Modeling Soviet Agriculture for Assessing Commanding Economy Policies

A fixed effect production function model is adopted and estimated in this paper for evaluating the agricultural policies of the Soviet planners in Russia (formerly RSFSR) and Kazakhstan from 1953 to 1980. In particular, the functional form allows the variance of output to increase or decrease as one input is increased. Our estimates suggest the following assessment of the Soviet planners' agricultural policies during the command economy days: First, the extension of farming in Kazakhstan with generally inferior soils and climate resulted in lower crop levels (with identical input applications). Second, the massive inflow of capital resources and sharp rise in fertilizer use in agriculture during the Brezhnev years (1964-1982) did not contribute significantly to crop levels in the two republics. Finally, the extension of the cultivated area into marginal lands generally, and of fertilizer use in wrong mix and form contributed to the instability of agricultural output. In conclusion, we emphasize the need for specifying and estimating appropriate models for evaluating Soviet agricultural policies especially because farm-level data are rapidly becoming available in the republics of the former Soviet Union

Functional brain activity constrained by structural connectivity reveals cohort-specific features for serum neurofilament light chain

Background: Neuro-axonal brain damage releases neurofilament light chain (NfL) proteins, which enter the blood. Serum NfL has recently emerged as a promising biomarker for grading axonal damage, monitoring treatment responses, and prognosis in neurological diseases. Importantly, serum NfL levels also increase with aging, and the interpretation of serum NfL levels in neurological diseases is incomplete due to lack of a reliable model for age-related variation in serum NfL levels in healthy subjects. Methods: Graph signal processing (GSP) provides analytical tools, such as graph Fourier transform (GFT), to produce measures from functional dynamics of brain activity constrained by white matter anatomy. Here, we leveraged a set of features using GFT that quantified the coupling between blood oxygen level dependent signals and structural connectome to investigate their associations with serum NfL levels collected from healthy subjects and former athletes with history of concussions. Results: Here we show that GSP feature from isthmus cingulate in the right hemisphere (r-iCg) is strongly linked with serum NfL in healthy controls. In contrast, GSP features from temporal lobe and lingual areas in the left hemisphere and posterior cingulate in the right hemisphere are the most associated with serum NfL in former athletes. Additional analysis reveals that the GSP feature from r-iCg is associated with behavioral and structural measures that predict aggressive behavior in healthy controls and former athletes. Conclusions: Our results suggest that GSP-derived brain features may be included in models of baseline variance when evaluating NfL as a biomarker of neurological diseases and studying their impact on personality traits

Glucose-induced down regulation of thiamine transporters in the kidney proximal tubular epithelium produces thiamine insufficiency in diabetes

Increased renal clearance of thiamine (vitamin B1) occurs in experimental and clinical diabetes producing thiamine insufficiency mediated by impaired tubular re-uptake and linked to the development of diabetic nephropathy. We studied the mechanism of impaired renal re-uptake of thiamine in diabetes. Expression of thiamine transporter proteins THTR-1 and THTR-2 in normal human kidney sections examined by immunohistochemistry showed intense polarised staining of the apical, luminal membranes in proximal tubules for THTR-1 and THTR-2 of the cortex and uniform, diffuse staining throughout cells of the collecting duct for THTR-1 and THTR-2 of the medulla. Human primary proximal tubule epithelial cells were incubated with low and high glucose concentration, 5 and 26 mmol/l, respectively. In high glucose concentration there was decreased expression of THTR-1 and THTR-2 (transporter mRNA: −76% and −53% respectively, p<0.001; transporter protein −77% and −83% respectively, p<0.05), concomitant with decreased expression of transcription factor specificity protein-1. High glucose concentration also produced a 37% decrease in apical to basolateral transport of thiamine transport across cell monolayers. Intensification of glycemic control corrected increased fractional excretion of thiamine in experimental diabetes. We conclude that glucose-induced decreased expression of thiamine transporters in the tubular epithelium may mediate renal mishandling of thiamine in diabetes. This is a novel mechanism of thiamine insufficiency linked to diabetic nephropathy

Minimizing Liability of the COVID-19 Pandemic on Construction Contracts—A Structural Equation Model for Risk Mitigation of Force Majeure Impacts

