Golden Ratio Versus Pi as Random Sequence Sources for Monte Carlo Integration

We discuss here the relative merits of these numbers as possible random sequence sources. The quality of these sequences is not judged directly based on the outcome of all known tests for the randomness of a sequence. Instead, it is determined implicitly by the accuracy of the Monte Carlo integration in a statistical sense. Since our main motive of using a random sequence is to solve real world problems, it is more desirable if we compare the quality of the sequences based on their performances for these problems in terms of quality/accuracy of the output. We also compare these sources against those generated by a popular pseudo-random generator, viz., the Matlab rand and the quasi-random generator ha/ton both in terms of error and time complexity. Our study demonstrates that consecutive blocks of digits of each of these numbers produce a good random sequence source. It is observed that randomly chosen blocks of digits do not have any remarkable advantage over consecutive blocks for the accuracy of the Monte Carlo integration. Also, it reveals that pi is a better source of a random sequence than theta when the accuracy of the integration is concerned

Long-term efficacy and safety of once-daily nevirapine in combination with tenofovir and emtricitabine in the treatment of HIV-infected patients: a 72-week prospective multicenter study (TENOR-Trial)

<p>Abstract</p> <p>Background</p> <p>There is an unmet medical need for simplified antiretroviral therapy regimens to improve patient's compliance and quality of life. The purpose of this study was to evaluate the efficacy and safety of a once-daily regimen with Tenofovir (TDF), Emtricitabine (FTC) and Nevirapine (NVP) for adult patients with HIV-1 infection.</p> <p>Methods</p> <p>70 patients were enrolled in a prospective, multicenter, non-randomized, single arm, open-label cohort study. Patients were either naïve or had problems with their current ART and needed to be changed to another regimen. Daily drug dosage was 300 mg Tenofovir, 200 mg Emtricitabine and 400 mg Nevirapine once daily. Follow-up was performed over 72 weeks.</p> <p>Results</p> <p>After 72 weeks, the regimen was still continued by 52 patients (74,3%). Of these, 44 patients (84,6%) had a viral load below detection limit. The median viral load had decreased by 2,5 log and the median CD4 cell count had increased by 44,8%. Most side-effects occurred at an early stage during the study. Resistances were rare (only two resistances were considered as newly developed) and occurred rather late during the study.</p> <p>Conclusion</p> <p>A once-daily regimen of Tenofovir, Emtricitabine and Nevirapine is an attractive treatment option since it is safe, effective, and well tolerated.</p

Predicting involuntary hospitalization in psychiatry: A machine learning investigation.

Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-learning (ML) models to identify socio-demographic, clinical and procedural characteristics that predict the use of compulsory admission on a large sample of psychiatric patients. We retrospectively analyzed the routinely collected data of all psychiatric admissions that occurred between 2013 and 2017 in the canton of Vaud, Switzerland (N = 25,584). The main predictors of involuntary hospitalization were identified using two ML algorithms: Classification and Regression Tree (CART) and Random Forests (RFs). Their predictive power was compared with that obtained through traditional logistic regression. Sensitivity analyses were also performed and missing data were imputed through multiple imputation using chain equations. The three models achieved similar predictive balanced accuracy, ranging between 68 and 72%. CART showed the lowest predictive power (68%) but the most parsimonious model, allowing to estimate the probability of being involuntarily admitted with only three checks: aggressive behaviors, who referred the patient to hospital and primary diagnosis. The results of CART and RFs on the imputed data were almost identical to those obtained on the original data, confirming the robustness of our models. Identifying predictors of coercion is essential to efficiently target the development of professional training, preventive strategies and alternative interventions. ML methodologies could offer new effective tools to achieve this goal, providing accurate but simple models that could be used in clinical practice

Subtypes of narcissistic personality disorder based on psychotherapy process: A longitudinal nonparametric analysis.

The present study aims at empirically exploring subtypes of narcissistic personality disorder (NPD), based on patient descriptors of the psychotherapeutic process. Subtype identification and characterization of NPD is central, in particular, to increase diagnostic precision, linking categorical and dimensional conceptualizations of psychopathology, and to individualize treatments. A total of N = 161 patients diagnosed with NPD undergoing clarification-oriented psychotherapy were included in the present reanalysis of a naturalistic pre-post process-outcome study. At three crucial time-points of the therapy (Sessions 15, 20, and 25), the patient's in-session quality of content, process, and relationship are assessed using intensive video- and audio analyses. Levels of psychopathology were assessed using self-reported questionnaires. Data were analyzed using longitudinal nonparametric analysis. Based on in-session processes across three time-points, a two-subtype solution was retained (optimal vs. suboptimal process qualities). Optimal process quality of time was linked with the intensity of narcissistic symptoms; suboptimal process quality was linked with a variety of general symptom loads and problematic personality traits. The two empirical subtypes were predicted by the quality of real-life functioning with an accuracy of more than 92% and were partially associated with outcome. NPD may be empirically differentiated between patients engaging in optimal psychotherapy process versus those who engage in suboptimal psychotherapy process. This differentiation has reliable clinical predictors at the outset of treatment. The present study has implications in terms of personalizing psychotherapy for patients presenting NPD, or pathological narcissism. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Should Pruning be a Pre-Processor of any Linear System?

