preprints of 3. IFAC Symp. on Identification and System Parameters Estimation, Paper TM-2, 1973, Hague. 2 Peterka

disturbance. Here the reliance has been placed on its most plausible feature, namely its independence of input. This yields a set of assumptions in excess of the minimal requirement and an endeavor has been made to exploit this excess to reduce the sum of squares of estimation errors. developed operational matrix, the expansion coefficients of the shifted Legendre series which represents the approximate responses of transfer functions are computed by the recursive formula. The significance of the present research is that the present method is simple, straightforward and the computational results are accurate as well as the final time of the system can be adjustable without any restriction. Based on the model reduction technique, the design of a feedback control system to satisfy the prescribed specifications is studied by the proposed new algebraic method. Satisfactory examples are given to illustrate the method. References Properties of Shifted Legendre Functions The shifted Legendre function, P" (t) is related to the wellknown Legendre function P" ( T) by transforming the independent variable as T = 2(t/ Tj) -1. One of the properties of shifted Legendre polynomial functions is, 2(2« +1) Thus, the integration of P"(t) with respect to t can be ob- Introduction Model reduction has been receiving great attention in the field of process analysis and synthesis with the last twenty years. The purpose of model reduction is to provide a lower order model which is computationally simpler than the original higher order system. In this paper, an effective method of shifted Legendre functions is employed to approach the problems of model reduction. The operational matrix for the integration of the shifted Legendre polynomial vectors whose elements are shifted Legendre function are first developed. Using th

Childhood adversity and midlife suicidal ideation.

BACKGROUND: Childhood adversity predicts adolescent suicidal ideation but there are few studies examining whether the risk of childhood adversity extends to suicidal ideation in midlife. We hypothesized that childhood adversity predicts midlife suicidal ideation and this is partially mediated by adolescent internalizing disorders, externalizing disorders and adult exposure to life events and interpersonal difficulties. METHOD: At 45 years, 9377 women and men from the UK 1958 British Birth Cohort Study participated in a clinical survey. Childhood adversity was prospectively assessed at the ages of 7, 11 and 16 years. Suicidal ideation at midlife was assessed by the depressive ideas subscale of the Revised Clinical Interview Schedule. Internalizing and externalizing disorders were measured by the Rutter scales at 16 years. Life events, periods of unemployment, partnership separations and alcohol dependence were measured through adulthood. RESULTS: Illness in the household, paternal absence, institutional care, parental divorce and retrospective reports of parental physical and sexual abuse predicted suicidal ideation at 45 years. Three or more childhood adversities were associated with suicidal ideation at 45 years [odds ratio (OR) 4.31, 95% confidence interval (CI) 2.67-6.94]. Psychological distress at 16 years partially mediated the associations of physical abuse (OR 3.41, 95% CI 2.29-5.75), sexual abuse (OR 4.99, 95% CI 2.90-11.16) with suicidal ideation. Adult life events partially mediated the association of parental divorce (OR 6.34, 95% CI -7.16 to 36.75) and physical (OR 9.59, 95% CI 4.97-27.88) and sexual abuse (OR 6.59, 95% CI 2.40-38.36) with suicidal ideation at 45 years. CONCLUSIONS: Adversity in childhood predicts suicidal ideation in midlife, partially mediated by adolescent internalizing and externalizing disorders, adult life events and interpersonal difficulties. Understanding the pathways from adversity to suicidal ideation can inform suicide prevention and the targeting of preventive interventions

Inflammation and epithelial repair predict mortality, hospital readmission, and growth recovery in complicated severe acute malnutrition.

Severe acute malnutrition (SAM) is the most high-risk form of undernutrition, particularly when children require hospitalization for complications. Complicated SAM is a multisystem disease with high inpatient and postdischarge mortality, especially in children with comorbidities such as HIV; however, the underlying pathogenesis of complicated SAM is poorly understood. Targeted multiplex biomarker analysis in children hospitalized with SAM (n = 264) was conducted on plasma samples, and inflammatory markers were assessed on stool samples taken at recruitment, discharge, and 12 to 24 and 48 weeks after discharge from three hospitals in Zimbabwe and Zambia. Compared with adequately nourished controls (n = 173), we found that at baseline, complicated SAM was characterized by systemic, endothelial, and intestinal inflammation, which was exacerbated by HIV infection. This persisted over 48 weeks despite nutritional recovery and was associated with children's outcomes. Baseline plasma concentrations of vascular endothelial growth factor, glucagon-like peptide-2, and intestinal fatty acid-binding protein were independently associated with lower mortality or hospital readmission over the following 48 weeks. Following principal components analysis of baseline biomarkers, higher scores of a component representing growth factors was associated with greater weight-for-height z score recovery and lower mortality or hospital readmission over the 48 weeks. Conversely, components representing higher gut and systemic inflammation were associated with higher mortality or hospital readmission. These findings highlight the interplay between inflammation, which damages tissues, and growth factors, which mediate endothelial and epithelial regeneration, and support further studies investigating interventions to reduce inflammation and promote epithelial repair as an approach to reducing mortality and improving nutritional recovery

