Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R2) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

The potential use of mass timber in mid-to high-rise construction and the associated carbon benefits in the United States.

Nonresidential and mid- to high-rise multifamily residential structures in the United States currently use little wood per unit floor area installed, because earlier building codes lacked provisions for structural wood use in those types of buildings. However, revisions to the International Building Code allow for increased wood use in the form of mass timber, as structural and fire safety concerns have been addressed through new science-based design standards and through newly specified construction materials and measures. This study used multiple models to describe alternative futures for new construction, mass timber adoption rates, and the associated carbon benefits in higher than three-story buildings in the United States. The use of mass timber, in place of traditional constructions (i.e., structures dominated by concrete and steel), in projected new higher than three-story buildings was shown to provide combined carbon benefits (i.e., global warming mitigation benefits), including avoided embodied carbon emissions due to the substitution of non-wood alternatives and additional biogenic carbon storage in mass timber materials, of between 9.9 and 16.5 million t CO2e/yr spanning 50 years, 2020 to 2070. These carbon benefits equate to 12% to 20% of the total U.S. harvested wood products carbon storage for 2020. Future research is needed to understand how greater mass timber adoption leads to changes in forest product markets, land use, and total forest sector carbon.

(Pro)renin receptor signaling in hypothalamic tyrosine hydroxylase neurons is required for obesity-associated glucose metabolic impairment.

Glucose homeostasis is achieved via complex interactions between the endocrine pancreas and other peripheral tissues and glucoregulatory neurocircuits in the brain that remain incompletely defined. Within the brain, neurons in the hypothalamus appear to play a particularly important role. Consistent with this notion, we report evidence that (pro)renin receptor (PRR) signaling within a subset of tyrosine hydroxylase (TH) neurons located in the hypothalamic paraventricular nucleus (PVNTH neurons) is a physiological determinant of the defended blood glucose level. Specifically, we demonstrate that PRR deletion from PVNTH neurons restores normal glucose homeostasis in mice with diet-induced obesity (DIO). Conversely, chemogenetic inhibition of PVNTH neurons mimics the deleterious effect of DIO on glucose. Combined with our finding that PRR activation inhibits PVNTH neurons, these findings suggest that, in mice, (a) PVNTH neurons play a physiological role in glucose homeostasis, (b) PRR activation impairs glucose homeostasis by inhibiting these neurons, and (c) this mechanism plays a causal role in obesity-associated metabolic impairment.

Methods for estimating insulin resistance from untargeted metabolomics data.

CONTEXT: Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming. OBJECTIVE: Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data. DESIGN: Insulin Resistance Atherosclerosis Family Study (IRASFS). SETTING: Community based. PARTICIPANTS: Mexican Americans (MA) and African Americans (AA). MAIN OUTCOME: Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR. RESULTS: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results. CONCLUSIONS: We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

California's harvested wood products: A time-dependent assessment of life cycle greenhouse gas emissions.

Following life-cycle assessment (LCA) methodology, this study presents a state-level estimation of embodied carbon of wood products harvested in 2019 from California and subsequently processed, manufactured, transported, used, and disposed at the end-of-life (EoL). In a conventional static approach to LCA, all GHG emissions were aggregated and considered to occur at year 0 of the given time horizon (500 years in this study) and used a static characterization factor (CF). In dynamic LCA, GHG emissions occurring in different years were considered, and their global warming impact (GWI) was determined using a time-dependent CF over the selected time horizon of 500 years. Four scenarios were developed to examine the impact of EoL choices on GWI. It was found that dynamic GWI for all scenarios ranged from 0.27 to 0.93 million tonne CO2e, which were 45-73 % lower than those estimated with static LCA approach, indicating that the static LCA approach could lead to an underestimation of the benefits of substituting wood for non-wood products, compared to those based on dynamic LCA approach. This analysis also demonstrated that the choice of EoL treatment option is a key factor affecting the estimated GWI as it directly determines the annual emission of GHGs released into atmosphere and subsequently their warming effect depending on the time harvested wood products (HWPs) spend in the horizon of assessment. Overall, the dynamic LCA performed in this study enabled more robust interpretations of embodied carbon by including temporal boundaries associated with the HWPs life cycle.

