Chronic idiopathic thrombocytopenia in the Bantu

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Functional Sequential Treatment Allocation

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present paper we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies

Harvesting and Feeding Drought-Stressed Corn

How to best harvest, store and use drought-stressed corn is a problem dairy and livestock producers may encounter at one time or another. The severity of the drought, cultural practices, plant growth, plant maturity and livestock feeding regimes are factors that influence how to harvest, store and feed most effectively the drought-stressed corn. Dairy and livestock producers attempting to salvage usable feed from their drought-stressed fields of corn must not only be wary of poisoning their livestock, but poisoning themselves as well. Accumulation of nitrates in drought-stressed corn can cause nitrate toxicity in animals and ensiled drought-stressed corn can produce poisonous nitrogen gases during the fermentation process, which may be lethal to livestock and humans

Prussic Acid Poisoning of Livestock: Causes and Prevention

What Is Prussic Acid? Sudangrass, sorghum, and sorghum-sudangrass hybrids are among a group of plants that produce cyanide, which can poison livestock under certain conditions. Also included in this group of plants are johnsongrass, chokecherry, and black cherry. These plants produce cyanogenic glycosides during their growing stage. Glycosides are compounds containing a carbohydrate (sugar) and a noncarbohydrate residue in the same molecule. They decompose (breakdown) into glucose sugar and noncarbohydrate residue by hydrolysis (addition of water) as a result of enzymatic action. In cyanogenic plants this decomposition frees the cyanide from its chemical bond, and it becomes toxic hydrocyanic acid, frequently called prussic acid, and abbreviated HCN. The intact, still-bonded cyanide and glucose are not poisonous. But, when certain enzymes are present that break the bond and free the cyanide, prussic acid (a highly toxic poison) is formed. The enzymes involved in this chemical decomposition of the cyanide and glycosides usually are present in the same plant—but may be available from other sources. Animal digestive juices are a probable source

Bacillary dysentery in African children on the Witwatersrand

'It has .... been shown in the United States and elsewhere that when cases of "diarrhoea, enteritis and dysentery" are carefully studied, the majority appear to be bacillary dysentery.'The investigation to be described was carried out to ascertain the importance of bacillary dysentery in the causation of diarrhoeal disorders among African children in the Johannesburg area. It was found that dysentery organisms were present in less than 20% of these patients

Fine-tuning implications for complementary dark matter and LHC SUSY searches

The requirement that SUSY should solve the hierarchy problem without undue fine-tuning imposes severe constraints on the new supersymmetric states. With the MSSM spectrum and soft SUSY breaking originating from universal scalar and gaugino masses at the Grand Unification scale, we show that the low-fine-tuned regions fall into two classes that will require complementary collider and dark matter searches to explore in the near future. The first class has relatively light gluinos or squarks which should be found by the LHC in its first run. We identify the multijet plus E_T^miss signal as the optimal channel and determine the discovery potential in the first run. The second class has heavier gluinos and squarks but the LSP has a significant Higgsino component and should be seen by the next generation of direct dark matter detection experiments. The combined information from the 7 TeV LHC run and the next generation of direct detection experiments can test almost all of the CMSSM parameter space consistent with dark matter and EW constraints, corresponding to a fine-tuning not worse than 1:100. To cover the complete low-fine-tuned region by SUSY searches at the LHC will require running at the full 14 TeV CM energy; in addition it may be tested indirectly by Higgs searches covering the mass range below 120 GeV.Comment: References added. Version accepted for publication in JHE

Tuning supersymmetric models at the LHC: A comparative analysis at two-loop level

We provide a comparative study of the fine tuning amount (Delta) at the two-loop leading log level in supersymmetric models commonly used in SUSY searches at the LHC. These are the constrained MSSM (CMSSM), non-universal Higgs masses models (NUHM1, NUHM2), non-universal gaugino masses model (NUGM) and GUT related gaugino masses models (NUGMd). Two definitions of the fine tuning are used, the first (Delta_{max}) measures maximal fine-tuning wrt individual parameters while the second (Delta_q) adds their contribution in "quadrature". As a direct result of two theoretical constraints (the EW minimum conditions), fine tuning (Delta_q) emerges as a suppressing factor (effective prior) of the averaged likelihood (under the priors), under the integral of the global probability of measuring the data (Bayesian evidence p(D)). For each model, there is little difference between Delta_q, Delta_{max} in the region allowed by the data, with similar behaviour as functions of the Higgs, gluino, stop mass or SUSY scale (m_{susy}=(m_{\tilde t_1} m_{\tilde t_2})^{1/2}) or dark matter and g-2 constraints. The analysis has the advantage that by replacing any of these mass scales or constraints by their latest bounds one easily infers for each model the value of Delta_q, Delta_{max} or vice versa. For all models, minimal fine tuning is achieved for M_{higgs} near 115 GeV with a Delta_q\approx Delta_{max}\approx 10 to 100 depending on the model, and in the CMSSM this is actually a global minimum. Due to a strong ($\approx$ exponential) dependence of Delta on M_{higgs}, for a Higgs mass near 125 GeV, the above values of Delta_q\approx Delta_{max} increase to between 500 and 1000. Possible corrections to these values are briefly discussed.Comment: 23 pages, 46 figures; references added; some clarifications (section 2

Nitrate Poisoning of Livestock Causes and Prevention

Nitrate poisoning is generally caused when animals eat too much forage that is high in nitrates not changed to protein in the plant. Poisoning can also happen when animals eat too much urea or nitrogen fertilizer spilled in the field or left where the animals can find it. Nitrate fertilizer is palatable, especially to cattle

Determining the Value of Drought-Stressed Corn

Drought-stressed corn for grain or silage does not automatically signal disaster, as both crops can provide high-quality forage for ruminant animals. Drought-stressed corn or corn that is unpollinated will produce little or no grain crop for the crop farmer to sell, but dairy producers can use the unpollinated corn for silage. On a dry matter basis, the drought-stressed corn will be approximately equal in feeding value to normal corn silage

Naturalness and Fine Tuning in the NMSSM: Implications of Early LHC Results

We study the fine tuning in the parameter space of the semi-constrained NMSSM, where most soft Susy breaking parameters are universal at the GUT scale. We discuss the dependence of the fine tuning on the soft Susy breaking parameters M_1/2 and m0, and on the Higgs masses in NMSSM specific scenarios involving large singlet-doublet Higgs mixing or dominant Higgs-to-Higgs decays. Whereas these latter scenarios allow a priori for considerably less fine tuning than the constrained MSSM, the early LHC results rule out a large part of the parameter space of the semi-constrained NMSSM corresponding to low values of the fine tuning.Comment: 19 pages, 10 figures, bounds from Susy searches with ~1/fb include