Narrative of the expedition of an American squadron to the China Seas and Japan : performed in the years 1852, 1853, and 1854, under the command of Commodore M.C. Perry, United States Navy, by order of the Government of the United States /

v. 3

Clinical Differences between Subtypes of Atrial Fibrillation and Flutter: Cross-Sectional Registry of 407 Patients

Introduction:Atrial fibrillation and atrial flutter account for one third of hospitalizations due to arrhythmias, determining great social and economic impacts. In Brazil, data on hospital care of these patients is scarce.Objective:To investigate the arrhythmia subtype of atrial fibrillation and flutter patients in the emergency setting and compare the clinical profile, thromboembolic risk and anticoagulants use.Methods:Cross-sectional retrospective study, with data collection from medical records of every patient treated for atrial fibrillation and flutter in the emergency department of Instituto de Cardiologia do Rio Grande do Sul during the first trimester of 2012.Results:We included 407 patients (356 had atrial fibrillation and 51 had flutter). Patients with paroxysmal atrial fibrillation were in average 5 years younger than those with persistent atrial fibrillation. Compared to paroxysmal atrial fibrillation patients, those with persistent atrial fibrillation and flutter had larger atrial diameter (48.6 ± 7.2 vs. 47.2 ± 6.2 vs. 42.3 ± 6.4; p < 0.01) and lower left ventricular ejection fraction (66.8 ± 11 vs. 53.9 ± 17 vs. 57.4 ± 16; p < 0.01). The prevalence of stroke and heart failure was higher in persistent atrial fibrillation and flutter patients. Those with paroxysmal atrial fibrillation and flutter had higher prevalence of CHADS2 score of zero when compared to those with persistent atrial fibrillation (27.8% vs. 18% vs. 4.9%; p < 0.01). The prevalence of anticoagulation in patients with CHA2DS2-Vasc ≤ 2 was 40%.Conclusions:The population in our registry was similar in its comorbidities and demographic profile to those of North American and European registries. Despite the high thromboembolic risk, the use of anticoagulants was low, revealing difficulties for incorporating guideline recommendations. Public health strategies should be adopted in order to improve these rates.

Existence, multiplicity and behaviour of solutions of some elliptic differential equations of higher order

