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This study aimed to systematize the diverse and rather controversial findings of empirical research on the relationship between the temperament and academic achievement of school children, as well as to determine the average effect size between these variables. We included 57 original studies of published and unpublished research conducted in 12 countries between 1985 and 2019, with cumulative sample size of 79,913 (varying from 6333 to 14,126 for links between particular temperament dimensions and specific domains of achievement). A random-effects and mixed-effects model was fitted to the data for the central tendency of the temperament–achievement relation and for analyzing moderators, respectively. The high heterogeneity of studies was tackled by selected specific moderators, namely, education level, transition status, family’s socio-economic level, and sources of report on achievement and temperament. The main findings of this meta-analysis affirmed the positive association of effortful control (EC) and inverse relationship of negative affectivity (NA) with a child’s academic performance, together with no apparent trend of surgency (SU) in this relationship; additionally, the sources of report significantly moderated the link between temperament and academic achievement.

In previously published articles, our research group has developed the Haphazard Intentional Sampling method and compared it to the Rerandomization method proposed by K.Morgan and D.Rubin. In this article, we compare both methods to the pure randomization method used for the Epicovid19 survey, conducted to estimate SARS-CoV-2 prevalence in 133 Brazilian Municipalities. We show that Haphazard intentional sampling can either substantially reduce operating costs to achieve the same estimation errors or, the other way around, substantially improve estimation precision using the same sample sizes.

Dynamic suit orthoses (DSO) are currently used as a complementary treatment method in children with Cerebral Palsy (cwCP). The aim of this review was to assess the effects of interventions with DSO on the altered spatio-temporal gait parameters (STGPs) in cwCP. An electronic search was conducted in the Web of Science, Scopus, PEDro, Cochrane Library, MEDLINE/PubMed, and CINAHL databases up to July 2021. We included a total of 12 studies, which showed great heterogeneity in terms of design type, sample size, and intervention performed (two employed a Therasuit, three employed the Adeli suit, three employed Theratogs, one employed elastomeric tissue dynamic orthosis, one employed a full-body suit, one employed external belt orthosis, and one employed dynamic orthosis composed of trousers and T-shirt). The Cochrane collaboration’s tool and the Checklist for Measuring Study Quality were used to assess the risk of bias and the methodological quality of the studies. It was variable according to the Checklist for Measuring Study Quality, and it oscillated between eight and 23. The studies of higher methodological quality showed significant post-intervention changes in walking speed (which is the most widely evaluated parameter), cadence, stride length, and step length symmetry. Although the evidence is limited, the intervention with DSO combined with a programme of training/physical therapy seems to have positive effects on the STGPs in cwCP, with the functional improvements that it entails. Despite the immediate effect after one session, a number of sessions between 18 and 60 is recommended to obtain optimum results. Future studies should measure all STGPs, and not only the main ones, such as gait speed, in order to draw more accurate conclusions on the functional improvement of gait after the use of this type of intervention.

Background: Driving is the main mode of transportation in many countries, and visual safety depends largely on good visual health. The objective of this study is to analyze the visual health of Spanish drivers, as well as analyze the difference between professional and non-professional drivers. Methods: A visual screening was carried out in Spanish drivers from all over Spain, in which the following tests were performed: monocular visual acuity in distance and near vision, visual field, stereopsis, contrast sensitivity, intraocular pressure and balance test binocular. Subsequently, a questionnaire was carried out on the patient’s driving data and ocular antecedents. Results: 74.5% of the drivers used glasses to drive, of which 61.5% used progressive glasses. However, 39.4% reported having difficulties seeing well. The mean visual acuity in the distance and near was 0.93 ± 0.13 and 0.94 ± 0.13, respectively. Significant differences have been found in accident risk based on visual acuity (p &lt, 0.001). But no significant differences have been found in terms of visual field, stereopsis, contrast sensitivity, binocular balance and intraocular pressure (p &gt, 0.05). Conclusion: Vision appears to play a key role in driving and a good visual assessment is recommended for early detection of visual problems that may affect road safety. A study with a larger sample size would be necessary to confirm the results of this pilot study.

Previous studies have reported abnormal muscle morphology and functions in patients with adolescent idiopathic scoliosis (AIS). To answer whether such abnormalities could be reflected in their circulation and their clinical implication for predicting curve progression to the surgical threshold, this preliminary study explored the presence of baseline muscle-related proteins and their association with curve progression. Plasma samples were collected at the first clinical visit for AIS, with patients divided into non-progressive or progressive groups (N = four and four) according to their Cobb angle in six-year follow-ups, with age- and sex-matched healthy subjects (N = 50). Then, the samples were subjected to isobaric tags for relative and absolute quantitation (iTRAQ) for global comparison of untargeted protein expression. Seventy-one differentially expressed proteins (DEPs) were found elevated in progressive AIS. Functional analysis showed that 18 of these are expressed in muscles and play an essential role in muscle activities. Among the muscle-related DEPs, α-actin had the highest fold change in progressive/non-progressive groups. This preliminary study firstly suggested higher circulating levels of muscle structural proteins in progressive AIS, indicating the likelihood of structural damage at the microscopic level and its association with progression to the surgical threshold. Further studies with larger sample sizes are warranted to validate these novel candidates for early diagnosis and predicting progression.

Simple Summary The causal impact of body fat distribution on female-specific cancers is largely unknown. For the first time we used a two-sample multivariable Mendelian randomization (MR) approach to elucidate the role and causal relations of body composition assessed by segmental bioelectrical impedance analysis on the risks of breast, endometrial and ovarian cancers and their subtypes. We found that abdominal fat content increases the risk for ovarian cancer and its endometrioid and clear cell subtypes independent of overall fat content. General adiposity has a protective effect on risk of breast cancer and its ER- and ER+ subtypes but increases the risk for endometrial cancer, ovarian cancer, and the endometrioid ovarian cancer subtype. This study extends the literature by addressing specifically the causal role of visceral fat on female-specific cancers. Abstract Background: Mounting evidence shows that adiposity increases female-specific cancer risk, but the role of body fat distribution is less clear. We used a two-sample Mendelian randomization (MR) approach to elucidate causal relations of body fat distribution to the risks of breast, endometrial and ovarian cancers and their subtypes. Methods: Body composition was assessed using segmental bioelectrical impedance analysis, yielding trunk, arm, and leg fat ratios (TFR, AFR, LFR) and BMI including 195,043 and 434,794 European women, respectively. The sample sizes for the outcomes ranged between 58,396 and 228,951. Causal effects were estimated per one standard deviation increment in the respective exposure within the radial regression framework. Robust sensitivity analyses were performed to verify MR assumptions. In a multivariable MR setting, the proportion of risk attributable to overall and abdominal fat content was assessed. Results: TFR, which represents abdominal fat content, was associated with ovarian cancer and its clear cell and endometrioid histotypes independent of overall fat content. BMI was inversely associated with breast cancer and its ER− and ER+ subtypes, but positively with endometrial cancer and ovarian cancer, including its endometrioid histotype. These estimates were confirmed using AFR as proxy for overall body fat. Conclusions: Visceral adiposity seems to be a driver of elevated ovarian cancer risk, particularly of the endometrioid and clear cell ovarian cancer histotypes. General adiposity decreases the risk of breast cancer but increases the risk of endometrial and ovarian cancer.

