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With a diverse group of students taking undergraduate Anatomy and Physiology (A&P), it is imperative to find methods that enable every student to be more successful. At National University, we have been applying a new approach in our online and onsite classes that includes more learners: offering multiple attempt homework assessments. The A&P homework is scaffolded to be done with either unlimited attempts or limited to 10 attempts, depending on the specific section. The homework is given online, while subsequent assessments are either given onsite or online. The homework assignments precede more traditional, single-attempt quizzes and/or exams. Some students only complete a homework assessment once, while other students complete the homework assessment multiple times (occasionally over 50 times) in order to achieve their desired grade. The number of attempts per student is specific to each student and not to the specific topic of the homework; some students routinely carry out multiple attempts while others do not. Although making multiple attempts available may seem to allow for grade inflation, our results show that is not the case. Analysis of the number of homework attempts in relation to homework assessment grade had no correlation. Further, the number of homework attempts has no correlation with the grade students earned on quizzes or the entire course. However, a positive correlation has been established between the achieved homework grade in relation to quizzes, exams, and course grade. This positive correlation exists in both online and onsite classes. Our results indicate that enabling students to work through homework assignments according to their needs until they master each topic increases their opportunities for success in the A&P course. Our approach of offering multiple homework attempts is inclusive because it provides any learner with an opportunity to succeed. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Supplementary Figure from Attenuating Adaptive VEGF-A and IL8 Signaling Restores Durable Tumor Control in AR Antagonist–Treated Prostate Cancers

Inhibiting androgen signaling using androgen signaling inhibitors (ASI) remains the primary treatment for castrate-resistant prostate cancer. Acquired resistance to androgen receptor (AR)-targeted therapy represents a major impediment to durable clinical response. Understanding resistance mechanisms, including the role of AR expressed in other cell types within the tumor microenvironment, will extend the clinical benefit of AR-targeted therapy. Here, we show the ASI enzalutamide induces vascular catastrophe and promotes hypoxia and microenvironment adaptation. We characterize treatment-induced hypoxia, and subsequent induction of angiogenesis, as novel mechanisms of relapse to enzalutamide, highlighting the importance of two hypoxia-regulated cytokines in underpinning relapse. We confirmed AR expression in CD34+ vascular endothelium of biopsy tissue and human vascular endothelial cells (HVEC). Enzalutamide attenuated angiogenic tubule formation and induced cytotoxicity in HVECs in vitro, and rapidly induced sustained hypoxia in LNCaP xenografts. Subsequent reoxygenation, following prolonged enzalutamide treatment, was associated with increased tumor vessel density and accelerated tumor growth. Hypoxia increased AR expression and transcriptional activity in prostate cells in vitro. Coinhibition of IL8 and VEGF-A restored tumor response in the presence of enzalutamide, confirming the functional importance of their elevated expression in enzalutamide-resistant models. Moreover, coinhibition of IL8 and VEGF-A resulted in a durable, effective resolution of enzalutamide-sensitive prostate tumors. We conclude that concurrent inhibition of two hypoxia-induced factors, IL8 and VEGF-A, prolongs tumor sensitivity to enzalutamide in preclinical models and may delay the onset of enzalutamide resistance.Implications:Targeting hypoxia-induced signaling may extend the therapeutic benefit of enzalutamide, providing an improved treatment strategy for patients with resistant disease.

We set up an agent-based model where the parameters of firms’ pricing heuristics are determined through evolutionary learning. We argue that there are several key ingredients that result in the emergence of the lead–lag pattern in the money growth–inflation relationship. First, a realistic representation of money creation through a lending mechanism is essential. Second, there must be considerable heterogeneity in the distribution of newly created deposits and in the associated changes in demand at the individual firm level.

