Multifaceted Microcephaly-Related Gene MCPH1

MCPH1, or BRIT1, is often mutated in human primary microcephaly type 1, a neurodevelopmental disorder characterized by a smaller brain size at birth, due to its dysfunction in regulating the proliferation and self-renewal of neuroprogenitor cells. In the last 20 years or so, genetic and cellular studies have identified MCPH1 as a multifaceted protein in various cellular functions, including DNA damage signaling and repair, the regulation of chromosome condensation, cell-cycle progression, centrosome activity and the metabolism. Yet, genetic and animal model studies have revealed an unpredicted essential function of MPCH1 in gonad development and tumorigenesis, although the underlying mechanism remains elusive. These studies have begun to shed light on the role of MPCH1 in controlling various pathobiological processes of the disorder. Here, we summarize the biological functions of MCPH1, and lessons learnt from cellular and mouse models of MCPH1.

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

Hydrodynamics in the Morganza Floodway and Atchafalaya Basin, report 3 : phase 3; a report for the US Army Corps of Engineers, MRG&P

The Morganza Floodway and the Atchafalaya Basin, located in Louisiana west of the Mississippi River, were evaluated using a two-dimensional Adaptive Hydraulics model. Prior to this study, Phase 1 and 2 model studies were performed that indicated that the existing floodway may not be able to pass the Project Design Flood discharge of 600,000 cubic feet per second due to levee overtopping. In this study, all elevations of exterior and interior levees were updated with current crest elevations. In addition, the Phase 3 effort evaluated the sensitivity of the floodway’s flow capacity to variations in tree/vegetation density conditions. These adjustments in roughness will improve the understanding of the role of land cover characteristics in the simulated water surfaces. This study also provides a number of inundation maps corresponding to certain flows through the Morganza Control Structure.

Improving the energy efficiency of a 6ChN13/15 gas engine with a Miller cycle by optimizing the valve timing

Introduction (problem statement and relevance). The object of research in this work is an inline six-cylinder gas engine 6ChN13/15 with a Miller thermodynamic cycle. On the basis of its computer model studies minimization of the specific effective fuel consumption has been reached due to variation study of gas distribution and air supply systems parameters.The purpose of the study was to investigate the parameters regulation effect of gas distribution and air supply systems on the performance of a 6ChN13/15 gas engine with a Miller cycle on the external speed characteristic basing on numerical modeling.Methodology and research methods. The research was carried out by the method of computer simulation. Numerical modeling was made on the basis of data obtained during a full-scale experiment of a 6ChN13/15 gas engine with Miller thermodynamic cycle.Scientific novelty and results. A comparative analysis of a gas engine optimization results has been carried out. The results obtained will be used to create a gas engine and its further optimization by controlling the working process and the air supply system.Practical significance. The results obtained may be of interest to truck car manufacturers and engine specialists.

Competing C–H and C–F bond activation reactions of a fluorinated olefin at Rh: a fluorido vinylidene complex as an intermediate in an unprecedented dehydrofluorination step

Dehydrofluorination of a fluorinated vinyl ligand takes place after C–H bond activation of Z-1,3,3,3-tetrafluropropene at a rhodium vinyl complex. Model studies support a vinylidene as reaction intermediate for the dehydrofluorination.

ADOBE FLASH MEDIA LEARNING ENGLISH THROUGH E-LEARNING SYSTEM IN UNIVERSITY OF MUHAMMADIYAH SUMATERA UTARA

<p>The Objective of study is to find the result of the learning process of Adobe Flash as media on the course of <em>Pengembangan Media Pembelajaran </em>through E-Learning system at English education study program in University of Muhammadiyah Sumatera Utara. E-learning provides a set of tools to enrich the value of conventional learning model, studies of textbooks, CD-ROMs, and computer-based training. Students can answer the challenges of globalization. Research Method used descriptive method through qualitative approach. Method of research conducted qualitative. Sample of research were forth semester students of English education study program. E learning system of UMSU provided schedule of course, attendance check, discussion forum between a lecture and students, task file, exercises file integrated Moodle and google form, module e book of <em>Pengembangan Media Pembelajaran</em>, slide file presentation, all activities viewed past, present and future time. Adobe flash media learning English implemented by virtual for one semester. The students can be active teaching English using Adobe Flash, simple material for presentation. They were enthusiastic when learn Adobe flash as media leaning English.</p>