Dapagliflozin prevents abdominal visceral and subcutaneous adipose tissue dysfunction in the insulin-resistant canine model.

OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) promote urinary glucose excretion, induce weight loss, and reduce fat accumulation. The effects of the SGLT2i dapagliflozin (DAPA) on subcutaneous (SC) and visceral (VIS) adipose tissue function remain unclear. The objective of this study is to evaluate SC and VIS adipose tissue function in an insulin-resistant canine model. METHODS: A total of 12 dogs were fed a high-fat diet (HFD) for 6 weeks and then were given a single low dose of streptozotocin (18.5 mg/kg) to induce insulin resistance. Animals were then randomized and exposed to DAPA (n = 6, 1.25 mg/kg) or placebo (n = 6) once per day for 6 weeks while remaining on the HFD. RESULTS: DAPA prevented further weight gain induced by the HFD and normalized fat mass. DAPA reduced fasting glucose and increased free fatty acids, adiponectin, and ß-hydroxybutyrate. DAPA reduced adipocyte diameter and cell distribution. Furthermore, DAPA increased genes associated with beiging, lipolysis, and adiponectin secretion and the expression of the adiponectin receptor ADR2, in SC and VIS adipose tissue. DAPA increased AMP-activated protein kinase activity and maximal mitochondrial respiratory function, especially in the SC depot. Furthermore, DAPA reduced cytokines and ceramide synthesis enzymes in SC and VIS depots. CONCLUSIONS: For the first time, to our knowledge, we identify mechanisms by which DAPA enhances adipose tissue function in regulating energy homeostasis in an insulin-resistant canine model.

Techno-economic analysis and life cycle assessment of cellulose nanocrystals production from wood pulp.

Cellulose nanocrystals (CNCs) are biobased materials with many desirable properties such as high aspect ratio, mechanical strength, crystalline nature, and biodegradability. This study developed a commercial-scale process model of CNC production from wood pulp using sulfuric acid treatment and evaluated its techno-economic and environmental performance with and without acid recovery. The results indicated that CNC produced with acid recovery process was financially more profitable with higher project net present values than without acid recovery process but required higher capital which resulted in a longer payback period and lower return on invested capital. The estimated minimum selling prices of CNC produced with and without acid recovery were $4.69/kg and $4.89/kg, respectively. The global warming (GW) impacts of 1 kg CNC production with and without acid recovery were 11.39 and 11.18 kgCO2eq, respectively, showing that higher steam use and consequently more fossil fuels were needed in the acid recovery process.

Measures of glucose homeostasis during and after duodenal exclusion using a duodenal-jejunal bypass liner in a normoglycemic, nonobese canine model.

BACKGROUND: Discovering the role duodenal exclusion plays in weight loss and resolution of type 2 diabetes (T2D) may help refine the surgical and nonsurgical treatment of obesity and T2D. OBJECTIVES: To assess changes in glucose homeostasis due to duodenal exclusion using a duodenal-jejunal bypass liner (DJBL) in a nonobese canine model. SETTING: Academic laboratory setting. METHODS: An intravenous glucose tolerance test (IVGTT), and a mixed-meal tolerance test (MMTT) at baseline, 1, and 6 weeks post DJBL implantation (I1 and I6, respectively), and 1 and 6 weeks post DJBL removal (R1 and R6, respectively) were done in canines (n = 7) fed a normal chow diet. RESULTS: Placement of the DJBL induced weight loss that was maintained until 4 weeks post removal (R4), despite normal food intake. Total bile acids (TBA) and glucagon-like peptide-1 (GLP-1) during the MMTT were significantly increased at I1 and were associated with increased lactate and free fatty acids. Hypoglycemia counter-regulation was blunted during the IVGTT at I1 and I6, returning to baseline at R1. While there were no changes to insulin sensitivity during the experiment, glucose tolerance was significantly increased following the removal of the DJBL at R1. CONCLUSION: These data show that in a normoglycemic, nonobese canine model, duodenal exclusion induces energy intake-independent weight loss and negative metabolic effects that are reversed following re-exposure of the small intestine to nutrients.