Modern computational models in supervised machine learning are often highly parameterized universal approximators. As such, the value of the parameters is unimportant, and only the out of sample performance is considered. On the other hand much of the literature on model estimation assumes that the parameters themselves have intrinsic value, and thus is concerned with bias and variance of parameter estimates, which may not have any simple relationship to out of sample model performance. Therefore, within supervised machine learning, heavy use is made of ridge regression (i.e., L2 regularization), which requires the the estimation of hyperparameters and can be rendered ineffective by certain model parameterizations. We introduce an objective function which we refer to as Information-Corrected Estimation (ICE) that reduces KL divergence based generalization error for supervised machine learning. ICE attempts to directly maximize a corrected likelihood function as an estimator of the KL divergence. Such an approach is proven, theoretically, to be effective for a wide class of models, with only mild regularity restrictions. Under finite sample sizes, this corrected estimation procedure is shown experimentally to lead to significant reduction in generalization error compared to maximum likelihood estimation and L2 regularization.

Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.

Refereed/Peer-reviewed It is imperative that the surgical treatment of hip fractures is followed up with rehabilitation to enhance recovery and quality of life. This randomized controlled trial aimed to determine if an individualised, combined exercise–nutrition intervention significantly improved health outcomes in older adults, after proximal femoral fracture. We commenced the community extended therapy while in hospital, within two weeks post-surgery. The primary outcome was gait speed and secondary outcomes included physical function, strength and balance, body composition, energy and protein intake. Eighty-six and 89 participants were randomized into six months individualised exercise and nutrition intervention and attention-control groups, respectively. There were no statistically significant differences in gait speed between the groups at six and 12 months. There were no major differences between groups with respect to the secondary outcomes, except estimated energy and protein intake. This may be explained by the sample size achieved. Participants in the intervention group had greater increment in energy (235 kcal; 95% CI, 95 to 375; p = 0.01) and protein intake (9.1 g; 95% CI, 1.5 to 16.8; p = 0.02), compared with those in the control group at six months but not significant at 12 months. This study has demonstrated that providing early, combined exercise and nutrition therapy can improve dietary energy and protein intake in older adults with hip fractures.

Background: Three-dimensional speckle-tracking echocardiography (3D-STE) allows simultaneous assessment of multidirectional components of strain. However, there are few data on its usefulness to predict prognosis in patients with acute myocardial infarction (AMI). The objective of our pilot study was to evaluate the prognostic value of four different 3D-STE parameters (global longitudinal strain (GLS-3D), global circumferential strain (GCS-3D), global radial strain (GRS-3D), and global area strain (GAS)) in AMI, after successful revascularization by primary PCI. Methods: We enrolled 94 AMI patients (66 ± 13 years, 56% men) who underwent coronary angiography. All patients had been 3D-STE assessed and followed-up for 1 year for the occurrence of MACE. Results: A total of 25 MACE were recorded over follow-up. Cut-off values of −17% for GAS (HR = 3.1, 95% CI: 1.39–6.92, p = 0.005), −12% for GCS-3D (HR = 3.06, 95% CI: 1.36–6.8, p = 0.006), −10% for GLS-3D (HR = 3.04, 95% CI: 1.36–6.78, p = 0.006), and 25% for GRS-3D (HR = 2.89, 95% CI: 1.29–6.46, p = 0.009) showed moderate accuracy in MACE prediction. Multivariate regression showed that GAS (HR = 1.1, 95% CI: 1.03–1.16), GLS-3D (HR = 1.13, 95% CI: 1.03–1.26), and GCS-3D (HR = 1.13, 95% CI: 1.03–1.23) remained independent predictors of MACE (HR = 1.07, 95% CI: 1.01–1.14 for GAS, and HR = 1.1, 95% CI: 1.01–1.2 for GCS-3D). However, post hoc power analysis indicated adequate sample size (power of 80%) only for GAS and GCS-3D for the ROC curve analysis and for GAS, GCS-3D, and GRS-3D for the log-rank test. Conclusion: Patients with AMI might benefit from early risk stratification with the aid of 3D-STE measurements, particularly GAS and GCS-3D, but larger studies are necessary to determine the optimal cut-off values to predict MACE.

This study aimed to find a potential biomarker that can be used to diagnose prediabetic condition by comparing the salivary bacterial microbiomes between Thai dental patients with normoglycemia (NG) and those with potential prediabetes (PPG) conditions. Thirty-three subjects were randomly recruited. Demographic data were collected along with oral examination and unstimulated salivary collections. The salivary bacterial microbiomes were identified by high-throughput sequencing on the V3–V4 region of the bacterial 16S rRNA gene. Microbiomes in this study were composed of 12 phyla, 19 classes, 29 orders, 56 families, 81 genera, and 184 species. To check the validity of the selection criterion for prediabetes, we adopted two separate criteria to divide samples into PPG and NG groups using glycated hemoglobin A1c (HbA1c) or fasting plasma glucose (FPG) levels. Using the HbA1c level resulted in the significant reduction of Alloprevotella, Neisseria, Rothia, and Streptococcus abundances in PPG compared with those in NG (p-value &lt, 0.05). On the other hand, the abundance of Absconditabacteriales was significantly reduced whereas Leptotrichia, Stomatobaculum, and Ruminococcaceae increased in the PPG group when the samples were classified by the FPG level (p-value &lt, 0.05). It is implied that the group classifying criterion should be carefully concerned when investigating relative abundances between groups. However, regardless of the criteria, Rothia is significantly dominant in the NG groups, suggesting that Rothia might be a potential prediabetic biomarker. Due to the small sample size of this study, further investigation with a larger sample size is necessary to ensure that Rothia can be a potential biomarker for prediabetes in Thai people.

Epidemiological studies suggest that high intake of soy isoflavones may protect against breast cancer, but causal relationships can only be established by experimental trials. Thus, we aimed to provide a systematic review of randomized controlled trials (RCTs) on the effect of an isoflavone intake on risk factors of breast cancer in healthy subjects. After a systematic literature search in PubMed, 18 different RCTs with pre- and/or postmenopausal women were included and investigated for details according to the PRISMA guideline. In these studies, isoflavones were provided by soy food or supplements in amounts between 36.5–235 mg/d for a period of 1–36 months. Breast density, estrogens including precursors, metabolites, estrogen response such as length of menstrual cycle, and markers of proliferation and inflammation were considered. However, in most studies, differences were not detectable between isoflavone and control/placebo treatment despite a good adherence to isoflavone treatment, irrespective of the kind of intervention, the dose of isoflavones used, and the duration of isoflavone treatment. However, the lack of significant changes in most studies does not prove the lack of effects as a sample size calculation was often missing. Taking into account the risk of bias and methodological limitations, there is little evidence that isoflavone treatment modulates risk factors of breast cancer in pre- and postmenopausal women. Future studies should calculate the sample size to detect possible effects and consider methodological details to improve the study quality.