Magdeburg, Univ., Fak. für Mathematik, Diss., 2009

Análise estatística da distribuição de Poisson

The general properties of POISSON distributions and their relations to the binomial distribuitions are discussed. Two methods of statistical analysis are dealt with in detail: X2-test. In order to carry out the X2-test, the mean frequency and the theoretical frequencies for all classes are calculated. Than the observed and the calculated frequencies are compared, using the well nown formula: f(obs) - f(esp) 2; i(esp). When the expected frequencies are small, one must not forget that the value of X2 may only be calculated, if the expected frequencies are biger than 5. If smaller values should occur, the frequencies of neighboroughing classes must ge pooled. As a second test reintroduced by BRIEGER, consists in comparing the observed and expected error standard of the series. The observed error is calculated by the general formula: δ + Σ f . VK n-1 where n represents the number of cases. The theoretical error of a POISSON series with mean frequency m is always ± Vm. These two values may be compared either by dividing the observed by the theoretical error and using BRIEGER's tables for # or by dividing the respective variances and using SNEDECOR's tables for F. The degree of freedom for the observed error is one less the number of cases studied, and that of the theoretical error is always infinite. In carrying out these tests, one important point must never be overlloked. The values for the first class, even if no concrete cases of the type were observed, must always be zero, an dthe value of the subsequent classes must be 1, 2, 3, etc.. This is easily seen in some of the classical experiments. For instance in BORKEWITZ example of accidents in Prussian armee corps, the classes are: no, one, two, etc., accidents. When counting the frequency of bacteria, these values are: no, one, two, etc., bacteria or cultures of bacteria. Ins studies of plant diseases equally the frequencies are : no, one, two, etc., plants deseased. Howewer more complicated cases may occur. For instance, when analising the degree of polyembriony, frequently the case of "no polyembryony" corresponds to the occurrence of one embryo per each seed. Thus the classes are not: no, one, etc., embryo per seed, but they are: no additional embryo, one additional embryo, etc., per seed with at least one embryo. Another interestin case was found by BRIEGER in genetic studies on the number os rows in maize. Here the minimum number is of course not: no rows, but: no additional beyond eight rows. The next class is not: nine rows, but: 10 rows, since the row number varies always in pairs of rows. Thus the value of successive classes are: no additional pair of rows beyond 8, one additional pair (or 10 rows), two additional pairs (or 12 rows) etc.. The application of the methods is finally shown on the hand of three examples : the number of seeds per fruit in the oranges M Natal" and "Coco" and in "Calamondin". As shown in the text and the tables, the agreement with a POISSON series is very satisfactory in the first two cases. In the third case BRIEGER's error test indicated a significant reduction of variability, and the X2 test showed that there were two many fruits with 4 or 5 seeds and too few with more or with less seeds. Howewer the fact that no fruit was found without seed, may be taken to indicate that in Calamondin fruits are not fully parthenocarpic and may develop only with one seed at the least. Thus a new analysis was carried out, on another class basis. As value for the first class the following value was accepted: no additional seed beyond the indispensable minimum number of one seed, and for the later classes the values were: one, two, etc., additional seeds. Using this new basis for all calculations, a complete agreement of the observed and expected frequencies, of the correspondig POISSON series was obtained, thus proving that our hypothesis of the impossibility of obtaining fruits without any seed was correct for Calamondin while the other two oranges were completely parthenocarpic and fruits without seeds did occur.

A determinação dos números de indivíduos mínimos necessários na experimentação genética