Modeling the Tropospheric Multiphase Chemistry of Biomass Burning Trace Compounds Using the Chemical Aqueous Phase Radical Mechanism (CAPRAM)

&lt;p&gt;Biomass burning (BB) is a significant contributor to air pollution on global, regional and local scale with impacts on air quality, public health and climate. Anhydrosugars and methoxyphenols are key tracers emitted through BB. Once emitted, they can undergo complex multiphase chemistry in the atmosphere contributing to secondary organic aerosol (SOA) formation. However, their chemical multiphase processing is not yet well understood and investigated by models. Thus, the present study aimed at a better understanding of the multiphase chemistry of these BB tracers by detailed model studies with a new developed CAPRAM biomass burning module (CAPRAM-BBM).This module was developed based on the kinetic data from our laboratory measurements at TROPOS and other literature studies. The developed CAPRAM-BBM includes 2991 reactions (9 phase transfers and 2982 aqueous-phase reactions). By coupling with the multiphase chemistry mechanism MCMv3.2/CAPRAM4.0 and the extended CAPRAM aromatics (CAPRAM-AM1.0) and halogen modules (CAPRAM-HM3.0), itis being applied for residential wood burning cases in Europeand wildfire cases in the US. Our model results show that levoglucosan and vanillin are effectively oxidized under cloud conditions. Furthermore, the results demonstrate that the chemistry of BB tracers can affect the budgets of key oxidants such as H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;, and contribute to the SOA formation especially by increasing the fraction of brown carbon and substituted organic acids.&lt;/p&gt;

Context-based sentiment analysis on customer reviews using machine learning linear models

Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach this with algorithms, methods, and models. These include machine learning and deep learning (DL) methods, unigram and skip-gram based algorithms, as well as the Artificial Neural Network (ANN) and bag-of-word (BOW) regression model. Studies and research have revealed incoherence in polarity, model overfitting and performance issues, as well as high cost in data processing. This experiment was conducted to solve these revealing issues, by building a high performance yet cost-effective model for predicting accurate sentiments from large datasets containing customer reviews. This model uses the fastText library from Facebook’s AI research (FAIR) Lab, as well as the traditional Linear Support Vector Machine (LSVM) to classify text and word embedding. Comparisons of this model were also done with the author’s a custom multi-layer Sentiment Analysis (SA) Bi-directional Long Short-Term Memory (SA-BLSTM) model. The proposed fastText model, based on results, obtains a higher accuracy of 90.71% as well as 20% in performance compared to LSVM and SA-BLSTM models.

Distinct Seasonal Primary Production Patterns in the Sub-Polar Gyre and Surrounding Seas

Primary production (PP) in the sub-polar region appears to be important for ocean carbon uptake but how the different water masses contribute to the PP occurring here has not yet been described. Using two models based on satellite observations of surface chlorophyll, light and temperature, seasonal patterns in the distribution of PP are shown here to differ in the sub-polar gyre south of the Greenland-Scotland Ridge (GSR) and surrounding water masses. Monthly averages of PP (2003–2013) were determined. Total and seasonal PP were similar in both models. Average PP in five of the domains (0.47–0.77 g C m–2 d–1) was well above the global average (0.37 g C m–2 d–1). Over the East Greenland shelf, however, total annual PP was estimated to be only 0.19 g C m–2 d–1. The Norwegian shelf was the most productive of the regions studied. “Spring blooms” appear sporadically as spikes in the annual distribution of PP in some regions/years, but do not emerge as a dominant feature in the average annual development of PP in any of the domains. For all regions, ∼25% of the annual PP takes place in the period January-May. PP peaked over most of the study area at or around maximum insolation or temperature. PP in the study region as a whole appears to be more related to latitude or water masses than to bathymetry. In waters over the East Greenland shelf, the Norwegian shelf, and north of the GSR up to 50% of annual PP had taken place when ∼50% of the annual flux of light has reached the surface. In contrast, only about 35% of annual PP had taken place in the sub-polar gyre and waters over the southern open shelf by this time. Light-use efficiency differences may be explained by differences in mixed layer depth (MLD). Multi-model Earth System model studies have indicated that climate change may decrease the MLD in the sub-polar gyre and suggest this may lead to a decrease in the PP occurring here. The results presented here, however, suggest that a shallower MLD could lead to an increase in PP.