Inhibiting androgen signaling using androgen signaling inhibitors (ASI) remains the primary treatment for castrate-resistant prostate cancer. Acquired resistance to androgen receptor (AR)-targeted therapy represents a major impediment to durable clinical response. Understanding resistance mechanisms, including the role of AR expressed in other cell types within the tumor microenvironment, will extend the clinical benefit of AR-targeted therapy. Here, we show the ASI enzalutamide induces vascular catastrophe and promotes hypoxia and microenvironment adaptation. We characterize treatment-induced hypoxia, and subsequent induction of angiogenesis, as novel mechanisms of relapse to enzalutamide, highlighting the importance of two hypoxia-regulated cytokines in underpinning relapse. We confirmed AR expression in CD34+ vascular endothelium of biopsy tissue and human vascular endothelial cells (HVEC). Enzalutamide attenuated angiogenic tubule formation and induced cytotoxicity in HVECs in vitro, and rapidly induced sustained hypoxia in LNCaP xenografts. Subsequent reoxygenation, following prolonged enzalutamide treatment, was associated with increased tumor vessel density and accelerated tumor growth. Hypoxia increased AR expression and transcriptional activity in prostate cells in vitro. Coinhibition of IL8 and VEGF-A restored tumor response in the presence of enzalutamide, confirming the functional importance of their elevated expression in enzalutamide-resistant models. Moreover, coinhibition of IL8 and VEGF-A resulted in a durable, effective resolution of enzalutamide-sensitive prostate tumors. We conclude that concurrent inhibition of two hypoxia-induced factors, IL8 and VEGF-A, prolongs tumor sensitivity to enzalutamide in preclinical models and may delay the onset of enzalutamide resistance. Implications: Targeting hypoxia-induced signaling may extend the therapeutic benefit of enzalutamide, providing an improved treatment strategy for patients with resistant disease.

We test the ability of early warning indicators that appear in the literature to predict credit cycle peaks in a cross-section of emerging markets, verifying our findings by cross-sectional validation. Our results confirm that the standard credit gap indicator performs satisfactorily. In fact, we find that, in emerging market economies, it seems rather difficult to outperform this indicator by means of augmented multivariate models. Nevertheless, we have found that the robustness of real-time credit cycle determination may potentially be improved (although with a risk of overfitting the data) by simultaneously monitoring GDP growth, banks’ non-core liabilities, the financial sector’s value added and (to a lesser extent) the change in the debt service ratio.

We examine the relationship between households’ demand for traditional money and the proliferation of alternative financial assets using a cross-section of countries. We have found that the inclusion of the disaggregated indicators of financial assets improves the money demand models. Notably, this relationship appears to be non-linear. At higher stages of financial development, an increase in alternative assets is associated with a larger decrease in money demand.

We analyse the ability of credit gap measures to predict banking crises by estimating the usefulness measure conditionally on policymaker’s preferences. The results show that the signals based on the credit gap indicators are most useful when the policymaker’s preferences regarding Type I and Type II errors are approximately equal. However, according to the current consensus, the preferences to avoid missing a crisis are higher than issuing a false signal. This means that the usefulness of the credit-gap-based early warning systems is likely to increase once the static Basel III regulative measures are implemented (assuming that their implementation results in lower financial crises’ costs).

We set up an agent-based model where the parameters of the firms’ pricing heuristics are determined via evolutionary learning. We argue that there are several key ingredients that result in the emergence of the lead-lag pattern in the money growth-inflation relationship. Firstly, the realistic representation of the money creation through lending mechanism is essential. Secondly, there should be considerable heterogeneity in the distribution of newly created deposits and in the associated changes in demand on the individual firm level.

This paper examines the relative roles of region-specific and commodity-specific developments in the consumer price setting in Russia. For this purpose, we estimate a dynamic hierarchical factor model using inflation rates across regions and sectors. We found little evidence of association between region-specific factors and inflation developments, although there are several regions (mostly located in the Far East and North Caucasus) where the idiosyncratic component may contribute substantially. Conversely, the role of cross-commodity relative price changes in inflation developments in Russia is substantial.