The main object of the present paper consists in giving formulas and methods which enable us to determine the minimum number of repetitions or of individuals necessary to garantee some extent the success of an experiment. The theoretical basis of all processes consists essentially in the following. Knowing the frequency of the desired p and of the non desired ovents q we may calculate the frequency of all possi- ble combinations, to be expected in n repetitions, by expanding the binomium (p-+q)n. Determining which of these combinations we want to avoid we calculate their total frequency, selecting the value of the exponent n of the binomium in such a way that this total frequency is equal or smaller than the accepted limit of precision n/pª{ 1/n1 (q/p)n + 1/(n-1)| (q/p)n-1 + 1/ 2!(n-2)| (q/p)n-2 + 1/3(n-3) (q/p)n-3... < Plim - -(1b) There does not exist an absolute limit of precision since its value depends not only upon psychological factors in our judgement, but is at the same sime a function of the number of repetitions For this reasen y have proposed (1,56) two relative values, one equal to 1-5n as the lowest value of probability and the other equal to 1-10n as the highest value of improbability, leaving between them what may be called the "region of doubt However these formulas cannot be applied in our case since this number n is just the unknown quantity. Thus we have to use, instead of the more exact values of these two formulas, the conventional limits of P.lim equal to 0,05 (Precision 5%), equal to 0,01 (Precision 1%, and to 0,001 (Precision P, 1%). The binominal formula as explained above (cf. formula 1, pg. 85), however is of rather limited applicability owing to the excessive calculus necessary, and we have thus to procure approximations as substitutes. We may use, without loss of precision, the following approximations: a) The normal or Gaussean distribution when the expected frequency p has any value between 0,1 and 0,9, and when n is at least superior to ten. b) The Poisson distribution when the expected frequecy p is smaller than 0,1. Tables V to VII show for some special cases that these approximations are very satisfactory. The praticai solution of the following problems, stated in the introduction can now be given: A) What is the minimum number of repititions necessary in order to avoid that any one of a treatments, varieties etc. may be accidentally always the best, on the best and second best, or the first, second, and third best or finally one of the n beat treatments, varieties etc. Using the first term of the binomium, we have the following equation for n: n = log Riim / log (m:) = log Riim / log.m - log a --------------(5) B) What is the minimun number of individuals necessary in 01der that a ceratin type, expected with the frequency p, may appaer at least in one, two, three or a=m+1 individuals. 1) For p between 0,1 and 0,9 and using the Gaussean approximation we have: on - ó. p (1-p) n - a -1.m b= δ. 1-p /p e c = m/p } -------------------(7) n = b + b² + 4 c/ 2 n´ = 1/p n cor = n + n' ---------- (8) We have to use the correction n' when p has a value between 0,25 and 0,75. The greek letters delta represents in the present esse the unilateral limits of the Gaussean distribution for the three conventional limits of precision : 1,64; 2,33; and 3,09 respectively. h we are only interested in having at least one individual, and m becomes equal to zero, the formula reduces to : c= m/p o para a = 1 a = { b + b²}² = b² = δ2 1- p /p }-----------------(9) n = 1/p n (cor) = n + n´ 2) If p is smaller than 0,1 we may use table 1 in order to find the mean m of a Poisson distribution and determine. n = m: p C) Which is the minimun number of individuals necessary for distinguishing two frequencies p1 and p2? 1) When pl and p2 are values between 0,1 and 0,9 we have: n = { δ p1 ( 1-pi) + p2) / p2 (1 - p2) n= 1/p1-p2 }------------ (13) n (cor) We have again to use the unilateral limits of the Gaussean distribution. The correction n' should be used if at least one of the valors pl or p2 has a value between 0,25 and 0,75. A more complicated formula may be used in cases where whe want to increase the precision : n (p1 - p2) δ { p1 (1- p2 ) / n= m δ = δ p1 ( 1 - p1) + p2 ( 1 - p2) c= m / p1 - p2 n = { b2 + 4 4 c }2 }--------- (14) n = 1/ p1 - p2 2) When both pl and p2 are smaller than 0,1 we determine the quocient (pl-r-p2) and procure the corresponding number m2 of a Poisson distribution in table 2. The value n is found by the equation : n = mg /p2 ------------- (15) D) What is the minimun number necessary for distinguishing three or more frequencies, p2 p1 p3. If the frequecies pl p2 p3 are values between 0,1 e 0,9 we have to solve the individual equations and sue the higest value of n thus determined : n 1.2 = {δ p1 (1 - p1) / p1 - p2 }² = Fiim n 1.2 = { δ p1 ( 1 - p1) + p1 ( 1 - p1) }² } -- (16) Delta represents now the bilateral limits of the : Gaussean distrioution : 1,96-2,58-3,29. 2) No table was prepared for the relatively rare cases of a comparison of threes or more frequencies below 0,1 and in such cases extremely high numbers would be required. E) A process is given which serves to solve two problemr of informatory nature : a) if a special type appears in n individuals with a frequency p(obs), what may be the corresponding ideal value of p(esp), or; b) if we study samples of n in diviuals and expect a certain type with a frequency p(esp) what may be the extreme limits of p(obs) in individual farmlies ? I.) If we are dealing with values between 0,1 and 0,9 we may use table 3. To solve the first question we select the respective horizontal line for p(obs) and determine which column corresponds to our value of n and find the respective value of p(esp) by interpolating between columns. In order to solve the second problem we start with the respective column for p(esp) and find the horizontal line for the given value of n either diretly or by approximation and by interpolation. 2) For frequencies smaller than 0,1 we have to use table 4 and transform the fractions p(esp) and p(obs) in numbers of Poisson series by multiplication with n. Tn order to solve the first broblem, we verify in which line the lower Poisson limit is equal to m(obs) and transform the corresponding value of m into frequecy p(esp) by dividing through n. The observed frequency may thus be a chance deviate of any value between 0,0... and the values given by dividing the value of m in the table by n. In the second case we transform first the expectation p(esp) into a value of m and procure in the horizontal line, corresponding to m(esp) the extreme values om m which than must be transformed, by dividing through n into values of p(obs). F) Partial and progressive tests may be recomended in all cases where there is lack of material or where the loss of time is less importent than the cost of large scale experiments since in many cases the minimun number necessary to garantee the results within the limits of precision is rather large. One should not forget that the minimun number really represents at the same time a maximun number, necessary only if one takes into consideration essentially the disfavorable variations, but smaller numbers may frequently already satisfactory results. For instance, by definition, we know that a frequecy of p means that we expect one individual in every total o(f1-p). If there were no chance variations, this number (1- p) will be suficient. and if there were favorable variations a smaller number still may yield one individual of the desired type. r.nus trusting to luck, one may start the experiment with numbers, smaller than the minimun calculated according to the formulas given above, and increase the total untill the desired result is obtained and this may well b ebefore the "minimum number" is reached. Some concrete examples of this partial or progressive procedure are given from our genetical experiments with maize.