A pandemic is a force majeure event, and contracting parties can invoke conditions under force majeure to minimize liability for unforeseen, uncontrollable, and unavoidable circumstances. This study develops a conceptual model to assist in the management of delays and cost overruns due to force majeure events arising from the construction sector in Small Island Developing States (SIDS). A critical case study analysis of past epidemics and pandemics was conducted to develop a survey questionnaire for administration to construction professionals in Trinidad and Tobago. Based on the empirical data of 65 construction professionals, the structural equation model shows that there are strong causal effects from the implications of COVID-19 and force majeure events, which in turn have a dire impact on the construction industry. The leading implication of COVID-19 is the drastic increases in the cost of materials. Also, granting an extension of time to contractors was the main risk variable under the force majeure conditions. From the results, the measurement model verifies that events under force majeure and its perceived implications strongly influence the construction industry, and proposes that force majeure contractual clauses require explicit treatment of the periodic reoccurrence of pandemics to avoid conflicts among contracting parties. This research explores and builds on new avenues from the latest COVID-19 scholarship to better understand existing impacts on the construction industry, and consequently add to the novel body of knowledge on the implications of pandemics on construction contracts. Overall, this research provides a risk-guidance framework for construction professionals and academia to mitigate unforeseen, uncontrollable, and unavoidable risks on construction projects

Cells activated for wound repair have the potential to direct collective invasion of an epithelium.

Mechanisms regulating how groups of cells are signaled to move collectively from their original site and invade surrounding matrix are poorly understood. Here we develop a clinically relevant ex vivo injury invasion model to determine whether cells involved in directing wound healing have invasive function and whether they can act as leader cells to direct movement of a wounded epithelium through a three-dimensional (3D) extracellular matrix (ECM) environment. Similar to cancer invasion, we found that the injured cells invade into the ECM as cords, involving heterotypical cell-cell interactions. Mesenchymal cells with properties of activated repair cells that typically locate to a wound edge are present in leader positions at the front of ZO-1-rich invading cords of cells, where they extend vimentin intermediate filament-enriched protrusions into the 3D ECM. Injury-induced invasion depends on both vimentin cytoskeletal function and MMP-2/9 matrix remodeling, because inhibiting either of these suppressed invasion. Potential push and pull forces at the tips of the invading cords were revealed by time-lapse imaging, which showed cells actively extending and retracting protrusions into the ECM. This 3D injury invasion model can be used to investigate mechanisms of leader cell-directed invasion and understand how mechanisms of wound healing are hijacked to cause disease

Increased Expression of Fatty-Acid and Calcium Metabolism Genes in Failing Human Heart

Heart failure (HF) involves alterations in metabolism, but little is known about cardiomyopathy-(CM)-specific or diabetes-independent alterations in gene expression of proteins involved in fatty-acid (FA) uptake and oxidation or in calcium-(Ca(2+))-handling in the human heart.RT-qPCR was used to quantify mRNA expression and immunoblotting to confirm protein expression in left-ventricular myocardium from patients with HF (n = 36) without diabetes mellitus of ischaemic (ICM, n = 16) or dilated (DCM, n = 20) cardiomyopathy aetiology, and non-diseased donors (CTL, n = 6).Significant increases in mRNA of genes regulating FA uptake (CD36) and intracellular transport (Heart-FA-Binding Protein (HFABP)) were observed in HF patients vs CTL. Significance was maintained in DCM and confirmed at protein level, but not in ICM. mRNA was higher in DCM than ICM for peroxisome-proliferator-activated-receptor-alpha (PPARA), PPAR-gamma coactivator-1-alpha (PGC1A) and CD36, and confirmed at the protein level for PPARA and CD36. Transcript and protein expression of Ca(2+)-handling genes (Two-Pore-Channel 1 (TPCN1), Two-Pore-Channel 2 (TPCN2), and Inositol 1,4,5-triphosphate Receptor type-1 (IP3R1)) increased in HF patients relative to CTL. Increases remained significant for TPCN2 in all groups but for TPCN1 only in DCM. There were correlations between FA metabolism and Ca(2+)-handling genes expression. In ICM there were six correlations, all distinct from those found in CTL. In DCM there were also six (all also different from those found in CTL): three were common to and three distinct from ICM.DCM-specific increases were found in expression of several genes that regulate FA metabolism, which might help in the design of aetiology-specific metabolic therapies in HF. Ca(2+)-handling genes TPCN1 and TPCN2 also showed increased expression in HF, while HF- and CM-specific positive correlations were found among several FA and Ca(2+)-handling genes

Disruption of Spectrin-Like Cytoskeleton in Differentiating Keratinocytes by PKCδ Activation Is Associated with Phosphorylated Adducin