Due to the low frequency of circulating tumor cells (CTC), the standard CellSearch method of enumeration and isolation using a single tube of blood is insufficient to measure treatment effects consistently, or to steer personalized therapy. Using diagnostic leukapheresis this sample size can be increased, however, this also calls for a suitable new method to process larger sample inputs. In order to achieve this, we have optimized the immunomagnetic enrichment process using a flow-through magnetophoretic system. An overview of the major forces involved in magnetophoretic separation is provided and the model used for optimizing the magnetic configuration in flow through immunomagnetic enrichment is presented. The optimal Halbach array element size was calculated and both optimal and non-optimal arrays were built and tested using anti-EpCAM ferrofluid in combination with cell lines of varying EpCAM antigen expression. Experimentally measured distributions of the magnetic moment of the cell lines used for comparison were combined with predicted recoveries and fit to the experimental data. Resulting predictions agree with measured data within measurement uncertainty. The presented method can be used not only to optimize magnetophoretic separation using a variety of flow configurations but could also be adapted to optimize other (static) magnetic separation techniques.

Based on the characteristics of remote sensing images of mine vegetation, this research studied the application of deep belief network model in mine vegetation identification. Through vegetation identification and classification, the ecological environment index of mining area was determined according to the analysis of vegetation and coverage. Deep learning algorithm is adopted to improve the depth study, the vegetation coverage in the analysis was studied. Parameters and parameter values were selected for identification by establishing the optimal experimental design. The experimental results were compared with remote sensing images to determine the accuracy of deep learning identification and the effectiveness of the algorithm. When the sample size is 2,000,000 pixels, through repeated tests and classification effect comparison, the optimal parameter setting suitable for mine vegetation identification is obtained. Parameter setting: the number of network layers is 3 layers, the number of hidden layer neurons is 60. The learning rate is 0.01 and the number of iterations is 2. The average recognition rate of vegetation coverage was 95.95%, outperforming some other models, and the accuracy rate of kappa coefficient was 0.95, which can accurately reflect the vegetation coverage. The clearer the satellite image is, the more accurate the recognition result is, and the accuracy is closer to 100%. The identification of vegetation coverage has important guiding significance for determining the area and area of ecological restoration.

The WHO considers hearing loss to be a major global problem. A literature search was conducted to see whether high-frequency audiometry (HFA) could be used for the early detection of hearing loss. A further aim was to see whether any differences exist in the hearing threshold using conventional audiometry (CA) and HFA in workers of different age groups exposed to workplace noise. Our search of electronic databases yielded a total of 5938 scientific papers. The inclusion criteria were the keywords “high frequency” and “audiometry” appearing anywhere in the article and the participation of unexposed people or a group exposed to workplace noise. Fifteen studies met these conditions; the sample size varied (51–645 people), and the age range of the people studied was 5–90 years. Commercial high-frequency audiometers and high-frequency headphones were used. In populations unexposed to workplace noise, significantly higher thresholds of 14–16 kHz were found. In populations with exposure to workplace noise, significantly higher statistical thresholds were found for the exposed group (EG) compared with the control group (CG) at frequencies of 9–18 kHz, especially at 16 kHz. The studies also showed higher hearing thresholds of 10–16 kHz in respondents aged under 31 years following the use of personal listening devices (PLDs) for longer than 5 years. The effect of noise-induced hearing loss (NIHL) first became apparent for HFA rather than CA. However, normative data have not yet been collected. Therefore, it is necessary to establish a uniform evaluation protocol accounting for age, sex, comorbidities and exposures, as well as for younger respondents using PLDs.

Simple Summary Thrips are tiny insects that cause significant damage to onion crops worldwide. They feed on the plants and can also transmit plant viral diseases. To prevent damage, it is necessary to estimate the population density (average number of insects per plant), through periodic sampling, and to apply a combination of control tactics to maintain thrips at acceptable levels. Conventional sampling methods are precise but require large investments of time and effort. In this study, binomial and sequential sampling plans were developed to estimate thrips population density in a precise and less time-consuming manner. More than 50 onion plots were sampled, and Thrips tabaci Lindeman was identified as the predominant pest species. The sampling plans reached acceptable levels of precision (D = 0.25) in less time than conventional sampling. Binomial and sequential sampling plans were reliable and easily implemented in practice, but sequential sampling showed better performance than binomial sampling under different field conditions. These findings may help to reduce time and work for T. tabaci sampling and, consequently, improve implementation of crop protection tactics on onion. Abstract Thrips tabaci Lindeman is a worldwide onion pest that causes economic losses of 10–60%, depending on many factors. Population sampling is essential for applying control tactics and preventing damage by the insect. Conventional sampling methods are criticized as time consuming, while fixed-precision binomial and sequential sampling plans may allow reliable estimations with a more efficient use of time. The aim of this work was to develop binomial and sequential sampling for fast reliable estimation of T. tabaci density on an onion. Forty-one commercial 1.0-ha onion plots were sampled (sample size n = 200) to characterize the spatial distribution of T. tabaci using Taylor’s power law (a = 2.586 and b = 1.511). Binomial and sequential enumerative sampling plans were then developed with precision levels of 0.10, 0.15 and 0.25. Sampling plans were validated with bootstrap simulations (1000 samples) using 10 independent data sets. Bootstrap simulation indicated that precision was satisfactory for all repetitions of the sequential sampling plan, while binomial sampling met the fixed precision in 80% of cases. Both methods reduced sampling time by around 80% relative to conventional sampling. These precise and less time-consuming sampling methods can contribute to implementation of control tactics within the integrated pest management approach.