Sôbre uma propriedade da equação utilizada para interpolação da Lei de Mitscherlich

The author proves that equation, Σy n ΣZx | ΣxyZx ΣxZx ΣxZ2x | = 0, Σy ΣZx Σy2x | where Z = 10-cq and q is a numerical constant, used by Pimentel Gomes and Malavolta in several articles for the interpolation of Mitscherlih's equation y = A [ 1 - 10 - c (x + b) ] by the least squares method, always has a zero of order three for Z = 1. Therefore, equation A Zm + A1Zm -1 + ........... + Am = 0 obtained from that determinant can be divided by (Z-1)³. This property provides a good test for the correctness of the computations and facilitates the solution of the equation.

Estudo comparativo entre métodos de contrôle quantitativo da produção leiteira

This paper deals with the estimation of milk production by means of weekly, biweekly, bimonthly observations and also by method known as 6-5-8, where one observation is taken at the 6th week of lactation, another at 5th month and a third one at the 8th month. The data studied were obtained from 72 lactations of the Holstein Friesian breed of the "Escola Superior de Agricultura "Luiz de Queiroz" (Piracicaba), S. Paulo, Brazil), being 6 calvings on each month of year and also 12 first calvings, 12 second calvings, and so on, up to the sixth. The authors criticize the use of "maximum error" to be found in papers dealing with this subject, and also the use of mean deviation. The former is completely supersed and unadvisable and latter, although equivalent, to a certain extent, to the usual standard deviation, has only 87,6% of its efficiency, according to KENDALL (9, pp. 130-131, 10, pp. 6-7). The data obtained were compared with the actual production, obtained by daily control and the deviations observed were studied. Their means and standard deviations are given on the table IV. Inspite of BOX's recent results (11) showing that with equal numbers in all classes a certain inequality of varinces is not important, the autors separated the methods, before carrying out the analysis of variance, thus avoiding to put together methods with too different standard deviations. We compared the three first methods, to begin with (Table VI). Then we carried out the analysis with the four first methods. (Table VII). Finally we compared the two last methods. (Table VIII). These analysis of variance compare the arithmetic means of the deviations by the methods studied, and this is equivalent to compare their biases. So we conclude tht season of calving and order of calving do not effect the biases, and the methods themselves do not differ from this view point, with the exception of method 6-5-8. Another method of attack, maybe preferrable, would be to compare the estimates of the biases with their expected mean under the null hypothesis (zero) by the t-test. We have: 1) Weekley control: t = x - 0/c(x) = 8,59 - 0/ = 1,56 2) Biweekly control: t = 11,20 - 0/6,21= 1,80 3) Monthly control: t = 7,17 - 0/9,48 = 0,76 4) Bimonthly control: t = - 4,66 - 0/17,56 = -0,26 5) Method 6-5-8 t = 144,89 - 0/22,41 = 6,46*** We denote above by three asterisks, significance the 0,1% level of probability. In this way we should conclude that the weekly, biweekly, monthly and bimonthly methods of control may be assumed to be unbiased. The 6-5-8 method is proved to be positively biased, and here the bias equals 5,9% of the mean milk production. The precision of the methods studied may be judged by their standard deviations, or by intervals covering, with a certain probability (95% for example), the deviation x corresponding to an estimate obtained by cne of the methods studied. Since the difference x - x, where x is the mean of the 72 deviations obtained for each method, has a t distribution with mean zero and estimate of standard deviation. s(x - x) = √1+ 1/72 . s = 1.007. s , and the limit of t for the 5% probability, level with 71 degrees of freedom is 1.99, then the interval to be considered is given by x ± 1.99 x 1.007 s = x ± 2.00. s The intervals thus calculated are given on the table IX.

