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Data quality is an important consideration in many engineering applications and projects. Data collection procedures do not always involve careful utilization of the most precise instruments and strictest protocols. As a consequence, data are invariably affected by imprecision and sometimes sharply varying levels of quality of the data. Different mathematical representations of imprecision have been suggested, including a classical approach to censored data which is considered optimal when the proposed error model is correct, and a weaker approach called interval statistics based on partial identification that makes fewer assumptions. Maximizing the quality of statistical results is often crucial to the success of many engineering projects, and a natural question that arises is whether data of differing qualities should be pooled together or we should include only precise measurements and disregard imprecise data. Some worry that combining precise and imprecise measurements can depreciate the overall quality of the pooled data. Some fear that excluding data of lesser precision can increase their overall uncertainty about results because lower sample size implies more sampling uncertainty. This paper explores these concerns and describes simulation results that show when it is advisable to combine fairly precise data with rather imprecise data by comparing analyses using different mathematical representations of imprecision. Pooling data sets is preferred when the low-quality data set does not exceed a certain level of uncertainty. However, so long as the data are random, it may be legitimate to reject the low-quality data if its reduction of sampling uncertainty does not counterbalance the effect of its imprecision on the overall uncertainty. [ABSTRACT FROM AUTHOR]

Objective. To map geographic clusters of rare disorders and congenital anomalies reported in South America. Methods. Qualitative systematic review conducted in Medline/PubMed, Lilacs, and Scielo electronic databases to identify studies meeting eligibility criteria. The strategy resulted in 1 672 unique articles, from which 164 were selected for full reading by a pair of reviewers. Results. Fifty-five articles reported at least one cluster of genetic disorders or congenital anomalies in South American territory. From these papers, 122 clusters were identified, of which half (61) were related to autosomal recessive disorders. Sixty-five (53.3%) of the clusters were located in Brazil. Conclusions. The results of the review reinforce that rare diseases and congenital anomalies can occur in a non-random way in space, which is discussed in the perspective of the complex history of formation, social organization, and genetic structure of the South American population. Mapping clusters in population medical genetics can be an important public health tool, given that such places concentrate cases of rare diseases that frequently require multiprofessional, specialized care. Therefore, these results can support important agendas in public health related to rare diseases and congenital anomalies, such as health promotion and surveillance. [ABSTRACT FROM AUTHOR]

Objectives. The aim of this initiative was to assess whether a novel training program - Understanding Stigma and Strengthening Cognitive Behavioral Interpersonal Skills - could improve primary health care providers' confidence in the quality of mental health care they provide in the Caribbean setting by using the Plan-Do-Study-Act rapid cycle for learning improvement. Methods. We conducted a prospective observational study of the impact of this training program. The training was refined during three cycles: first, the relevance of the program for practice improvement in the Caribbean was assessed. Second, pilot training of 15 local providers was conducted to adapt the program to the culture and context. Third, the course was launched in fall 2021 with 96 primary care providers. Pre- and post-program outcomes were assessed by surveys, including providers' confidence in the quality of the mental health care they provided, changes in stigma among the providers and their use of and comfort with the tools. This paper describes an evaluation of the results of cycle 3, the official launch. Results. A total of 81 participants completed the program. The program improved primary care providers' confidence in the quality of mental health care that they provided to people with lived experience of mental health disorders, and it reduced providers' stigmatization of people with mental health disorders. Conclusions. The program's quality improvement model achieved its goals in enhancing health care providers' confidence in the quality of the mental health care they provided in the Caribbean context; the program provides effective tools to support the work and it helped to empower and engage clients. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques. [ABSTRACT FROM AUTHOR]

Objective. To determine the relative risk of a lethal outcome associated with chronic degenerative conditions in patients with COVID-19. Methods. A cohort study was conducted using electronic medical records belonging to patients who tested positive for COVID-19 on RT-PCR while receiving care as outpatients or inpatients in a social security system facility between March 2020 and March 2021. Two study groups were formed. The exposed group was divided into four subgroups, each of which was diagnosed with one and only one chronic condition (diabetes, hypertension, obesity, or chronic kidney disease); the unexposed group was obtained from the medical records of patients without comorbidities. A total of 1 114 medical records were examined using simple random sampling. Once the minimum sample size was reached, the relative risk was calculated for each chronic condition. Combinations of two, three, and four conditions were created, and each of them was included in the analysis. Results. In the absence of a chronic degenerative condition, the prevalence of a lethal outcome from COVID-19 is 3.8%; in the presence of type 2 diabetes mellitus, 15.8%; in the presence of arterial hypertension, 15.6%; and in the presence of obesity, 15.0%. For diabetes and hypertension combined, the prevalence of a lethal outcome is 54.1%; for diabetes and obesity combined, 36.8%, and for obesity and hypertension combined, 28.1%. Conclusion. In patients with COVID-19, the relative risk of a lethal outcome is 4.17 for those with diabetes, 4.13 for those with hypertension, and 3.96 for those with obesity. For two chronic conditions combined, the relative risk doubles or triples. The relative risk of a lethal outcome is 14.27 for diabetes plus hypertension; 9.73 for diabetes plus obesity, and 7.43 for obesity plus hypertension. Chronic conditions do not present alone; they generally occur together, hence the significance of the relative risks for lethal outcomes presented in this paper. [ABSTRACT FROM AUTHOR]