(1) Background: The prevalence of knee osteoarthritis (OA) in women is significantly higher than in men. The estrogen receptor α (ERα) has been considered to play a key role due to a large gender difference in its expression. ERα is encoded by the gene estrogen receptor 1 (ESR1), which is widely studied to explore the gender difference in knee OA. Several polymorphisms in ESR1 [PvuII (rs2234693) and BtgI (rs2228480)] were confirmed as the risk factors of OA. However, the evidence of the last widely investigated polymorphism, ESR1 Xbal (rs9340799), is still insufficient for concluding its effect on knee OA. (2) Objective: This study proposed a case–control study to investigate the association between ESR1 Xbal and knee OA. Moreover, a meta-analysis and trial sequential analysis (TSA) were conducted to enlarge the sample size for obtaining a conclusive evidence. (3) Methods: In total, 497 knee OA cases and 473 healthy controls were recruited between March 2015 and July 2018. The Kellgren–Lawrence grading system was used to identify the knee OA cases. To improve the evidence level of our study, we conducted a meta-analysis including the related studies published up until December 2018 from PubMed, Embase, and previous meta-analysis. The results are expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) for evaluating the effect of this polymorphism on knee OA risk. TSA was used to estimate the sample sizes required in this issue. (4) Results: We found non-significant association between the G allele and knee OA [Crude-OR: 0.97 (95% CI: 0.78–1.20) and adjusted-OR: 0.90 (95% CI: 0.71–1.15) in allele model] in the present case–control study, and the analysis of other genetic models showed a similar trend. After including six published studies and our case–control studies, the current evidence with 3174 Asians showed the conclusively null association between ESR1 XbaI and knee OA [OR: 0.78 (95% CI: 0.59–1.04)] with a high heterogeneity (I2: 78%). The result of Caucasians also concluded the null association [OR: 1.05 (95% CI: 0.56–1.95), I2: 87%]. (5) Conclusions: The association between ESR1 XbaI and knee OA was not similar with other polymorphisms in ESR1, which is not a causal relationship. This study integrated all current evidence to elaborate this conclusion for suggesting no necessity of future studies.

This research received external funding by Project HUM-983 (ITACA): “Research through transforming learning and contexts.” Principal Researcher: Eva María Olmedo-Moreno., The aim of this study was to analyze the significant differences in satisfaction with educational quality in higher education in Italy (Naples) among students and professors. The sample consisted of 501 higher education students and 121 professors, resulting in a total sample size of 622 subjects. Once the quality parameters of the instrument were determined, reliability was confirmed, and data collection was initiated. In order to analyze the results, a test of independent means (Student’s t-test) was performed, interrelating the variables of educational quality, concerning both management and satisfaction with higher education. Based on the results, we concluded that there are significant differences between the group of students and the group of professors, highlighting a higher level of satisfaction with quality reported by students regarding the coordination of teachers and staff in the educational process; therefore, the inclusion of students in the direct management of the center should be more active and an indicator to be taken into account in self-evaluation. Despite the limitations in the sample at the regional level, it offers many possibilities for future research., HUM-983

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.

This study evaluated severe psychological symptoms in the United Kingdom and Austria after four weeks of lockdown due to COVID-19. Two cross-sectional online surveys were performed with representative population samples according to age, gender, region, and education. Depressive symptoms were measured with the Patient Health Questionnaire (PHQ-9), anxiety symptoms with the Generalized Anxiety Disorder scale (GAD-7), and insomnia symptoms with the Insomnia Severity Index (ISI). The sample size was N = 1005 for Austria (52% women) and N = 1006 (54% women) for the UK. In total, 3.2% of the Austrian sample and 12.1% of the UK sample had severe depressive symptoms (PHQ-9 ≥ 20 points, χ2(1) = 57.24, p &lt, 0.001), 6.0% in Austria vs. 18.9% in the UK had severe anxiety symptoms (GAD-7 ≥ 15 points, χ2(1) = 76.17, 0.001), and 2.2% in Austria and 7.3% in the UK had severe insomnia (ISI, ≥22 points, χ2(1) = 28.89, 0.001). The prevalence of severe depressive, anxiety or insomnia symptoms was around three times higher in the UK than in Austria.

Simulation-based forecast model selection considers two candidate forecast model classes, simulates from both models fitted to data, applies both forecast models to simulated structures, and evaluates the relative benefit of each candidate prediction tool. This approach, for example, determines a sample size beyond which a candidate predicts best. In an application, aggregate household consumption and disposable income provide an example for error correction. With panel data for European countries, we explore whether and to what degree the cointegration properties benefit forecasting. It evolves that statistical evidence on cointegration is not equivalent to better forecasting properties by the implied cointegrating structure.

A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease, and a negative result indicates the absence. Positive and negative predictive values represent the accuracy of a binary diagnostic test when it is applied to a cohort of individuals, and they are measures of the clinical accuracy of the binary diagnostic test. In this manuscript, we study the comparison of the positive (negative) predictive values of two binary diagnostic tests subject to a paired design through confidence intervals. We have studied confidence intervals for the difference and for the ratio of the two positive (negative) predictive values. Simulation experiments have been carried out to study the asymptotic behavior of the confidence intervals, giving some general rules for application. We also study a method to calculate the sample size to compare the parameters using confidence intervals. We have written a program in R to solve the problems studied in this manuscript. The results have been applied to the diagnosis of colorectal cancer.

In Bayesian analysis of clinical trials data, credible intervals are widely used for inference on unknown parameters of interest, such as treatment effects or differences in treatments effects. Highest Posterior Density (HPD) sets are often used because they guarantee the shortest length. In most of standard problems, closed-form expressions for exact HPD intervals do not exist, but they are available for intervals based on the normal approximation of the posterior distribution. For small sample sizes, approximate intervals may be not calibrated in terms of posterior probability, but for increasing sample sizes their posterior probability tends to the correct credible level and they become closer and closer to exact sets. The article proposes a predictive analysis to select appropriate sample sizes needed to have approximate intervals calibrated at a pre-specified level. Examples are given for interval estimation of proportions and log-odds.

Introduction: In the last two decades, telemedicine has been increasingly applied to telemonitoring (TM) of patients with pacemakers; however, presently, its growth has significantly accelerated because of the COVID-19 pandemic, which has pushed patients and healthcare work-ers alike to seek new ways to stay healthy with minimal physical contact. Therefore, the main objective of this study was to update the current knowledge on the differences in the medium-and long-term effectiveness of TM and conventional monitoring (CM) in relation to costs and health outcomes. (2) Methods: Three databases and one scientific registry were searched (PubMed, EM-BASE, Scopus, and Google Scholar), with no restrictions on language or year of publication. Studies published until July 2021 were included. The inclusion criteria were: (a) experimental or observational design, (b) complete economic evaluation, (c) patients with implanted pacemakers, and (d) comparison of TM with CM. Measurements of study characteristics (author, study duration, sample size, age, sex, major indication for implantation, and pacemaker used), analysis, significant results of the variables (analysis performed, primary endpoints, secondary endpoints, health out-comes, and cost outcomes), and further miscellaneous measurements (methodological quality, variables coded, instrument development, coder training, and intercoder reliability, etc.) were included. (3) Results: 11 studies met the inclusion criteria, consisting of 3372 enrolled patients; 1773 (52.58%) of them were part of randomized clinical trials. The mean age was 72 years, and the atri-oventricular block was established as the main indication for device implantation. TM was significantly effective in detecting the presence or absence of pacemaker problems, leading to a reduction in the number of unscheduled hospital visits (8.34¿55.55%). The cost of TM was up to 87% lower than that of CM. There were no significant differences in health-related quality of life (HRQoL) and the number of cardiovascular events. (4) Conclusions: Most of the studies included in this systematic review confirm that in the TM group of patients with pacemakers, cardiovascular events are detected and treated earlier, and the number of unscheduled visits to the hospital is significantly reduced, without affecting the HRQoL of patients. In addition, with TM modality, both formal and informal costs are significantly reduced in the medium and long term. Funding: This study was funded by the Instituto de Salud Carlos III through the project “No. PI17/02056” (Co-funded by European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future”).