The Physiology of Insulin Clearance.

In the 1950's, Dr. I. Arthur Mirsky first recognized the possible importance of insulin degradation changes to the pathogenesis of type 2 diabetes. While this mechanism was ignored for decades, insulin degradation is now being recognized as a possible factor in diabetes risk. After Mirsky, the relative importance of defects in insulin release and insulin resistance were recognized as risk factors. The hyperbolic relationship between secretion and sensitivity was introduced, as was the relationship between them, as expressed as the disposition index (DI). The DI was shown to be affected by environmental and genetic factors, and it was shown to be differentiated among ethnic groups. However, the importance of differences in insulin degradation (clearance) on the disposition index relationship remains to be clarified. Direct measure of insulin clearance revealed it to be highly variable among even normal individuals, and to be affected by fat feeding and other physiologic factors. Insulin clearance is relatively lower in ethnic groups at high risk for diabetes such as African Americans and Hispanic Americans, compared to European Americans. These differences exist even for young children. Two possible mechanisms have been proposed for the importance of insulin clearance for diabetes risk: in one concept, insulin resistance per se leads to reduced clearance and diabetes risk. In a second and new concept, reduced degradation is a primary factor leading to diabetes risk, such that lower clearance (resulting from genetics or environment) leads to systemic hyperinsulinemia, insulin resistance, and beta-cell stress. Recent data by Chang and colleagues appear to support this latter hypothesis in Native Americans. The importance of insulin clearance as a risk factor for metabolic disease is becoming recognized and may be treatable.

Hyperinsulinemic Compensation for Insulin Resistance Occurs Independent of Elevated Glycemia in Male Dogs.

Insulin resistance engenders a compensatory increase in plasma insulin. Inadequate compensation is a primary element in the pathogenesis of type 2 diabetes. The signal that heralds developing insulin resistance and initiates hyperinsulinemic compensation is not known. It has often been assumed to be increased glucose. We tested this assumption by determining whether development of fasting and/or glucose-stimulated hyperinsulinemia with diet-induced insulin resistance occurs because of concomitant elevation of glycemia. Male dogs (n = 58) were fed a hypercaloric, fat-supplemented diet for 6 weeks. Dogs underwent magnetic resonance imaging to quantify total and regional (visceral, subcutaneous) adiposity as well as euglycemic hyperinsulinemic clamps. A subset of animals also underwent an insulin-modified intravenous glucose tolerance test to assess insulin sensitivity, acute insulin response (AIRg), and glucose effectiveness. Fat feeding caused modest weight gain, increased visceral and subcutaneous fat, and insulin resistance at both peripheral and hepatic levels. Hyperinsulinemic compensation was observed in fasting levels as well as increased AIRg. However, we observed absolutely no increase in carefully measured fasting, evening (6 to 8 pm) or nocturnal glycemia (2 to 4 am). Insulin resistance and hyperinsulinemia occurred despite no elevation in 24-hour glucose. Compensatory development of hyperinsulinemia during diet-induced insulin resistance occurs without elevated fasting or 24-hour glycemia. These data refute the idea that glucose itself is a requisite signal for ß-cell upregulation. Alternative feedback mechanisms need to be identified.

Correction: Paszkiewicz et al. A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting That Central Effects Can Be Avoided. Int. J. Mol. Sci. 2020, 21, 6639.

The authors wish to make the following corrections to this paper [...].

Voices: Insulin and beyond.

Marking insulin's centennial, we share stories of researchers and clinicians whose seminal work has advanced our understanding of insulin, islet biology, insulin resistance, and diabetes. The past century of pursuing the "hormone of hormones" and advancing diabetes therapies is replete with stories of collaboration, perseverance, and triumph.