We estimated a Non-Stationary Dynamic Factor model and used it to generate artificial episodes of disinflation (permanent change in the mean inflation rate). These datasets were used to test the performance of alternative underlying inflation measures. We found that the benchmark underlying inflation measures (based on unobserved trend extraction) are more severely affected by disinflation than the alternative simpler methods (based on exclusion or reweighting approaches). Alternatively, a Non-Stationary Dynamic Factor model may be employed for extraction of the unobserved trend to be used as an underlying inflation measure.

We estimate a dynamic factor model for the cross section of monetary and price indicators. We extract the common part of the dataset’s fluctuations and decompose it into structural shocks. We argue that one of the shocks identified has empirical properties (in terms of impulse response functions) that are fully in line with the theoretically expected relationship between money growth and inflation, confirming that the process identified has the capacity for economic interpretation. Based on this finding, we decompose recent inflationary developments in Russia into those that are associated with changes in monetary stance and other shorter-lived shocks.

Real-time assessment of quarterly GDP growth rates is crucial for evaluation of economy’s current perspectives given the fact that respective data is normally subject to substantial publication delays by national statistical agencies. Large information sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are in practice characterized by unbalanced data, mixed frequencies, systematic data revisions, as well as a more general curse of dimensionality problem. The latter issues could, however, be practically resolved by means of dynamic factor modeling that has recently been recognized as a helpful tool to evaluate current economic conditions by means of higher frequency indicators. Our major results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior as compared to other common alternative specifications. At the same time, we empirically show that the arrival of new data seems to consistently improve DFM’s predictive accuracy throughout sequential nowcast vintages. We also introduce the analysis of nowcast evolution resulting from the gradual expansion of the dataset of explanatory variables, as well as the framework for estimating contributions of different blocks of predictors into now-casts of Russian GDP.

We evaluate the reliability of credit gap measures estimated over time samples of different lengths. We augment our empirical analysis (which turned out to be somewhat inconclusive) with Monte Carlo experiments. For this purpose we build an agent-based model that realistically reproduces credit cycles and use it to generate the artificial data set. We found that 12–15 years of available data is sufficient for the estimation of reliable credit gaps (i.e. the reliability of credit gap estimates will not improve substantially as more data are added to the sample).

We apply an econometric approach developed specifically to address the “curse of dimensionality” in Russian data and estimate a Bayesian vector autoregression model comprising sixteen major macroeconomic indicators. We conduct several types of exercises to validate our model: impulse response analysis, recursive forecasting and counterfactual simulations. We also show that real sector developments in Russia in 2010–13 could be accurately forecasted if conditioned on oil price and EU GDP (but not if conditioned on oil price alone). Real growth rates were notably lower than projected in 2014, presumably due to increased economic uncertainty.

This paper provides an analysis of the debt burden of Russian companies and raises the issue of debt-level heterogeneity across economic sectors. To identify the causes of this heterogeneity, it estimates a regression model that includes both fundamental explanatory variables of companies and industry fixed effects. The results of the analysis demonstrate that standard variables, such as profitability, company size, asset turnover, and fixed-asset turnover ratio have a strong statistical significance. However, these do not fully explain the variation in the debt levels of companies in different sectors. According to model estimation, there are other industry specific factors that produce an imbalance between fundamental factors and companies’ debt levels. An understanding of the formation process and structure of debt burden in individual industries is extremely important for the financial stability of companies and for an effective monetary policy.

We test the ability of early warning indicators that appear in the literature to predict credit cycle peaks in a cross-section of emerging markets. Our results confirm that the standard credit gap indicator performs satisfactorily. The robustness of real-time credit cycle determination may potentially (and with a risk of overfitting the data) be improved by simultaneously monitoring GDP growth, banks’ non-core liabilities, the financial sector’s value added and (to a lesser extent) the change in the debt service ratio.