Helicoverpa armigera is one of the key pests affecting processing tomatoes and many other crops. A three-year study was conducted to describe the oviposition preferences of this species on determinate tomato plants (mainly the stratum, leaf, leaflet, and leaf side) and the spatial pattern of the eggs in the field, to form a sequential sampling plan. Eggs were found mainly in the exposed canopy, on leaves a (upper stratum) and b (upper-middle stratum) and significantly fewer eggs on leaf c (middle-lower stratum) below flower clusters. This vertical pattern in the plant was found in all phenological growth stages. The spatial pattern was found to be aggregated, with a trend towards a random pattern at lower densities. A sequential sampling plan was developed, based on Iwao&rsquo, s method with the parameters of Taylor&rsquo, s power law, with minimum and maximum sample size of 20 and 80 sample units (plants), respectively (two leaves/plant). For its validation, operating characteristic (OC) and average sample number (ASN) curves were calculated by means of simulation with independent data sets. The &beta, error was higher than desirable in the vicinity of the economic threshold, but this sampling plan is regarded as an improvement both in effort and precision, compared with the fixed sample plan, and further improvements are discussed.

Evidence supports the implementation of concurrent strength and running training, within the same mesocycle, to improve performances in middle- and long-distance events. However, very little is known about the effects of concurrent training cessation. The purpose of this investigation was to describe the effects of 4 weeks of explosive strength training cessation after an 8-week concurrent training protocol. Eight runners completed this study, which first included either plyometric (n = 4) or dynamic weight training (n = 4) in addition to the usual running regimen. Explosive strength training was thereafter interrupted for 4 weeks, during which running sessions were maintained. Participants were tested at baseline, after concurrent training and after concurrent training cessation. The results suggest that the energy cost of running improvements observed after the intervention (−5.75%; 95% CI = −8.47 to −3.03) were maintained once explosive strength training was interrupted (−6.31%; 95% CI = −10.30 to −2.32). The results also suggest that neuromuscular performances were maintained after 4 weeks of concurrent training cessation, especially when tests were specific to the training intervention. Furthermore, a 3000m time trial revealed a similar pattern, with improvements after the concurrent mesocycle (−2.40%; 95% CI = −4.65 to −0.16) and after concurrent training cessation (−4.43%; 95% CI = −6.83 to −2.03). Overall, only trivial changes were observed for aerobic endurance and V˙O2peak. Together, these results suggest that short-term explosive strength training cessation might be beneficial and could be considered as a taper strategy for middle-distance runners. However, coaches and athletes must interpret these results cautiously considering the study’s low sample size and the very limited available literature in this domain.

Non-response in surveys can lead to bias, which is often difficult to investigate. The aim of this analysis was to compare factors available from claims data associated with survey non-response and to compare them among two samples. A stratified sample of 4471 persons with a diagnosis of axial spondyloarthritis (axSpA) and a sample of 8995 persons with an osteoarthritis (OA) diagnosis from a German statutory health insurance were randomly selected and sent a postal survey. The association of age, sex, medical prescriptions, specialist physician contact, influenza vaccination, hospitalization, and Elixhauser comorbidity index with the survey response was assessed. Multiple logistic regression models were used with response as the outcome. A total of 47% of the axSpA sample and 40% of the OA sample responded to the survey. In both samples, the response was highest in the 70&ndash, 79-year-olds. Women in all age groups responded more often, except for the 70&ndash, 79-year-olds. Rheumatologist/orthopedist contact, physical therapy prescription, and influenza vaccination were more frequent among responders. In the logistic regression models, rheumatologist/orthopedist treatment, influenza vaccination, and physical therapy were associated with a higher odds ratio for response in both samples. The prescription of biologic drugs was associated with higher response in axSpA. A high Elixhauser comorbidity index and opioid use were not relevantly associated with response. Being reimbursed for long-term care was associated with lower response&mdash, this was only significant in the OA sample. The number of quarters with a diagnosis in the survey year was associated with higher response. Similar factors were associated with non-response in the two samples. The results can help other investigators to plan sample sizes of their surveys in similar settings.

Bandwidth is the crucial knowledge to sampling, reconstruction or estimation of the graph signal (GS). However, it is typically unknown in practice. In this paper, we focus on detecting the bandwidth of bandlimited GS with a small sample size, where the number of spectral components of GS to be tested may greatly exceed the sample size. To control the significance of the result, the detection procedure is implemented by multi-stage testing. In each stage, a Bayesian score test, which introduces a prior to the spectral components, is adopted to face the high dimensional challenge. By setting different priors in each stage, we make the test more powerful against alternatives that have similar bandwidth to the null hypothesis. We prove that the Bayesian score test is locally most powerful in expectation against the alternatives following the given prior. Finally, numerical analysis shows that our method has a good performance in bandwidth detection and is robust to the noise.

Objective: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease and still lacks effective methods of diagnosis. Dysfunction of the N-methyl-D-aspartate receptor (NMDAR) has been found to be involved in synapse dysfunction and neurotoxicity of AD mechanisms. d-Serine, an NMDAR receptor coagonist, is reported as a potential new biomarker for AD. However, the results of serum and cerebrospinal fluid (CSF) d-serine levels are conflicting. We conducted a meta-analysis to investigate the serum and CSF d-serine levels in patients with AD. Methods: We searched PubMed, the Cochrane central register of controlled trials, and the Cochrane database of systematic reviews for trials that measured d-serine levels both in patients with AD and in controls. We included controlled trials that analyzed d-serine levels in human samples (e.g., serum and CSF). Studies were pooled using a random-effect model for comparisons between AD and control group. We used effect size (ES; expressed as d-serine levels) in each selected meta-analysis to calculate standardized mean difference (SMD). Positive values indicated increased d-serine levels in AD group. We presented results with 95% confidence intervals (CIs). The heterogeneity of the included trials was evaluated through visually inspecting funnel plots and using the I2 statistic. Moderators of effects were explored using metaregression. Results: Seven trials with more than 1186 participants were included in this meta-analysis. d-serine levels in patients with AD were significantly higher than those in controls (SMD = 0.679, 95% CI = 0.335 to 1.022, p < 0.001). Subgroup analyses showed that the AD group had significantly higher d-serine levels in serum and CSF compared with the control group (SMD = 0.566 (serum) and 1.008 (CSF); 95% CI = 0.183 to 0.948 (serum) and 0.168 to 1.849 (CSF)). Moreover, a metaregression revealed a significant negative association between ES and mean mini-mental state examination score in AD group (slope = −0.1203, p = 0.0004). Conclusions: Our results revealed higher d-serine levels in the serum and CSF of patients with AD relative to the controls. Further studies with a larger sample size and longer follow-up are recommended to clarify this association.

