Institutional Herding, Business Groups, and Economic Regimes: Evidence from Japan

To gain new and important insights into institutional herding, we study Japan for the following reasons: we can examine a market that is known for its active institutional investors, we can investigate the impacts of business grouping (i.e., the keiretsu), and we can see if herding and feedback trading behaviors differ under three distinct economic regimes (i.e., a regulated period, a bubble economy, and a bear market). We argue that the culture in Japan causes institutions to have both a long-term focus and close relationships with management. Consistent with the first view, we find that herding in Japan occurs on a lower level than it does in the U.S., and that the subsequent short-run returns to herding seem to be unimportant. Consistent with the second view, we find that when herding does occur, it has a large impact on price movements, and the use of past information (feedback trading) on herding behavior seems only marginally important. Much of these findings are more pronounced for keiretsu firms. Lastly, the effects and behavior of institutional herding is dependent on the economic environment.

Textile and Clothing Safeguards: from the ATC to the Future

The Agreement on Textiles and Clothing established the textile and clothing safeguards regime from 1995 to 2004. The current safeguards regime for these products is defined in terms of the Agreement on Safeguards, the China Textile Safeguards, and the China Product-specific Safeguards. This article examines each of these three current safeguard options and assesses them in terms of a number of relevant dimensions. It also reviews safeguard actions to date to provide a sense of continued managed trade in this area.managed trade, protectionism, safeguards, textiles and clothing, International Relations/Trade,

A Note on Shareholder Oversight and the Regulatory Environment: The Japanese Banking Experience

During a period where Japanese banks operated under a less restrictive regulatory environment, 1986-88, we find positive relationships between bank risk and ownership concentration. This empirical evidence reveals shareholder activism by the largest shareholders. During the periods immediately before and immediately after this particular subperiod, which are characterized by stricter regulatory environments, we do not observe evidence of shareholder activism. Taken together, these results are consistent with the argument that restrictive regulation and shareholder oversight are substitutes for one another. Time-series results and bank performance results yield consistent evidence.Large shareholders, Japanese banks, bank risk, shareholder oversight

Associations of PON1 and genetic ancestry with obesity in early childhood.

Obesity in children has become an epidemic in the U.S. and is particularly prominent in minority populations such as Mexican-Americans. In addition to physical activity and diet, genetics also plays a role in obesity etiology. A few studies in adults and adolescents suggest a link between obesity and paraoxonase 1 (PON1), a multifunctional enzyme that can metabolize organophosphate pesticides and also has antioxidant properties. We determined PON1192 genotype and arylesterase levels (ARYase, measure of PON1 enzyme quantity), to characterize the relationship between PON1 and obesity in young Mexican-American children (n = 373) living in an agricultural community in California. Since PON1 polymorphisms and obesity both vary between ethnic groups, we estimated proportional genetic ancestry using 106 ancestral informative markers (AIMs). Among children, PON1192 allele frequencies were 0.5 for both alleles, and the prevalence of obesity was high (15% and 33% at ages two and five, respectively). The average proportion of European, African, and Native American ancestry was 0.40, 0.09, and 0.51, yet there was wide inter-individual variation. We found a significantly higher odds of obesity (9.3 and 2.5- fold) in PON1192QQ children compared to PON1192RR children at ages two and five, respectively. Similar relationships were seen with BMI Z-scores at age two and waist circumference at age five. After adjusting for genetic ancestry in models of PON1 and BMI Z-score, effect estimates for PON1192 genotype changed 15% and 9% among two and five year old children, respectively, providing evidence of genetic confounding by population stratification. However even after adjustment for genetic ancestry, the trend of increased BMI Z-scores with increased number of PON1192 Q alleles remained. Our findings suggest that PON1 may play a role in obesity independent of genetic ancestry and that studies of PON1 and health outcomes, especially in admixed populations, should account for differences due to population stratification

Evaluating duration of response to treatment in systemic lupus erythematosus clinical trials.