The Measurement of Insulin Clearance.

Insulin clearance has recently been highlighted as a fundamental aspect of glucose metabolism, as it has been hypothesized that its impairment could be related to an increased risk of developing type 2 diabetes. This review focuses on methods used to calculate insulin clearance: from the early surrogate indices employing C-peptide:insulin molar ratio, to direct measurement methods used in animal models, to modeling-based techniques to estimate the components of insulin clearance (hepatic versus extrahepatic). The methods are explored and interpreted by critically highlighting advantages and limitations.

A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting that Central Effects Can Be Avoided.

With the increased prevalence of obesity and related co-morbidities, such as type 2 diabetes (T2D), worldwide, improvements in pharmacological treatments are necessary. The brain- and peripheral-cannabinoid receptor 1 (CB1R) antagonist rimonabant (RIM) has been shown to induce weight loss and improve glucose homeostasis. We have previously demonstrated that RIM promotes adipose tissue beiging and decreased adipocyte cell size, even during maintenance on a high-fat diet. Given the adverse side-effects of brain-penetrance with RIM, in this study we aimed to determine the site of action for a non-brain-penetrating CB1R antagonist AM6545. By using in vitro assays, we demonstrated the direct effects of this non-brain-penetrating CB1R antagonist on cultured adipocytes. Specifically, we showed, for the first time, that AM6545 significantly increases markers of adipose tissue beiging, mitochondrial biogenesis, and lipolysis in 3T3-L1 adipocytes. In addition, the oxygen consumption rate (OCR), consisting of baseline respiratory rate, proton leak, maximal respiratory capacity, and ATP synthase activity, was greater for cells exposed to AM6545, demonstrating greater mitochondrial uncoupling. Using a lipolysis inhibitor during real-time OCR measurements, we determined that the impact of CB1R antagonism on adipocytes is driven by increased lipolysis. Thus, our data suggest the direct role of CB1R antagonism on adipocytes does not require brain penetrance, supporting the importance of focus on peripheral CB1R antagonism pharmacology for reducing the incidence of obesity and T2D.

Mortality Attributed to COVID-19 in High-Altitude Populations.

Woolcott, Orison O., and Richard N. Bergman. Mortality attributed to COVID-19 in high-altitude populations. High Alt Med Biol. 21:409-416, 2020. Background: Since partial oxygen pressure decreases as altitude increases, environmental hypoxia could worsen Coronavirus Disease 2019 (COVID-19) patient's hypoxemia. We compared COVID-19 mortality at different altitudes. Methods: Retrospective analysis of population-level data on COVID-19 deaths was conducted in the United States (1,016 counties) and Mexico (567 municipalities). Mixed-model Poisson regression analysis of the association between altitude and COVID-19 mortality was conducted using individual-level data from 40,168 Mexican subjects with COVID-19, adjusting for multiple covariates. Results: Between January 20 and April 13, 2020, mortality rates were higher in U.S. counties located at ≥2,000 m elevation versus those located <1,500 m (12.3 vs. 3.2 per 100,000; p < 0.001). In Mexico, between March 13 and May 13, 2020, mortality rates were higher in municipalities located at ≥2,000 m versus those located <1,500 m (5.3 vs. 3.9 per 100,000; p < 0.001). Among Mexican subjects younger than 65 years, the risk of death was 36% higher in those living at ≥2,000 m versus those living at <1,500 m (adjusted incidence rate ratio [IRR]: 1.36; confidence interval [95% CI], 1.05-1.78; p = 0.022). Among Mexican men, the risk of death was 31% higher at ≥2,000 m versus that at <1,500 m (adjusted IRR: 1.31; 95% CI, 1.03-1.66; p = 0.025). No association between altitude and COVID-19 mortality was found among Mexican women or among Mexican subjects 65 years of age and older. Conclusions: Altitude is associated with COVID-19 mortality in men younger than 65 years.