In this meta-study, we analyzed 2442 effect sizes from 131 meta-analyses in intelligence research, published from 1984 to 2014, to estimate the average effect size, median power, and evidence for bias. We found that the average effect size in intelligence research was a Pearson&rsquo, s correlation of 0.26, and the median sample size was 60. Furthermore, across primary studies, we found a median power of 11.9% to detect a small effect, 54.5% to detect a medium effect, and 93.9% to detect a large effect. We documented differences in average effect size and median estimated power between different types of intelligence studies (correlational studies, studies of group differences, experiments, toxicology, and behavior genetics). On average, across all meta-analyses (but not in every meta-analysis), we found evidence for small-study effects, potentially indicating publication bias and overestimated effects. We found no differences in small-study effects between different study types. We also found no convincing evidence for the decline effect, US effect, or citation bias across meta-analyses. We concluded that intelligence research does show signs of low power and publication bias, but that these problems seem less severe than in many other scientific fields.

The dielectric properties of biological tissues are typically measured using the open-ended coaxial probe technique, which is based on the assumption that the tissue sample is homogeneous. Therefore, for heterogeneous tissue samples, additional post-measurement sample processing is conducted. Specifically, post-measurement histological analysis may be performed in order to associate the measured dielectric properties with the tissue types present in a heterogeneous sample. Accurate post-measurement histological analysis enables identification of the constituent tissue types that contributed to the measured dielectric properties, and their relative distributions. There is no standard protocol for conducting post-measurement histological analysis, which leads to high numbers of excluded tissue samples and inconsistencies in the resulting reported data for heterogeneous tissues. To this extent, this study examines the post-measurement histological process and the challenges in associating the acquired dielectric properties with the different tissue types present in heterogeneous samples. The results demonstrate that the histological process inevitably alters the morphology of samples, thus introducing errors in the interpretation of the dielectric properties acquired from heterogeneous biological samples. Notably, sample size was seen to shrink by up to 90% through the histological process, meaning that sensing volume determined from fresh tissues is not directly applicable to histology images. The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n. 637780. This work was also supported by Science Foundation Ireland (SFI) (grant number 15/ERCS/3276), and the Hardiman Research Scholarship from the National University of Ireland Galway. This work has been developed in the framework of COST Action MyWAVE (CA17115). peer-reviewed

Background: So far, numerous meta-analyses have been published regarding the correlation between peroxisome proliferator-activated receptor gamma (PPARG) proline 12 alanine (Pro12Ala) gene polymorphism and chronic kidney disease (CKD), however, the results appear to be contradictory. Hence, this study is formulated with the objective of using existing meta-analysis data together with our research population to study the correlation between PPARG Pro12Ala gene polymorphism and CKD and evaluate whether an accurate result can be obtained. Methods: First, literature related to CKD and PPARG Pro12Ala available on the PubMed and EMBASE databases up to December 2016 was gathered from 20 publications. Then, the gathered results were combined with our case-control study of 1693 enrolled subjects and a trial sequential analysis (TSA) was performed to verify existing evidence and determine whether a firm conclusion can be drawn. Results: The TSA results showed that the cumulative sample size for the Asian sample was 6078 and was sufficient to support a definite result. The results of this study confirmed that there is no obvious correlation between PPARG Pro12Ala and CKD for Asians (OR = 0.82 (95% CI = 0.66&ndash, 1.02), I2 = 63.1%), but this was not confirmed for Caucasians. Furthermore, the case-control sample in our study was shown to be the key for reaching this conclusion. Conclusions: The meta-analysis results of this study suggest no significant correlation between PPARG Pro12Ala gene polymorphism and CKD for Asians after adding our samples, but not for Caucasian.

Objectives: The SF-6D is a preference-based measure of health developed to generate utility values from the SF-36. The aim of this pilot study was to examine the feasibility and acceptability of using the standard gamble (SG) technique to generate preference-based values for the Arabic version of SF-6D in a Lebanese population. Methods: The SF-6D was translated into Arabic using forward and backward translations. Forty-nine states defined by the SF-6D were selected using an orthogonal design and grouped into seven sets. A gender-occupation stratified sample of 126 Lebanese adults from the American University of Beirut were recruited to value seven states and the pits using SG. The sample size is appropriate for a pilot study, but smaller than the sample required for a full valuation study. Both interviewers and interviewees reported their understanding and effort levels in the SG tasks. Mean and individual level multivariate regression models were fitted to estimate preference weights for all SF-6D states. The models were compared with those estimated in the UK. Results: Interviewers reported few problems in completing SG tasks (0.8% with a lot of problems) and good respondent understanding (5.6% with little effort and concentration), and 25% of respondents reported the SG task was difficult. A total of 992 SG valuations were useable for econometric modeling. There was no significant change in the test&ndash, retest values from 21 subjects. The mean absolute errors in the mean and individual level models were 0.036 and 0.050, respectively, both of which were lower than the UK results. The random effects model adequately predicts the SG values, with the worst state having a value of 0.322 compared to 0.271 in the UK. Conclusion: This pilot confirmed that it was feasible and acceptable to generate preference values with the SG method for the Arabic SF-6D in a Lebanese population. However, further work is needed to extend this to a more representative population, and to explore why no utility values below zero were observed.

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

The adoption of a vegetarian diet has been associated with positive health outcomes. However, few studies evaluate the effect of this eating pattern on quality of life. Moreover, no specific instrument for the vegetarian population to measure the quality of life is available worldwide. Therefore, this study aimed to elaborate and validate a specific questionnaire to measure the quality of life in vegetarians. The Specific Vegetarian Quality of Life Questionnaire (VEGQOL) was constructed based on other instruments and studies related to vegetarianism. The content and semantic validation were performed by a group of experts, followed by a pilot study to evaluate the questionnaire acceptability and reproducibility. Discriminant validation was tested using the WHOQOL as the gold standard measure (Pearson correlation ranging from 0.302 of the domain 3 to 0.392 of the domain 2). Afterward, a nationwide survey was conducted using VEGQOL. Content and semantic validation selected 19 of the initial 30 items. VEGQOL presented good reproducibility (Cohen&rsquo, s Kappa coefficient ranging from 0.361 to 0.730 and intraclass correlation coefficient of 0.820) and internal consistency (0.708), both adequate to evaluate the quality of life in vegetarians. The sample size (n = 5014 individuals, error of 3% at a level of significance of 5%) and distribution was representative of the Brazilian vegetarian population. In general, the quality of life of Brazilian vegetarians was considered satisfactory (VEGQOL cut off points 70&ndash, 80). Among different types of vegetarians, the vegans showed better results with a VEGQOL mean value of 79.2 &plusmn, 10.7. Older individuals, the ones who adopted the diet for a longer time (VEGQOL mean value of 75.8 &plusmn, 12.7) and the ones who had other vegetarians in their social network (VEGQOL mean value of 74.6 &plusmn, 12.2) also had a better quality of life score. Individuals who adopted it for ethical or health reasons had a higher quality of life score. The questionnaire produced in this study is a useful tool for future research in this area. Results were better for vegans and for the ones who adopt the diet for ethical or health reasons.