Spectrin is a central component of the cytoskeletal protein network in a variety of erythroid and non-erythroid cells. In keratinocytes, this protein has been shown to be pericytoplasmic and plasma membrane associated, but its characteristics and function have not been established in these cells. Here we demonstrate that spectrin increases dramatically in amount and is assembled into the cytoskeleton during differentiation in mouse and human keratinocytes. The spectrin-like cytoskeleton was predominantly organized in the granular and cornified layers of the epidermis and disrupted by actin filament inhibitors, but not by anti-mitotic drugs. When the cytoskeleton was disrupted PKCδ was activated by phosphorylation on Thr505. Specific inhibition of PKCδ(Thr505) activation with rottlerin prevented disruption of the spectrin-like cytoskeleton and the associated morphological changes that accompany differentiation. Rottlerin also inhibited specific phosphorylation of the PKCδ substrate adducin, a cytoskeletal protein. Furthermore, knock-down of endogenous adducin affected not only expression of adducin, but also spectrin and PKCδ, and severely disrupted organization of the spectrin-like cytoskeleton and cytoskeletal distribution of both adducin and PKCδ. These results demonstrate that organization of a spectrin-like cytoskeleton is associated with keratinocytes differentiation, and disruption of this cytoskeleton is mediated by either PKCδ(Thr505) phosphorylation associated with phosphorylated adducin or due to reduction of endogenous adducin, which normally connects and stabilizes the spectrin-actin complex

Predictive models for estimation of labyrinth weir aeration efficiency

Purpose: The purpose of the study is to estimate the aeration efficiency (E20) of Labyrinth weir using artificial intelligent (AI)-based models. Design/methodology/approach: The aeration efficiency (E20) was collected by using the nine models of Labyrinth weir with different shapes and dimensions. A total of 180 observations were used out of which 126 used to train the AI-based models and the remaining used to test the model. This observation consists of input variables such as Fraud number (Fr), Reynolds number (Re), numbers of keys (N), the ratio of head to the width of the channel (H/W), the ratio of crest length to width of the channel (L/W), the ratio of drop height to width of the channel (D/W) and shape factor (SF) and E20 as the output variables. The AI-based models used were Fuzzy Logic, multi-linear regression (MLR), adaptive neuro fuzzy interface system (ANFIS), and artificial neural network (ANN). Findings: The main findings of this investigation are that ANN is the best AI-based model that can estimate the E20 accurately than MLR, ANFIS, and Fuzzy Logic. Sensitivity analysis depicts that drop height at labyrinth weir is the essential factors for the estimation of E20; further, parametric studies have also been performed. Research limitations/implications: The proposed AI-based models can be used in the estimation of E20 with different shapes of labyrinth weir but still it needs improvement for the different dimensions. Practical implications: The best AI-based model can be used to calculate the E20 with the different values of input variables. Originality/value: There are no such AI-based models such as ANN, ANFIS, and Fuzzy Logic, available in the literature which can estimate the values of E20 accurately

Estimation of the recharging rate of groundwater using random forest technique

Accurate knowledge of the recharging rate is essential for several groundwater-related studies and projects mainly in the water scarcity regions. In this study, a comparison between different methods of soft computing-based models was obtained in order to evaluate and select the most suitable and accurate method for predicting the recharging rate of groundwater, as the natural recharging rate of the groundwater is important in efficient groundwater resource management and aquifer recharge. Experimental data have been used to investigate the improved performance of Gaussian process (GP), M5P and random forest (RF)-based regression method and evaluate the potential of these techniques in the prediction of natural recharging rate. The study also compares the prediction of recharging rate to empirical (Kostiakov model, multilinear regression, multi-nonlinear regression) equations. The RF method was selected for the recharging rate prediction and was compared with the M5P tree, GP and also empirical models. While GP, M5P tree and empirical models provide good quality of prediction performance, RF model showed superiority among them with coefficient of correlation (R) values as 0.98 and 0.91 for training and testing, respectively. Out of 106 observations collected from laboratory experiments, 73 were used for developing different models, whereas rest 33 observations were used for the assessment of the models’ performance. Sensitivity analysis recommends that time parameter (t) is the main influencing parameter, which is crucial for the prediction of the recharging rate. RF-based model is suitable for accurate prediction of recharging rate of groundwater. © 2020, The Author(s)

Comparative analysis of artificial intelligence techniques for the prediction of infiltration process

Knowledge of the infiltration process is beneficial in designing and planning of irrigation networks, soil erosion, hydrologic design, and watershed management. In this study, the infiltration process was analyzed using predictive models of artificial neural network (ANN), multi-linear regression (MLR), Random Forest regression (RF), M5P tree, and their performances were compared with the empirical model: Kostiakov model. Field experimental data was implemented for training and testing the above models, and their outcomes were assessed with the help of suitable performance assessment parameters. These models were assessed using a field dataset containing 340 observations, out of which 70% were used for the training purpose and the remaining for the testing. The RF-based models perform better than other models with Nash-Sutcliffe model efficiency (NSE) equal to 0.9963 and 0.9904 for the training and testing stages, correspondingly. ANN, MLR, and M5P model also give a good prediction performance, but the Kostiakov model’s performance is inferior. Sensitivity investigation suggests that the parameters, cumulative time, and moisture content in the soil are the most influential parameters for assessing the cumulative infiltration of soil. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON)