Nowadays Molecular Cell Biology (MCB) must be taught as science is practiced. Even though there are several approaches based on scientific practices, a key aspect is to define the purpose of each of these teaching strategies and, most importantly, their implementation. Our goal was to train students to acquire, understand, and communicate new scientific knowledge in the field. The main feature of our new teaching methodology was progressive training in scientific practices associated with a back-and-forward interplay between activities and assessments. The methodology was implemented over 4 years, in students attending the MCB course of the undergraduate degree in Biological Sciences. In the first two modules, the students were prepared to comprehend MCB concepts and techniques and to experience activities based on scientific practices. In the third module, the students analyzed a primary paper in-depth. They were assessed by midterm exams based on a primary paper, written laboratory reports, and the oral presentation of a scientific paper. Our teaching proposal was evaluated through the students' academic performance and by their opinion on the teaching methodology. Most students were satisfied since they improved their acquisition of concepts, their interpretation and integration of scientific knowledge, and developed skills to communicate scientific knowledge in writing and orally. The novelty of transversal interconnections and progressive training in scientific practices provides students with skills in acquiring and understanding new scientific information, even beyond the MCB course.
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http://dx.doi.org/10.1002/bmb.21423 | DOI Listing |
J Oral Pathol Med
January 2025
Laboratory of Oral Histopathology, Health Sciences Faculty, University of Brasília, Brasília, Brazil.
Background: Cell culture studies play an important role in addressing fundamental scientific questions. However, inadequate reporting of these studies results in a lack of transparency and reproducibility. Recognizing the need for improvement, several ongoing efforts, such as CRIS guidelines and the ICLAC checklist, are focused on enhancing best practices for in vitro studies.
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January 2025
College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China.
The construction industry is generally characterized by high emissions, making its transition to low-carbon practices essential for achieving a low-carbon economy. However, due to information asymmetry, there remains a gap in research regarding the strategic interactions and reward/punishment mechanisms between governments and firms throughout this transition. This paper addresses this gap by investigating probabilistic and static reward and punishment evolutionary games.
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January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
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January 2025
Science and Technology on Vacuum and Physics Laboratory, Lanzhou Institute of Physics, Lanzhou, 730000, China.
The Laser Interferometer Space Antenna (LISA) mission is designed to detect space gravitational wave sources in the millihertz band. A critical factor in the success of this mission is the residual acceleration noise metric of the internal test mass (TM) within the ultra-precise inertial sensors. Existing studies indicate that the coupling effects of residual gas and temperature gradient fluctuations significantly influence this metric, primarily manifesting as the radiometer effect and the outgassing effect.
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January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
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