Several measures of skewness and kurtosis were proposed by Hogg (1974) in order to reduce the bias of conventional estimators when the distribution is non-normal. Here we conducted a Monte Carlo simulation study to compare the performance of conventional and Hogg&rsquo, s estimators, considering the most frequent continuous distributions used in health, education, and social sciences (gamma, lognormal and exponential distributions). In order to determine the bias, precision and accuracy of the skewness and kurtosis estimators for each distribution we calculated the relative bias, the coefficient of variation, and the scaled root mean square error. The effect of sample size on the estimators is also analyzed. In addition, a SAS program for calculating both conventional and Hogg&rsquo, s estimators is presented. The results indicated that for the non-normal distributions investigated, the estimators of skewness and kurtosis which best reflect the shape of the distribution are Hogg&rsquo, s estimators. It should also be noted that Hogg&rsquo, s estimators are not as affected by sample size as are conventional estimators.

We have read with interest the recently published paper by Schraders et al [...]

We have read with interest the recently published paper by Schraders et al [...].

In empirical science, random sampling is the golden standard to ensure unbiased, impartial, or fair results, as it works as a technological barrier designed to prevent spurious communication or illegitimate interference between parties in the application of interest. However, the chance of at least one covariate showing a “significant difference” between two treatment groups increases exponentially with the number of covariates. In 2012, Morgan and Rubin proposed a coherent approach to solve this problem based on rerandomization in order to ensure that the final allocation obtained is balanced, but with an exponential computation cost in the number of covariates. Haphazard Intentional Sampling is a statistical technique that combines intentional sampling using goal optimization techniques with random perturbations. On one hand, it has all the benefits of standard randomization and, on the other hand, avoid exponentially large (and costly) sample sizes. In this work, we compare the haphazard and rerandomization methods in a case study regarding the re-engineering of the network of measurement stations for atmospheric pollutants. In comparison with rerandomization, the haphazard method provided groups with a better balance and permutation tests consistently more powerful.

This study aimed to systematically review previous studies on the reliability and concurrent validity of the Global Physical Activity Questionnaire (GPAQ). A systematic literature search was conducted (n = 26) using the online EBSCOHost databases, PubMed, Web of Science, and Google Scholar up to September 2019. A previously developed coding sheet was used to collect the data. The Modified Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was employed to assess risk of bias and study quality. It was found that GPAQ was primarily revalidated in adult populations in Asian and European countries. The sample size ranged from 43 to 2657 with a wide age range (i.e., 15–79 years old). Di erent populations yielded inconsistent results concerning the reliability and validity of the GPAQ. Short term (i.e., one- to two-week interval) and long-term (i.e., two- to three-month apart) test–retest reliability was good to very good. The concurrent validity using accelerometers, pedometers, and physical activity (PA) log was poor to fair. The GPAQ data and accelerometer/pedometer/PA log data were not compared using the same measurements in some validation studies. Studies with more rigorous research designs are needed before any conclusions concerning the concurrent validity of GPAQ can be reached.

Demonstration of similar microstructure is essential for demonstrating the equivalence of generic topical products since the microstructure of semisolids may affect the drug release. The objective of this study was to compare the microstructure-defining physical parameters of different batches of a reference ointment containing calcipotriol and betamethasone (Daivobet 50 &micro, g/0.5 mg/g) in order to define the acceptance range that allows concluding equivalence between these batches. Being batches of the same reference product, they are expected to be clinically equivalent and possess similar microstructure. The 90% confidence intervals for the test/reference ratio of these physical parameters were calculated with parametric and non-parametric approaches. Both methods conclude that equivalent microstructure between batches cannot be demonstrated with a reasonable sample size when the acceptance range was set at &plusmn, 10%, since several physical parameters exhibit inter-batch variability &gt, 10%. An acceptance range of &plusmn, 10% is therefore too strict to conclude equivalence in the microstructure of semisolid dosage forms, given the inter-batch variability observed between batches of the reference product. A wider fixed acceptance range or an acceptance range widened based on the inter-batch variability of the reference product would be advisable.

We aimed to estimate the seroprevalence and the prevalence of coeliac disease (CD) in women with reproductive problems. A systematic review of English published articles until June 2019 was performed in PubMed and Scopus using the terms: (infertility and (coeliac disease OR gluten) OR (miscarriage and (coeliac disease OR gluten) OR (abortion and (coeliac disease OR gluten). All articles showing numerical data of anti-transglutaminase type 2 or anti-endomisium antibodies, or intestinal biopsy information were included. The study group comprised women with overall infertility, unexplained infertility, or recurrent spontaneous abortions. Two authors independently performed data extraction using a predefined data sheet. The initial search yielded 310 articles, and 23 were selected for data extraction. After meta-analysis, the pooled seroprevalence was very similar for overall and unexplained infertility, with a pooled proportion of around 1.3%–1.6%. This implies three times higher odds of having CD in infertility when compared to controls. The pooled prevalence could not be accurately calculated due to the small sample sizes. Further studies with increased sample sizes are necessary before giving specific recommendations for CD screening in women with reproductive problems, but current data seem to support a higher risk of CD in these women.

Background: Adjustment disorder (AD) is a condition commonly encountered by clinicians in emergency departments and liaison psychiatry settings and has been frequently reported among patients presenting with suicidal behaviours. A number of previous studies have noted the strong association between suicidal ideation and behaviours, and AD. In this paper, we aimed to explore this relationship, by establishing the incidence of AD in patients who present with self-harm and suicidal ideation, and the rates of self-harm among patients with a diagnosis of AD. Methods: We conducted a review of the literature of well-established databases using specific key words then synthesised the results into a descriptive narrative as well as representing it in table form. Results: Sample sizes and study methods varied significantly across the review. A majority of studies were retrospective chart-based reviews, and only three used structured diagnostic instruments. A high prevalence of AD (ranging from 9.8 to 100%) was found, with self-poisoning representing the most common form of suicide attempt in the majority of studies. Interpersonal difficulties were the main precipitant in studies which examined this. Conclusions: This study suggests there is a strong association between AD and suicidal behaviours. Given the paucity of research in the area, there is a need to build the evidence base for effective treatment strategies.

