Combining parametric and nonparametric models to estimate treatment effects in observational studies

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.     

Physiotherapy students’ perceptions of ageing – Playing narratives as a pedagogical innovation

Introduction and objectivesThere is a considerable lack of structure in training health professionals in geriatrics. The narratives can promote a collaborative reflection on different topics and might be used as a pedagogic strategy for undergraduate health students. This study aimed to explore the adoption of new perspectives on ageing after the implementation of dynamic narratives in the first graduate year for physiotherapy students.Materials and methodsAn exploratory qualitative study was performed. Participants were included if they were: ≥18 years old, physiotherapy students and agreed to participate. Physiotherapy students (n = 44) were recruited from the School of Health Sciences, Polytechnic Institute of Leiria. Two gaming sessions were performed to help students, as narrators, express their visions and ways to deal with the geriatrics field. Students’ perspectives about ageing at baseline (T1) and after the narratives experience (T2) were collected by answering the following question: What is your perception regarding ageing? Two evaluators were involved in qualitative data analysis by performing: (i) an individual analysis of themes/subthemes and a discussion meeting to evaluate discrepancies and to reach a consensus.ResultsNegative perceptions about ageing were referred 39 times at T1 (most of them related to restriction; deterioration subthemes). There were no negative perceptions registered at T2. Positive perceptions increased at T2, from n = 39 to n = 52, and three new subthemes emerged (beginning of something, fighting ageism, challenge).ConclusionThis study demonstrated the potential of narrative-based experiences as a desirable pedagogic methodology (board games-oriented) for geriatric education in undergraduate health students.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

By-Products of Zea mays L.: A Promising Source of Medicinal Properties with Phytochemistry and Pharmacological Activities: A Comprehensive Review

Zea mays (Z. mays) is one of the main cereal crops in the world, and it′s by-products have exhibited medicinal properties to explore. This article intends to review the chemical compositions and pharmacological activities of by-products of Z. mays (corn silks, roots, bract, stems, bran, and leaves) which support the therapeutic potential in the treatment of different diseases, with emphasis on the natural occurring compounds and detailed pharmacological developments. Based on this review, 231 natural compounds are presented. Among them, flavonoids, terpenes, phenylpropanoids, and alkaloids are the most frequently reported. The by-products of Z. mays possess diuretic effects, hepatoprotective, anti-diabetic, antioxidant, neuroprotective, anti-inflammatory, anti-cancer, plant protection activity, and other activities. This article reviewed the phytochemistry and pharmacological activities of Z. mays for comprehensive quality control and the safety and effectiveness to enhance future application.     

Membrane inlet—ion mobility spectrometry with automatic spectra evaluation as online monitoring tool for the process control of microalgae cultivation

The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high-frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process.     

Spatial autocorrelation invalidates the Dow-Cheverud test

Dow and Cheverud (Am. J. Phys. Anthropol. 68:367–373, 1985) have published a statistical test for comparing any three similarity matrices. Using both simulations and analytical arguments, I establish that the presence of spatial autocorrelation, a common feature of geographically based anthropological and biological data, causes this test to reject too often. Increasing the spatial autocorrelation increases the spurious rejection rate. About 20% of the papers that reference Dow and Cheverud's paper have used their test with spatially autocorrelated data. Mantel's (Cancer Res. 27:209–220, 1967) method, when used as a test of spatial autocorrelation, is unaffected by these considerations, since its null hypothesis is that the data are uncorrelated. © 1992 Wiley-Liss, Inc.     

第四纪甲虫研究方法及意义

第四纪甲虫的研究在国外已开展多年,形成了一套完整的方法,而在我国几乎为一空白。第四纪甲虫研究方法一般包括如下几个步骤：野外采样,实验室分离,标本保存,标本鉴定,结果分析。第四纪甲虫研究的意义主要表现在如下三个方面：重建古气候、恢复昆虫地理史和第四纪考古。     

A Technique to Analyse Succession Data from Permanent Plots

IIntroductionSuccessionstudyIsaspeclalfleldInvegetationecology（Greig—Smith1983）．ItreferstocummulatlvechangeInvegetationovertime．Florl幻IcgradientsIndicativeofsuccessionaltrendsmayemergefromconventionalordinationIfextrinsic！nformatlonallowsthemtobel－dentlfledassuch．Inthespedalcaseofforestvegetation,forInstance,Informationonthesize—classesoftreespedespresentmaypermitsuccessionalgradientslobeldentlfleddirectly（Goff1968,Carleton＆Maycock1978）．Fewauthors,however,haveattempted…     

Models for relating pH measurements in water and calcium chloride for a wide range of pH,soil types and depths

Soil pH is commonly measured in water (pHw) or 0.01 M CaCl₂ (pH_Ca). The need to convert between these methods has led to the publication of linear, quadratic and cubic polynomial relationships for limited suites of soils. Concerns over the applicability of such relationships when mapping a wide range of soils and pH led to the establishment of a database of pH_W and pH_Ca values on each of 7894 samples from soil survey and field experimental sites in Queensland. The relationship between pH_W and pH_Ca across all soils was investigated and preliminary results examining the effect of soil depth and soil type on the relationship are presented.For all soils and depths, a linear regression accounted for 93.2% of the variation but did not predict pH_Ca well at very high or low pH_W values. The inclusion of second and third powers of pH_W accounted for significantly more of the variation (R²=0.94) in pH_Ca and the resultant curve matched the data better at high and low pH.Analysis of surface, sub-surface and subsoil groupings did not reveal any appreciable differences in the relationship between pH_W and pH_Ca attributable to depth. In contrast, differences in the relationship were evident between soil types. Generally, the mildly leached soils had linear relationships, while the weathered soils were distinctly curvilinear at low pH.