This article presents three alternative models for decomposing loan developments into components associated with changes in loan demand and supply fundamentals. Two models are based on macro data (error correction model and structural vector autoregression with sign restrictions) and one is based on bank-specific Bank Lending Survey results. We conclude that although loan growth in Russia converges to a long-run equilibrium determined by macroeconomic (demand) factors the convergence is likely to be driven by bank-side (supply) shocks. We identify large and unexplained supply shocks in loan fluctuations during the crisis of 2008-2009, signifying an impairment of credit markets. We also find contractionary shocks unrelated to demand fundamentals or balance sheet structures in 2013, although in general loan developments in 2013 and the first half of 2014 were not at all extraordinary.

​We apply several tests to the underlying inflation metrics used in practice by central banks and/or proposed in the scientific literature, in an attempt to find the best-performing indicators. We find that although there is no single best measure of underlying inflation, indicators calculated on the basis of dynamic factor models are generally among the best performers. These best performers not only outdid the simpler traditional underlying indicators (trimmed and exclusion-based measures) but also proved to be economically meaningful and interpretable.

The volume collects the essays presented at the 15th Workshop on Public Finance organised by Banca d'Italia in Perugia from 4 to 6 April 2013. The workshop focused on the link between fiscal policy and macroeconomic imbalances and comprised four sessions. The first session concentrated on the interaction between fiscal policy and macroeconomic imbalances. The second session dealt with the specific problems of the euro area, where insufficient attention to macroeconomic developments contributed to increase fiscal and financial tensions. The third session scrutinized fiscal policy with an eye to the inequalities of emerging economies and resource-rich countries. The last session analysed the experiences so far and the potential prescriptions about the use of fiscal tools in addressing macroeconomic problems.

The authors estimate a dynamic factor model for the cross-section of monetary and price indicators for Russia. They extract the common part of the dataset's fluctuations and decompose it into structural shocks. One of the shocks identified has empirical properties (in terms of impulse response functions) that are fully in line with the theoretically expected relationship between money growth and inflation, confirming that the process identified has the capacity for economic interpretation. Based on the finding, recent inflationary developments in Russia are decomposed into those that are associated with changes in monetary stance and other shorter-lived shocks. The analysis in this paper is based on the course material taught in the CCBS course: 'Applied Bayesian Econometrics for central bankers'.Â

We examine the drivers behind loan supply fluctuations in Russia using Bayesian vector autoregressive model with sign restrictions on impulse response functions. We identify two types of structural innovations: loan supply shock and monetary stance shock. We find that contractionary shocks of both types contributed significantly and in the roughly equal measure to the decrease of bank lending after the Lehman Brothers collapse.

Serine proteases are secreted from cells as single-chain zymogens, typically having activities orders of magnitude lower than those of the mature two-chain enzymes. Activation occurs by a conformational change initiated by cleavage of a specific peptide bond. We have derived a monoclonal antibody (mAb-112) which binds with subnanomolar affinity to pro-uPA, the zymogen form of urokinase-type plasminogen activator (uPA). We mapped the epitope of the antibody to the autolysis loop, one of the structural elements known to change conformation during zymogen activation. A mechanistic evaluation with biophysical methods elucidated a novel bifunctional inhibitory mechanism whereby mAb-112 not only delays the proteolytic conversion of single-chain pro-uPA into the two-chain form but also subsequently averts the conformational transition to a mature protease by sequestering the two-chain form in a zymogen-like, noncatalytic state. Functional studies employing two variants of human HT-1080 cells, exhibiting high and low levels of dissemination in a chorioallantoic membrane assay, demonstrate that mAb-112 is an effective inhibitor of tumor cell intravasation. These findings show that pharmacological interference with zymogen activation is a plausible and robust means to regulate uPA activity and the downstream effects of plasminogen activation in the spread of cancer and other processes of pathological tissue remodeling. A strategy that targets regions related to pro-enzyme activation likely provide a unique inhibitor-protease interaction surface and is, thus, expected to enhance the chances of engineering high inhibitor specificity. Our results provide new information about the structural flexibility underlying the equilibrium between active and inactive forms of serine proteases.