The relation of the concentration of macronutrients in the substrate and in the foliage to cell wall thickness and cellulose concentration in the xylem of slash pine (Pinus elliotti)

Sand culture experiments, using a sub-irrigation technique, were installed in order to find out the effects of the macronutrients N, P, K, Ca, Mg and S on growth, aspect, mineral composition, length of fibers, thickness of cell wall and cellulose concentration in slash pine. The aim was to obtain, under controlled conditions, basic information which could eventually lead to practical means designed to increase the rate of growth and to make of slash pine a richer source of cellulose. Nitrogen, Phosphorus, Potassium Experiment A 3 x 3 x 3 factorial design with two replicates was used. Nitrogen was supplied initially at the levels of 25, 50 and 100 ppm; phosphorus was given at the rates of 5, 10 and 20 ppm; potassium was supplied at the rates of 25, 50 and 100 ppm; six months after the experiment was started the first level for each element was dropped to zero. Others macro and all micronutrients were supplied at uniform rates. Fifteen hours of illumination per day were provided. The experimental technique for growing the slash pine seedlings proved quite satisfactory. Symptoms of deficiency of nitrogen, phosphorus and potassium were observed, described and recorded in photographs and water colors. These informations will help to identify abnormalities which may appear under field conditions. Chemical analysis of the several plant parts, on the other hand, give a valuable means to assess the nutritional status of slash pine, thus confirming when needed, the visual diagnosis. The correctness of manurial pratices, on the other hand, can be judged with the help of the analytical data tabulated. Under the experimental conditions nitrogen caused the highest increases on growth, as measured by increments in height and dry weights, whereas the effects of phosphorus and potassium were less marked. Cellulose concentration was not significantly affected by the treatments used. Higher levels of N seemed to decrease both length of fiber elements and the thickness of cell wall. The effects of P and K were not well defined. Calcium, Magnesium, Sulfur Experiment A 3 x 3 x 3 factorial design with two replicates was used. Calcium was supplied initially at the levels of 12.5, 25 and 50 ppm; magnesium and sulfur were given at the rates of 6, 12.5 and 25 ppm. Other macro and micronutrients were supplied at uniform rates, common to all treatments. Three months after starting the experiment the first level for each element was dropped to zero. Symptoms of deficiency of calcium, magnesium and sulfur were observed, described and recorded as in the case of the previous experiment. Chemical analysis were made, both for mineral content and cellulose concentration. Length of fibers and thickness of cell wall were measured. Both calcium and magnesium increase height, sulfur failing to give significant response. Dry weight was beneficially affected by calcium and sulfur. The levels of calcium, magnesium and sulfur in the needles associated with deficiency and maximum growth are comparable with those found in the literature. Cellulose concentration increased when the level of sulfur in the substrate was raised. The thickness of cell wall was negatively affected by the treatments; no effect was observed with regards to length of fibers.

Higher order finite elements and the fictitious domain concept for wave propagation analysis

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2014

Bilinear H 2-optimal Model Order-Reduction with applications to thermal parametric systems

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015