MED27 Variants Cause Developmental Delay, Dystonia, and Cerebellar Hypoplasia

The Mediator multiprotein complex functions as a regulator of RNA polymerase II-catalyzed gene transcription. In this study, exome sequencing detected biallelic putative disease-causing variants in MED27, encoding Mediator complex subunit 27, in 16 patients from 11 families with a novel neurodevelopmental syndrome. Patient phenotypes are highly homogeneous, including global developmental delay, intellectual disability, axial hypotonia with distal spasticity, dystonic movements, and cerebellar hypoplasia. Seizures and cataracts were noted in severely affected individuals. Identification of multiple patients with biallelic MED27 variants supports the critical role of MED27 in normal human neural development, particularly for the cerebellum. ANN NEUROL 2021Peer reviewe

Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference

Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are discrete, and the very peculiar shape of the noise distribution, asymmetric with fat tails, with a large majority zeros and a few unreliable and very large values. We compare several machine learning methods for estimating insurance premia, and test them on a large data base of car insurance policies. We find that function approximation methods that do not optimize a squared loss, like Support Vector Machines regression, do not work well in this context. Compared methods include decision trees and generalized linear models. The best results are obtained with a mixture of experts, which better identifies the least and most risky contracts, and allows to reduce the median premium by charging more to the most risky customers

Modification and Characterization of Fe3O4 Nanoparticles for Use in Adsorption of Alkaloids

Magnetite (Fe3O4) is a ferromagnetic iron oxide of both Fe(II) and Fe(III), prepared by FeCl2 and FeCl3. XRD was used for the confirmation of Fe3O4. Via the modification of Tetraethyl orthosilicate (TEOS), (3-Aminopropyl)trimethoxysilane (APTMS), and Alginate (AA), Fe3O4@SiO2, Fe3O4@SiO2-NH2, and Fe3O4@SiO2-NH2-AA nanoparticles could be obtained, and IR and SEM were used for the characterizations. Alkaloid adsorption experiments exhibited that, as for Palmatine and Berberine, the most adsorption could be obtained at pH 8 when the adsorption time was 6 min. The adsorption percentage of Palmatine was 22.2%, and the adsorption percentage of Berberine was 23.6% at pH 8. Considering the effect of adsorption time on liquid phase system, the adsorption conditions of 8 min has been chosen when pH 7 was used. The adsorption percentage of Palmatine was 8.67%, and the adsorption percentage of Berberine was 7.25%. Considering the above conditions, pH 8 and the adsorption time of 8min could be chosen for further uses

Novel Surrogates for Membrane Fouling and the Application of Support Vector Machine in Analyzing Fouling Mechanism

It is difficult to recognize specific fouling mechanisms due to the complexity of practical feed water, thus the current studies usually employ foulant surrogates to carry out research, such as alginate and xanthan gum. However, the representativeness of these surrogates is questionable. In this work, the classical surrogates (i.e., alginate and xanthan gum) were systematically studied, and results showed that they behaved differently during filtration. For the mixture of alginate and xanthan gum, both filtration behaviors and adsorption tests performed by quartz-crystal microbalance with dissipation monitoring (QCM-D) indicated that alginate plays a leading role in fouling development. Furthermore, by examining the filtration behaviors of extracellular polymeric substances (EPS) extracted from practical source water, it turns out that the gel layer formation is responsible for EPS fouling, and the properties of gel layer formed by EPS share more similarities with that formed from pectin instead of alginate. In addition, with the use of experimental data sets extracted from this study and our previous studies, a modeling method was established and tested by the support vector machine (SVM) to predict complex filtration behaviors. Results showed that the small differences of fouling mechanisms lying between alginate and pectin cannot be recognized by Hermia’s models, and SVM can show a discrimination as high as 76.92%. As such, SVM may be a powerful tool to predict complex filtration behaviors

Influencing factors and contribution analysis of CO2 emissions originating from final energy consumption in Sichuan Province, China

Within the context of CO2 emission peaking and carbon neutrality, the study of CO2 emissions at the provincial level is few. Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO2 emissions. Therefore, using logarithmic mean Divisia index (LMDI) model to analysis the influence degree of different influencing factors on CO2 emissions from final energy consumption in Sichuan Province, so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors. Based on the data of final energy consumption in Sichuan Province from 2010 to 2019, we calculated CO2 emission by the indirect emission calculation method. The influencing factors of CO2 emissions originating from final energy consumption in Sichuan Province were decomposed into population size, economic development, industrial structure, energy consumption intensity, and energy consumption structure by the Kaya–logarithmic mean Divisia index (LMDI) decomposition model. At the same time, grey correlation analysis was used to identify the correlation between CO2 emissions originating from final energy consumption and the influencing factors in Sichuan Province. The results showed that population size, economic development and energy consumption structure have positive contributions to CO2 emissions from final energy consumption in Sichuan Province, and economic development has a significant contribution to CO2 emissions from final energy consumption, with a contribution rate of 519.11%. The industrial structure and energy consumption intensity have negative contributions to CO2 emissions in Sichuan Province, and both of them have significant contributions, among which the contribution rate of energy consumption structure was 325.96%. From the perspective of industrial structure, secondary industry makes significant contributions and will maintain a restraining effect; from the perspective of energy consumption structure, industry sector has a significant contribution. The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province

Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN

Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required