Football injuries at the College of the Pacific 1924 to 1933

The interest in football injuries has increased with the increased enthusiasm for the sport. The coach is finding it more and more important to have all of his players in the best possible condition to withstand the terrific strain under which they must compete. The public wants to see a well played, exciting game, but the days of brutality seem to be vanishing. The average fan would rather see the best players play in the game than to have them sit on the bench in plaster casts. This has led to a series of quite wide-spread investigations dealing with the problem during the past few seasons. The results, while not final, seem to show that a large percentage of the injuries are preventable. If this is true, further research will certainly be worthwhile. The inspiration for this present investigation came as a result of thirteen years\u27 experience as trainer for athletic teams. During this period most of the games were played by a small squad, frequently further reduced by injuries. It is natural, then, to endeavor to discover hew to strengthen the squads by a study of the causes and treatment of these injuries

Subseasonal precipitation forecasts of opportunity over central southwest Asia

Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation, and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Niño events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to 3-fold of an SFO 3–4 and 5–6 weeks in advance. Strong La Niña events, as observed in 1989, 1999, 2000, also significantly increase the likelihood of an SFO at those same lead times. High-amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increase the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for enhanced tropical convection in the South Pacific convergence zone (SPCZ) region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.</p

Stabilizing machine learning models with Age-Period-Cohort inputs for scoring and stress testing

Machine learning models have been used extensively for credit scoring, but the architectures employed suffer from a significant loss in accuracy out-of-sample and out-of-time. Further, the most common architectures do not effectively integrate economic scenarios to enable stress testing, cash flow, or yield estimation. The present research demonstrates that providing lifecycle and environment functions from Age-Period-Cohort analysis can significantly improve out-of-sample and out-of-time performance as well as enabling the model's use in both scoring and stress testing applications. This method is demonstrated for behavior scoring where account delinquency is one of the provided inputs, because behavior scoring has historically presented the most difficulties for combining credit scoring and stress testing. Our method works well in both origination and behavior scoring. The results are also compared to multihorizon survival models, which share the same architectural design with Age-Period-Cohort inputs and coefficients that vary with forecast horizon, but using a logistic regression estimation of the model. The analysis was performed on 30-year prime conforming US mortgage data. Nonlinear problems involving large amounts of alternate data are best at highlighting the advantages of machine learning. Data from Fannie Mae and Freddie Mac is not such a test case, but it serves the purpose of comparing these methods with and without Age-Period-Cohort inputs. In order to make a fair comparison, all models are given a panel structure where each account is observed monthly to determine default or non-default

The monthly evolution of precipitation and warm conveyor belts during the central southwest Asia wet season

Understanding the nature of precipitation over central southwest Asia (CSWA), a data-sparse, semi-arid region, is important given its relation to agricultural productivity and the likelihood of hazards such as flooding. The present study considers how daily precipitation and local vertical motion – represented by warm conveyor belts (WCBs) – evolve from November to April over CSWA. First we compare several precipitation datasets, revealing that the seasonality of daily precipitation is consistent across estimates that incorporate satellite information, while total accumulation amounts differ substantially. A common feature across datasets is that the majority of precipitation occurs on the few days when area-averaged accumulation exceeds 4 mm, which are most frequent in February and March. The circulation pattern associated with heavy (&lt; 4 mm d−1) precipitation days evolves within the wet season from a southwest–northeast tilted couplet of circulation anomalies in January and February to a neutrally tilted monopole pattern in April. El Niño conditions are associated with more heavy precipitation days than La Niña conditions, with both enhanced WCB frequency and moisture transport observed during the former. An exception to this is found in January, when precipitation, WCB frequency, and moisture do not increase, despite a similar increase in surface cyclones to other months, suggesting that precipitation changes cannot always be inferred from cyclone frequency changes. Nonetheless, our results generally support prior connections made between the El Niño–Southern Oscillation (ENSO) and seasonal-to-interannual precipitation anomalies and extend this connection to one between the slowly evolving ENSO influence and transient and local vertical motion represented by WCBs.</p

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking

Robust pricing and hedging of double no-touch options

Double no-touch options, contracts which pay out a fixed amount provided an underlying asset remains within a given interval, are commonly traded, particularly in FX markets. In this work, we establish model-free bounds on the price of these options based on the prices of more liquidly traded options (call and digital call options). Key steps are the construction of super- and sub-hedging strategies to establish the bounds, and the use of Skorokhod embedding techniques to show the bounds are the best possible. In addition to establishing rigorous bounds, we consider carefully what is meant by arbitrage in settings where there is no {\it a priori} known probability measure. We discuss two natural extensions of the notion of arbitrage, weak arbitrage and weak free lunch with vanishing risk, which are needed to establish equivalence between the lack of arbitrage and the existence of a market model.Comment: 32 pages, 5 figure

Pre-transplant antibody screening and anti-CD154 costimulation blockade promote long-term xenograft survival in a pig-to-primate kidney transplant model

Xenotransplantation has the potential to alleviate the organ shortage that prevents many patients with end-stage renal disease from enjoying the benefits of kidney transplantation. Despite significant advances in other models, pig-to-primate kidney xenotransplantation has met limited success. Preformed anti-pig antibodies are an important component of the xenogeneic immune response. To address this, we screened a cohort of 34 rhesus macaques for anti-pig antibody levels. We then selected animals with both low and high titers of anti-pig antibodies to proceed with kidney transplant from galactose-α1,3-galactose knockout/CD55 transgenic pig donors. All animals received T-cell depletion followed by maintenance therapy with costimulation blockade (either anti-CD154 mAb or belatacept), mycophenolate mofetil, and steroid. The animal with the high titer of anti-pig antibody rejected the kidney xenograft within the first week. Low-titer animals treated with anti-CD154 antibody, but not belatacept exhibited prolonged kidney xenograft survival (>133 and >126 vs. 14 and 21 days, respectively). Long-term surviving animals treated with the anti-CD154-based regimen continue to have normal kidney function and preserved renal architecture without evidence of rejection on biopsies sampled at day 100. This description of the longest reported survival of pig-to-non-human primate kidney xenotransplantation, now >125 days, provides promise for further study and potential clinical translation