are used and present population estimates of TPG model parameters for young, healthy (without diabetes) Nordic men. Conclusion: Researchers now have a practical tool to enable kinetic and epidemiological analysis of TPG data sets

Star formation efficiency across large-scale galactic environments

Environmental effects on the evolution of galaxies have been one of the leading questions in galaxy studies for decades. In this work, we investigate the relationship between the star formation activity of galaxies and their environmental matter density using the cosmological hydrodynamic simulation Simba. The star formation activity indicators we explore include the star formation efficiency (SFE), specific star formation rate (sSFR) and molecular hydrogen mass fraction ($f^*_{H_2}$) and the environment is considered as the large-scale environmental matter density, calculated based on the stellar mass of nearby galaxies on a 1 Mpc/h grid using the cloud in cell (CIC) method. Our sample includes galaxies with $9<\log(M_*/M_{\odot})$ at $0<z<4$, divided into three mass bins to disentangle the effects of mass and environment on the galactic star formation activity. For low- to intermediate-mass galaxies at low-redshifts ($z<1.5$), we find that the star formation efficiency of those in high-density regions are $\sim 0.3$ dex lower than those in low-density regions. However, there is no significant environmental dependence of the star formation efficiency for massive galaxies over all our redshift range, and low- to intermediate-mass galaxies at high redshifts ($z > 1.5$). We present a scaling relation for the depletion time of molecular hydrogen (${t_{depl}}=1/SFE$) as a function of galaxy parameters including environmental density. Our findings provide a framework for quantifying the environmental effects on the star formation activities of galaxies as a function of stellar mass and redshift. The most significant environmental dependence is seen at later cosmic times ($z<1.5$) and towards lower stellar masses ($9<\log(M_*/M_{\odot})<10$). Future large galaxy surveys can use this framework to look for the environmental dependence of the star formation activity and examine our predictions.Comment: 17 pages, 6 figure

Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

In this paper, we show that by combining experimental data from different optical fibers, we can build a fiber-agnostic neural-network to model the Raman amplifier. The fiber-agnostic NN model can predict the gain profile of a new fiber type (unseen by the model during training) with a maximum absolute error as low as 0.22 dB. We show that this generalization is only possible when the unseen fiber parameters are similar to the fibers used to build the model. Therefore, a training dataset with a wide range of optical fibers parameters is needed to enhance the chance of accurately predicting the gain of a new fiber. This implies that time-consuming experimental measurements of various fiber types can be avoided. For that, here we extend and improve our general model by numerically generating the dataset. By doing so, it is possible to generate uniformly distributed data that covers a wide range of optical fiber types. The results show that the averaged maximum prediction error is reduced when compared to the limited experimental data-based general models. As the second and final contribution of this work, we propose the use of transfer learning (TL) to re-train the numerical data-based general model using just a few experimental measurements. Compared with the fiber-specific models, this TL-upgraded general model reaches very similar accuracy, with just 3.6% of the experimental data . These results demonstrate that the already fast and accurate NN-based RA models can be upgraded to have strong fiber generalization capabilities