Greenspan’s Monetary Policy in Retrospect: Discretion or Rules?

Is Alan Greenspan to blame for the current housing bubble and the ongoing financial crisis? A growing chorus charges the former Federal Reserve Chairman with being an "inflationist" whose loose monetary policy caused or significantly contributed to our current economic troubles. However, although Greenspan's policies weren't perfect, his monetary policy was in fact tight, and his legacy is one of having overseen low and stable inflation and a striking dampening of the business cycle. Critics charge Greenspan with having carried on an excessively expansionary monetary policy, particularly following the recession of 2001. They note how low interest rates were from 2002 through 2004 and argue that those low rates paved the way for everything from high prices at the pump to high prices at the supermarket, from the housing crisis to the financial crisis. In so doing, those critics make the classic mistake of using interest rates to evaluate monetary policy, reasoning that if interest rates are low, recent monetary policy must have been expansionary. It is not the Federal Reserve but supply and demand that ultimately determines interest rates. Although central banks can push rates up or down to some degree, the globally integrated financial system reduces the Fed’s ability to significantly influence rates. This paper should not be construed as a defense of all of Greenspan’s policies, nor of central banking or the Federal Reserve. In fact, our preference would be to abolish the Fed and deregulate the banking industry. Barring that, we argue that Federal Reserve policy ought to abide by the rules rather than the discretion of its chairman

Method for Semi-Automated Improvement of Smart Factories Using Synthetic Data and Cause-Effect-Relationships

Smart factories, driven by the integration of automation and digital technologies, have revolutionized industrial production by enhancing efficiency, productivity, and flexibility. However, the optimization and continuous improvement of these complex systems present numerous challenges, especially when real-world data collection is time-consuming, expensive, or limited. In this paper, we propose a novel method for semi-automated improvement of smart factories using synthetic data and cause-effect-relations, while incorporating the aspect of self-organization. The method leverages the power of synthetic data generation techniques to create representative datasets that mimic the behaviour of real-world manufacturing systems. These synthetic datasets serve together with the cause-and-effect relationships as a valuable resource for factory optimization, as they enable extensive experimentation and analysis without the constraints of limited or costly real-world data. Furthermore, the method embraces the concept of self-organization within smart factories. By allowing the system to adapt and optimize itself based on feedback from the synthetic data, cause-effect-relationships, the factory can dynamically reconfigure and adjust its processes. To facilitate the improvement process, the method integrates the synthetic data with advanced analytics and machine learning algorithms as well as and the cause-and-effect relationships. This synergy between human expertise and technological advancements represents a compelling path towards a truly optimized smart factory of the future

Decrease in Protein Level in Final Finishing Phase of High Lean Gain Swine

In an attempt to increase the fat-to-lean ratio on modern lean pigs destined for a niche market, a high lean genetic line of finishing pigs capable of gaining more than 2.0 lbs per day (in latter growth stages over 100 lbs of body weight) were fed two diets with differing protein levels. Pigs consuming a lower protein diet ate less feed (1.46 lbs/day) and gained less (0.65 lbs/day). Backfat, loin muscle area, percent lean and lean gain per day were not different. Gender performance differences were as expected. Lowering the diet’s protein content below the pig’s requirement was not effective in creating change in fat-tolean ratio in the final finishing phase. The balance of all amino acids must be considered when attempting to reduce soybean meal content.

Proteomic analysis of metronidazole resistance in the human facultative pathogen Bacteroides fragilis

The anaerobic gut bacteria and opportunistic pathogen Bacteroides fragilis can cause life-threatening infections when leaving its niche and reaching body sites outside of the gut. The antimicrobial metronidazole is a mainstay in the treatment of anaerobic infections and also highly effective against Bacteroides spp. Although resistance rates have remained low in general, metronidazole resistance does occur in B . fragilis and can favor fatal disease outcomes. Most metronidazole-resistant Bacteroides isolates harbor nim genes, commonly believed to encode for nitroreductases which deactivate metronidazole. Recent research, however, suggests that the mode of resistance mediated by Nim proteins might be more complex than anticipated because they affect the cellular metabolism, e.g., by increasing the activity of pyruvate:ferredoxin oxidoreductase (PFOR). Moreover, although nim genes confer only low-level metronidazole resistance to Bacteroides , high-level resistance can be much easier induced in the laboratory in the presence of a nim gene than without. Due to these observations, we hypothesized that nim genes might induce changes in the B . fragilis proteome and performed comparative mass-spectrometric analyses with B . fragilis 638R, either with or without the nimA gene. Further, we compared protein expression profiles in both strains after induction of high-level metronidazole resistance. Interestingly, only few proteins were repeatedly found to be differentially expressed in strain 638R with the nimA gene, one of them being the flavodiiron protein FprA, an enzyme involved in oxygen scavenging. After induction of metronidazole resistance, a far higher number of proteins were found to be differentially expressed in 638R without nimA than in 638R with nimA . In the former, factors for the import of hemin were strongly downregulated, indicating impaired iron import, whereas in the latter, the observed changes were not only less numerous but also less specific. Both resistant strains, however, displayed a reduced capability of scavenging oxygen. Susceptibility to metronidazole could be widely restored in resistant 638R without nimA by supplementing growth media with ferrous iron sulfate, but not so in resistant 638R with the nimA gene. Finally, based on the results of this study, we present a novel hypothetic model of metronidazole resistance and NimA function