Objective. To describe the editorial processing time of published COVID-19 research articles and compare this with a similar topic, human influenza, and analyze the number of publications, withdrawals, and retractions. Methods. A descriptive-analytical study using PubMed on research articles with the MeSH terms human influenza and COVID-19. Time to acceptance (from submission to acceptance) and time to publication (from acceptance to publication) were compared. Retractions and withdrawals were reviewed both qualitatively and quantitatively. Results. There were 31 319 research articles on COVID-19 and 4 287 on human influenza published during 2020. The median time to acceptance for COVID-19 was lower than that for human influenza (8 vs. 92 days). The median time to publication for COVID-19 articles was shorter than those on human influenza (12 vs. 16 days); 47.0% of COVID-19 research articles were accepted within the first week of submission, and 19.5% within one day. There were 82 retractions and withdrawals for COVID-19 articles, 1 for human influenza, and 5 for articles that contain both terms; these were mainly related to ethical misconduct, and 27 (31.0%) were published by the same group of authors in one highest-quartile journal. Conclusions. The conundrum between fast publishing and adequate standards is shown in this analysis of COVID-19 research articles. The speed of acceptance for COVID-19 manuscripts was 11.5 times faster than for human influenza. The high number of acceptances within a day or week of submission and the number of retractions and withdrawals of COVID-19 papers might be a warning sign about the possible lack of a quality control process in scientific publishing and the peer review process. [ABSTRACT FROM AUTHOR]

Objective. To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer. Methods. A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan-Meier method, Cox regression, and conditional decision tree. Results. Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days. Conclusions. This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient. [ABSTRACT FROM AUTHOR]

Objectives: This review sought to evaluate the evidence for embryo formation during intrauterine device (IUD) use, to articulate how often embryo loss occurs in well-designed studies, and to comment on other bodies of literature suggestive of postfertilization mechanisms of action of IUDs. Methods: The MEDLINE, EMBASE, and Ovid databases were searched for English-language studies of markers of pregnancy in IUD users in May 2018. Studies of human chorionic gonadotropin (hCG) were subjected to quality assessment based on the US Preventive Services Task Force quality tool. Bias of studies assessing pregnancy in other ways was assessed on a study-to-study basis. Results: In all, 1,073 studies were identified and 138 were read in detail. Twenty-three studies of β-hCG, 4 studies of direct observation of embryos in fallopian tubes, 2 studies of pregnancy-specific binding globulin (PSBG), and 1 study of heat-shock protein 10 (Hsp10) or chaperonin 10 were included. In all studies considered together, 7.3 percent of IUD users had evidence of fertilization and pregnancy failure. In good-quality studies, 4.5 percent had evidence of fertilization and pregnancy failure. Discussion: There are no randomized trials of embryo formation and loss in IUD users compared with noncontracepting controls. Studies of β-hCG span a large spectrum of quality, but several good-quality studies exist, which support embryo formation and loss in IUD users. Evidence of embryos found in tubes is moderate and evidence of PSBG and Hsp10 elaboration was limited, but these are also concerning for embryo formation and loss. Conclusion: There is good-quality evidence of embryo formation and loss in IUD users. Studies are inconsistent, and the stated conclusions of several papers inaccurately diminish postfertilization evidence of embryo formation. To better assess the rate of embryo loss in IUD users compared with non-users, future research should include well-designed prospective trials and less subjective assessments of embryos in fallopian tubes. Summary: A systematic review was carried out examining the English-language literature in the MEDLINE, EMBASE, and Ovid databases for evidence of embryo formation and loss during IUD use. In all, 1,073 studies were identified and 138 were read. There are no randomized trials and evidence ranges in quality, but evidence for the embryo formation and loss in 4.5 percent of IUD users exists in good-quality research. Further research is needed to compare embryo loss in IUD users to loss in controls. [ABSTRACT FROM AUTHOR]