We conducted a systematic review to describe and summarize possible associations between the walkability index, overweight, and obesity. Systematic searches using seven electronic databases and reference lists were conducted to identify papers published until December 2017. Observational studies, describing associations using regression-based statistical methods, published in English and Portuguese, reporting markers of overweight and obesity, and involving adults (≥18 years) were included. Of the 2469 references initially retrieved, ten were used for the descriptive synthesis. Seven studies showed significant inverse associations between walkability and overweight and obesity, however, all were cross-sectional studies. High risk of bias scores were observed in “selection bias” and “withdrawals and dropouts”. All studies were published in high-income countries with sample sizes ranging among 75 to 649,513 participants. Weight and height as measures for determining BMI tended to be self-reported. Indicators of walkability, such as land-use mix, street connectivity and residential density were used as components of the indices. Based on this review, more studies should be conducted in low, middle, and middle-high income countries, using longitudinal designs that control neighborhood self-selection; other indicators of the neighborhood environment, such as food access, physical activity facilities, sidewalks, and safety and crime prevention should be considered.

Background and objectives: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in children and adolescents. Mind−body therapies (MBTs) seem to be effective for improving health in different populations; however, whether a positive effect occurs in children and adolescents with ADHD is still controversial. The main aim of this systematic review was to analyse the interventions based on MBT aimed to improve the main ADHD symptoms in children and adolescents. Materials and Methods: A systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify MBT studies on children and adolescents (4−18 years) with a clinical diagnosis of ADHD. Study quality was evaluated by the NIH quality tool (U.S. National Institute of Health). Results: There were positive results in eleven out of twelve included studies regarding the effect of the MBT interventions on ADHD symptoms. With respect to ADHD symptoms, we observed differences across studies. In relation to the studies’ quality, eleven studies were rated “poor” and one was rated as “fair”. Conclusions: MBTs, such as yoga or mindfulness, could be positive strategies to mitigate ADHD symptoms in children and adolescents. However, further research with high-quality designs, with randomization, greater sample sizes, and more intensive supervised practice programs are needed.

The prevalence of food insecurity (FI) among college students is alarmingly high, yet the impact on student health has not been well investigated. The aim of the current study was to examine the simultaneous relationships between food insecurity and health-related outcomes including body mass index (BMI) and overall health in a college student population. Randomly sampled students in the University of California 10 campus system were invited to participate in an online survey in spring 2015. The analytic sample size was 8705 graduate and undergraduate students. Data were collected on FI in the past year, daily servings of fruits and vegetables (FV), number of days in the past week of enough sleep and moderate- to vigorous-intensity physical activity (MVPA), height and weight, self-rated health, and student characteristics. Using path analysis, mediated pathways between FI, BMI, and poor health were examined through FV intake, number of days of MVPA and enough sleep. Analyses controlled for student characteristics. Mean BMI was 23.6 kg/m2 (SD, 5.0), and average self-rated health was good. FI was directly and indirectly related to higher BMI and poor health through three pathways. First, FI was related to fewer days of enough sleep, which in turn was related to increased BMI and poor health. Second, FI was related to fewer days of MVPA, which in turn was related to increased BMI and poor health. Third, FI was related to fewer daily servings of FV, which in turn was related to poor health. FI is associated with poor health behaviors among college students, which may contribute to higher weight status and poor health. These findings highlight the importance of food security for a healthy college experience.

Objective: To assess the scientific evidence on the relationship between body image and body weight control in overweight 55-year-old adults. Methods: The literature search was conducted on MEDLINE database via PubMed, using terms related to body image, weight control and body composition. Inclusion criteria were scientific papers, written in English or Spanish, made on older adults. Exclusion criteria were eating and psychological disorders, low sample size, cancer, severe diseases, physiological disorders other than metabolic syndrome, and bariatric surgery. Results: Fifty-seven studies were included. Only thirteen were conducted exclusively among 55-year-old adults or performed analysis adjusted by age. Overweight perception was related to spontaneous weight management, which usually concerned dieting and exercising. More men than women showed over-perception of body image. Ethnics showed different satisfaction level with body weight. As age increases, conformism with body shape, as well as expectations concerning body weight decrease. Misperception and dissatisfaction with body weight are risk factors for participating in an unhealthy lifestyle and make it harder to follow a healthier lifestyle. Body image disturbance also made it more likely to underreport calorie intake. Conclusions: Aging is associated with a decrease in weight concerns and lower overweight perception, especially in women. However, when designing a program to improve body image in overweight 55-year-old adults, three items ought to be considered: physical activity, dietary and behavioral treatments., Instituto de Salud Carlos III through the Fondo de Investigacion para la Salud (FIS), which is co-funded by the European Regional Development Fund (Projects PI14/00636 and PI17/01827, Red Predimed-RETIC RD06/0045/1004, and CIBEROBN CB12/03/30038), EU-COST Action CA16112, Grant of support to research groups no. 35/2011 (Balearic Islands Gov.), and Fundacio La Marato TV3 (Spain) project ref. 201630.10. Cristina Bouzas received a Fernando Tarongi Bauza Grant. The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results.

This cross-sectional study compares the effectiveness of an active MMP-8 (aMMP-8) point-of-care (PoC)/chairside mouthrinse test to the conventional bleeding on probing (BOP) (cutoff 20%) test in detecting subclinical periodontitis/pre-periodontitis in Finnish adolescents. The study was carried out at the Kotka Health Center, Finland. A total of 47 adolescents (30 boys/17 girls) aged 15&ndash, 17 were first tested with the aMMP-8 PoC test, followed by a full-mouth evaluation of clinical parameters of oral health including periodontal, oral mucosal, and caries assessment. A periodontist performed these clinical examinations. The aMMP-8 PoC test result had much stronger association with subclinical periodontitis than the BOP 20% test (2.8&ndash, 5.3 times stronger in terms of odds ratio). The aMMP-8 PoC test had &ge, 2 times higher sensitivity than the BOP 20% test with, generally, the same specificity. Further, the aMMP-8 PoC test had generally better accuracy and lower false negative percentages. The aMMP-8 PoC test seemed to be more effective than the conventional BOP test in detecting subclinical periodontitis/pre-periodontitis in adolescents reducing the risk of their undertreatment. However, the sample size may be a limiting factor, and more studies are needed to confirm our results for both adolescents and adults.

In the modeling of successive arrival times with a monotone trend, the alpha-series process provides quite successful results. Both selecting the distribution of the first arrival time and making an optimal statistical inference play a crucial role in the modeling performance of the alpha-series process. In this study, when the distribution of the first arrival time is the generalized Rayleigh, the problem of statistical inference for the &alpha, &beta, and &lambda, parameters of the alpha-series process is considered. Further, in order to obtain optimal modeling performance from the mentioned alpha-series process, various estimators for the model parameters are obtained by employing different estimation methodologies such as maximum likelihood, modified maximum spacing, modified least-squares, modified moments, and modified L-moments. By a series of Monte Carlo simulations, the estimation efficiencies of the obtained estimators are evaluated through the different sample sizes. Finally, two real datasets are analyzed to illustrate the importance of modeling with the alpha-series process.