Feeding DDGS to Finishing Pigsin Deep-Bedded Hoop Barns

The ethanol industry in Iowa has rapidly expanded in the last several years. A major coproduct of ethanol production is dried distillers grains with solubles(DDGS). Higher prices for corn and ample supplies of DDGS has generated interest in feeding DDGS to finishing pigs. Challenges in feeding DDGS supplemented diets to finishing swine include problems of feed flowability in bulk bins and feeders, reduced feed intake, and softer, oilier fat in pork carcasses. Pelleted diets have improved flowability and may stimulate feed intake, but thus far no studies have examined feeding DDGS-supplemented dietsin pelletted form to pigs. The objective of this study was to evaluate pelletted DDGS-based diets fed to finishing pigs

Maximizing DDGS for Finishing Pigs in Bedded Hoop Barns: Trial One

Iowa’s ethanol industry continues to expand rapidly. A major coproduct of ethanol production is dried distillers grains with solubles (DDGS). Higher prices for corn and increasing supplies of DDGS have generated questions about feeding DDGS to market swine. The objective of this study was to evaluate various programs to maximize DDGS feeding to finishing pigs in bedded hoop barns. The work reported is the first of several trials planned

Maximizing DDGS for Finishing Pigs in Bedded Hoop Barns

Higher prices for corn and increasing supplies of DDGS have generated questions about feeding DDGS to market swine. The objective of this study was to evaluate various programs to maximize DDGS feeding to finishing pigs in bedded hoop barns. The project was conducted during 2008 and 2009 at the ISU Western Research Farm, Castana, IA. The pens were in small hoop barns with two pens per barn. Each pen was assigned to one of three dietary treatments— continuous 20% DDGS (Cont), a step-up program from 0% to 30% DDGS (Step), and a high DDGS program that rapidly got pigs to 30% DDGS (High). All treatments were fed a 20% DDGS diet for the last phase of the trial. There were 4 dietary phases in the 98-day trial. Phase 1 and 4 were each 21d. Phase 2 and 3 were each 28d. The diets were pelletted and fed ad libitum. Within each phase, the diets were formulated to be equal in apparent digestible amino acids—lysine, threonine, and tryptophan. The pigs consumed the diets readily with no apparent problems making the transition among the diets. Feed intake (ADFI), growth (ADG), and feed per liveweight gain (F/G) did not differ among treatments (P \u3e 0.05). No major differences were noted in backfat thickness (BF) and loin muscle area (LMA) (P \u3e 0.05). Also, based on the means of fatty acid saturation, iodine values, and belly flop scores of selected pigs in trial (one, two or three), the differences in unsaturation percentages, iodine value and belly flop scores between treatments were minor. On average, a pig fed the continuous program consumed 119 lb of DDGS or 20% of the total feed over the 98-day feeding trial (from 54 to 274 lb). A pig fed the Step-up program consumed 106 lb of DDGS or 17% of the total feed. A pig fed the High program consumed 162 lb of DDGS or 26% of the total feed. This work suggests that diets and feeding programs can be designed to increase DDGS usage by market swine without negatively affecting pig performance. Also formulating diets on apparent digestible amino acid content may be advantageous when using DDGS on swine diets. The pelleted diets worked well with no problems in feed flow or fines separation

Use of the Palliative Performance Scale to estimate survival among home hospice patients with heart failure

AimsEstimating survival is challenging in the terminal phase of advanced heart failure. Patients, families, and health‐care organizations would benefit from more reliable prognostic tools. The Palliative Performance Scale Version 2 (PPSv2) is a reliable and validated tool used to measure functional performance; higher scores indicate higher functionality. It has been widely used to estimate survival in patients with cancer but rarely used in patients with heart failure. The aim of this study was to identify prognostic cut‐points of the PPSv2 for predicting survival among patients with heart failure receiving home hospice care.Methods and resultsThis retrospective cohort study included 1114 adult patients with a primary diagnosis of heart failure from a not‐for‐profit hospice agency between January 2013 and May 2017. The primary outcome was survival time. A Cox proportional‐hazards model and sensitivity analyses were used to examine the association between PPSv2 scores and survival time, controlling for demographic and clinical variables. Receiver operating characteristic curves were plotted to quantify the diagnostic performance of PPSv2 scores by survival time. Lower PPSv2 scores on admission to hospice were associated with decreased median (interquartile range, IQR) survival time [PPSv2 10 = 2 IQR: 1–5 days; PPSv2 20 = 3 IQR: 2–8 days] IQR: 55–207. The discrimination of the PPSv2 at baseline for predicting death was highest at 7 days [area under the curve (AUC) = 0.802], followed by an AUC of 0.774 at 14 days, an AUC of 0.736 at 30 days, and an AUC of 0.705 at 90 days.ConclusionsThe PPSv2 tool can be used by health‐care providers for prognostication of hospice‐enrolled patients with heart failure who are at high risk of near‐term death. It has the greatest utility in patients who have the most functional impairment.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148390/1/ehf212398_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148390/2/ehf212398.pd