ObjectiveTo evaluate response duration and identify predictors of transitioning into and out of the response state in patients with SLE receiving standard of care (SoC) in 52-week clinical trials.MethodsA multistate model (MSM) allowing for bidirectional transitions between response and non-response states was fit to data on 759 patients with SLE with active disease randomised to SoC. The probability of being in response at 52 weeks, average duration of response (sojourn time) and mean total time in response for SLE Responder Index (SRI-4, SRI-5, SRI-6) and BILAG-based Composite Lupus Assessment (BICLA) were estimated. Predictors of attainment and loss of SRI-5 response were also assessed.ResultsThe MSM estimated probability of being in response at 52 weeks ranged from 42% (SRI-6) to 61% (SRI-4). Mean duration of response ranged from 20.4 weeks (BICLA) to 31.5 weeks (SRI-4). Mean total time in response was 16.4-24.8 weeks. Baseline characteristics predictive of shorter SRI-5 response duration were African descent (p=0.005), longer history of disease (p=0.03), higher anti-dsDNA antibody titres (p=0.039), lower lymphocyte count (p=0.008) and lower haemoglobin (p=0.006). Younger age (p<0.001) and higher protein/creatinine ratio (p<0.001) were associated with higher likelihood of achieving SRI-5 but also shorter response duration.ConclusionFactors associated with disease severity were more predictive of shorter response duration than of 52-week response status. Analysing landmark response rates and response duration using MSM may be a more powerful way to distinguish effective investigational treatments from background SoC, although this remains to be evaluated in future trials

Second Stage Short Run (X,vc) and (X,sc) Control Charts

In their'70 paper titled "Mean and Variance Control Chart Limits Based on a Small Number of Subgroups" (Journal of Quality Technology, Volume 2, Number 1, pp. 9-16), Yang and Hillier originally derived equations for calculating the factors required to determine second stage short run control limits for ) v,X c( and )s ,X( c charts. Two issues have restricted the applicability of this particular control chart methodology. These are the limited tabulated values of factors Yang and Hillier present and no example to illustrate the use of the methodology. This paper addresses the first issue by presenting a computer program that accurately calculates the factors regardless of the values of the required inputs. An example shows how to incorporate the methodology into a two stage short run control charting procedure. The computer program is available at http://program.20m.com.

Relative timing based verification of timed circuits and systems

Journal ArticleAggressive timed circuits, including synchronous and asynchronous self-resetting circuits, are particularly challenging to design and verify due to complicated timing constraints that must hold to ensure correct operation. Identifying a small, sufficient, and easily verifiable set of relative timing constraints simplifies both design and verification. However, the manual identification of these constraints is a complex and error-prone process. This paper presents the first systematic algorithm to generate and optimize relative timing constraints sufficient to guarantee correctness. The algorithm has been implemented in our RTCG tool and has been applied to several real-life circuits. In all cases, the tool successfully generates a sufficient set of easily verifiable relative timing constraints. Moreover, the generated constraint sets are the same size or smaller than that of the hand-optimized constraints

Predicted structures of agonist and antagonist bound complexes of adenosine A_3 receptor

We used the GEnSeMBLE Monte Carlo method to predict ensemble of the 20 best packings (helix rotations and tilts) based on the neutral total energy (E) from a vast number (10 trillion) of potential packings for each of the four subtypes of the adenosine G protein-coupled receptors (GPCRs), which are involved in many cytoprotective functions. We then used the DarwinDock Monte Carlo methods to predict the binding pose for the human A_3 adenosine receptor (hAA_3R) for subtype selective agonists and antagonists. We found that all four A_3 agonists stabilize the 15th lowest conformation of apo-hAA_3R while also binding strongly to the 1st and 3rd. In contrast the four A_3 antagonists stabilize the 2nd or 3rd lowest conformation. These results show that different ligands can stabilize different GPCR conformations, which will likely affect function, complicating the design of functionally unique ligands. Interestingly all agonists lead to a trans χ1 angle for W6.48 that experiments on other GPCRs associate with G-protein activation while all 20 apo-AA_3R conformations have a W6.48 gauche+ χ1 angle associated experimentally with inactive GPCRs for other systems. Thus docking calculations have identified critical ligand-GPCR structures involved with activation. We found that the predicted binding site for selective agonist Cl-IB-MECA to the predicted structure of hAA_3R shows favorable interactions to three subtype variable residues, I253^(6.58), V169^(EL2), and Q167^(EL2), while the predicted structure for hAA_(2A)R shows weakened to the corresponding amino acids: T256^(6.58), E169^(EL2), and L167^(EL2), explaining the observed subtype selectivity