Визначення гострої та підгострої токсичності препарату “Тиловет 20 %”

The article presents the results of determination of acute and subacute toxicity of the drug “Tilovet 20 %”, which was made on the basis of tylosin tartrate. In result of the conducted researches, it was found out, that LD50 of the drug “Tilovet 20 %” by intramuscular injection to white mice (calculated by the method of G. Kerber) is 10000 and mg/kg, and for white rats – 9583.33 mg/kg. Long-term use of the drug in a therapeutic dose caused a tendency to the decrease of hemoglobin concentration, amount of erythrocytes, platelets, hematocrit value, mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), content of the total protein, creatinine level, ALP activity on the background of a slight an increase of amount of leukocytes, urea level, AST and ALT activity compared to the values of the control group. The use of the drug in a dose that is 10 times higher than the therapeutic, caused a probable decrease of hemoglobin concentration by – 6.6 % (P < 0.05), platelet count by – 32.5 % (P < 0.05), mean corpuscular hemoglobin (MCH) by – 4.2 % (P < 0.05), mean corpuscular hemoglobin concentration (MCHC) by – 1.5 % (P < 0.1), hematocrit value by – 4.95 %, and total protein content by – 10.7 % (P < 0.05), on the background of a slight an increase of amount of leukocytes by 5.3 %, ALP activity by – 17.5 %, compared to the values oft he control group. Therefore, all of the above may indicate to the suppression of hematopoietic processes and a decrease in the protein-synthesizing function of the liver.У статті наведені результати вивчення гострої та підгострої токсичності препарату “Тиловет 20 %”, виготовленого на основі тилозину тартрат. У результаті проведених досліджень було встановлено, що LD50 препарату “Тиловет 20 %” за внутрішньом’язового введення білим мишам (обчислення за методом Г. Кербера) становить 10000 та мг/кг, а для білих щурів – 9583,33 мг/кг. Довготривале застосування препарату у терапевтичній дозі викликало тенденцію до зниження концентрації гемоглобіну, кількості еритроцитів, тромбоцитів, величини гематокриту, середнього вмісту гемоглобіну в еритроциті (МСН), середнього об’єму еритроцита (МСV), вмісту загального білка, рівня креатиніну, активності ЛФ на тлі незначного зростання кількості лейкоцитів, рівня сечовини, активності АсАТ та АлАТ порівняно з величинами контрольної групи. Застосування препарату у дозі, яка в 10 разів перевищує терапевтичну, викликало вірогідне зниження концентрації гемоглобіну на 6,6 % (Р < 0,05), кількості тромбоцитів – на 32,5 % (Р < 0,05), середнього вмісту гемоглобіну в еритроциті (МСН) – на 4,2 % (Р < 0,05), середньої концентрації гемоглобіну в еритроциті (МСНС) – на 1,5 % (Р < 0,01), величини гематокриту – на  4,95 % та вмісту загального білка – на 10,7 % (Р < 0,05) на тлі незначного зростання кількості лейкоцитів на 5,3 %, активності ЛФ – на 17,5 % порівняно з величинами контрольної групи. Отже, усе вищевказане може свідчити про пригнічення процесів  кровотворення та зниження протеїнсинтезувальної функції печінки

External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

Mid-life psychosocial work environment as a predictor of work exit by age 50.

OBJECTIVES: To examine whether psychosocial work characteristics at age 45 years predict exit from the labour market by the age of 50 years in data from the 1958 British Birth Cohort. METHODS: Psychosocial work characteristics (decision latitude, job demands, job strain and work social support at 45 years and job insecurity at 42 years) measured by questionnaire were linked to employment outcomes (unemployment, retirement, permanent sickness, homemaking) at 50 years in 6510 male and female participants. RESULTS: Low decision latitude (RR = 2.01, 95%CI 1.06,3.79), low work social support (RR = 1.96, 95%CI 1.12,3.44), and high job insecurity (RR = 2.27, 95%CI 1.41, 3.67) predicted unemployment at 50, adjusting for sex, housing tenure, socioeconomic status, marital status, and education. High demands were associated with lower risk of unemployment (RR = 0.50, 95%CI 0.29,0.88) but higher risk of permanent sickness (RR = 2.14, 95%CI 1.09,4.21). CONCLUSIONS: Keeping people in the workforce beyond 50 years may contribute to both personal and national prosperity. Employers may wish to improve working conditions for older workers, in particular, increase control over work, increase support and reduce demands to retain older employees in the workforce

External validation of prognostic models predicting pre-eclampsia : individual participant data meta-analysis

Abstract Background Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. Methods IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. Results Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model’s calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. Conclusions The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. Trial registration PROSPERO ID: CRD42015029349