In a nonideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used as a sampling clock to an analog-to-digital converter (ADC), it creates spurious sidebands in the sampled data as well. In spectrum sensing applications, the presence of spurious sidebands can lead to false detection of signals in otherwise empty channels. To remove the spurious sidebands, we first estimate the distortions to the sampled data and then compensate the data in the digital domain. To reduce hardware complexity and computation cost, we propose an algorithm that uses a Fourier Transform block (a typical component in spectrum sensing architectures) to estimate the sampling errors from the spurious sidebands. We also analyze the effects of the spurious sidebands on spectrum sensing. Computer simulations are included to show that the proposed solution can remove the spurious sidebands and improve the detection performance. [ABSTRACT FROM PUBLISHER]

Abstract In this paper, we investigate a granular data description for facial components in which a characterization of facial components is presented by a collection of information granules. Firstly, the facial landmark detector is utilized to extract facial components automatically. Secondly, semantic concepts are formed by involving various mechanisms of fuzzy clustering based on these detected landmarks. A collection of numeric prototypes can be sought as a blueprint of the descriptors. Consequently, the information granules are being formed around the prototypes that are engaged by the fundamental ideas of Granular Computing, especially the principle of justifiable granularity. Multiple experiments on Multi-PIE facial database illustrate the proposed facial semantic descriptors based on information granules not only can characterize the key semantics of facial components of data, but also can improve the semantic classification performance in comparison with human perception. [ABSTRACT FROM AUTHOR]

Abstract Determination of shape parameter has a major role in the accuracy of the radial basis functions method. In this paper, we present a new method called Leave-Two-Out Cross Validation to determine the best shape parameter. In the proposed method, by deleting two data from the data set, a new formula is derived for determining the error value and then the optimal shape parameter is determined. Num results show that the method will be more accurate in comparison with other methods. [ABSTRACT FROM AUTHOR]

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and $k$ -means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to $k$ -means. In particular, our approach leads to a new interpretation of $k$ -means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD. [ABSTRACT FROM AUTHOR]

Statistical depth and related quantile functions, originally introduced for nonparametric description and analysis of multivariate data in a way sensitive to inherent geometry, are in active development for functional data and in this setting offer special options since the data may be visualized regardless of dimension. This paper provides depth-based methods for revealing the structure of a functional data set in terms of relevant sample quantile curves displayed at selected levels, and for constructing and displaying confidence bands for corresponding “population” versions. Also, the usual functional boxplot is enhanced, by adding inner fences to flag possible shape outliers, along with the outer fences that flag location outliers. This enables the boxplot to serve as a stand-alone tool for functional data, as with univariate and multivariate data. Further, the spatial depth approach, well-established for multivariate data, is investigated for nonparametric description of functional data along these lines. In comparison with four other commonly used depth approaches for functional data, over a range of actual and simulated data sets, the spatial depth approach is seen to offer a very competitive combination of robustness, efficiency, computational ease, simplicity, and versatility. [ABSTRACT FROM AUTHOR]

In order to realize the patient privacy protection in medical image, opposite to traditional reversible data hiding (RDH) methods which prior to embed message into the smooth area for pursuing high PSNR value, the proposed method priors to embed message into the texture area of the medical images for improving the quality of the details information and helping accurate diagnosis. Furthermore, in order to decrease the embedding distortion while enhancing the contrast of the texture area, this paper also proposes a message sparse representation method. Experiments implemented on medical images showed that the proposed method enhances the contrast of texture area when compared with previous methods. [ABSTRACT FROM AUTHOR]

Marginalized models (Heagerty, 1999, 2002) are often used for short longitudinal series when population averaged effects are of interest. Lee and Daniels (2007, 2008) proposed marginalized models for the analysis of longitudinal ordinal data to permit likelihood-based estimation of marginal mean parameters. In this paper, we extend their work to accommodate the response dependence that we have seen with long series of response data (the functional form of response dependence has both serial and long-range components). Maximum likelihood estimation is proposed utilizing the Quasi-Newton algorithm with a Quasi Monte Carlo method for integration of the random effects. The methods are illustrated on quality of life data from a recent lung cancer clinical trial. [ABSTRACT FROM AUTHOR]

The article informs that according to a new paper by Martin Schweinsberg, a psychologist at the European School of Management and Technology, in Berlin, different data can lead scientists to opposite conclusions. It mentions that if many different analytical approaches point in the same direction, then scientists can be confident that their conclusion is the right one.