There are many real-world problems whose mathematical models turn out to be linear systems Ax = b, where A is an m x n matrix. Each equation of the linear system is an information. An information, in a physical problem, such as 4 mangoes, 6 bananas, and 5 oranges cost $10, is mathematically modeled as an equation 4x(sub 1) + 6x(sub 2) + 5x(sub 3) = 10 , where x(sub 1), x(sub 2), x(sub 3) are each cost of one mango, that of one banana, and that of one orange, respectively. All the information put together in a specified context, constitutes the physical problem and need not be all distinct. Some of these could be redundant, which cannot be readily identified by inspection. The resulting mathematical model will thus have equations corresponding to this redundant information and hence are linearly dependent and thus superfluous. Consequently, these equations once identified should be better pruned in the process of solving the system. The benefits are (i) less computation and hence less error and consequently a better quality of solution and (ii) reduced storage requirements. In literature, the pruning concept is not in vogue so far although it is most desirable. It is assumed that at least one information, i.e. one equation is known to be correct and which will be our first equation. In a numerical linear system, the system could be slightly inconsistent or inconsistent of varying degree. If the system is too inconsistent, then we should fall back on to the physical problem (PP), check the correctness of the PP derived from the material universe, modify it, if necessary, and then check the corresponding mathematical model (MM) and correct it. In nature/material universe, inconsistency is completely nonexistent. If the MM becomes inconsistent, it could be due to error introduced by the concerned measuring device and/or due to assumptions made on the PP to obtain an MM which is relatively easily solvable or simply due to human error. No measuring device can usually measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The . error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning

The specificity of the familial aggregation of early-onset bipolar disorder: A controlled 10-year follow-up study of offspring of parents with mood disorders.

BACKGROUND: Two major sources of heterogeneity of mood disorders that have been demonstrated in clinical, family and genetic studies are the mood disorder subtype (i.e. bipolar (BPD) and major depressive disorder (MDD)) and age of onset of mood episodes. Using a prospective high-risk study design, our aims were to test the specificity of the parent-child transmission of BPD and MDD and to establish the risk of psychopathology in offspring in function of the age of onset of the parental disorder. METHODS: Clinical information was collected on 208 probands (n=81 with BPD, n=64 with MDD, n=63 medical controls) as well as their 202 spouses and 372 children aged 6-17 years at study entry. Parents and children were directly interviewed every 3 years (mean duration of follow-up=10.6 years). Parental age of onset was dichotomized at age 21. RESULTS: Offspring of parents with early onset BPD entailed a higher risk of BPD HR=7.9(1.8-34.6) and substance use disorders HR=5.0(1.1-21.9) than those with later onset and controls. Depressive disorders were not significantly increased in offspring regardless of parental mood disorder subtype or age of onset. LIMITATIONS: Limited sample size, age of onset in probands was obtained retrospectively, age of onset in co-parents was not adequately documented, and a quarter of the children had no direct interview. CONCLUSIONS: Our results provide support for the independence of familial aggregation of BPD from MDD and the heterogeneity of BPD based on patterns of onset. Future studies should further investigate correlates of early versus later onset BPD

Prospective associations of depression subtypes with cardio-metabolic risk factors in the general population.

The mechanisms and temporal sequence underlying the association between major depressive disorder (MDD) and cardio-metabolic diseases are still poorly understood. Recent research suggests subtyping depression to study the mechanisms underlying its association with biological correlates. Accordingly, our aims were to (1) assess the prospective associations of the atypical, melancholic and unspecified subtypes of MDD with changes of fasting glucose, high-density lipoprotein-cholesterol, triglycerides, systolic blood pressure and the incidence of the metabolic syndrome, (2) determine the potential mediating role of inflammatory marker or adipokine concentrations, eating behaviors and changes in waist circumference during follow-up. Data stemmed from CoLaus|PsyCoLaus, a prospective cohort study including 35-66-year-old randomly selected residents of an urban area. Among the Caucasian participants who underwent the physical and psychiatric baseline evaluations, 2813 (87% participation rate) also accepted the physical follow-up exam (mean follow-up duration=5.5 years). Symptoms of mental disorders were elicited using a semi-structured interview. The atypical MDD subtype, and only this subtype, was prospectively associated with a higher incidence of the metabolic syndrome (OR=2.49; 95% CI 1.30-4.77), a steeper increase of waist circumference (β=2.41; 95% CI 1.19-3.63) and independently of this, with a steeper increase of the fasting glucose level (β=131; 95% CI 38-225) during follow-up. These associations were not attributable to or mediated by inflammatory marker or adipokine concentrations, eating behaviors, comorbid psychiatric disorders or lifestyle factors. Accordingly, our results further support the subtyping of MDD and highlight the particular need for prevention and treatment of metabolic consequences in patients with atypical MDD

Anomalous codeposition of cobalt and ruthenium from chloride-sulfate baths

Codeposition of Ru and Co was studied at room temperature and at 50oC with various Ru3+ and Co2+ concentrations in the electrolyte. The codeposition of Co and Ru proved to be anomalous since no pure Ru could be obtained in the presence of Co2+ in the electrolyte, but a significant Co incorporation into the deposit was detected at potentials where the deposition of pure Co was not possible. The composition of the deposits varied monotonously with the change of the concentration ratio of Co2+ and Ru3+. The deposition of Ru was much hindered and the current efficiency was a few percent only when the molar fraction of Co in the deposit was low. Continuous deposits could be obtained only when the molar fraction of Co in the deposit was at least 40 at.%. The deposit morphology was related to the molar fraction of Co in the deposit. The X-ray diffractograms are in conformity with a hexagonal close-packed alloy and indicate the formation of nanocrystalline deposits. Two-pulse plating did not lead to a multilayer but to a Co-rich alloy. Magnetoresistance of the samples decreased with increasing Ru content

Prediction of early weight gain during psychotropic treatment using a combinatorial model with clinical and genetic markers.

Psychotropic drugs can induce significant (&gt;5%) weight gain (WG) already after 1 month of treatment, which is a good predictor for major WG at 3 and 12 months. The large interindividual variability of drug-induced WG can be explained in part by genetic and clinical factors. The aim of this study was to determine whether extensive analysis of genes, in addition to clinical factors, can improve prediction of patients at risk for more than 5% WG at 1 month of treatment. Data were obtained from a 1-year naturalistic longitudinal study, with weight monitoring during weight-inducing psychotropic treatment. A total of 248 Caucasian psychiatric patients, with at least baseline and 1-month weight measures, and with compliance ascertained were included. Results were tested for replication in a second cohort including 32 patients. Age and baseline BMI were associated significantly with strong WG. The area under the curve (AUC) of the final model including genetic (18 genes) and clinical variables was significantly greater than that of the model including clinical variables only (AUCfinal: 0.92, AUCclinical: 0.75, P&lt;0.0001). Predicted accuracy increased by 17% with genetic markers (Accuracyfinal: 87%), indicating that six patients must be genotyped to avoid one misclassified patient. The validity of the final model was confirmed in a replication cohort. Patients predicted before treatment as having more than 5% WG after 1 month of treatment had 4.4% more WG over 1 year than patients predicted to have up to 5% WG (P≤0.0001). These results may help to implement genetic testing before starting psychotropic drug treatment to identify patients at risk of important WG

Effect of Quetiapine, from Low to High Dose, on Weight and Metabolic Traits: Results from a Prospective Cohort Study.

The atypical antipsychotic quetiapine is known to induce weight gain and other metabolic complications. The underlying mechanisms are multifactorial and poorly understood with almost no information on the effect of dosage. Concerns were thus raised with the rise in low-dose quetiapine off-label prescription (i. e.,&lt;150 mg/day). In this study, we evaluated the influence of quetiapine dose for 474 patients included in PsyMetab and PsyClin studies on weight and metabolic parameter evolution. Weight, blood pressure, lipid, and glucose profiles were evaluated during a follow-up period of 3 months after treatment initiation. Significant dose-dependent metabolic alterations were observed. The daily dose was found to influence weight gain and increase the risk of undergoing clinically relevant weight gain (≥7% from baseline). It was also associated with a change in plasma levels of cholesterol (total cholesterol, LDL cholesterol, and HDL cholesterol) as well as with increased odds of developing hypertriglyceridemia, as well as total and LDL hypercholesterolemia. No impact of a dose increase on blood pressure and plasma glucose level was observed. The dose-dependent effect highlighted for weight gain and lipid alterations emphasizes the importance of prescribing the minimal effective dose. However, as the effect size of a dose increase on metabolic worsening is low, the potential harm of low-dose quetiapine should not be dismissed. Prescriptions must be carefully evaluated and regularly questioned in light of side effect onset